Microsoft SQLServer To Amazon Aurora with Post-
greSQL Compatibility
Migration Playbook
1.0 Preliminary September 2018
© 2018 Amazon Web Services, Inc. or its affiliates. All rights reserved.
Notices
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tomers.
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Table of Contents
Introduction 9
Tables of Feature Compatibility 12
AWS Schema and Data Migration Tools 20
AWS Schema Conversion Tool (SCT) 21
Overview 21
Migrating a Database 21
SCT Action Code Index 31
Creating Tables 32
Data Types 32
Collations 33
PIVOT and UNPIVOT 33
TOP and FETCH 34
Cursors 34
Flow Control 35
Transaction Isolation 35
Stored Procedures 36
Triggers 36
MERGE 37
Query hints and plan guides 37
Full Text Search 38
Indexes 38
Partitioning 39
Backup 40
SQL Server Mail 40
SQL Server Agent 41
Service Broker 41
XML 42
Constraints 42
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Linked Servers 42
AWS Database Migration Service (DMS) 43
Overview 43
Migration Tasks Performed by AWS DMS 43
How AWS DMS Works 44
ANSI SQL 46
Migrate from: SQL Server Constraints 47
Migrate to: Aurora PostgreSQL Table Constraints 51
Migrate from: SQL Server Creating Tables 58
Migrate to: Aurora PostgreSQL Creating Tables 62
Migrate from: SQL Server Common Table Expressions 67
Migrate to: Aurora PostgreSQL Common Table Expressions (CTE) 70
Migrate from: SQL Server Data Types 74
Migrate to: Aurora PostgreSQL Data Types 76
Migrate from: SQL Server Derived Tables 81
Migrate to: Aurora PostgreSQL Derived Tables 82
Migrate from: SQL Server GROUP BY 83
Migrate to: Aurora PostgreSQL GROUP BY 87
Migrate from: SQL Server Table JOIN 90
Migrate to: Aurora PostgreSQL Table JOIN 95
Migrate from: SQL Server Temporal Tables 99
Migrate to: Aurora PostgreSQL Triggers (Temporal Tables alternative) 101
Migrate from: SQL Server Views 102
Migrate to: Aurora PostgreSQL Views 105
Migrate from: SQL Server Window Functions 108
Migrate to: Aurora PostgreSQL Window Functions 110
T-SQL 114
Migrate from: SQL Server Service Broker Essentials 115
Migrate to: Aurora PostgreSQL AWS Lambda or DB links 119
Migrate from: SQL Server Cast and Convert 120
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Migrate to: Aurora PostgreSQL CAST and CONVERSION 122
Migrate from: SQL Server Common Library Runtime (CLR) 124
Migrate to: Aurora PostgreSQL PL/Perl 125
Migrate from: SQL Server Collations 126
Migrate to: Aurora PostgreSQL Encoding 129
Migrate from: SQL Server Cursors 132
Migrate to: Aurora PostgreSQL Cursors 134
Migrate from: SQL Server Date and Time Functions 139
Migrate to: Aurora PostgreSQL Date and Time Functions 141
Migrate from: SQL Server String Functions 143
Migrate to: Aurora PostgreSQL String Functions 145
Migrate from: SQL Server Databases and Schemas 148
Migrate to: Aurora PostgreSQL Databases and Schemas 151
Migrate from: SQL Server Dynamic SQL 153
Migrate to: Aurora PostgreSQL EXECUTE and PREPARE 156
Migrate from: SQL Server Transactions 159
Migrate to: Aurora PostgreSQL Transactions 163
Migrate from: SQL Server Synonyms 169
Migrate to: Aurora PostgreSQL Views, Types & Functions 171
Migrate from: SQL Server DELETE and UPDATE FROM 173
Migrate to: Aurora PostgreSQL DELETE and UPDATE FROM 176
Migrate from: SQL Server Stored Procedures 179
Migrate to: Aurora PostgreSQL Stored Procedures 182
Migrate from: SQL Server Error Handling 186
Migrate to: Aurora PostgreSQL Error Handling 191
Migrate from: SQL Server Flow Control 194
Migrate to: Aurora PostgreSQL Control Structures 197
Migrate from: SQL Server Full-Text Search 200
Migrate to: Aurora PostgreSQL Full-Text Search 203
Migrate from: SQL Server JSON and XML 206
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Migrate to: Aurora PostgreSQL JSON and XML 209
Migrate from: SQL Server MERGE 213
Migrate to: Aurora PostgreSQL MERGE 216
Migrate from: SQL Server PIVOT and UNPIVOT 218
Migrate to: Aurora PostgreSQL PIVOT and UNPIVOT 222
Migrate from: SQL Server Triggers 225
Migrate to: Aurora PostgreSQL Triggers 228
Migrate from: SQL Server TOP and FETCH 232
Migrate to: Aurora PostgreSQL LIMIT and OFFSET (TOP and FETCH Equivalent) 235
Migrate from: SQL Server User Defined Functions 238
Migrate to: Aurora PostgreSQL User Defined Functions 241
Migrate from: SQL Server User Defined Types 242
Migrate to: Aurora PostgreSQL User Defined Types 245
Migrate from: SQL Server Sequences and Identity 248
Migrate to: Aurora PostgreSQL Sequences and SERIAL 253
Configuration 258
Migrate from: SQL Server Session Options 259
Migrate to: Aurora PostgreSQL Session Options 262
Migrate from: SQL Server Database Options 265
Migrate to: Aurora PostgreSQL Database Options 266
Migrate from: SQL Server Server Options 267
Migrate to: Aurora PostgreSQL Aurora Parameter Groups 269
High Availability and Disaster Recovery (HADR) 276
Migrate from: SQL Server Backup and Restore 277
Migrate to: Aurora PostgreSQL Backup and Restore 281
Migrate from: SQL Server High Availability Essentials 289
Migrate to: Aurora PostgreSQL High Availability Essentials 293
Indexes 303
Migrate from: SQL Server Clustered and Non Clustered Indexes 304
Migrate to: Aurora PostgreSQL Clustered and Non Clustered Indexes 308
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Management 313
Migrate from: SQL Server Agent 314
Migrate to: Aurora PostgreSQL Scheduled Lambda 315
Migrate from: SQL Server Alerting 316
Migrate to: Aurora PostgreSQL Alerting 318
Migrate from: SQL Server Database Mail 323
Migrate to: Aurora PostgreSQL Database Mail 326
Migrate from: SQL Server ETL 334
Migrate to: Aurora PostgreSQL ETL 336
Migrate from: SQL Server Export and Import with Text files 361
Migrate to: Aurora PostgreSQL pg_dump and pg_restore 363
Migrate from: SQL Server Viewing Server Logs 366
Migrate to: Aurora PostgreSQL Viewing Server Logs 368
Migrate from: SQL Server Maintenance Plans 371
Migrate to: Aurora PostgreSQL Maintenance Plans 373
Migrate from: SQL Server Monitoring 378
Migrate to: Aurora PostgreSQL Monitoring 381
Migrate from: SQL Server Resource Governor 383
Migrate to: Aurora PostgreSQL Dedicated Amazon Aurora Clusters 385
Migrate from: SQL Server Linked Servers 388
Migrate to: Aurora PostgreSQL DBLink and FDWrapper 391
Migrate from: SQL Server Scripting 393
Migrate to: Aurora PostgreSQL Scripting 395
Performance Tuning 399
Migrate from: SQL Server Execution Plans 400
Migrate to: Aurora PostgreSQL Execution Plans 402
Migrate from: SQL Server Query Hints and Plan Guides 404
Migrate to: Aurora PostgreSQL DB Query Planning 408
Migrate from: SQL Server Managing Statistics 409
Migrate to: Aurora PostgreSQL Table Statistics 411
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Physical Storage 414
Migrate from: SQL Server Columnstore Index 415
Migrate to: Aurora PostgreSQL 416
Migrate from: SQL Server Indexed Views 417
Migrate to: Aurora PostgreSQL Materialized Views 419
Migrate from: SQL Server Partitioning 422
Migrate to: Aurora PostgreSQL Table Inheritance 424
Security 429
Migrate from: SQL Server Column Encryption 430
Migrate to: Aurora PostgreSQL Column Encryption 432
Migrate from: SQL Server Data Control Language 434
Migrate to: Aurora PostgreSQL Data Control Language 435
Migrate from: SQL Server Transparent Data Encryption 438
Migrate to: Aurora PostgreSQL Transparent Data Encryption 439
Migrate from: SQL Server Users and Roles 444
Migrate to: Aurora PostgreSQL Users and Roles 446
Appendix A:SQL Server 2018 Deprecated Feature List 448
Migration Quick Tips 449
Migration Quick Tips 450
Management 450
SQL 450
Glossary 453
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Introduction
The migration process from SQL Server to Amazon Aurora PostgreSQL typically involves several stages.
The first stage is to use the AWS Schema Conversion Tool (SCT) and the AWS Database Migration Ser-
vice (DMS) to convert and migrate the schema and data. While most of the migration work can be auto-
mated, some aspects require manual intervention and adjustments to both database schema objects
and database code.
The purpose of this Playbook is to assist administrators tasked with migrating SQL Server databases to
Aurora PostgreSQL with the aspects that can't be automatically migrated using the Amazon Web Ser-
vices Schema Conversion Tool (AWS SCT). It focuses on the differences, incompatibilities, and sim-
ilarities between SQL Server and Aurora PostgreSQL in a wide range of topics including T-SQL,
Configuration, High Availability and Disaster Recovery (HADR), Indexing, Management, Performance
Tuning, Security, and Physical Storage.
The first section of this document provides an overview of AWS SCT and the AWS Data Migration Ser-
vice (DMS) tools for automating the migration of schema, objects and data. The remainder of the doc-
ument contains individual sections for SQL Server features and their Aurora PostgreSQL counterparts.
Each section provides a short overview of the feature, examples, and potential workaround solutions
for incompatibilities.
You can use this playbook either as a reference to investigate the individual action codes generated by
the AWS SCT tool, or to explore a variety of topics where you expect to have some incompatibility
issues. When using the AWSSCT, you may see a report that lists Action codes, which indicates some
manual conversion is required, or that a manual verification is recommended. For your convenience,
this Playbook includes an SCT Action Code Index section providing direct links to the relevant topics
that discuss the manual conversion tasks needed to address these action codes. Alternatively, you can
explore the Tables of Feature Compatibility section that provides high-level graphical indicators and
descriptions of the feature compatibility between SQL Server and Aurora PostgreSQL. It also includes a
graphical compatibility indicator and links to the actual sections in the playbook.
There are two appendices at the end of this playbook: Appendix A: SQL Server 2008 Deprecated
Feature List provides focused links on features that were deprecated in SQL Server 2008R2. Appendix
B: Migration Quick Tips provides a list of tips for SQL Server administrators or developers who have
little experience with PostgreSQL. It briefly highlights key differences between SQL Server and Aurora
PostgreSQL that they are likely to encounter.
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Note that not all SQL Server features are fully compatible with Aurora PostgreSQL, or have simple work-
arounds. From a migration perspective, this document does not yet cover all SQL Server features and
capabilities. This first release focuses on some of the most important features and will be expanded
over time.
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Disclaimer
The various code snippets, commands, guides, best practices, and scripts included in this document
should be used for reference only and are provided as-is without warranty. Test all of the code snip-
petts, commands, best practices, and scripts outlined in this document in a non-production envir-
onment first. Amazon and its affiliates are not responsible for any direct or indirect damage that may
occur from the information contained in this document.
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Tables of Feature Compatibility
Feature Compatibility Legend
Compatibility
Score Symbol
Description
Very high compatibility: None or minimal low-risk and low-effort rewrites
needed
High compatibility: Some low-risk rewrites needed, easy workarounds exist for
incompatible features
Medium compatibility: More involved low-medium risk rewrites needed, some
redesign may be needed for incompatible features
Low compatibility: Medium to high risk rewrites needed, some incompatible fea-
tures require redesign and reasonable-effort workarounds exist
Very low compatibility: High risk and/or high-effort rewrites needed, some fea-
tures require redesign and workarounds are challenging
Not compatible: No practical workarounds yet, may require an application level
architectural solution to work around incompatibilities
SCT Automation Level Legend
SCT Automation
Level Symbol
Description
Full Automation SCT performs fully automatic conversion, no manual con-
version needed.
High Automation: Minor, simple manual conversions may be needed.
Medium Automation: Low-medium complexity manual conversions may be
needed.
Low Automation: Medium-high complexity manual conversions may be needed.
Very Low Automation: High risk or complex manual conversions may be
needed.
No Automation: Not currently supported by SCT, manual conversion is required
for this feature.
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ANSI SQL
SQLServer Aurora PostgreSQL Key Differences Compatibility
Constraints Constraints l SET DEFAULT option is
missing
Creating Tables Creating Tables l Auto generated value
column is different
l Can't use physical
attribute ON
l Missing table variable
and memory optim-
ized table
Common Table
Expressions
Common Table
Expressions
GROUP BY GROUP BY
Table JOIN Table JOIN l OUTER JOIN with com-
mas
l CROSS APPLY and
OUTER APPLY are not
supported
Data Types Data Types l Syntax and handling
differences
Views Views l Indexed and Par-
titioned view are not
supported
Windowed Func-
tions
Windowed Functions
Derived Tables Derived Tables
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SQLServer Aurora PostgreSQL Key Differences Compatibility
Temporal Tables Temporal Tables l Temporal tables are
not supported
T-SQL
SQLServer Aurora PostgreSQL Key Differences Compatibility
Collations Collations l UTF16 and
NCHAR/NVARCHAR
data types are not
supported
Cursors Cursors l Different cursor
options
Date and Time Func-
tions
Date and Time Func-
tions
l PostgreSQL is
using different
function names
String Functions String Functions l Syntax and option
differences
Databases and
Schemas
Databases and
Schemas
Transactions Transactions l Nested trans-
actions are not
supported
l syntax diffrences
for initializing a
transaction
DELETE and UPDATE
FROM
DELETE and UPDATE
FROM
l DELETE...FROM
from_list is not
supported -
rewrite to use sub-
queries
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SQLServer Aurora PostgreSQL Key Differences Compatibility
Stored Procedures Stored Procedures l Syntax and option
differences
Error Handling Error Handling l Different
paradigm and syn-
tax will require
rewrite of error
handling code
Full Text Search Full Text Search l Different
paradigm and syn-
tax will require
application/drivers
rewrite.
Flow Control Flow Control l Postgres does not
support GOTO and
WAITFOR TIME
JSON and XML JSON and XML l Syntax and option
differences, sim-
ilar functionality
PIVOT PIVOT l Straight forward
rewrite to use tra-
ditional SQL syn-
tax
MERGE MERGE l Rewrite to use
INSERT… ON
CONFLICT
Triggers Triggers l Syntax and option
differences, sim-
ilar functionality -
PostgreSQL trigger
calling a function
User Defined Functions User Defined Functions l Syntax and option
differences
User Defined Types User Defined Types
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SQLServer Aurora PostgreSQL Key Differences Compatibility
Sequences and Identity Sequences and Identity l Less options with
SERIAL
l Reseeding need to
be rewrited
Synonyms Synonyms l PostgreSQL does
not support Syn-
onym - there is an
available work-
around
TOP and FETCH LIMIT and OFFSET l TOP is not sup-
portd
Dynamic SQL Dynamic SQL l Different
paradigm and syn-
tax will require
application/drivers
rewrite.
CAST and CONVERT CAST and CONVERT l CONVERT is used
only to convert
between collations
l CAST uses dif-
ferent syntax
Broker Broker l Use Amazon
Lambda for sim-
ilar functionality
CLR Objects CLR Objects l Migrating CLR
objects will require
a full code rewrite
Configuration
SQLServer Aurora PostgreSQL Key Differences Compatibility
Session Options Session Options l SET options are
significantly dif-
ferent, except for
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SQLServer Aurora PostgreSQL Key Differences Compatibility
transaction isol-
ation control
Database Options Database Options l Use Cluster and
Database/Cluster
Parameters
Server Options Server Options l Use Cluster and
Database/Cluster
Parameters
High Availability and Disaster Recovery (HADR)
SQLServer Aurora PostgreSQL Key Differences Compatibility
Backup and Restore Backup and Restore l Storage level
backup managed
by Amazon RDS
High Availability Essen-
tials
High Availability Essen-
tials
l Multi replica,
scale out solution
using Amazon Aur-
ora clusters and
Availability Zones
Indexes
SQLServer Aurora PostgreSQL Key Differences Compatibility
Clustered and Non
Clustered Indexes
Clustered and Non
Clustered Indexes
l CLUSTERED INDEX
is not supported
l There are few
missing options
Indexed Views Indexed Views l Different
paradigm and syn-
tax will require
application/drivers
rewrite.
Columnstore Columnstore l Aurora Post-
greSQL offers no
comparable fea-
ture
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Management
SQLServer
Aurora Post-
greSQL
Key Differences Compatibility
SQL Server
Agent
SQL Agent l See Alerting and Maintenance
Plans
Alerting Alerting l Use Event Notifications Sub-
scription with Amazon Simple Noti-
fication Service (SNS)
ETL ETL l Use Amazon Glue for ETL
Database Mail Database Mail l Use Lambda Integration
Viewing Server
Logs
Viewing Server Logs l View logs from the Amazon RDS
console, the Amazon RDS API, the
AWS CLI, or the AWS SDKs
Maintenance
Plans
Maintenance Plans l Backups via the RDS services
l Table maintenance via SQL
Monitoring Monitoring l Use Amazon Cloud Watch service
Resource
Governor
Resource Governor l Distribute load/applications/users
across multiple instances
Linked Servers Linked Servers l Syntax and option differences, sim-
ilar functionality
Scripting &
PowerShell
Scripting & Power-
Shell
l Non-compatible tool sets and
scripting languages
l Use PostgreSQL pgAdmin, Amazon
RDS API, AWS Management Con-
sole, and Amazon CLI
Import and
Export
Import and Export l Non compatible tool
Performance Tuning
SQLServer Aurora PostgreSQL Key Differences Compatibility
Execution
Plans
Execution Plans l Syntax differences
l Completely different optimizer
with different operators and rules
Query Hints
and Plan
Guides
Query Hints and
Plan Guides
l Very limited set of hints - Index
hints and optimizer hints as com-
ments
- 18 -
SQLServer Aurora PostgreSQL Key Differences Compatibility
l Syntax differences
Managing Stat-
istics
Managing Statistics l Syntax and option differences,
similar functionality
Physical Storage
SQLServer Aurora PostgreSQL Key Differences Compatibility
Partitioning Partitioning l Does not support
LEFT partition or
foreign keys ref-
erencing par-
titioned tables
Security
SQLServer Aurora PostgreSQL Key Differences Compatibility
Column Encryption Column Encryption l Syntax and option
differences, sim-
ilar functionality
Data Control Language Data Control Language l Similar syntax and
similar func-
tionality
Transparent Data
Encryption
Transparent Data
Encryption
l Storage level
encryption man-
aged by Amazon
RDS
Users and Roles Users and Roles l Syntax and option
differences, sim-
ilar functionality
l There are no
users - only roles
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AWS Schema and Data Migration Tools
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AWS Schema Conversion Tool (SCT)
Overview
The AWS Schema Conversion Tool (SCT) is a stand alone tool that connects to source and target data-
bases, scans the source database schema objects (tables, views, indexes, procedures, etc.), and con-
verts them to target database objects.
This section provides a step-by-step process for using AWSSCT to migrate an SQL Server database to
an Aurora PostgreSQL database cluster. Since AWS SCT can automatically migrate most of the data-
base objects, it greatly reduces manual effort.
It is recommended to start every migration with the process outlined in this section and then use the
rest of the Playbook to further explore manual solutions for objects that could not be migrated auto-
matically. Even though AWS SCT can automatically migrate most schema objects, it is highly recom-
mended that you allocate sufficient resources to perform adequate testing, and performance tuning
due to the differences between the SQL Server engine and the Aurora PostgreSQL engine. For more
information, see http://docs.aws.amazon.com/SchemaConversionTool/latest/userguide/Welcome.html
Migrating a Database
Note: This walkthrough uses the AWS DMS Sample Database. You can download it from
https://github.com/aws-samples/aws-database-migration-samples.
Download the Software and Drivers
1. Download and install the AWS SCT from https://-
docs.aws.amazon.com/SchemaConversionTool/latest/userguide/CHAP_Installing.html.
2. Download the SQL Server driver from https://www.microsoft.com/en-us/-
download/details.aspx?displaylang=en&id=11774
3. Download the PostgreSQL driver from https://jdbc.postgresql.org/
Configure SCT
Launch SCT. Click the Settings button and select Global Settings.
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On the left navigation bar, click Drivers. Enter the paths for the SQL Server and PostgreSQL drivers
downloaded in the first step. Click Apply and then OK.
Create a New Migration Project
Click File > New project wizard. Alternatively, use the keyboard shortcut <Ctrl+W>.
Enter a project name and select a location for the project files. Click Next.
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Enter connection details for the source SQL Server database and click Test Connection to verify. Click
Next.
Select the schema or database to migrate and click Next.
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The progress bar displays the objects being analyzed.
The Database Migration Assessment Report is displayed when the analysis completes. Read the Exec-
utive summary and other sections. Note that the information on the screen is only partial. To read the
full report, including details of the individual issues, click Save to PDF and open the PDFdocument.
- 24 -
Scroll down to the section Database objects with conversion actions for Amazon Aurora (Post-
greSQL compatible).
- 25 -
Scroll further down to the section Detailed recommendations for Amazon Aurora (PostgreSQL com-
patible) migrations.
Return to AWS SCT and click Next. Enter the connection details for the target Aurora PostgreSQL data-
base and click Finish.
Note: The changes have not yet been saved to the target.
When the connection is complete, AWS SCTdisplays the main window. In this interface, you can
explore the individual issues and recommendations discovered by AWSSCT.
For example, expand sample database > dbo default schema > SQL scalar functions > rand_int.
This issue has a red marker indicating it could not be automatically converted and requires a manual
code change (issue 811 above). Select the object to highlight the incompatible code section.
- 26 -
Right-click the database and then click Create Report to create a report tailored for the target data-
base type. It can be viewed in AWSSCT.
The progress bar updates while the report is generated.
- 27 -
The executive summary page displays. Click the Action Items tab.
In this window, you can investigate each issue in detail and view the suggested course of action. For
each issue, drill down to view all instances of that issue.
Right-click the database name and click Convert Schema.
Note: Be sure to uncheck the sys and information_schema system schemas. Aurora Post-
greSQL already has an information_schema schema.
Note: This step does not make any changes to the target database.
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On the right pane, the new virtual schema is displayed as if it exists in the target database. Drilling
down into individual objects displays the actual syntax generated by AWSSCT to migrate the objects.
Right-click the database on the right pane and choose either Apply to database to automatically
execute the conversion script against the target database, or click Save as SQL to save to an SQL file.
Saving to an SQL file is recommended because it allows you to verify and QA the SCT code. Also, you
can make the adjustments needed for objects that could not be automatically converted.
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SCT Action Code Index
Legend
SCT Automation
Level Symbol
Description
Full Automation SCT performs fully automatic conversion, no manual con-
version needed.
High Automation: Minor, simple manual conversions may be needed.
Medium Automation: Low-medium complexity manual conversions may be
needed.
Low Automation: Medium-high complexity manual conversions may be
needed.
Very Low Automation: High risk or complex manual conversions may be
needed.
No Automation: Not currently supported by SCT, manual conversion is
required for this feature.
The following sections list the Schema Conversion Tool Action codes for topics that are covered in this
playbook.
Note: The links in the table point to the Microsoft SQL Server topic pages, which are imme-
diately followed by the PostgreSQL pages for the same topics.
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Creating Tables
AWS SCT automatically converts the most commonly used constructs of the CREATE TABLE statement
as both SQL Server and Aurora PostgreSQL support the entry level ANSI compliance. These items
include table names, containing security schema (or database), column names, basic column data
types, column and table constraints, column default values, primary, candidate (UNIQUE), and foreign
keys. Some changes may be required for computed columns and global temporary tables.
For more details, see Creating Tables.
Action Code Action Message
7659 The scope table-variables and temporary tables is different. You must apply
manual conversion, if you are using recursion
7679 A computed column is replaced by the triggers 7680
7680 PostgreSQL doesn't support global temporary tables
7812 Temporary table must be removed before the end of the function
Data Types
Data type syntax and rules are very similar between SQL Server and Aurora PostgreSQL and most are
converted automatically by AWS SCT. Note that date and time handling paradigms are different for SQL
Server and Aurora PostgreSQL and require manual verifications and/or conversion. Also note that due
to differences in data type behavior between SQL Server and Aurora PostgreSQL , manual verification
and strict testing are highly recommended.
For more details, see Data Types.
Action Code Action Message
7657 PostgreSQL doesn't support this type. A manual conversion is required
7658 PostgreSQL doesn't support this type. A manual conversion is required
7662 PostgreSQL doesn't support this type. A manual conversion is required
7664 PostgreSQL doesn't support this type. A manual conversion is required
7690 PostgreSQL doesn't support table types
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Action Code Action Message
7706 Unable convert the variable declaration of unsupported %s datatype
7707 Unable convert variable reference of unsupported %s datatype
7708 Unable convert complex usage of unsupported %s datatype
7773 Unable to perform an automated migration of arithmetic operations with several
dates
7775 Check the data type conversion. Possible loss of accuracy
Collations
The collation paradigms of SQL Server and Aurora PostgreSQL are significantly different. The AWS SCT
tool can not migrate collation automaticly to PostgreSQL
For more details, see Collations.
Action Code Action Message
7646 Automatic conversion of collation is not supported
PIVOT and UNPIVOT
Aurora PostgreSQL version 9.6 does not support the PIVOT and UNPIVOT syntax and it cannot be auto-
matically converted by AWS SCT.
For workarounds using traditional SQL syntax, see PIVOT and UNPIVOT.
Action Code Action Message
7905 PostgreSQL doesn't support the PIVOT clause for the SELECT statement
7906 PostgreSQL doesn't support the UNPIVOT clause for the SELECT statement
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TOP and FETCH
Aurora PostgreSQL supports the non-ANSI compliant (but popular with other engines) LIMIT... OFFSET
operator for paging results sets. Some options such as WITH TIES cannot be automatically converted
and require manual conversion.
For more details, see TOP and FETCH.
Action Code Action Message
7605 PostgreSQL doesn't support the WITH TIES option
7796 PostgreSQL doesn't support TOP option in the operator UPDATE
7798 PostgreSQL doesn't support TOP option in the operator DELETE
Cursors
PostgreSQL has PL/pgSQL cursors that enable you to iterate business logic on rows read from the data-
base. They can encapsulate the query and read the query results a few rows at a time. All access to curs-
ors in PL/pgSQL is performed through cursor variables, which are always of the refcursor data type.
There are specific options which are not supported for automaic conversion by SCT.
For more details, see Cursors.
Action Code Action Message
7637 PostgreSQL doesn't support GLOBAL CURSORS. Requires manual Conversion
7639 PostgreSQL doesn't support DYNAMIC cursors
7700 The membership and order of rows never changes for cursors in PostgreSQL, so
this option is skipped
7701 Setting this option corresponds to the typical behavior of cursors in PostgreSQL, so
this option is skipped
7702 All PostgreSQL cursors are read-only, so this option is skipped
7704 PostgreSQL doesn't support the option OPTIMISTIC, so this option is skipped
7705 PostgreSQL doesn't support the option TYPE_WARNING, so this option is skipped
7803 PostgreSQL doesn't support the option FOR UPDATE, so this option is skipped
- 34 -
Flow Control
Although the flow control syntax of SQL Server differs from Aurora PostgreSQL , the AWS SCT can con-
vert most constructs automatically including loops, command blocks, and delays. Aurora PostgreSQL
does not support the GOTO command nor the WAITFOR TIME command, which require manual con-
version.
For more details, see Flow Control.
Action Code Action Message
7628 PostgreSQL doesn't support the GOTO option. Automatic conversion can't be per-
formed
7691 PostgreSQL doesn't support WAITFOR TIME feature
7801 The table can be locked open cursor
7802 A table that is created within the procedure, must be deleted before the end of the
procedure
7821 Automatic conversion operator WAITFOR with a variable is not supported
7826 Check the default value for a DateTime variable
7827 Unable to convert default value
Transaction Isolation
Aurora PostgreSQL supports the four transaction isolation levels specified in the SQL:92 standard:
READ UNCOMMITTED, READ COMMITTED, REPEATABLE READ, and SERIALIZABLE, all of which are auto-
matically converted by AWS SCT. AWS SCT also converts BEGIN / COMMIT and ROLLBACK commands
that use slightly different syntax. Manual conversion is required for named, marked, and delayed dur-
ability transactions that are not supported by Aurora PostgreSQL .
For more details, see Transaction Isolation.
Action Code Action Message
7807 PostgreSQL does not support explicit transaction management in functions
- 35 -
Stored Procedures
Aurora PostgreSQL Stored Procedures (functions) provides very similar functionality to SQL Server
stored procedures and can be automatically converted by AWS SCT. Manual conversion is required for
procedures that use RETURN values and some less common EXECUTE options such as the RECOMPILE
and RESULTS SETS options.
For more details, see Stored Procedures.
Action Code Action Message
7640 The EXECUTE with RECOMPILE option is ignored
7641 The EXECUTE with RESULT SETS UNDEFINED option is ignored
7642 The EXECUTE with RESULT SETS NONE option is ignored
7643 The EXECUTE with RESULT SETS DEFINITION option is ignored
7672 Automatic conversion of this command is not supported
7695 PostgreSQL doesn't support the execution of a procedure as a variable
7800 PostgreSQL doesn't support result sets in the style of MSSQL
7830 Automatic conversion arithmetic operations with operand CASE is not supported
7838 The EXECUTE with LOGIN | USER option is ignored
7839 Converted code might be incorrect because of the parameter names
Triggers
Aurora PostgreSQL supports BEFORE and AFTER triggers for INSERT, UPDATE, and DELETE. However,
Aurora PostgreSQL triggers differ substantially from SQL Server's triggers, but most common use cases
can be migrated with minimal code changes.
For more details, see Triggers.
Action Code Action Message
7809 PostgreSQL does not support INSTEAD OF triggers on tables
7832 Unable to convert INSTEAD OF triggers on view
7909 Unable to convert the clause
- 36 -
MERGE
Aurora PostgreSQL version 9.6 does not support the MERGE statement and it cannot be automatically
converted by AWS SCT. Manual conversion is straight-forward in most cases.
For more details and potential workarounds, see MERGE.
Action Code Action Message
7915 Please check unique(exclude) constraint existence on field %s
7916 Current MERGE statement can not be emulated by INSERT ON CONFLICT usage
Query hints and plan guides
Basic query hints such as index hints can be converted automatically by AWS SCT, except for DML state-
ments. Note that specific optimizations used for SQL Server may be completely inapplicable to a new
query optimizer. It is recommended to start migration testing with all hints removed. Then, selectively
apply hints as a last resort if other means such as schema, index, and query optimizations have failed.
Plan guides are not supported by Aurora PostgreSQL.
For more details, see Query hints and Plan Guides.
Action Code Action Message
7823 PostgreSQL doesn't support table hints in DML statements
- 37 -
Full Text Search
Migrating Full-Text indexes from SQL Server to Aurora PostgreSQL requires a full rewrite of the code
that deals with both creating, managing, and querying Full-Text indexes. They cannot be automatically
converted by AWS SCT.
For more details, see Full Text Search.
Action Code Action Message
7688 PostgreSQL doesn't support the FREETEXT predicate
Indexes
Basic non-clustered indexes, which are the most commonly used type of indexes are automatically
migrated by AWS SCT. In addition, filtered indexes, indexes with included columns, and some SQL
Server specific index options can not be migrated automatically and require manual conversion.
For more details, see Indexes.
Action Code Action Message
7675 PostgreSQL doesn't support sorting options (ASC | DESC) for constraints
7681 PostgreSQL doesn't support clustered indexes
7682 PostgreSQL doesn't support the INCLUDE option in indexes
7781 PostgreSQL doesn't support the PAD_INDEX option in indexes
7782 PostgreSQL doesn't support the SORT_IN_TEMPDB option in indexes
7783 PostgreSQL doesn't support the IGNORE_DUP_KEY option in indexes
7784 PostgreSQL doesn't support the STATISTICS_NORECOMPUTE option in indexes
7785 PostgreSQL doesn't support the STATISTICS_INCREMENTAL option in indexes
7786 PostgreSQL doesn't support the DROP_EXISTING option in indexes
7787 PostgreSQL doesn't support the ONLINE option in indexes
7788 PostgreSQL doesn't support the ALLOW_ROW_LOCKS option in indexes
7789 PostgreSQL doesn't support the ALLOW_PAGE_LOCKS option in indexes
- 38 -
Action Code Action Message
7790 PostgreSQL doesn't support the MAXDOP option in indexes
7791 PostgreSQL doesn't support the DATA_COMPRESSION option in indexes
Partitioning
Aurora PostgreSQL uses "table inheritance", some of the physical aspects of partitioning in SQL Server
do not apply to Aurora PostgreSQL . For example, the concept of file groups and assigning partitions
to file groups. Aurora PostgreSQL supports a much richer framework for table partitioning than SQL
Server, with many additional options such as hash partitioning, and sub partitioning.
For more details, see Partitioning.
Action Code Action Message
7910 NULL columns not supported for partitioning
7911 PostgreSQL does not support foreign keys referencing partitioned tables
7912 PostgreSQL does not support foreign key references from a partitioned table to
some other table
7913 PostgreSQL does not support LEFT partitioning - partition values distribution could
vary
7914 Update of the partitioned table may lead to errors
- 39 -
Backup
Migrating from a self-managed backup policy to a Platform as a Service (PaaS) environment such as
Aurora PostgreSQL is a complete paradigm shift. You no longer need to worry about transaction logs,
file groups, disks running out of space, and purging old backups. Amazon RDS provides guaranteed
continuous backup with point-in-time restore up to 35 days. Therefor, AWS SCT does not automatically
convert backups.
For more details, see Backup and Restore.
Action Code Action Message
7903 PostgreSQL does not have functionality similar to SQL Server Backup
SQL Server Mail
Aurora PostgreSQL does not provide native support sending mail from the database.
For more details and potential workarounds, see Database Mail.
Action Code Action Message
7900 PostgreSQL does not have functionality similar to SQL Server Database Mail
- 40 -
SQL Server Agent
Aurora PostgreSQL does not provide functionality similar to SQL Server Agent as an external, cross-
instance scheduler. However, Aurora PostgreSQL does provide a native, in-database scheduler. It is lim-
ited to the cluster scope and can't be used to manage multiple clusters. Therefore, AWS SCT can not
automatically convert Agent jobs and alerts.
For more details, see SQL Server Agent.
Action Code Action Message
7902 PostgreSQL does not have functionality similar to SQL Server Agent
Service Broker
Aurora PostgreSQL does not provide a compatible solution to the SQL Server Service Broker. However,
you can use DB Links and AWS Lambda to achieve similar functionality.
For more details, see Service Broker.
Action Code Action Message
7901 PostgreSQL does not have functionality similar to SQL Server Service Broker
- 41 -
XML
The XML options and features in Aurora PostgreSQL are similar to SQL Server and the most important
functions (XPATH and XQUERY)or almost identical.
PostgreSQL does not support FOR XML clause, the walkaround for that is using string_agg instead. In
some cases, it might be more efficient to use JSON instead of XML.
For more details, see XML.
Action Code Action Message
7816 PostgreSQL doesn't support any methods for datatype XML
7817 PostgreSQL doesn't support option [for xml path] in the SQL-queries
Constraints
Constraints feature is almost fully automated and compatible between SQL Server and Aurora Post-
greSQL.
The differences are: missing SET DEFAULT and Check constraint with sub-query.
For more details, see Constraints.
Action Code Action Message
7825 The default value for a DateTime column removed
7915 Please check unique(exclude) constraint existence on field %s
Linked Servers
Aurora PostgreSQL does support remote data access from the database. Connectivity between
schemas is trivial, but connectivity to other instances require an extension installation
For more details, see Constraints.
Action Code Action Message
7645 PostgreSQL doesn't support executing a pass-through command on a linked server
- 42 -
AWS Database Migration Service (DMS)
Overview
The AWS Database Migration Service (DMS) helps you migrate databases to AWS quickly and securely.
The source database remains fully operational during the migration, minimizing downtime to applic-
ations that rely on the database. The AWS Database Migration Service can migrate your data to and
from most widely-used commercial and open-source databases.
The service supports homogenous migrations such as Oracle to Oracle as well as heterogeneous migra-
tions between different database platforms such as Oracle to Amazon Aurora or Microsoft SQL Server
to MySQL. It also allows you to stream data to Amazon Redshift, Amazon DynamoDB, and Amazon S3
from any of the supported sources, which are Amazon Aurora, PostgreSQL, MySQL, MariaDB, Oracle
Database, SAP ASE, SQL Server, IBM DB2 LUW, and MongoDB, enabling consolidation and easy ana-
lysis of data in a petabyte-scale data warehouse. The AWS Database Migration Service can also be used
for continuous data replication with high-availability.
When migrating databases to Aurora, Redshift or DynamoDB, you can use DMS free for six months.
For all supported sources for DMS, see
https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Source.html
For all supported targets for DMS, see
https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Target.html
Migration Tasks Performed by AWS DMS
l In a traditional solution, you need to perform capacity analysis, procure hardware and software,
install and administer systems, and test and debug the installation. AWS DMS automatically man-
ages the deployment, management, and monitoring of all hardware and software needed for
your migration. Your migration can be up and running within minutes of starting the AWS DMS
configuration process.
l With AWS DMS, you can scale up (or scale down) your migration resources as needed to match
your actual workload. For example, if you determine that you need additional storage, you can
easily increase your allocated storage and restart your migration, usually within minutes. On the
other hand, if you discover that you aren't using all of the resource capacity you configured, you
can easily downsize to meet your actual workload.
l AWS DMS uses a pay-as-you-go model. You only pay for AWS DMS resources while you use them
as opposed to traditional licensing models with up-front purchase costs and ongoing main-
tenance charges.
l AWS DMS automatically manages all of the infrastructure that supports your migration server
including hardware and software, software patching, and error reporting.
- 43 -
l AWS DMS provides automatic failover. If your primary replication server fails for any reason, a
backup replication server can take over with little or no interruption of service.
l AWS DMS can help you switch to a modern, perhaps more cost-effective database engine than
the one you are running now. For example, AWS DMS can help you take advantage of the man-
aged database services provided by Amazon RDS or Amazon Aurora. Or, it can help you move to
the managed data warehouse service provided by Amazon Redshift, NoSQL platforms like
Amazon DynamoDB, or low-cost storage platforms like Amazon S3. Conversely, if you want to
migrate away from old infrastructure but continue to use the same database engine, AWS DMS
also supports that process.
l AWS DMS supports nearly all of today’s most popular DBMS engines as data sources, including
Oracle, Microsoft SQL Server, MySQL, MariaDB, PostgreSQL, Db2 LUW, SAP, MongoDB, and
Amazon Aurora.
l AWS DMS provides a broad coverage of available target engines including Oracle, Microsoft SQL
Server, PostgreSQL, MySQL, Amazon Redshift, SAP ASE, S3, and Amazon DynamoDB.
l You can migrate from any of the supported data sources to any of the supported data targets.
AWS DMS supports fully heterogeneous data migrations between the supported engines.
l AWS DMS ensures that your data migration is secure. Data at rest is encrypted with AWS Key Man-
agement Service (AWS KMS) encryption. During migration, you can use Secure Socket Layers (SSL)
to encrypt your in-flight data as it travels from source to target.
How AWS DMS Works
At its most basic level, AWS DMS is a server in the AWS Cloud that runs replication software. You create
a source and target connection to tell AWS DMS where to extract from and load to. Then, you schedule
a task that runs on this server to move your data. AWS DMS creates the tables and associated primary
keys if they don't exist on the target. You can pre-create the target tables manually if you prefer. Or you
can use AWS SCT to create some or all of the target tables, indexes, views, triggers, and so on.
The following diagram illustrates the AWS DMS process.
- 44 -
For a complete guide with a step-by-step walkthrough including all the latest notes for migrating SQL
Server to Aurora MySQL (which is very similar to migrate from SQL Server to Aurora PostgerSQL) with
DMS, see
https://docs.aws.amazon.com/dms/latest/sbs/CHAP_SQLServer2Aurora.html
For more information about DMS, see:
l https://docs.aws.amazon.com/dms/latest/userguide/Welcome.html
l https://docs.aws.amazon.com/dms/latest/userguide/CHAP_BestPractices.html
- 45 -
ANSI SQL
- 46 -
Migrate from: SQL Server Constraints
Feature Com-
patibility
SCT Automation Level
SCT Action Code
Index
Key Differences
Constraints SET DEFAULT option is miss-
ing
Check constraint with sub-
query
Overview
Column and table constraints are defined by the SQL standard and enforce relational data consistency.
There are four types of SQLconstraints:Check Constraints, Unique Constraints, Primary Key Con-
straints, and Foreign Key Constraints.
Check Constraints
Syntax
CHECK (<Logical Expression>)
CHECK constraints enforce domain integrity by limiting the data values stored in table columns. They
are logical boolean expressions that evaluate to one of three values: TRUE, FALSE, and UNKNOWN.
Note: CHECK constraint expressions behave differently than predicates in other query
clauses. For example, in a WHEREclause, a logical expression that evaluates to UNKNOWN is
functionally equivalent to FALSE and the row is filtered out. For CHECK constraints, an expres-
sion that evaluates to UNKNOWN is functionally equivalent to TRUE because the value is per-
mitted by the constraint.
Multiple CHECK constraints may be assigned to a column. A single CHECK constraint may apply to mul-
tiple columns (in this case, it is known as a Table-Level Check Constraint).
In ANSI SQL, CHECK constraints can not access other rows as part of the expression. SQL Server allows
using User Defined Functions in constraints to access other rows, tables, or databases.
Unique Constraints
Syntax
UNIQUE [CLUSTERED | NONCLUSTERED] (<Column List>)
- 47 -
UNIQUE constraints should be used for all candidate keys. A candidate key is an attribute or a set of
attributes (columns) that uniquely identify each tuple (row) in the relation (table data).
UNIQUE constraints guarantee that no rows with duplicate column values exist in a table.
A UNIQUE constraint can be simple or composite. Simple constraints are composed of a single
column. Composite constraints are composed of multiple columns. A column may be a part of more
than one constraint.
Although the ANSI SQLstandard allows multiple rows having NULLvalues for UNIQUE constraints, SQL
Server allows a NULLvalue for only one row. Use a NOTNULL constraint in addition to a UNIQUE con-
straint to disallow all NULL values.
To improve efficiency, SQL Server creates a unique index to support UNIQUE constraints. Otherwise,
every INSERT and UPDATE would require a full table scan to verify there are no duplicates. The default
index type for UNIQUE constraints is non- clustered.
Primary Key Constraints
Syntax
PRIMARY KEY [CLUSTERED | NONCLUSTERED] (<Column List>)
A PRIMARY KEY is a candidate key serving as the unique identifier of a table row. PRIMARY KEYS may
consist of one or more columns. All columns that comprise a primary key must also have a NOT NULL
constraint. Tables can have one primary key.
The default index type for PRIMARY KEYS is a clustered index.
Foreign Key Constraints
Syntax
FOREIGN KEY (<Referencing Column List>)
REFERENCES <Referenced Table>(<Referenced Column List>)
FOREIGN KEY constraints enforce domain referential integrity. Similar to CHECK constraints, FOREIGN
KEYS limit the values stored in a column or set of columns.
FOREIGN KEYS reference columns in other tables, which must be either PRIMARY KEYS or have UNIQUE
constraints. The set of values allowed for the referencing table is the set of values existing the ref-
erenced table.
Although the columns referenced in the parent table are indexed (since they must have either a
PRIMARY KEY or UNIQUE constraint), no indexes are automatically created for the referencing columns
in the child table. A best practice is to create appropriate indexes to support joins and constraint
enforcement.
- 48 -
FOREIGN KEY constraints impose DML limitations for the referencing child and parent tables. The pur-
pose of a constraint is to guarantee that no "orphan" rows (rows with no corresponding matching val-
ues in the parent table) exist in the referencing table. The constraint limits INSERT and UPDATE to the
child table and UPDATE and DELETE to the parent table. For example, you can not delete an order hav-
ing associated order items.
Foreign keys support Cascading Referential Integrity (CRI). CRI can be used to enforce constraints and
define action paths for DML statements that violate the constraints. There are four CRI options:
l NO ACTION:When the constraint is violated due to a DML operation, an error is raised and the
operation is rolled back.
l CASCADE: Values in a child table are updated with values from the parent table when they are
updated or deleted along with the parent.
l SET NULL: All columns that are part of the foreign key are set to NULL when the parent is deleted
or updated.
l SET DEFAULT: All columns that are part of the foreign key are set to their DEFAULT value when
the parent is deleted or updated.
These actions can be customized independently of others in the same constraint. For example, a cas-
cading constraint may have CASCADE for UPDATE, but NO ACTION for UPDATE.
Examples
Create a composite non-clustered PRIMARY KEY.
CREATE TABLE MyTable
(
Col1 INT NOT NULL,
Col2 INT NOT NULL,
Col3 VARCHAR(20) NULL,
CONSTRAINT PK_MyTable
PRIMARY KEY NONCLUSTERED (Col1, Col2)
);
Create a table-level CHECK constraint
CREATE TABLE MyTable
(
Col1 INT NOT NULL,
Col2 INT NOT NULL,
Col3 VARCHAR(20) NULL,
CONSTRAINT PK_MyTable
PRIMARY KEY NONCLUSTERED (Col1, Col2),
CONSTRAINT CK_MyTableCol1Col2
CHECK (Col2 >= Col1)
);
Create a simple non-null UNIQUE constraint.
CREATE TABLE MyTable
(
- 49 -
Col1 INT NOT NULL,
Col2 INT NOT NULL,
Col3 VARCHAR(20) NULL,
CONSTRAINT PK_MyTable
PRIMARY KEY NONCLUSTERED (Col1, Col2),
CONSTRAINT UQ_Col2Col3
UNIQUE (Col2, Col3)
);
Create a FOREIGN KEY with multiple cascade actions.
CREATE TABLE MyParentTable
(
Col1 INT NOT NULL,
Col2 INT NOT NULL,
Col3 VARCHAR(20) NULL,
CONSTRAINT PK_MyTable
PRIMARY KEY NONCLUSTERED (Col1, Col2)
);
CREATE TABLE MyChildTable
(
Col1 INT NOT NULL PRIMARY KEY,
Col2 INT NOT NULL,
Col3 INT NOTNULL,
CONSTRAINT FK_MyChildTable_MyParentTable
FOREIGN KEY (Col2, Col3)
REFERENCES MyParentTable (Col1, Col2)
ON DELETE NO ACTION
ON UPDATE CASCADE
);
For more information, see:
l https://docs.microsoft.com/en-us/sql/relational-databases/tables/unique-constraints-and-check-constraints
l https://docs.microsoft.com/en-us/sql/relational-databases/tables/primary-and-foreign-key-constraints
- 50 -
Migrate to: Aurora PostgreSQL Table Constraints
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
Constraints SET DEFAULT option is miss-
ing
Check constraint with sub-
query
Overview
PostgreSQL supports the following types of table constraints:
l PRIMARY KEY
l FOREIGN KEY
l UNIQUE
l NOT NULL
l EXCLUDE (unique to PostgreSQL)
Similar to constraint declaration in SQL Server, PostgreSQL allows creating constraints in-line or out-
of-line when specifying table columns.
PostgreSQL constraints can be specified using CREATE / ALTER TABLE. Constraints on views are not sup-
ported.
You must have privileges (CREATE / ALTER) on the table in which constrains are created. For foreign key
constraints, you must also have the REFERENCES privilege.
Primary Key Constraints
l Uniquely identify each row and cannot contain NULL values.
l Use the same ANSI SQL syntax as SQL Server.
l Can be created on a single column or on multiple columns (composite primary keys) as the only
PRIMARY KEY in a table.
l Creating a PRIMARY KEY constraint automatically creates a unique B-Tree index on the column or
group of columns marked as the primary key of the table.
l Constraint names can be generated automatically by PostgreSQL or explicitly specified during
constraint creation.
Examples
Create an inline primary key constraint with a system-generated constraint name.
- 51 -
CREATE TABLE EMPLOYEES (
EMPLOYEE_ID NUMERIC PRIMARY KEY,
FIRST_NAME VARCHAR(20),
LAST_NAME VARCHAR(25),
EMAIL VARCHAR(25));
Create an inline primary key constraint with a user-specified constraint name.
CREATE TABLE EMPLOYEES (
EMPLOYEE_ID NUMERIC CONSTRAINT PK_EMP_ID PRIMARY KEY,
FIRST_NAME VARCHAR(20),
LAST_NAME VARCHAR(25),
EMAIL VARCHAR(25));
Create an out-of-line primary key constraint.
CREATE TABLE EMPLOYEES(
EMPLOYEE_ID NUMERIC,
FIRST_NAME VARCHAR(20),
LAST_NAME VARCHAR(25),
EMAIL VARCHAR(25)),
CONSTRAINT PK_EMP_ID PRIMARY KEY (EMPLOYEE_ID));
Add a primary key constraint to an existing table.
ALTER TABLE SYSTEM_EVENTS
ADD CONSTRAINT PK_EMP_ID PRIMARY KEY (EVENT_CODE, EVENT_TIME);
Drop the primary key.
ALTER TABLE SYSTEM_EVENTS DROP CONSTRAINT PK_EMP_ID;
Foreign Key Constraints
l Enforce referential integrity in the database. Values in specific columns or a group of columns
must match the values from another table (or column).
l Creating a FOREIGN KEY constraint in PostgreSQL uses the same ANSI SQL syntax as SQL Server.
l Can be created in-line or out-of-line during table creation.
l Use the REFERENCES clause to specify the table referenced by the foreign key constraint.
l When specifying REFERENCES in the absence of a column list in the referenced table, the
PRIMARY KEY of the referenced table is used as the referenced column or columns.
l A table can have multiple FOREIGN KEY constraints.
l Use the ON DELETE clause to handle FOREIGN KEY parent record deletions (such as cascading
deletes).
l Foreign key constraint names are generated automatically by the database or specified explicitly
during constraint creation.
- 52 -
ON DELETE Clause
PostgreSQL provides three main options to handle cases where data is deleted from the parent table
and a child table is referenced by a FOREIGN KEY constraint. By default, without specifying any addi-
tional options, PostgreSQL uses the NO ACTION method and raises an error if the referencing rows still
exist when the constraint is verified.
l ON DELETE CASCADE: Any dependent foreign key values in the child table are removed along
with the referenced values from the parent table.
l ON DELETE RESTRICT: Prevents the deletion of referenced values from the parent table and the
deletion of dependent foreign key values in the child table.
l ON DELETE NO ACTION: Performs no action (the default). The fundamental difference between
RESTRIC and NO ACTION is that NO ACTION allows the check to be postponed until later in the
transaction; RESTRICT does not.
ON UPDATE Clause
Handling updates on FOREIGN KEY columns is also available using the ON UPDATE clause, which
shares the same options as the ON DELETE clause:
l ON UPDATE CASCADE
l ON UPDATE RESTRICT
l ON UPDATE NO ACTION
Examples
Create an inline foreign key with a user-specified constraint name.
CREATE TABLE EMPLOYEES (
EMPLOYEE_ID NUMERIC PRIMARY KEY,
FIRST_NAME VARCHAR(20),
LAST_NAME VARCHAR(25),
EMAIL VARCHAR(25),
DEPARTMENT_ID NUMERIC REFERENCES DEPARTMENTS(DEPARTMENT_ID));
Create an out-of-line foreign key constraint with a system-generated constraint name.
CREATE TABLE EMPLOYEES (
EMPLOYEE_ID NUMERIC PRIMARY KEY,
FIRST_NAME VARCHAR(20),
LAST_NAME VARCHAR(25),
EMAIL VARCHAR(25),
DEPARTMENT_ID NUMERIC,
- 53 -
CONSTRAINT FK_FEP_ID
FOREIGN KEY(DEPARTMENT_ID) REFERENCES DEPARTMENTS(DEPARTMENT_ID));
Create a foreign key using the ON DELETE CASCADE clause.
CREATE TABLE EMPLOYEES (
EMPLOYEE_ID NUMERIC PRIMARY KEY,
FIRST_NAME VARCHAR(20),
LAST_NAME VARCHAR(25),
EMAIL VARCHAR(25),
DEPARTMENT_ID NUMERIC,
CONSTRAINT FK_FEP_ID
FOREIGN KEY(DEPARTMENT_ID) REFERENCES DEPARTMENTS(DEPARTMENT_ID)
ON DELETE CASCADE);
Add a foreign key to an existing table.
ALTER TABLE EMPLOYEES ADD CONSTRAINT FK_DEPT
FOREIGN KEY (department_id)
REFERENCES DEPARTMENTS (department_id) NOT VALID;
ALTER TABLE EMPLOYEES VALIDATE CONSTRAINT FK_DEPT;
UNIQUE Constraints
l Ensure that values in a column, or a group of columns, are unique across the entire table.
l PostgreSQL UNIQUE constraint syntax is ANSI SQL compatible.
l Automatically creates a B-Tree index on the respective column, or a group of columns, when cre-
ating a UNIQUE constraint.
l If duplicate values exist in the column(s) on which the constraint was defined during UNIQUE con-
straint creation, the UNIQUE constraint creation fails and returns an error message.
l UNIQUE constraints in PostgreSQL accept multiple NULL values (similar to SQL Server).
l UNIQUE constraint naming can be system-generated or explicitly specified.
Example
Create an inline unique constraint ensuring uniqueness of values in the email column.
CREATE TABLE EMPLOYEES (
EMPLOYEE_ID NUMERIC PRIMARY KEY,
FIRST_NAME VARCHAR(20),
LAST_NAME VARCHAR(25),
EMAIL VARCHAR(25) CONSTRAINT UNIQ_EMP_EMAIL UNIQUE,
DEPARTMENT_ID NUMERIC);
- 54 -
CHECK Constraints
l Enforce that values in a column satisfy a specific requirement.
l CHECK constraints in PostgreSQL use the same ANSI SQL syntax as SQL Server.
l Can only be defined using a Boolean data type to evaluate the values of a column.
l CHECK constraints naming can be system-generated or explicitly specified by the user during con-
straint creation.
Check constraints are using Boolean data datatype, therefor sub-query can't be used in CHECK con-
straint. if you want to use a similar feature you can create a Boolean function that will check the query
resulsts and return TRUE or FALSE values accordingly.
Example
Create an inline CHECK constraint using a regular expression to enforce the email column contains
email addresses with an “@aws.com” suffix.
CREATE TABLE EMPLOYEES (
EMPLOYEE_ID NUMERIC PRIMARY KEY,
FIRST_NAME VARCHAR(20),
LAST_NAME VARCHAR(25),
EMAIL VARCHAR(25) CHECK(EMAIL ~ '(^[A-Za-z][email protected]$)'),
DEPARTMENT_ID NUMERIC);
NOT NULL Constraints
l Enforce that a column cannot accept NULL values. This behavior is different from the default
column behavior in PostgreSQL where columns can accept NULL values.
l NOT NULL constraints can only be defined inline during table creation.
l You can explicitly specify names for NOT NULL constraints when used with a CHECK constraint.
Example
Define two not null constraints on the FIRST_NAME and LAST_NAME columns. Define a check con-
straint (with an explicitly user-specified name) to enforce not null behavior on the EMAIL column.
CREATE TABLE EMPLOYEES (
EMPLOYEE_ID NUMERIC PRIMARY KEY,
FIRST_NAME VARCHAR(20) NOT NULL,
LAST_NAME VARCHAR(25) NOT NULL,
EMAIL VARCHAR(25) CONSTRAINT CHK_EMAIL
CHECK(EMAIL IS NOT NULL));
- 55 -
SETConstraints Syntax
SET CONSTRAINTS {ALL | name [, ...] } {DEFERRED | IMMEDIATE }
PostgreSQL provides controls for certain aspects of constraint behavior:
l DEFERRABLE | NOT DEFERRABLE:Using the PostgreSQL SET CONSTRAINTS statement. Con-
straints can be defined as:
l DEFERRABLE:Allows you to use the SET CONSTRAINTS statement to set the behavior of con-
straint checking within the current transaction until transaction commit.
l IMMEDIATE:Constraints are enforced only at the end of each statement. Note that each
constraint has its own IMMEDIATE or DEFERRED mode.
l NOT DEFERRABLE:This statement always runs as IMMEDIATE and is not affected by the
SET CONSTRAINTS command.
l VALIDATE CONSTRAINT | NOT VALID:
l VALIDATE CONSTRAINT:Validates foreign key or check constraints (only) that were pre-
viously created as NOT VALID. This action performs a validation check by scanning the
table to ensure all records satisfy the constraint definition.
l NOT VALID:Can be used only for foreign key or check constraints. When specified, new
records are not validated with the creation of the constraint. Only when the VALIDATE
CONSTRAINT state is applied is the constraint state enforced on all records.
Using Existing Indexes During Constraint Creation
PostgreSQL can add a new primary key or unique constraints based on an existing unique Index . All
index columns are included in the constraint. When creating constraints using this method, the index
is owned by the constraint. When dropping the constraint, the index is also dropped.
Use an existing unique Index to create a primary key constraint.
CREATE UNIQUE INDEX IDX_EMP_ID ON EMPLOYEES(EMPLOYEE_ID);
ALTER TABLE EMPLOYEES
ADD CONSTRAINT PK_CON_UNIQ PRIMARY KEY USING INDEX IDX_EMP_ID;
Summary
The following table identifies similarities, differences, and key migration considerations.
Feautre SQL Server Aurora PostgreSQL
CHECK constraints CHECK CHECK
UNIQUE constraints UNIQUE UNIQUE
- 56 -
Feautre SQL Server Aurora PostgreSQL
PRIMARY KEY constraints PRIMARY KEY PRIMARY KEY
FOREIGN KEY constraints FOREIGN KEY FOREIGN KEY
Cascaded referential actions NO ACTION | CASCADE | SET NULL
| SET DEFAULT
RESTRICT | CASCADE | SET
NULL |
NO ACTION
Indexing of referencing
columns
Not required N/A
Indexing of referenced
columns
PRIMARY KEY or UNIQUE PRIMARY KEY or UNIQUE
For additional details:
l https://www.postgresql.org/docs/9.6/static/ddl-constraints.html
l https://www.postgresql.org/docs/9.6/static/sql-set-constraints.html
l https://www.postgresql.org/docs/9.6/static/sql-altertable.html
- 57 -
Migrate from: SQL Server Creating Tables
Feature Com-
patibility
SCT Automation
Level
SCT Action Code Index Key Differences
SCT Action Codes -
CREATE TABLE
Auto generated value column is dif-
ferent
Can't use physical attribute ON
Missing table variable and memory
optimized table
Overview
ANSI Syntax Conformity
Tables in SQL Server are created using the CREATE TABLE statement and conform to the ANSI/ISO entry
level standard. The basic features of CREATE TABLE are similar for most relational database man-
agement engines and are well defined in the ANSI/ISO standards.
In its most basic form, the CREATE TABLE statement in SQL Server is used to define:
l Table names, the containing security schema, and database
l Column names
l Column data types
l Column and table constraints
l Column default values
l Primary, candidate (UNIQUE), and foreign keys
T-SQL Extensions
SQL Server extends the basic syntax and provides many additional options for the CREATE TABLE or
ALTER TABLE statements. The most often used options are:
l Supporting index types for primary keys and unique constraints, clustered or non-clustered, and
index properties such as FILLFACTOR
l Physical table data storage containers using the ON <File Group> clause
l Defining IDENTITY auto-enumerator columns
l Encryption
l Compression
l Indexes
- 58 -
For more information, see Data Types, Column Encryption, and Databases and Schemas.
Table Scope
SQL Server provides five scopes for tables:
l Standard tables are created on disk, globally visible, and persist through connection resets and
server restarts.
l Temporary Tables are designated with the "# " prefix. They are persisted in TempDB and are vis-
ible to the execution scope where they were created (and any sub-scopes). Temporary tables are
cleaned up by the server when the execution scope terminates and when the server restarts.
l Global Temporary Tables are designated by the "## " prefix. They are similar in scope to tem-
porary tables, but are also visible to concurrent scopes.
l Table Variables are defined with the DECLARE statement, not with CREATE TABLE. They are visible
only to the execution scope where they were created.
l Memory-Optimized tables are special types of tables used by the In-Memory Online Transaction
Processing (OLTP) engine. They use a non-standard CREATE TABLE syntax.
Creating a Table Based on an Existing Table or Query
SQL Server allows creating new tables based on SELECT queries as an alternate to the CREATE TABLE
statement. A SELECT statement that returns a valid set with unique column names can be used to cre-
ate a new table and populate data.
SELECT INTO is a combination of DML and DDL. The simplified syntax for SELECT INTO is:
SELECT <Expression List>
INTO <Table Name>
[FROM <Table Source>]
[WHERE <Filter>]
[GROUP BY <Grouping Expressions>...];
When creating a new table using SELECT INTO, the only attributes created for the new table are column
names, column order, and the data types of the expressions. Even a straight forward statement such
as SELECT * INTO <New Table> FROM <Source Table> does not copy constraints, keys, indexes, identity
property, default values, or any other related objects.
TIMESTAMP Syntax for ROWVERSION Deprecated Syntax
The TIMESTAMP syntax synonym for ROWVERSION has been deprecated as of SQL Server 2008R2 in
accordance with https://docs.microsoft.com/en-us/previous-versions/sql/sql-server-2008-r2/ms143729
(v=sql.105).
Previously, you could use either the TIMESTAMP or the ROWVERSION keywords to denote a special
data type that exposes an auto-enumerator. The auto-enumerator generates unique eight-byte binary
- 59 -
numbers typically used to version-stamp table rows. Clients read the row, process it, and check the
ROWVERSION value against the current row in the table before modifying it. If they are different, the
row has been modified since the client read it. The client can then apply different processing logic.
Note that when migrating to Aurora PostgreSQL using the Amazon RDS Schema Conversion Tool (SCT),
neither ROWVERSION nor TIMESTAMP are supported. You must add customer logic, potentially in the
form of a trigger, to maintain this functionality.
See a full example in Creating Tables.
Syntax
Simplified syntax for CREATE TABLE:
CREATE TABLE [<Database Name>.<Schema Name>].<TableName> (<Column Definitions>)
[ON{<Partition Scheme Name> (<Partition Column Name>)];
<Column Definition>:
<Column Name> <Data Type>
[CONSTRAINT <Column Constraint>
[DEFAULT <Default Value>]]
[IDENTITY [(<Seed Value>, <Increment Value>)]
[NULL | NOT NULL]
[ENCRYPTED WITH (<Encryption Specifications>)
[<Column Constraints>]
[<Column Index Specifications>]
<Column Constraint>:
[CONSTRAINT <Constraint Name>]
{{PRIMARY KEY | UNIQUE} [CLUSTERED | NONCLUSTERED]
[WITH FILLFACTOR = <Fill Factor>]
| [FOREIGN KEY]
REFERENCES <Referenced Table> (<Referenced Columns>)]
<Column Index Specifications>:
INDEX <Index Name> [CLUSTERED | NONCLUSTERED]
[WITH(<Index Options>]
Examples
Create a basic table.
CREATE TABLE MyTable
(
Col1 INT NOT NULL PRIMARY KEY,
Col2 VARCHAR(20) NOT NULL
);
Create a table with column constraints and an identity.
CREATE TABLE MyTable
(
- 60 -
Col1 INT NOT NULL PRIMARY KEY IDENTITY (1,1),
Col2 VARCHAR(20) NOT NULL CHECK (Col2 <> ''),
Col3 VARCHAR(100) NULL
REFERENCES MyOtherTable (Col3)
);
Create a table with an additional index.
CREATE TABLE MyTable
(
Col1 INT NOT NULL PRIMARY KEY,
Col2 VARCHAR(20) NOT NULL
INDEX IDX_Col2 NONCLUSTERED
);
For more information, see https://docs.microsoft.com/en-us/sql/t-sql/statements/create-table-transact-sql
- 61 -
Migrate to: Aurora PostgreSQL Creating Tables
Feature Com-
patibility
SCT Automation
Level
SCT Action Code Index Key Differences
SCT Action Codes -
CREATE TABLE
Auto generated value column is dif-
ferent
Can't use physical attribute ON
Missing table variable and memory
optimized table
Overview
Like SQL Server, Aurora PostgreSQL provides ANSI/ISO syntax entry level conformity for CREATE TABLE
and custom extensions to support Aurora PostgreSQL specific functionality.
In its most basic form, and very similar to SQL Server, the CREATE TABLE statement in Aurora Post-
greSQL is used to define:
l Table names containing security schema and/or database
l Column names
l Column data types
l Column and table constraints
l Column default values
l Primary, candidate (UNIQUE), and foreign keys
Aurora PostgreSQL Extensions
Aurora PostgreSQL extends the basic syntax and allows many additional options to be defined as part
of the CREATE TABLE or ALTER TABLE statements. The most often used option is in-line index defin-
ition.
Table Scope
Aurora PostgreSQL provides two table scopes:
l Standard Tables are created on disk, visible globally, and persist through connection resets and
server restarts.
l Temporary Tables are created using the CREATE GLOBAL TEMPORARY TABLE statement. A
TEMPORARY table is visible only to the session that creates it and is dropped automatically when
the session is closed.
- 62 -
Creating a Table Based on an Existing Table or Query
Aurora PostgreSQL provides two ways to create standard or temporary tables based on existing tables
and queries:
CREATE TABLE <New Table> LIKE <Source Table> and CREATE TABLE ... AS <Query Expres-
sion>.
CREATE TABLE <New Table> LIKE <Source Table> creates an empty table based on the defin-
ition of another table including any column attributes and indexes defined in the ori-
ginal table.
CREATE TABLE ... AS <Query Expression> is very similar to SQL Server's SELECT INTO. It
allows creating a new table and populating data in a single step.
For example:
CREATE TABLE SourceTable(Col1 INT);
INSERT INTO SourceTable VALUES (1)
CREATE TABLE NewTable(Col1 INT) AS SELECT Col1 AS Col2 FROM SourceTable;
INSERT INTO NewTable (Col1, Col2) VALUES (2,3);
SELECT * FROM NewTable
Col1 Col2
---- ----
NULL 1
23
Converting TIMESTAMP and ROWVERSION Columns
SQL server provides an automatic mechanism for stamping row versions for application concurrency
control. For example:
CREATE TABLE WorkItems
(
WorkItemID INT IDENTITY(1,1) PRIMARY KEY,
WorkItemDescription XML NOT NULL,
Status VARCHAR(10) NOT NULL DEFAULT ('Pending'),
-- other columns...
VersionNumber ROWVERSION
);
The VersionNumber column automatically updates when a row is modified. The actual value is mean-
ingless. Just the fact that it changed is what indicates a row modification. The client can now read a
work item row, process it, and ensure no other clients updated the row before updating the status.
- 63 -
SELECT @WorkItemDescription = WorkItemDescription,
@Status = Status,
@VersionNumber = VersionNumber
FROM WorkItems
WHERE WorkItemID = @WorkItemID;
EXECUTE ProcessWorkItem @WorkItemID, @WorkItemDescription, @Stauts OUTPUT;
IF (
SELECT VersionNumber
FROM WorkItems
WHERE WorkItemID = @WorkItemID
) = @VersionNumber;
EXECUTE UpdateWorkItems @WorkItemID, 'Completed'; -- Success
ELSE
EXECUTE ConcurrencyExceptionWorkItem; -- Row updated while processing
In Aurora PostgreSQL, you can add a trigger to maintain the updated stamp per row.
CREATE OR REPLACE FUNCTION IncByOne()
RETURNS TRIGGER
AS $$
BEGIN
UPDATE WorkItems SET VersionNumber = VersionNumber+1
WHERE WorkItemID = OLD.WorkItemID;
END; $$
LANGUAGE PLPGSQL;
CREATE TRIGGER MaintainWorkItemVersionNumber
AFTER UPDATE OF WorkItems
FOR EACH ROW
EXECUTE PROCEDURE IncByOne();
For more information on PostgreSQLtriggers, see the Triggers.
Syntax
CREATE [[GLOBAL | LOCAL ] {TEMPORARY | TEMP } | UNLOGGED ] TABLE [IF NOT EXISTS ]
table_name ([
{column_name data_type [COLLATE collation ] [column_constraint [... ] ]
| table_constraint
| LIKE source_table [like_option ... ] }
[, ... ]
] )
[INHERITS (parent_table [, ... ] ) ]
[WITH (storage_parameter [= value] [, ... ] ) | WITH OIDS | WITHOUT OIDS ]
[ON COMMIT {PRESERVE ROWS | DELETE ROWS | DROP } ]
[TABLESPACE tablespace_name ]
CREATE [[GLOBAL | LOCAL ] {TEMPORARY | TEMP } | UNLOGGED ] TABLE [IF NOT EXISTS ]
table_name
OF type_name [(
- 64 -
{column_name WITH OPTIONS [column_constraint [... ] ]
| table_constraint }
[, ... ]
) ]
[WITH (storage_parameter [= value] [, ... ] ) | WITH OIDS | WITHOUT OIDS ]
[ON COMMIT {PRESERVE ROWS | DELETE ROWS | DROP } ]
[TABLESPACE tablespace_name ]
where column_constraint is:
[CONSTRAINT constraint_name ]
{NOT NULL |
NULL |
CHECK (expression ) [NO INHERIT ] |
DEFAULT default_expr |
UNIQUE index_parameters |
PRIMARY KEY index_parameters |
REFERENCES reftable [(refcolumn ) ] [MATCH FULL | MATCH PARTIAL | MATCH SIMPLE ]
[ON DELETE action ] [ON UPDATE action ] }
[DEFERRABLE | NOT DEFERRABLE ] [INITIALLY DEFERRED | INITIALLY IMMEDIATE ]
and table_constraint is:
[CONSTRAINT constraint_name ]
{CHECK (expression ) [NO INHERIT ] |
UNIQUE (column_name [, ... ] ) index_parameters |
PRIMARY KEY (column_name [, ... ] ) index_parameters |
EXCLUDE [USING index_method ] (exclude_element WITH operator [, ... ] ) index_para-
meters [WHERE (predicate ) ] |
FOREIGN KEY (column_name [, ... ] ) REFERENCES reftable [(refcolumn [, ... ] ) ]
[MATCH FULL | MATCH PARTIAL | MATCH SIMPLE ] [ON DELETE action ] [ON UPDATE
action ] }
[DEFERRABLE | NOT DEFERRABLE ] [INITIALLY DEFERRED | INITIALLY IMMEDIATE ]
and like_option is:
{INCLUDING | EXCLUDING } {DEFAULTS | CONSTRAINTS | INDEXES | STORAGE | COMMENTS |
ALL }
index_parameters in UNIQUE, PRIMARY KEY, and EXCLUDE constraints are:
[WITH (storage_parameter [= value] [, ... ] ) ]
[USING INDEX TABLESPACE tablespace_name ]
exclude_element in an EXCLUDE constraint is:
{column_name | (expression ) } [opclass ] [ASC | DESC ] [NULLS {FIRST | LAST } ]
Examples
Create a basic table.
CREATE TABLE MyTable
(
- 65 -
Col1 INT PRIMARY KEY,
Col2 VARCHAR(20) NOT NULL
);
Create a table with column constraints.
CREATE TABLE MyTable
(
Col1 INT PRIMARY KEY,
Col2 VARCHAR(20) NOT NULL
CHECK (Col2 <> ''),
Col3 VARCHAR(100) NULL
REFERENCES MyOtherTable (Col3)
);
Summary
Feature SQL Server Aurora PostgreSQL
ANSI compliance Entry level Entry level
Auto generated enumerator IDENTITY SERIAL
Reseed auto generated value DBCC CHECKIDENT N/A
Index types CLUSTERED / NONCLUSTERED See the Clustered and Non
Clustered Indexes .
Physical storage location ON <File Group> Not supported
Temporary tables #TempTable CREATE GLOBAL TEMPORARY
TABLE
Global Temporary Tables ##GlobalTempTable CREATE TEMPORARY TABLE
Table Variables DECLARE @Table Not supported
Create table as query SELECT... INTO CREATE TABLE... AS
Copy table structure Not supported CREATE TABLE... LIKE
Memory optimized tables Supported N/A
For more information, see https://www.postgresql.org/docs/9.6/static/sql-createtable.html
- 66 -
Migrate from: SQL Server Common Table Expres-
sions
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A Must use RECURSIVE key word for recursive
CTE queries
Overview
Common Table Expressions (CTE), which have been a part of the ANSI standard since SQL:1999, sim-
plify queries and make them more readable by defining a temporary view, or derived table, that a sub-
sequent query can reference. SQLServer CTEs can be the target of DML modification statements. They
have similar restrictions as updateable views.
SQLServer CTEs provide recursive functionality in accordance with the the ANSI 99 standard. Recursive
CTEs can reference themselves and re-execute queries until the data set is exhausted, or the maximum
number of iterations is exceeded.
CTE Syntax (simplified)
WITH <CTE NAME>
AS
(
SELECT ....
)
SELECT ...
FROM CTE
Recursive CTE syntax
WITH <CTE NAME>
AS (
<Anchor SELECTquery>
UNION ALL
<Recursive SELECTquery with reference to <CTE NAME>>
)
SELECT ... FROM <CTE NAME>...
Examples
Create and populate an OrderItems table.
CREATE TABLE OrderItems
(
- 67 -
OrderID INT NOT NULL,
Item VARCHAR(20) NOT NULL,
Quantity SMALLINT NOT NULL,
PRIMARY KEY(OrderID, Item)
);
INSERT INTO OrderItems (OrderID, Item, Quantity)
VALUES
(1, 'M8 Bolt', 100),
(2, 'M8 Nut', 100),
(3, 'M8 Washer', 200),
(3, 'M6 Washer', 100);
Define a CTE to calculate the total quantity in every order and then join to the OrderItems table to
obtain the relative quantity for each item.
WITH AggregatedOrders
AS
(SELECT OrderID, SUM(Quantity) AS TotalQty
FROM OrderItems
GROUP BY OrderID
)
SELECT O.OrderID, O.Item,
O.Quantity,
(O.Quantity / AO.TotalQty) * 100 AS PercentOfOrder
FROM OrderItems AS O
INNER JOIN
AggregatedOrders AS AO
ON O.OrderID = AO.OrderID;
The example above produces the following results:
OrderID ItemQuantity PercentOfOrder
--------------------------------
1 M8Bolt100 100.0000000000
2 M8Nut100 100.0000000000
3 M8Washer 100 33.3333333300
3 M6Washer200 66.6666666600
Using a Recursive CTE, create and populate the Employees table with the DirectManager for each
employee.
CREATE TABLE Employees
(
Employee VARCHAR(5) NOT NULL PRIMARY KEY,
DirectManager VARCHAR(5) NULL
);
INSERT INTO Employees(Employee, DirectManager)
VALUES
('John', 'Dave'),
('Jose', 'Dave'),
('Fred', 'John'),
('Dave', NULL);
- 68 -
Use a recursive CTE to display the employee-management hierarchy.
WITH EmpHierarchyCTE AS
(
-- Anchor query retrieves the top manager
SELECT 0 AS LVL,
Employee,
DirectManager
FROM Employees AS E
WHERE DirectManager IS NULL
UNION ALL
-- Recursive query gets all Employees managed by the previous level
SELECT LVL + 1 AS LVL,
E.Employee,
E.DirectManager
FROM EmpHierarchyCTE AS EH
INNER JOIN
Employees AS E
ON E.DirectManager = EH.Employee
)
SELECT *
FROM EmpHierarchyCTE;
The example above displays the following results:
LVL EmployeeDirectManager
------------------------
0 Dave NULL
1 John Dave
1 Jose Dave
2 Fred John
For more information, see https://technet.microsoft.com/en-us/library/ms186243.aspx
- 69 -
Migrate to: Aurora PostgreSQL Common Table
Expressions (CTE)
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A Must use RECURSIVE key word for recursive
CTE queries
Overview
PostgreSQL conforms to the ANSI SQL-99 standard and implementing CTEs in PostgreSQL is similar to
SQL Server.
CTE also non as WITH query, this type of query help you to simplify long queries, it similar to defining
temporary tables that exist only for the running of the query. The statement in a WITH clause can be a
SELECT, INSERT, UPDATE, or DELETE, and the WITH clause itself is attached to a primary statement that
can also be a SELECT, INSERT, UPDATE, or DELETE.
CTE Syntax (simplified)
WITH <CTE NAME>
AS
(
SELECT OR DML
)
SELECT OR DML
Recursive CTE syntax
WITH RECURSIVE <CTE NAME>
AS (
<Anchor SELECTquery>
UNION ALL
<Recursive SELECTquery with reference to <CTE NAME>>
)
SELECT OR DML
- 70 -
Examples
Create and populate an OrderItems table.
CREATE TABLE OrderItems
(
OrderID INT NOT NULL,
Item VARCHAR(20) NOT NULL,
Quantity SMALLINT NOT NULL,
PRIMARY KEY(OrderID, Item)
);
INSERT INTO OrderItems (OrderID, Item, Quantity)
VALUES
(1, 'M8 Bolt', 100),
(2, 'M8 Nut', 100),
(3, 'M8 Washer', 200),
(3, 'M6 Washer', 100);
Create a CTE.
WITH DEPT_COUNT
(DEPARTMENT_ID, DEPT_COUNT) AS (
SELECT DEPARTMENT_ID, COUNT(*) FROM EMPLOYEES GROUP BY DEPARTMENT_ID)
SELECT E.FIRST_NAME ||' '|| E.LAST_NAME AS EMP_NAME,
D.DEPT_COUNT AS EMP_DEPT_COUNT
FROM EMPLOYEES E JOIN DEPT_COUNT D USING (DEPARTMENT_ID) ORDER BY 2;
PostgreSQL provides an additional feature when using a CTE as a recursive modifier. The following
example uses a recursive WITH clause to access its own result set.
WITH RECURSIVE t(n) AS (
VALUES (0)
UNION ALL
SELECT n+1 FROM t WHERE n < 5)
SELECT * FROM t;
WITH RECURSIVE t(n) AS (
VALUES (0)
UNION ALL
SELECT n+1 FROM t WHERE n < 5)
SELECT * FROM t;
n
--
0
...
5
- 71 -
Note that using the SQL Server example will get undesired results:
Define a CTE to calculate the total quantity in every order and then join to the OrderItems table to
obtain the relative quantity for each item.
WITH AggregatedOrders
AS
(SELECT OrderID, SUM(Quantity) AS TotalQty
FROM OrderItems
GROUP BY OrderID
)
SELECT O.OrderID, O.Item,
O.Quantity,
(O.Quantity / AO.TotalQty) * 100 AS PercentOfOrder
FROM OrderItems AS O
INNER JOIN
AggregatedOrders AS AO
ON O.OrderID = AO.OrderID;
The example above produces the following results:
OrderID ItemQuantity PercentOfOrder
--------------------------------
1 M8Bolt100 100
2 M8Nut100 100
3 M8Washer 100 0
3 M6Washer200 0
This is because devide INT by INT will return round result, if another data type is in used such as
DECIMAL there will be no problem, in order to fix the current issue the columns can be casted using
'::decimal'.
WITH AggregatedOrders
AS
(SELECT OrderID, SUM(Quantity) AS TotalQty
FROM OrderItems
GROUP BY OrderID
)
SELECT O.OrderID, O.Item,
O.Quantity,
trunc((O.Quantity::decimal / AO.TotalQty::decimal)*100,2) AS PercentOfOrder
FROM OrderItems AS O
INNER JOIN
AggregatedOrders AS AO
ON O.OrderID = AO.OrderID;
The example above produces the following results:
OrderID ItemQuantity PercentOfOrder
--------------------------------
1 M8Bolt100 100.00
2 M8Nut100 100.00
3 M8Washer 100 66.66
3 M6Washer200 33.33
- 72 -
For RECURSIVE WITH query, the 'RECURSIVE' word must be used (unlike in SQL Server).
The equivalent query to SQL Server example will be:
Use a recursive CTE to display the employee-management hierarchy.
WITH RECURSIVE EmpHierarchyCTE AS
(
-- Anchor query retrieves the top manager
SELECT 0 AS LVL,
Employee,
DirectManager
FROM Employees AS E
WHERE DirectManager IS NULL
UNION ALL
-- Recursive query gets all Employees managed by the previous level
SELECT LVL + 1 AS LVL,
E.Employee,
E.DirectManager
FROM EmpHierarchyCTE AS EH
INNER JOIN
Employees AS E
ON E.DirectManager = EH.Employee
)
SELECT *
FROM EmpHierarchyCTE;
The example above displays the following results:
LVL EmployeeDirectManager
------------------------
0 Dave
1 John Dave
1 Jose Dave
2 Fred John
For additional details, see: https://www.postgresql.org/docs/9.6/static/queries-with.html
- 73 -
Migrate from: SQL Server Data Types
Feature Com-
patibility
SCT Automation
Level
SCT Action Code Index Key Differences
SCT Action Codes - Data
Types
Syntax and handling dif-
ferences
Overview
In SQL Server, each table column, variable, expression, and parameter has an associated data type.
SQLServer provides a rich set of built-in data types as summarized in the following table.
Category Data Types
Numeric BIT, TINYINT, SMALLINT, INT, BIGINT, NUMERIC, DECIMAL, MONEY,
SMALLMONEY, FLOAT, REAL
String and Character CHAR, VARCHAR, NCHAR, NVARCHAR
Temporal DATE, TIME, SMALLDATETIME, DATETIME, DATETIME2,
DATETIMEOFFSET
Binary BINARY, VARBINARY
Large Object (LOB) TEXT, NTEXT, IMAGE, VARCHAR(MAX), NVARCHAR(MAX), VARBINARY
(MAX)
Cursor CURSOR
GUID UNIQUEIDENTIFIER
Hierarchical identifier HIERARCHYID
Spatial GEOMETRY, GEOGRAPHY
Sets (Table type) TABLE
XML XML
Other Specialty Types ROW VERSION, SQL_VARIANT
Note: You can create custom user defined data types using T-SQL, and the .NET Framework.
Custom data types are based on the built-in system data types and are used to simplify devel-
opment. For more information, see User Defined Types.
TEXT, NTEXT, and IMAGE Deprecated Data Types
The TEXT, NTEXT, and IMAGE data types have been deprecated as of SQL Server 2008R2 in accordance
with https://docs.microsoft.com/en-us/previous-versions/sql/sql-server-2008-r2/ms143729(v=sql.105).
- 74 -
These data types are legacy types for storing BLOB and CLOB data. The TEXT data type was used to
store ASCII text CLOBS, the NTEXT data type to store UNICODE CLOBS, and IMAGE was used as a gen-
eric data type for storing all BLOB data. In SQL Server 2005, Microsoft introduced the new and
improved VARCHAR(MAX), NVARCHAR(MAX), and VARBINARY(MAX)data types as the new BLOB and
CLOB standard. These new types support a wider range of functions and operations. They also provide
enhanced performance over the legacy types.
If your code uses TEXT, NTEXT or IMAGE data types, SCT automatically converts them to the appro-
priate Aurora PostgreSQL BYTEA data type. TEXT and NTEXT are converted to LONGTEXT and image to
LONGBLOB. Make sure you use the proper collations. For more details, see the Collations.
Examples
Define table columns.
CREATE TABLE MyTable
(
Col1 ASINTEGER NOT NULL PRIMARY KEY,
Col2 ASNVARCHAR(100) NOT NULL
);
Define variable types.
DECLARE @MyXMLType ASXML,
@MyTemporalType AS DATETIME2
DECLARE@MyTableType
AS TABLE
(
Col1 AS BINARY(16) NOT NULL PRIMARY KEY,
Col2 AS XML NULL
);
For more information, see https://docs.microsoft.com/en-us/sql/t-sql/data-types/data-types-transact-sql
- 75 -
Migrate to: Aurora PostgreSQL Data Types
Feature Com-
patibility
SCT Automation
Level
SCT Action Code Index Key Differences
SCT Action Codes - Data
Types
Syntax and handling dif-
ferences
Overview
PostgreSQL provides multiple data types equivalent to certain SQL Server data types. The following
table provides the full list of PostgreSQL data types:
SQL Server
Data Type
Family
SQL ServerData
Type
SQL Server Data Type Char-
acteristic
PostgreSQL
Identical
Compatibility
PostgreSQL Cor-
responding
Data Type
Character CHAR Fixed length 1-8,000 Yes CHAR
VARCHAR Variable length 1-8,000 Yes VARCHAR
NCHAR Fixed length 1-4,000 Yes NCHAR
NVARCHAR Variable length 1-4,000 Yes NVARCHAR
Numeric BIT first 8 BIT column will consume 1
byte, 9 to 16 BIT columns will be
2 bytes etc..
Yes BIT
TINYINT 8-bit unsigned integer, 0 to 255 No SMALLINT
SMALLINT 16-bit integer Yes SMALLINT
INT, INTEGER 32-bit integer Yes INT, INTEGER
BIGINT 64-bit integer Yes BIGINT
NUMERIC Fixed-point number Yes NUMERIC
DECIMAL Fixed-point number Yes DECIMAL
MONEY 64-bit currency amount Yes MONEY
SMALLMONEY 32-bit currency amount No MONEY
FLOAT Floating-point number Yes FLOAT
REAL Single-precision floating-point
number
Yes REAL
- 76 -
SQL Server
Data Type
Family
SQL ServerData
Type
SQL Server Data Type Char-
acteristic
PostgreSQL
Identical
Compatibility
PostgreSQL Cor-
responding
Data Type
Temporal DATE Date (year, month and day) Yes DATE
TIME Time (hour, minute, second and
fraction)
Yes TIME
SMALLDATETIME Date and time No TIMESTAMP(0)
DATETIME Date and time with fraction No TIMESTAMP(3)
DATETIME2 Date and time with fraction No TIMESTAMP(p)
Temporal DATETIMEOFFSET Date and time with fraction and
time zone
No TIMESTAMP(p)
WITH TIME
ZONE
Binary BINARY Fixed-length byte string No BYTEA
VARBINARY Variable length 1-8,000 No BYTEA
LOB TEXT Variable-length character data up
to 2 GB
Yes TEXT
NTEXT Variable-length Unicode UCS-2
data up to 2 GB
No TEXT
IMAGE Variable-length character data up
to 2 GB
No BYTEA
VARCHAR(MAX) Variable-length character data up
to 2 GB
Yes TEXT
NVARCHAR(MAX) Variable-length Unicode UCS-2
data up to 2 GB
No TEXT
VARBINARY(MAX) Variable-length character data up
to 2 GB
No BYTEA
XML XML XML data Yes XML
GUID UNIQUEIDENTIFIER 16-byte GUID (UUID) No CHAR(16)
Hierarchical
identifier
HIERARCHYID Approximately 5 bytes No NVARCHAR
(4000)
Spatial - For
using with
Aurora Post-
greSQL, see:
AWS Docs
GEOMETRY Euclidean (flat) coordinate system Yes GEOMETRY
GEOGRAPHY Round-earth coordinate system Yes GEOGRAPHY
SQL_VARIANT Maximum length of 8016 No No equivalent
Other ROWVERSION 8 bytes No TIMESTAMP(p)
- 77 -
PostgreSQL Character Column Semantics
PostgreSQL only supports CHAR for column size semantics. If you define a field as VARCHAR (10), Post-
greSQL can store 10 characters regardless of how many bytes it takes to store each non-English char-
acter. VARCHAR(n) stores strings up to n characters (not bytes) in length.
Migration of SQL Server Data Types to PostgreSQL Data Types
Automatic migration and conversion of SQL Server Tables and Data Types can be performed using
Amazon’s Schema Conversion Tool (Amazon SCT).
Examples
To demonstrate SCT’s capability for migrating SQL Server tables to their PostgreSQL equivalents, a
table containing columns representing the majority of SQL Server data types was created and con-
verted using Amazon SCT.
Source SQL Server compatible DDL for creating the DATATYPES table:
CREATE TABLE "DataTypes"(
"BINARY_FLOAT" REAL,
"BINARY_DOUBLE" FLOAT,
"BLOB" VARBINARY(4000),
"CHAR" CHAR(10),
"CHARACTER" CHAR(10),
"CLOB" VARCHAR(4000),
"DATE" DATE,
"DECIMAL" NUMERIC(3,2),
"DOUBLE_PRECISION" FLOAT(52),
"FLOAT" FLOAT(3),
"INTEGER" INTEGER,
"LONG" TEXT,
"NCHAR" NCHAR(10),
"NUMBER" NUMERIC(9,9),
"NUMBER1" NUMERIC(9,0),
"NUMERIC" NUMERIC(9,9),
"RAW" BINARY(10),
"REAL" FLOAT(52),
"SMALLINT" SMALLINT,
"TIMESTAMP" TIMESTAMP,
"TIMESTAMP_WITH_TIME_ZONE" DATETIMEOFFSET(5),
"VARCHAR" VARCHAR(10),
"VARCHAR2" VARCHAR(10),
"XMLTYPE" XML
);
Target PostgreSQL compatible DDL for creating the DATATYPES table migrated from SQL Server with
Amazon SCT:
- 78 -
CREATE TABLE IF NOT EXISTS datatypes(
binary_float real DEFAULT NULL,
binary_double double precision DEFAULT NULL,
blob bytea DEFAULT NULL,
char character(10) DEFAULT NULL,
character character(10) DEFAULT NULL,
clob text DEFAULT NULL,
date TIMESTAMP(0) without time zone DEFAULT NULL,
decimal numeric(3,2) DEFAULT NULL,
dec numeric(3,2) DEFAULT NULL,
double_precision double precision DEFAULT NULL,
float double precision DEFAULT NULL,
integer numeric(38,0) DEFAULT NULL,
long text DEFAULT NULL,
nchar character(10) DEFAULT NULL,
number numeric(9,9) DEFAULT NULL,
number1 numeric(9,0) DEFAULT NULL,
numeric numeric(9,9) DEFAULT NULL,
raw bytea DEFAULT NULL,
real double precision DEFAULT NULL,
smallint numeric(38,0) DEFAULT NULL,
timestamp TIMESTAMP(5) without time zone DEFAULT NULL,
timestamp_with_time_zone TIMESTAMP(5) with time zone DEFAULT NULL,
varchar character varying(10) DEFAULT NULL,
varchar2 character varying(10) DEFAULT NULL,
xmltype xml DEFAULT NULL
)
WITH (
OIDS=FALSE
);
Summary
All incompatible data type being converted by SCT
SQL Server CREATE TABLE command:
Create table scttest(
SMALLDATETIMEcol SMALLDATETIME,
datetimecol DATETIME,
datetime2col DATETIME2,
datetimeoffsetcol DATETIMEOFFSET,
binarycol BINARY,
varbinarycol VARBINARY,
ntextcol NTEXT,
imagecol IMAGE,
nvarcharmaxcol NVARCHAR(MAX),
varbinarymaxcol VARBINARY(MAX),
uniqueidentifiercol UNIQUEIDENTIFIER,
hierarchyiDcol HIERARCHYID,
sql_variantcol SQL_VARIANT,
rowversioncol ROWVERSION);
The eqivelant command that was created using the SCT:
- 79 -
CREATE TABLE scttest(
smalldatetimecol TIMESTAMP WITHOUT TIME ZONE,
datetimecol TIMESTAMP WITHOUT TIME ZONE,
datetime2col TIMESTAMP(6) WITHOUT TIME ZONE,
datetimeoffsetcol TIMESTAMP(6) WITH TIME ZONE,
binarycol BYTEA,
varbinarycol BYTEA,
ntextcol TEXT,
imagecol BYTEA,
nvarcharmaxcol TEXT,
varbinarymaxcol BYTEA,
uniqueidentifiercol UUID,
hierarchyidcol VARCHAR(8000),
sql_variantcol VARCHAR(8000),
rowversioncol VARCHAR(8000) NOT NULL);
For additional details, see:
l https://www.postgresql.org/docs/current/static/ddl-system-columns.html
l https://www.postgresql.org/docs/current/static/datatype.html
l https://aws.amazon.com/documentation/SchemaConversionTool
- 80 -
Migrate from: SQL Server Derived Tables
Feature Compatibility SCT Automation Level SCT Action Code Index Key Differences
N/A
Overview
SQL Server implements Derived Tables as specified in ANSI SQL:2011.Aderived tables are similar to a
CTE, but the reference to another query is used inside the FROM clause of a query.
This feature enables you to write more sophisticated, complex join queries.
Examples
SELECT name, salary, average_salary
FROM (SELECT AVG(salary)
FROM employee) AS workers (average_salary), employee
WHERE salary > average_salary
ORDER BY salary DESC;
For more information, see https://docs.microsoft.com/en-us/sql/t-sql/queries/from-transact-sql
- 81 -
Migrate to: Aurora PostgreSQL Derived Tables
Feature Compatibility SCT Automation Level SCT Action Code Index Key Differences
N/A
Overview
PostgreSQL implements Derived Tables and is fully compatible with SQL Server Derived Tables.
Examples
SELECT name, salary, average_salary
FROM (SELECT AVG(salary)
FROM employee) AS workers (average_salary), employee
WHERE salary > average_salary
ORDER BY salary DESC;
For more information, see https://www.postgresql.org/docs/9.6/static/queries-table-expressions.html
- 82 -
Migrate from: SQL Server GROUP BY
Feature Compatibility SCT Automation Level SCT Action Code Index Key Differences
N/A
Overview
GROUP BY is an ANSI SQL query clause used to group individual rows that have passed the WHERE fil-
ter clause into groups to be passed on to the HAVING filter and then to the SELECT list. This grouping
supports the use of aggregate functions such as SUM, MAX, AVG, and others.
Syntax
ANSI compliant GROUP BYSyntax:
GROUP BY
[ROLLUP |CUBE]
<Column Expression> ...n
[GROUPING SETS (<Grouping Set>)...n
Backward compatibility syntax:
GROUP BY
[ALL ] <Column Expression> ...n
[WITH CUBE | ROLLUP ]
The basic ANSIsyntax for GROUP BY supports multiple grouping expressions, the CUBE and ROLLUP
keywords, and the GROUPING SETS clause; all used to add super-aggregate rows to the output.
Up to SQL Server 2008 R2, the database engine supported a legacy, proprietary syntax (not ANSI Com-
pliant) using the WITH CUBE and WITH ROLLUP clauses. These clauses added super-aggregates to the
output.
Also, up to SQL Server 2008 R2, SQL Server supported the GROUP BY ALL syntax, which was used to cre-
ate an empty group for rows that failed the WHEREclause.
SQL Server supports the following aggregate functions:
AVG, CHECKSUM_AGG, COUNT, COUNT_BIG, GROUPING, GROUPING_ID, STDEV, STDEVP, STRING_AGG,
SUM, MIN, MAX, VAR, VARP
- 83 -
Examples
Legacy CUBE and ROLLUPSyntax
CREATE TABLE Orders
(
OrderID INT IDENTITY(1,1) NOT NULL
PRIMARY KEY,
Customer VARCHAR(20) NOT NULL,
OrderDate DATE NOT NULL
);
INSERT INTO Orders(Customer, OrderDate)
VALUES ('John', '20180501'), ('John', '20180502'), ('John', '20180503'),
('Jim', '20180501'), ('Jim', '20180503'), ('Jim', '20180504')
SELECT Customer,
OrderDate,
COUNT(*) AS NumOrders
FROM Orders AS O
GROUP BY Customer, OrderDate
WITH ROLLUP
Customer OrderDate NumOrders
-------------------- ---------- -----------
Jim 2018-05-01 1
Jim 2018-05-03 1
Jim 2018-05-04 1
Jim NULL 3
John 2018-05-01 1
John 2018-05-02 1
John 2018-05-03 1
John NULL 3
NULL NULL 6
The highlighted rows were added as a result of the WITH ROLLUP clause and contain super aggregates
for the following:
l All orders for Jim and John regardless of OrderDate (Orange).
l A super aggregated for all customers and all dates (Red).
Using CUBE instead of ROLLUP adds super aggregates in all possible combinations, not only in GROUP
BY expression order.
SELECT Customer,
OrderDate,
COUNT(*) AS NumOrders
FROM Orders AS O
GROUP BY Customer, OrderDate
WITH CUBE
- 84 -
Customer OrderDate NumOrders
-------------------- ---------- -----------
Jim 2018-05-01 1
John 2018-05-01 1
NULL 2018-05-01 2
John 2018-05-02 1
NULL 2018-05-02 1
Jim 2018-05-03 1
John 2018-05-03 1
NULL 2018-05-03 2
Jim 2018-05-04 1
NULL 2018-05-04 1
NULL NULL 6
Jim NULL 3
John NULL 3
Note the additional four green highlighted rows, which were added by the CUBE. They provide super
aggregates for every date for all customers that were not part of the ROLLUP results above.
Legacy GROUP BY ALL
Use the Orders table from the previous example.
SELECT Customer,
OrderDate,
COUNT(*) AS NumOrders
FROM Orders AS O
WHERE OrderDate <= '20180503'
GROUP BY ALL Customer, OrderDate
Customer OrderDate NumOrders
-------------------- ---------- -----------
Jim 2018-05-01 1
John 2018-05-01 1
John 2018-05-02 1
Jim 2018-05-03 1
John 2018-05-03 1
Jim 2018-05-04 0
Warning: Null value is eliminated by an aggregate or other SET operation.
The row highlighted in orange for 2018-05-04 failed the WHERE clause and was returned as an empty
group as indicated by the warning for the empty COUNT(*) = 0.
Use GROUPING SETS
The following query uses the ANSI compliant GROUPING SETS syntax to provide all possible aggregate
combinations for the Orders table, similar to the result of the CUBE syntax. This syntax requires spe-
cifying each dimension that needs to be aggregated.
SELECT Customer,
OrderDate,
- 85 -
COUNT(*) AS NumOrders
FROM Orders AS O
GROUP BY GROUPING SETS (
(Customer, OrderDate),
(Customer),
(OrderDate),
()
)
Customer OrderDate NumOrders
-------------------- ---------- -----------
Jim 2018-05-01 1
John 2018-05-01 1
NULL 2018-05-01 2
John 2018-05-02 1
NULL 2018-05-02 1
Jim 2018-05-03 1
John 2018-05-03 1
NULL 2018-05-03 2
Jim 2018-05-04 1
NULL 2018-05-04 1
NULL NULL 6
Jim NULL 3
John NULL 3
For more information, see:
l https://docs.microsoft.com/en-us/sql/t-sql/functions/aggregate-functions-transact-sql
l https://docs.microsoft.com/en-us/sql/t-sql/queries/select-group-by-transact-sql
- 86 -
Migrate to: Aurora PostgreSQL GROUP BY
Feature Compatibility SCT Automation Level SCT Action Code Index Key Differences
N/A
Overview
Aurora PostgreSQL supports the basic ANSI syntax for GROUP BY and also supports GROUPING SETS
CUBE, and ROLLUP.
Like SQL Server, Aurora PostgreSQL does allow using ROLLUP and ORDER BY clauses in the same
query, but the syntax is a bit different from SQL Server; there is no WITH clause in the statement.
SELECT Customer,
OrderDate,
COUNT(*) AS NumOrders
FROM Orders AS O
GROUP BY ROLLUP (Customer, OrderDate)
The main difference is the need to move from writing the column to GROUP BY after the ROLLUP.
For the CUBE option, it's the same change:
SELECT Customer,
OrderDate,
COUNT(*) AS NumOrders
FROM Orders AS O
GROUP BY CUBE (Customer, OrderDate);
GROUPING SET:
SELECT Customer,
OrderDate,
COUNT(*) AS NumOrders
FROM Orders AS O
GROUP BY GROUPING SETS (
(Customer, OrderDate),
(Customer),
(OrderDate),());
For more information, see https://www.postgresql.org/docs/9.6/static/queries-table-expressions.html
Syntax
SELECT <Select List>
FROM <Table Source>
WHERE <Row Filter>
- 87 -
GROUP BY
[ROLLUP |CUBE |GROUPING SETS]
<Column Name> | <Expression> | <Position>
Migration Considerations
The GROUP BY functionality exists (except for the ALL option).
Every query must be converted to use the column name after the GROUP BY option (CUBE, ROLLUP, or
CUBE).
Examples
Rewrite SQL Server WITH CUBE modifier for migration. Also, see the example in SQL Server GROUP BY.
CREATE TABLE Orders
(
OrderID serial NOT NULL
PRIMARY KEY,
Customer VARCHAR(20) NOT NULL,
OrderDate DATE NOT NULL
);
INSERT INTO Orders(Customer, OrderDate)
VALUES ('John', '20180501'), ('John', '20180502'), ('John', '20180503'),
('Jim', '20180501'), ('Jim', '20180503'), ('Jim', '20180504');
SELECT Customer,
OrderDate,
COUNT(*) AS NumOrders
FROM Orders AS O
GROUP BY CUBE (Customer, OrderDate);
Customer OrderDate NumOrders
--------------------------
Jim 2018-05-011
Jim 2018-05-03 1
Jim 2018-05-04 1
Jim NULL 3
John 2018-05-01 1
John 2018-05-02 1
John 2018-05-03 1
John NULL 3
NULLNULL 6
NULL2018-05-01 2
NULL 2018-05-02 1
NULL 2018-05-03 2
NULL 2018-05-04 1
Rewrite SQL Server GROUP BY ALL for migration. Also see the example in SQL Server GROUP BY.
- 88 -
SELECT Customer,
OrderDate,
COUNT(*) AS NumOrders
FROM Orders AS O
WHERE OrderDate <= '20180503'
GROUP BY Customer, OrderDate
UNION ALL -- Add the empty groups
SELECT DISTINCTCustomer,
OrderDate,
0
FROM Orders AS O
WHERE OrderDate > '20180503';
Customer OrderDate NumOrders
--------------------------
Jim 2018-05-011
Jim 2018-05-03 1
John 2018-05-01 1
John 2018-05-02 1
John 2018-05-03 1
Jim 2018-05-04 0
Summary
Table of similarities, differences, and key migration considerations.
SQL Server feature
Aurora PostgreSQL fea-
ture
Comments
MAX, MIN, AVG, COUNT,
COUNT_BIG
MAX, MIN, AVG, COUNT In Aurora PostgreSQL, COUNT returns a BIGINT
and is compatible with SQL Server's COUNT
and COUNT_BIG.
CHECKSUM_AGG N/A Use a loop to calculate checksums.
GROUPING, GROUPING_
ID
GROUPING Reconsider query logic to avoid having NULL
groups that are ambiguous with the super
aggregates.
STDEV, STDEVP, VAR,
VARP
STDDEV, STDDEV_POP,
VARIANCE, VAR_POP
Rewrite keywords only.
STRING_AGG STRING_AGG
WITH ROLLUP ROLLUP Remove WITH and change the columns names
to be after the ROLLUP keyword
WITH CUBE CUBE Remove WITH and change the columns names
to be after the CUBE keyword
GROUPING SETS GROUPING SETS
For more information, see https://www.postgresql.org/docs/9.6/static/functions-aggregate.html
- 89 -
Migrate from: SQL Server Table JOIN
Feature Com-
patibility
SCT Auto-
mation Level
SCT Action
Code Index
Key Differences
N/A OUTER JOIN with commas and CROSS APPLY and
OUTER APPLY are not supported
Overview
ANSI JOIN
SQL Server supports the standard ANSI join types:
l <Set A> CROSS JOIN <Set B>: Results in a Cartesian product of the two sets. Every JOIN starts as
a Cartesian product.
l <Set A> INNER JOIN <Set B> ON <Join Condition>: Filters the Cartesian product to only the
rows where the join predicate evaluates to TRUE.
l <Set A> LEFT OUTER JOIN <Set B> ON <Join Condition>: Adds to the INNER JOIN all the rows
from the reserved left set with NULL for all the columns that come from the right set.
l <Set A> RIGHT OUTER JOIN <Set B> ON <Join Condition>: Adds to the INNER JOIN all the rows
from the reserved right set with NULL for all the columns that come from the left set.
l <Set A> FULL OUTER JOIN <Set B> ON <Join Condition>: Designates both sets as reserved and
adds non matching rows from both, similar to a LEFT OUTERJOIN and a RIGHT OUTER JOIN.
APPLY
SQL Server also supports the APPLY operator, which is somewhat similar to a join. However, APPLY
operators enable the creation of a correlation between <Set A> and <Set B> such that <Set B> may con-
sist of a sub query, a VALUES row value constructor, or a table valued function that is evaluated per
row of <Set A> where the <Set B> query can reference columns from the current row in <Set A>. This
functionality is not possible with any type of standard JOIN operator.
There are two APPLY types:
l <Set A> CROSS APPLY <Set B>: Similar to a CROSSJOIN in the sense that every row from <Set A>
is matched with every row from <Set B>.
l <Set A> OUTER APPLY <Set B>: Similar to a LEFT OUTER JOIN in the sense that rows from <Set
A> are returned even if the sub query for <Set B> produces an empty set. In that case, NULLis
assigned to all columns of <Set B>.
- 90 -
ANSI SQL 89 JOINSyntax
Up until version 2008R2, SQL Server also supported the "old style" JOIN syntax including LEFT and
RIGHTOUTER JOIN.
The ANSI syntax for a CROSS JOIN operator was to list the sets in the FROM clause using commas as
separators. For example:
SELECT *
FROM Table1,
Table2,
Table3...
To perform an INNER JOIN, you only needed to add the JOIN predicate as part of the WHERE clause. For
example:
SELECT *
FROM Table1,
Table2
WHERETable1.Column1 = Table2.Column1
Although the ANSI standard didn't specify outer joins at the time, most RDBMS supported them in one
way or another. T-SQL supported outer joins by adding an asterisk to the left or the right of equality
sign of the join predicate to designate the reserved table. For example:
SELECT *
FROM Table1,
Table2
WHERETable1.Column1 *= Table2.Column1
To perform a FULLOUTER JOIN, asterisks were placed on both sides of the equality sign of the join pre-
dicate.
As of SQL Server 2008R2, outer joins using this syntax have been depracated in accordance with
https://technet.microsoft.com/it-it/library/ms143729(v=sql.105).aspx.
Note: Even though INNER JOINs using the ANSI SQL 89 syntax are still supported, they are
highly discouraged due to being notorious for introducing hard-to-catch programming bugs.
Syntax
CROSSJOIN
FROM <Table Source 1>
CROSS JOIN
<Table Source 2>
-- ANSI 89
FROM <Table Source 1>,
<Table Source 2>
- 91 -
INNER / OUTER JOIN
FROM <Table Source 1>
[{INNER | {{LEFT | RIGHT | FULL } [OUTER ] } }] JOIN
<Table Source 2>
ON <JOIN Predicate>
-- ANSI 89
FROM <Table Source 1>,
<Table Source 2>
WHERE <Join Predicate>
<Join Predicate>:: <Table Source 1 Expression> | = | *= | =* | *=* <Table Source 2
Expression>
APPLY
FROM <Table Source 1>
{CROSS | OUTER } APPLY
<Table Source 2>
<Table Source 2>:: <SELECT sub-query> | <Table Valued UDF> | <VALUES clause>
Examples
Create the Orders and Items tables.
CREATE TABLE Items
(
Item VARCHAR(20) NOT NULL
PRIMARY KEY
Category VARCHAR(20) NOT NULL,
Material VARCHAR(20) NOT NULL
);
INSERT INTO Items (Item, Category, Material)
VALUES
('M8 Bolt', 'Metric Bolts', 'Stainless Steel'),
('M8 Nut', 'Metric Nuts', 'Stainless Steel'),
('M8 Washer', 'Metric Washers', 'Stainless Steel'),
('3/8" Bolt', 'Imperial Bolts', 'Brass')
CREATE TABLE OrderItems
(
OrderID INT NOT NULL,
Item VARCHAR(20) NOT NULL
REFERENCES Items(Item),
Quantity SMALLINT NOT NULL,
PRIMARY KEY(OrderID, Item)
);
- 92 -
INSERT INTO OrderItems (OrderID, Item, Quantity)
VALUES
(1, 'M8 Bolt', 100),
(2, 'M8 Nut', 100),
(3, 'M8 Washer', 200)
INNER JOIN
SELECT *
FROMItems AS I
INNERJOIN
OrderItems AS OI
ON I.Item = OI.Item;
-- ANSI SQL 89
SELECT *
FROM Items AS I,
OrderItems AS OI
WHERE I.Item = OI.Item;
LEFT OUTER JOIN
Find Items that were never ordered.
SELECT I.Item
FROMItems AS I
LEFT OUTERJOIN
OrderItems AS OI
ON I.Item = OI.Item
WHERE OI.OrderID IS NULL;
-- ANSI SQL 89
SELECT Item
FROM
(
SELECT I.Item, O.OrderID
FROM Items AS I,
OrderItems AS OI
WHERE I.Item *= OI.Item
) AS LeftJoined
WHERE LeftJoined.OrderID IS NULL;
FULL OUTERJOIN
CREATE TABLE T1(Col1 INT, COl2 CHAR(2));
CREATE TABLE T2(Col1 INT, COl2 CHAR(2));
INSERT INTO T1 (Col1, Col2)
VALUES (1, 'A'), (2,'B');
- 93 -
INSERT INTO T2 (Col1, Col2)
VALUES (2,'BB'), (3,'CC');
SELECT *
FROM T1
FULL OUTER JOIN
T2
ON T1.Col1 = T2.Col1;
Result:
Col1 COl2 Col1 COl2
----------------
1 A NULL NULL
2 B 2 BB
NULL NULL 3 CC
For more information, see https://docs.microsoft.com/en-us/sql/t-sql/queries/from-transact-sql
- 94 -
Migrate to: Aurora PostgreSQL Table JOIN
Feature Com-
patibility
SCT Automation
Level
SCT Action
Code Index
Key Differences
N/A OUTER JOIN with commas and CROSS APPLY and
OUTER APPLY are not supported
Overview
Aurora PostgreSQL supports all types of joins in the same way as SQL Server:
l <Set A> CROSS JOIN <Set B>: Results in a Cartesian product of the two sets. Every JOIN starts as
a Cartesian product.
l <Set A> INNER JOIN <Set B> ON <Join Condition>: Filters the Cartesian product to only the
rows where the join predicate evaluates to TRUE.
l <Set A> LEFT OUTER JOIN <Set B> ON <Join Condition>: Adds to the INNER JOIN all the rows
from the reserved left set with NULL for all the columns that come from the right set.
l <Set A> RIGHT OUTER JOIN <Set B> ON <Join Condition>: Adds to the INNER JOIN all the rows
from the reserved right set with NULL for all the columns that come from the left set.
l <Set A> FULL OUTER JOIN <Set B> ON <Join Condition>: Designates both sets as reserved and
adds non matching rows from both, similar to a LEFT OUTERJOIN and a RIGHT OUTER JOIN
SQL Server's APPLY options are not supported but can be replaced with INNER JOIN LATERAL and LEFT
JOIN LATERAL.
Syntax
FROM
<Table Source 1> CROSS JOIN <Table Source 2>
| <Table Source 1> INNER JOIN <Table Source 2>
ON <Join Predicate>
| <Table Source 1> {LEFT|RIGHT|FULL} [OUTER] JOIN <Table Source 2>
ON <Join Predicate>
Migration Considerations
For most JOINs, the syntax should be equivalent and no rewrites should be needed, a few differences
can be found below:
l ANSI SQL 89 is not supported.
l FULL OUTER JOIN and OUTER JOIN using the pre-ANSI SQL 92 syntax are not supported, but they
can be easily worked around (see the examples below).
- 95 -
l CROSS APPLY and OUTER APPLY are not supported and need to be rewritten using INNER JOIN
LATERAL and LEFT JOIN LATERAL.
Examples
Create the Orders and Items tables.
CREATE TABLE Items
(
Item VARCHAR(20) NOT NULL
PRIMARY KEY
Category VARCHAR(20) NOT NULL,
Material VARCHAR(20) NOT NULL
);
INSERT INTO Items (Item, Category, Material)
VALUES
('M8 Bolt', 'Metric Bolts', 'Stainless Steel'),
('M8 Nut', 'Metric Nuts', 'Stainless Steel'),
('M8 Washer', 'Metric Washers', 'Stainless Steel'),
('3/8" Bolt', 'Imperial Bolts', 'Brass')
CREATE TABLE OrderItems
(
OrderID INT NOT NULL,
Item VARCHAR(20) NOT NULL
REFERENCES Items(Item),
Quantity SMALLINT NOT NULL,
PRIMARY KEY(OrderID, Item)
);
INSERT INTO OrderItems (OrderID, Item, Quantity)
VALUES
(1, 'M8 Bolt', 100),
(2, 'M8 Nut', 100),
(3, 'M8 Washer', 200)
INNER JOIN
SELECT *
FROMItems AS I
INNERJOIN
OrderItems AS OI
ON I.Item = OI.Item;
LEFT OUTER JOIN
Find Items that were never ordered.
- 96 -
SELECT Item
FROMItems AS I
LEFT OUTERJOIN
OrderItems AS OI
ON I.Item = OI.Item
WHERE OI.OrderID IS NULL;
FULL OUTERJOIN
CREATE TABLE T1(Col1 INT, COl2 CHAR(2));
CREATE TABLE T2(Col1 INT, COl2 CHAR(2));
INSERT INTO T1 (Col1, Col2)
VALUES (1, 'A'), (2,'B');
INSERT INTO T2 (Col1, Col2)
VALUES (2,'BB'), (3,'CC');
SELECT *
FROM T1
FULL OUTER JOIN
T2
ON T1.Col1 = T2.Col1;
Result:
Col1 COl2 Col1 COl2
----------------
1 A NULL NULL
2 B 2 BB
NULL NULL 3 CC
Summary
Table of similarities, differences, and key migration considerations.
SQL Server Aurora PostgreSQL Comments
INNERJOIN with ON clause or commas Supported
OUTER JOIN with ON cluase Supported
OUTER JOIN with commas Not supported Requires T-SQL rewrite
post SQL Server 2008R2.
CROSS JOIN or using commas Supported
CROSS APPLY and OUTER APPLY Not Supported Rewrite required.
- 97 -
Migrate from: SQL Server Temporal Tables
Feature Compatibility SCT Automation Level SCT Action Code Index Key Differences
N/A
Overview
Temporal database tables were introduced in ANSI SQL 2011. T-SQL began supporting system ver-
sioned temporal tables in SQL Server 2016.
Each temporal table has two explicitly defined DATETIME2 columns known as period columns. The sys-
tem uses these columns to record the period of availability for each row when it is modified. An addi-
tional history table retains the previous version of the data. The system can automatically create the
history table, or a user can specify an existing table.
To query the history table, use FORSYSTEM TIME after the table name in the FROM clause and com-
bine it with the following options:
l ALL: All changes
l CONTAINED IN: Change is valid only within a period
l AS OF: Change was valid somewhere in a specific period
l BETWEEN: change was valid from a time range
Temporal Tables are mostly used when to track data change history as described in the scenarios
below.
Anomaly Detection
Use this option when searching for data with unusual values. For example, detecting when a customer
returns items too often.
CREATE TABLE Products_returned
(
ProductID int NOT NULL PRIMARY KEY CLUSTERED,
ProductName varchar(60) NOT NULL,
return_count INT NOT NULL,
ValidFrom datetime2(7) GENERATED ALWAYS AS ROW START NOT NULL,
ValidTo datetime2(7) GENERATED ALWAYS AS ROW END NOT NULL,
PERIOD FOR SYSTEM_TIME (ValidFrom, ValidTo)
)
WITH(SYSTEM_VERSIONING = ON (HISTORY_TABLE = dbo.ProductHistory,
DATA_CONSISTENCY_CHECK = ON ))
Query the Product table and run calculations on the data.
- 99 -
SELECT
ProductId,
LAG (return_count, 1, 1)
over (partition by ProductId order by ValidFrom) as PrevValue,
return_count,
LEAD (return_count, 1, 1)
over (partition by ProductId order by ValidFrom) as NextValue ,
ValidFrom, ValidTo from Product
FOR SYSTEM_TIME ALL
Audit
Track changes to critical data such as salaries or medical data.
CREATE TABLE Employee
(
EmployeeID int NOT NULL PRIMARY KEY CLUSTERED,
Name nvarchar(60) NOT NULL,
Salary decimal (6,2) NOT NULL,
ValidFrom datetime2 (2) GENERATED ALWAYS AS ROW START,
ValidTo datetime2 (2) GENERATED ALWAYS AS ROW END,
PERIOD FOR SYSTEM_TIME (ValidFrom, ValidTo)
)
WITH (SYSTEM_VERSIONING = ON (HISTORY_TABLE = dbo.EmployeeTrackHistory));
Use FORSYSTEM_TIME ALL to retrieve changes from the history table.
SELECT * FROM Employee
FOR SYSTEM_TIME ALL WHERE
EmployeeID = 1000 ORDER BY ValidFrom;
Other Scenarios
Additional scenarios include:
l Fixing row-level corruption
l Slowly Changing Dimension
l Over time changes analysis
For more information, see https://docs.microsoft.com/en-us/sql/relational-databases/tables/temporal-tables
- 100 -
Migrate to: Aurora PostgreSQL Triggers
(Temporal Tables alternative)
Feature Compatibility SCT Automation Level SCT Action Code Index Key Differences
N/A
Overview
PostgreSQL provides an extension for supporting temporal tables, but it's not supported by Amazon
Aurora. A workaround will be to create table triggers to update a custom history table to track changes
to data. For additional information, see PostgreSQL triggers.
- 101 -
Migrate from: SQL Server Views
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A Indexed and Partitioned view are not
supported
Overview
Views are schema objects that provide stored definitions for virtual tables. Similar to tables, views are
data sets with uniquely named columns and rows. With the exception of indexed views, view objects
do not store data. They consist only of a query definition and are reevaluated for each invocation.
Views are used as abstraction layers and security filters for the underlying tables. They can JOIN and
UNION data from multiple source tables and use aggregates, window functions, and other SQL fea-
tures as long as the result is a semi-proper set with uniquely identifiable columns and no order to the
rows. You can use Distributed Views to query other databases and data sources using linked servers.
As an abstraction layer, a view can decouple application code from the database schema. The under-
lying tables can be changed without the need to modify the application code as long as the expected
results of the view do not change. You can use this approach to provide backward compatible views of
data.
As a security mechanism, a view can screen and filter source table data. You can perform permission
management at the view level without explicit permissions to the base objects, provided the ownership
chain is maintained. For more information on ownership chains in SQL Server, see
https://docs.microsoft.com/en-us/dotnet/framework/data/adonet/sql/overview-of-sql-server-security.
View definitions are evaluated when they are created and are not affected by subsequent changes to
the underlying tables. For example, a view that uses SELECT * does not display columns that were
added later to the base table. Similarly, if a column was dropped from the base table, invoking the
view results in an error. Use the SCHEMABINDING option to prevent changes to base objects.
Modifying Data Through Views
Updatable Views can both SELECT and modify data. For a view to be updatable, the following con-
ditions must be met:
l The DML targets only one base table.
l Columns being modified must be directly referenced from the underlying base tables. Computed
columns, set operators, functions, aggregates, or any other expressions are not permitted.
l If a view is created with the CHECK OPTION, rows being updated can not be filtered out of the
view definition as the result of the update.
- 102 -
Special View Types
SQL Server also provides three types of specialized views:
l Indexed Views (also known as materialized views or persisted views) are standard views that
have been evaluated and persisted in a unique clustered index, much like a normal clustered
primary key table. Each time the source data changes, SQL Server re-evaluates the indexed views
automatically and updates them. Indexed views are typically used as a means to optimize per-
formance by pre-processing operators such as aggregations, joins, and others. Queries needing
this pre-processing don't have to wait for it to be reevaluated on every query execution.
l Partitioned Views are views that rejoin horizontally partitioned data sets from multiple under-
lying tables, each containing only a subset of the data. The view uses a UNION ALL query where
the underlying tables can reside locally or in other databases (or even other servers). These types
of views are called Distributed Partitioned Views (DPV).
l System Views are used to access server and object meta data. SQL Server also supports a set of
standard INFORMATION_SCHEMA views for accessing object meta data.
Syntax
CREATE [OR ALTER] VIEW [<Schema Name>.] <View Name> [(<Column Aliases> ])]
[WITH [ENCRYPTION][SCHEMABINDING][VIEW_METADATA]]
AS <SELECT Query>
[WITH CHECK OPTION][;]
Examples
Create a view that aggregates items for each customer.
CREATE TABLE Orders
(
OrderID INT NOT NULL PRIMARY KEY,
OrderDate DATETIME NOT NULL
DEFAULT GETDATE()
);
CREATE TABLE OrderItems
(
OrderID INT NOT NULL
REFERENCES Orders(OrderID),
Item VARCHAR(20) NOT NULL,
Quantity SMALLINT NOT NULL,
PRIMARY KEY(OrderID, Item)
);
CREATE VIEW SalesView
AS
SELECT O.Customer,
OI.Product,
- 103 -
SUM(CAST(OI.Quantity AS BIGINT)) AS TotalItemsBought
FROM Orders AS O
INNERJOIN
OrderItems AS OI
ON O.OrderID = OI.OrderID;
Create an indexed view that pre-aggregates items for each customer.
CREATE VIEW SalesViewIndexed
AS
SELECT O.Customer,
OI.Product,
SUM_BIG(OI.Quantity) AS TotalItemsBought
FROM Orders AS O
INNERJOIN
OrderItems AS OI
ON O.OrderID = OI.OrderID;
CREATE UNIQUE CLUSTEREDINDEX IDX_SalesView
ON SalesViewIndexed (Customer, Product);
Create a Partitioned View.
CREATE VIEW dbo.PartitioneView
WITH SCHEMABINDING
AS
SELECT *
FROM Table1
UNION ALL
SELECT *
FROM Table2
UNION ALL
SELECT *
FROM Table3
For more information, see:
l https://docs.microsoft.com/en-us/sql/relational-databases/views/views
l https://docs.microsoft.com/en-us/sql/relational-databases/views/modify-data-through-a-view
l https://docs.microsoft.com/en-us/sql/t-sql/statements/create-view-transact-sql
- 104 -
Migrate to: Aurora PostgreSQL Views
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A Indexed and Partitioned view are not
supported
Overview
The basic form of Views is similar between PostgreSQL and SQL Server. A view defines a stored query
based on one or more physical database tables that executes every time the view is accessed.
More complex option such as Indexed Views or Partitioned Views are not supported, and may require
a redesign or might application rewrite.
PostgreSQL View Privileges
A Role or User must be granted SELECT and DML privileges on the base tables or views in order to cre-
ate a view. For additional details, see https://www.postgresql.org/docs/9.6/static/sql-grant.html
PostgreSQL View Parameters
CREATE [OR REPLACE] VIEW
When re-creating an existing view, the new view must have the same column structure as generated by
the original view (column names, column order, and data types). It is sometimes preferable to drop the
view and use the CREATE VIEW statement instead.
hr=# CREATE [OR REPLACE] VIEW VW_NAME AS
SELECT COLUMNS
FROM TABLE(s)
[WHERE CONDITIONS];
hr=# DROP VIEW [IF EXISTS] VW_NAME;
Note: The IF EXISTS parameter is optional.
WITH [ CASCADED | LOCAL ] CHECK OPTION
DML INSERT and UPDATE operations are verified against the view-based tables to ensure new rows sat-
isfy the original structure conditions or the view-defining condition. If a conflict is detected, the DML
operation fails.
CHECK OPTION
- 105 -
l LOCAL:Verifies the view without a hierarchical check.
l CASCADED: Verifies all underlying base views using a hierarchical check.
Executing DML Commands On views
PostgreSQL simple views are automatically updatable. No restrictions exist when performing DML
operations on views. An updatable view may contain a combination of updatable and non-updatable
columns. A column is updatable if it references an updatable column of the underlying base table. If
not, the column is read-only and an error is raised if an INSERT or UPDATE statement is attempted on
the column.
Syntax
CREATE [OR REPLACE ] [TEMP | TEMPORARY ] [RECURSIVE ] VIEW name [(column_name [,
...] ) ]
[WITH (view_option_name [= view_option_value] [, ... ] ) ]
AS query
[WITH [CASCADED | LOCAL ] CHECK OPTION ]
Examples
Create and update a view without the CHECK OPTION parameter.
CREATE OR REPLACE VIEW VW_DEP AS
SELECT DEPARTMENT_ID, DEPARTMENT_NAME, MANAGER_ID, LOCATION_ID
FROM DEPARTMENTS
WHERE LOCATION_ID=1700;
view VW_DEP created.
UPDATE VW_DEP SET LOCATION_ID=1600;
21 rows updated.
Create and update a view with the LOCAL CHECK OPTION parameter.
CREATE OR REPLACE VIEW VW_DEP AS
SELECT DEPARTMENT_ID, DEPARTMENT_NAME, MANAGER_ID, LOCATION_ID
FROM DEPARTMENTS
WHERE LOCATION_ID=1700
WITH LOCAL CHECK OPTION;
view VW_DEP created.
UPDATE VW_DEP SET LOCATION_ID=1600;
SQL Error: ERROR: new row violates check option for view "vw_dep"
- 106 -
Summary
Feature SQL Server Aurora PostgreSQL
Indexed Views Supported N/A
Partitioned Views Supported N/A
Updateable Views Supported Supported
Prevent schema conflicts SCHEMABINDING option N/A
Triggers on views INSTEAD OF INSTEAD OF
Temporary Views CREATE VIEW #View... CREATE [ OR REPLACE ] [ TEMP |
TEMPORARY ] VIEW
Refresh view definition sp_refreshview / ALTER VIEW ALTER VIEW
For more information, see:
l https://www.postgresql.org/docs/9.6/static/tutorial-views.html
l https://www.postgresql.org/docs/9.6/static/sql-createview.html
- 107 -
Migrate from: SQL Server Window Functions
Feature Compatibility SCT Automation Level SCT Action Code Index Key Differences
N/A
Overview
Windowed functions use an OVER clause to define the window and frame for a data set to be pro-
cessed. They are part of the ANSI standard and are typically compatible among various SQL dialects.
However, most RDBMS do not yet support the full ANSI specification.
Windowed functions are a relatively new, advanced, and efficient T-SQL programming tool. They are
highly utilized by developers to solve numerous programming challenges.
SQL Server currently supports the following windowed functions:
l Ranking functions:ROW_NUMBER, RANK, DENSE_RANK, and NTILE
l Aggregate functions:AVG, MIN, MAX, SUM, COUNT, COUNT_BIG, VAR, STDEV, STDEVP, STRING_
AGG, GROUPING, GROUPING_ID, VAR, VARP, and CHECKSUM_AGG
l Analytic functions: LAG, LEAD, FIRST_Value, LAST_VALUE, PERCENT_RANK, PERCENTILE_CONT,
PERCENTILE_DISC, and CUME_DIST
l Other functions:NEXT_VALUE_FOR (See the Identity and Sequences section)
Syntax
<Function()>
OVER
(
[<PARTITION BY clause> ]
[<ORDER BY clause> ]
[<ROW or RANGE clause> ]
)
Examples
Create and populate an OrderItems table.
CREATE TABLE OrderItems
(
OrderID INT NOT NULL,
Item VARCHAR(20) NOT NULL,
Quantity SMALLINT NOT NULL,
PRIMARY KEY(OrderID, Item)
);
INSERT INTO OrderItems (OrderID, Item, Quantity)
VALUES
- 108 -
(1, 'M8 Bolt', 100),
(2, 'M8 Nut', 100),
(3, 'M8 Washer', 200),
(3, 'M6 Locking Nut', 300);
Use a windowed ranking function to rank items based on the ordered quantity.
SELECT Item,
Quantity,
RANK() OVER(ORDER BY Quantity) AS QtyRank
FROM OrderItems;
Item Quantity QtyRank
-------------------
M8 Bolt 100 1
M8 Nut 1001
M8 Washer 200 3
M6 Locking Nut 300 4
Use a partitioned windowed aggregate function to calculate the total quantity per order (without using
a GROUP BY clause).
SELECT Item,
Quantity,
OrderID,
SUM(Quantity)
OVER (PARTITION BY OrderID) AS TotalOrderQty
FROM OrderItems;
Item Quantity OrderIDTotalOrderQty
--------------------------------
M8 Bolt 100 1 100
M8 Nut 100 2 100
M6 Locking Nut 300 3 500
M8 Washer 200 3 500
Use an analytic LEAD function to get the next largest quantity for the order.
SELECT Item,
Quantity,
OrderID,
LEAD(Quantity)
OVER (PARTITION BY OrderID ORDER BY Quantity) AS NextQtyOrder
FROM OrderItems;
Item Quantity OrderID NextQtyOrder
-------------------------------
M8 Bolt 100 1 NULL
M8 Nut 100 2 NULL
M8 Washer 200 3 300
M6 Locking Nut 300 3 NULL
For more information, see https://docs.microsoft.com/en-us/sql/t-sql/queries/select-over-clause-transact-sql
- 109 -
Migrate to: Aurora PostgreSQL Window Functions
Feature Compatibility SCT Automation Level SCT Action Code Index Key Differences
N/A
Overview
PostgreSQL refers to ANSI SQL analytical functions as “Window Functions”. They provide the same core
functionality as SQL Analytical Functions. Window functions in PostgreSQL operate on a logical “par-
tition” or "window" of the result set and return a value for rows in that “window”.
From a database migration perspective, you should examine PostgreSQL Window Functions by type
and compare them with the equivalent SQL Server window functions to verify compatibility of syntax
and output.
Note: Even if a PostgreSQL window function provides the same functionality of a specific SQL
Server window function, the returned data type may be different and require application
changes.
PostgreSQL provides support for two main types of Window Functions:Aggregation functions and
Ranking functions.
PostgreSQL Window Functions by Type
Function Type Related Functions
Aggregate avg, count, max, min, sum, string_agg
Ranking row_number, rank, dense_rank, percent_rank, cume_dist, ntile, lag, lead, first_
value, last_value, nth_value
PostgreSQL Window Functions
PostgreSQL Window Func-
tion
Returned Data Type
Compatible Syn-
tax
Count bigint Yes
Max numeric, string, date/time, network or enum type Yes
Min numeric, string, date/time, network or enum type Yes
Avg numeric, double, otherwise same datatype as the
argument
Yes
Sum bigint, otherwise same datatype as the argument Yes
- 110 -
PostgreSQL Window Func-
tion
Returned Data Type
Compatible Syn-
tax
rank() bigint Yes
row_number() bigint Yes
dense_rank() bigint Yes
percent_rank() double Yes
cume_dist() double Yes
ntile() integer Yes
lag() same type as value Yes
lead() same type as value Yes
first_value() same type as value Yes
last_value() same type as value Yes
Examples
Use the PostgreSQL rank() function.
SELECT department_id, last_name, salary, commission_pct,
RANK() OVER (PARTITION BY department_id
ORDER BY salary DESC, commission_pct) "Rank"
FROM employees WHERE department_id = 80;
DEPARTMENT_ID LAST_NAME SALARY COMMISSION_PCT Rank
------------- ------------------------- ---------- -------------- ----------
80 Russell 14000.00 0.40 1
80 Partners 13500.00 0.30 2
80 Errazuriz 12000.00 0.30 3
Note: The returned formatting for certain numeric data types is different.
Query the total salary for department 80
SELECT SUM(salary)
FROM employees WHERE department_id = 80;
SUM(SALARY)
----------
39500.00
Create and populate an OrderItems table.
CREATE TABLE OrderItems
(
OrderID INT NOT NULL,
Item VARCHAR(20) NOT NULL,
Quantity SMALLINT NOT NULL,
- 111 -
PRIMARY KEY(OrderID, Item)
);
INSERT INTO OrderItems (OrderID, Item, Quantity)
VALUES
(1, 'M8 Bolt', 100),
(2, 'M8 Nut', 100),
(3, 'M8 Washer', 200),
(3, 'M6 Locking Nut', 300);
Use a windowed ranking function to rank items based on the ordered quantity.
SELECT Item,
Quantity,
RANK() OVER(ORDER BY Quantity) AS QtyRank
FROM OrderItems;
Item Quantity QtyRank
-------------------
M8 Bolt 100 1
M8 Nut 1001
M8 Washer 200 3
M6 Locking Nut 300 4
Use a partitioned windowed aggregate function to calculate the total quantity per order (without using
a GROUP BY clause).
SELECT Item,
Quantity,
OrderID,
SUM(Quantity)
OVER (PARTITION BY OrderID) AS TotalOrderQty
FROM OrderItems;
Item Quantity OrderIDTotalOrderQty
--------------------------------
M8 Bolt 100 1 100
M8 Nut 100 2 100
M6 Locking Nut 300 3 500
M8 Washer 200 3 500
Use an analytic LEAD function to get the next largest quantity for the order.
SELECT Item,
Quantity,
OrderID,
LEAD(Quantity)
OVER (PARTITION BY OrderID ORDER BY Quantity) AS NextQtyOrder
FROM OrderItems;
Item Quantity OrderID NextQtyOrder
-------------------------------
M8 Bolt 100 1 NULL
M8 Nut 100 2 NULL
- 112 -
T-SQL
- 114 -
Migrate from: SQL Server Service Broker Essentials
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
SCT Action Codes -
Broker
Use Amazon Lambda for similar func-
tionality
Overview
SQL Server Service Broker provides native support for messaging and queuing applications. It's makes
it easier for developers to create complex applications that use the Database Engine components to
communicate between several SQL Server databases. Developers can use Service Broker to easily build
distributed and more reliable applications.
Benefits of using messaging queues:
l Decouple dependencies between applications by communicating through messages.
l Scale out your architecture by moving queues / message processors to separate servers as
needed.
l Maintain individual parts with a minimal impact to the end users.
l Control when the messages are processed (for example: off-peak hours).
l Process queued messages on multiple servers / processes / threads.
The following sections describe the Service Broker commands.
CREATE MESSAGE TYPE
Create a message with name and structure.
CREATE MESSAGE TYPE message_type_name
[AUTHORIZATION owner_name ]
[VALIDATION = { NONE
| EMPTY
| WELL_FORMED_XML
| VALID_XML WITH SCHEMA COLLECTION schema_collection_name
} ]
[; ]
For more information, see:
https://docs.microsoft.com/en-us/sql/t-sql/statements/create-message-type-transact-sql?view=sql-server-2017
- 115 -
CREATE QUEUE
Create a queue to store messages.
CREATE QUEUE <object>
[WITH
[STATUS = {ON | OFF } [, ] ]
[RETENTION = {ON | OFF } [, ] ]
[ACTIVATION (
[STATUS = {ON | OFF } , ]
PROCEDURE_NAME = <procedure> ,
MAX_QUEUE_READERS = max_readers ,
EXECUTE AS {SELF | 'user_name' | OWNER }
) [, ] ]
[POISON_MESSAGE_HANDLING (
[STATUS = {ON | OFF } ] ) ]
]
[ON {filegroup | [DEFAULT ] } ]
[; ]
<object> ::=
{
[database_name. [schema_name ] . | schema_name. ]
queue_name
}
<procedure> ::=
{
[database_name. [schema_name ] . | schema_name. ]
stored_procedure_name
}
For more information, see:
https://docs.microsoft.com/en-us/sql/t-sql/statements/create-queue-transact-sql?view=sql-server-2017
CREATE CONTRACT
Specify the role and what type of messages a service can handle.
CREATE CONTRACT contract_name
[AUTHORIZATION owner_name ]
( { {message_type_name | [DEFAULT ] }
SENT BY {INITIATOR | TARGET | ANY }
} [,...n] )
[; ]
For more information, see:
https://docs.microsoft.com/en-us/sql/t-sql/statements/create-contract-transact-sql?view=sql-server-2017
- 116 -
CREATE SERVICE
Create a named Broker Service for a specified task or set of tasks.
CREATE SERVICE service_name
[AUTHORIZATION owner_name ]
ON QUEUE [schema_name. ]queue_name
[(contract_name | [DEFAULT][,...n ] ) ]
[; ]
For more information, see:
https://docs.microsoft.com/en-us/sql/t-sql/statements/create-service-transact-sql?view=sql-server-2017
BEGIN DIALOG CONVERSATION
Start the interaction between Broker Services.
BEGIN DIALOG [CONVERSATION ] @dialog_handle
FROM SERVICE initiator_service_name
TO SERVICE 'target_service_name'
[, {'service_broker_guid' | 'CURRENT DATABASE' }]
[ON CONTRACT contract_name ]
[WITH
[ {RELATED_CONVERSATION = related_conversation_handle
| RELATED_CONVERSATION_GROUP = related_conversation_group_id } ]
[[, ] LIFETIME = dialog_lifetime ]
[[, ] ENCRYPTION = {ON | OFF } ] ]
[; ]
For more information, see:
https://docs.microsoft.com/en-us/sql/t-sql/statements/begin-dialog-conversation-transact-sql?view=sql-server-2017
WAITFOR(RECEIVE TOP(1))
Specify that a code block to wait until one message is received .
[WAITFOR (]
RECEIVE [TOP (n ) ]
<column_specifier> [,...n ]
FROM <queue>
[INTO table_variable ]
[WHERE { conversation_handle = conversation_handle
| conversation_group_id = conversation_group_id } ]
[) ] [, TIMEOUT timeout ]
[; ]
<column_specifier> ::=
- 117 -
{ *
| {column_name | [] expression } [[AS ] column_alias ]
| column_alias = expression
} [,...n ]
<queue> ::=
{
[database_name . [schema_name ] . | schema_name . ]
queue_name
}
For more information, see:
https://docs.microsoft.com/en-us/sql/t-sql/statements/receive-transact-sql?view=sql-server-2017
All of the above commands can be combined in to achieve your architecture goals.
For more information see:
https://docs.microsoft.com/en-us/sql/database-engine/configure-windows/sql-server-service-broker?view=sql-server-
2017
- 118 -
Migrate to: Aurora PostgreSQL AWS Lambda or DB
links
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
SCT Action Codes -
Broker
Use Amazon Lambda for similar func-
tionality
Overview
Aurora PostgreSQL does not provide a compatible solution to the SQL Server Service Broker. However,
you can use DB Links and AWS Lambda to achieve similar functionality.
AWS Lambda can be combined with AWS SQS in order to reduce costs and remove some loads from
the database into the AWS Lambda and SQS (this will be much more efficient), for more information
see: https://docs.aws.amazon.com/lambda/latest/dg/with-sqs.html
For example, you can create a table in each database and connect each database with a DB link to read
the tables and process the data. For more information, seeDB Links.
You can also use AWS Lambda to query a table from the database, process the data, and insert it to
another database (even another database type).This approach is the best option for moving workloads
out of the database to a less expensive instance type.
For even more decoupling and reducing workloads from the database SQS can be used with Lambda,
SQS is the Amazon messages queues service.
For more information see AWS Lambda for sending mails
- 119 -
Migrate from: SQL Server Cast and Convert
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
CONVERT is used only to convert between
collations
CAST uses different syntax
Overview
The CAST and CONVERT functions are commonly used to convert one data type to another. CAST and
CONVERT behave mostly the same (they share the same topic in MSDN) , but there are few differences :
l CAST is part of the ANSI-SQL specification, but CONVERT is not.
l CONVERT accepts an optional style parameter used for formatting.
For more information about styles, see
https://docs.microsoft.com/en-us/sql/t-sql/functions/cast-and-convert-transact-sql?view=sql-server-
2017#date-and-time-styles
Conversion matrix
To view all conversion data types available, see
https://docs.microsoft.com/en-us/sql/t-sql/functions/cast-and-convert-transact-sql?view=sql-server-
2017#implicit-conversions
Syntax
-- CAST Syntax:
CAST (expression AS data_type [(length ) ] )
-- CONVERT Syntax:
CONVERT (data_type [(length ) ] , expression [, style ] )
Examples
Cast a string to int and int to decimal.
SELECT CAST('23.7' AS varchar) AS int, CAST(23.7 AS int) AS decimal;
Convert string to int and int to decimal.
SELECT CONVERT(VARCHAR, '23.7') AS int, CONVERT(int, 23.7) AS decimal;
- 120 -
In both examples above, the results will be:
int |decimal |
-----|--------|
23.7 |23 |
Convert a date with option style input (109 - mon dd yyyy hh:mi:ss:mmmAM (or PM))
SELECT CONVERT(nvarchar(30), GETDATE(), 109);
|
-------------------------------|
Jul 25 2018 5:20:10.8975085PM |
For more information, see:
https://docs.microsoft.com/en-us/sql/t-sql/functions/cast-and-convert-transact-sql?view=sql-server-2017
- 121 -
Migrate to: Aurora PostgreSQL CAST and
CONVERSION
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
CONVERT is used only to convert between
collations
CAST uses different syntax
Overview
Aurora PostgreSQL provides the same CAST function as SQLServer for conversion between data types.
It also provides a CONVERSION function, but it is not equivalent to SQL Server's CONVERT.
PostgreSQL CONVERSION is used to convert between character set encoding.
CREATE A CAST defines a new cast on how to convert between two data types.
Cast can be EXPLICITLY or IMPLICIT.
The behavior is similar to SQL Server's casting, but in PostgreSQL, you can also create your own casts
to change the default behavior. For example, checking if a string is a valid credit card number by cre-
ating the CAST with the WITHOUT FUNCTION clause.
CREATE CONVERSION is used to convert between encoding such as UTF8 and LATIN. If CONVERT is cur-
rently in use in SQL Server code, it must be rewritten to use CAST instead.
Note: Not all SQL Server's data types are supported on Aurora PostgreSQL, besides changing
the CAST or CONVERT commands, you might need to also change the source of the target
data type, for more information about supported data types, see: Data Types
Another way to convert between data types in PostgreSQL will be to use the '::' characters, this option is
useful and can make your pgsql code look cleaner and simpler, see examples below.
Syntax
CREATE CAST (source_type AS target_type)
WITH FUNCTION function_name (argument_type [, ...]) [AS ASSIGNMENT | AS IMPLICIT ]
CREATE CAST (source_type AS target_type)
WITHOUT FUNCTION [AS ASSIGNMENT | AS IMPLICIT ]
CREATE CAST (source_type AS target_type)
WITH INOUT [AS ASSIGNMENT | AS IMPLICIT ]
- 122 -
Examples
Convert a numeric value to float.
SELECT 23 + 2.0;
or
SELECT CAST (23 AS numeric ) + 2.0;
Convert a date with format input ('mon dd yyyy hh:mi:ss:mmmAM (or PM)')
SELECT TO_CHAR(NOW(),'Mon DD YYYY HH:MI:SS:MSAM');
|
-------------------------------|
Jul 25 2018 5:20:10.8975085PM |
Use '::' characters
SELECT '2.35'::DECIMAL + 4.5 AS results;
results |
--------|
6.85 |
Summary
Option SQL Server Aurora PostgreSQL
Explicit CAST SELECT CAST('23.7' AS varchar)
AS int
SELECT CAST('23.7' AS varchar) AS int
Explicit CONVERT SELECT CONVERT(VARCHAR,
'23.7')
Need to use CAST:
SELECT CAST('23.7' AS varchar) AS int
Implicit casting SELECT 23 + 2.0 SELECT 23 + 2.0
Convert to a specific date
format: 'mon dd yyyy
hh:mi:ss:mmmAM'
SELECT CONVERT(nvarchar(30),
GETDATE(), 109)
SELECT TO_CHAR(NOW(),'Mon DD YYYY
HH:MI:SS:MSAM')
For more information, see:
l https://www.postgresql.org/docs/9.6/static/sql-createcast.html
l https://www.postgresql.org/docs/9.6/static/typeconv.html
l https://www.postgresql.org/docs/9.6/static/sql-createconversion.html
- 123 -
Migrate from: SQL Server Common Library Runtime
(CLR)
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A Migrating CLR objects will require a
full code rewrite
Overview
SQL Server provides the capability of implementing .NET objects in the database using the Common
Runtime Library (CLR). The CLR enables development of functionality that would be complicated using
T-SQL.
The CLR provides robust solutions for string manipulation, date manipulation, and calling external ser-
vices such as Windows Communication Foundation (WCF) services and web services.
The objects that can be created with the EXTERNAL NAME clause are:
l Procedures -For more information, see:
https://msdn.microsoft.com/en-us/library/ms131094.aspx
l Functions - For more information, see:
https://docs.microsoft.com/en-us/sql/relational-databases/user-defined-functions/create-clr-func-
tions?view=sql-server-2017
l Triggers - For more information, see:
https://docs.microsoft.com/en-us/sql/relational-databases/triggers/create-clr-triggers?view=sql-
server-2017
l Types - For more information, see: https://docs.microsoft.com/en-us/sql/relational-data-
bases/clr-integration-database-objects-user-defined-types/clr-user-defined-types?view=sql-
server-2017
l Aggregates - user-defined aggregate function. For more information, see:
https://docs.microsoft.com/en-us/sql/relational-databases/clr-integration-database-objects-user-
defined-functions/clr-user-defined-aggregates?view=sql-server-2017
- 124 -
Migrate to: Aurora PostgreSQL PL/Perl
Feature Com-
patibility
SCT Automation
Level
SCT Action Code Index Key Differences
N/A Migrating CLR objects will require a
full code rewrite
Overview
Aurora PostgreSQL does not support .NET code. However, you can create Perl functions. You must con-
vert all C# code to PL/pgSQL or PL/Perl.
In order to use PL/Perl language you must install the perl extension:
CREATE EXTENSION plperl;
After it is installed, you can create functions using perl code. Specify plperl in the the LANGUAGE
clause.
The objects that can be created with Perl are:
l Functions
l Void functions (procedures)
l Triggers
l Event Triggers
l Values for session level
Examples
Create a function that returns the greater value of two integers.
CREATE FUNCTION perl_max (integer, integer) RETURNS integer AS $$
if ($_[0] > $_[1]) {return $_[0]; }
return $_[1];
$$LANGUAGE plperl;
For more information see: https://www.postgresql.org/docs/9.6/static/plperl.html
- 125 -
Migrate from: SQL Server Collations
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
SCT Action Codes -
Collation
UTF16 and NCHAR/NVARCHAR data types
are not supported
Overview
SQL Server collations define the rules for string management and storage in terms of sorting, case
sensitivity, accent sensitivity, and code page mapping. SQL Server supports both ASCII and UCS-2
UNICODE data.
UCS-2 UNICODE data uses a dedicated set of UNICODEdata types denoted by the prefix "N":Nchar and
Nvarchar. Their ASCII counterparts are CHAR and VARCHAR.
Choosing a collation and a character set has significant implications on data storage, logical predicate
evaluations, query results, and query performance.
Note: To view all collations supported by SQL Server, use the fn_helpcollations
function:SELECT * FROM sys.fn_helpcollations().
Collations define the actual bitwise binary representation of all string characters and the associated
sorting rules. SQL Server supports multiple collations down to the column level. A table may have mul-
tiple string columns that use different collations. Collations for non-UNICODE character sets determine
the code page number representing the string characters.
Note: UNICODE and non-UNICODE data types in SQL Server are not compatible. A predicate
or data modification that introduces a type conflict is resolved using predefined collation pre-
cedence rules.
For more information, see
https://docs.microsoft.com/en-us/sql/t-sql/statements/collation-precedence-transact-sql
Collations define sorting and matching sensitivity for the following string characteristics:
l Case
l Accent
l Kana
l Width
l Variation selector
SQL Server uses a suffix naming convention that appends the option name to the collation name. For
example, the collation Azeri_Cyrillic_100_CS_AS_KS_WS_SC, is an Azeri-Cyrillic-100 collation that is case-
sensitive, accent-sensitive, kana type-sensitive, width-sensitive, and has supplementary characters.
SQL Server supports three types of collation sets:
- 126 -
l Windows Collations use the rules defined for collations by the operating system locale where
UNICODE and non-UNICODE data use the same comparison algorithms.
l Binary Collations use the binary bit-wise code for comparison. Therefore, the locale does not
affect sorting.
l SQLServer Collations provide backward compatibility with previous SQL Server versions. They
are not compatible with the windows collation rules for non-UNICODE data.
Collations can be defined at various levels:
l Server Level Collations determine the collations used for all system databases and is the
default for future user databases. While the system databases collation can not be changed, an
alternative collation can be specified as part of the CREATEDATABASE statement
l Database Level Collations inherit the server default unless the CREATE DATABASE statement
explicitly sets a different collation. This collation is used as a default for all CREATE TABLE and
ALTER TABLE statements
l Column Level Collations can be specified as part of the CREATE TABLE or ALTER TABLE state-
ments to override the database's default collation setting.
l Expression Level Collations can be set for individual string expressions using the COLLATE func-
tion. For example, SELECT * FROM MyTable ORDER BY StringColumn COLLATE Latin1_General_
CS_AS.
Note: SQL Server supports UCS-2 UNICODEonly.
Syntax
CREATE DATABASE <Database Name>
[ON <File Specifications> ]
COLLATE <Collation>
[WITH <Database Option List> ];
CREATETABLE <Table Name>
(
<Column Name> <String Data Type>
COLLATE <Collation> [<Column Constraints> ]...
);
Examples
Create a database with a default Bengali_100_CS_AI collation.
CREATE DATABASE MyBengaliDatabase
ON
(NAME = MyBengaliDatabase_Datafile,
FILENAME = 'C:\Program Files\Microsoft SQL Server-
\MSSQL13.MSSQLSERVER\MSSQL\DATA\MyBengaliDatabase.mdf',
SIZE = 100)
LOG ON
- 127 -
(NAME = MyBengaliDatabase_Logfile,
FILENAME = 'C:\Program Files\Microsoft SQL Server-
\MSSQL13.MSSQLSERVER\MSSQL\DATA\MyBengaliDblog.ldf',
SIZE = 25)
COLLATE Bengali_100_CS_AI;
Create a table with two different collations.
CREATE TABLE MyTable
(
Col1 CHAR(10) COLLATE Hungarian_100_CI_AI_SC NOT NULL PRIMARY KEY,
COL2 VARCHAR(100) COLLATE Sami_Sweden_Finland_100_CS_AS_KS NOT NULL
);
For more information, see
https://docs.microsoft.com/en-us/sql/relational-databases/collations/collation-and-unicode-support
- 128 -
Migrate to: Aurora PostgreSQL Encoding
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
SCT Action Codes -
Collation
UTF16 and NCHAR/NVARCHAR data types
are not supported
Overview
PostgreSQL supports a variety of different character sets, also known as encoding, including support
for both single-byte and multi-byte languages. The default character set is specified when initializing a
PostgreSQL database cluster with initdb. Each individual database created on the PostgreSQL cluster
supports individual character sets defined as part of database creation.
Notes:
l All supported character sets can be used by clients. However, some client-side only characters
are not supported for use within the server.
l Unlike SQLServer, PostgreSQL does not natively support an NVARHCHAR data type and does not
provide support for UTF-16.
Type Function
Implementation
Level
Encoding Defines the basic rules on how alphanumeric characters are rep-
resented in binary format. For example, Unicode Encoding.
Database
Locale A superset that includes LC_COLLATE and LC_CTYPE among others. For
example, LC_COLLATE defines how strings are sorted and must be a sub-
set supported by the database Encoding.
Table-Column
Examples
Create a database named test01 which uses the Korean EUC_KR Encoding the and the ko_KR locale.
CREATE DATABASE test01 WITH ENCODING 'EUC_KR' LC_COLLATE='ko_KR.euckr' LC_CTYPE='ko_
KR.euckr' TEMPLATE=template0;
View the character sets configured for each database by querying the System Catalog.
select datname, datcollate, datctype from pg_database;
- 129 -
Changing Character Sets/Encoding
In-place modification of the database encoding is not recommended nor supported. You must export
all data, create a new database with the new encoding, and import the data.
Export the data using the pg_dump utility.
pg_dump mydb1 > mydb1_export.sql
Rename (or delete) a database.
ALTER DATABASE mydb1 TO mydb1_backup;
Create a new database using the modified encoding.
CREATE DATABASE mydb1_new_encoding WITH ENCODING 'UNICODE' TEMPLATE=template0;
Import data using the pg_dump file previously created. Verify that you set your client encoding to the
encoding of your “old” database.
PGCLIENTENCODING=OLD_DB_ENCODING psql -f mydb1_export.sql mydb1_new_encoding
Note: The client_encoding parameter overrides the use of PGCLIENTENCODING.
Client/Server Character Set Conversions
PostgreSQL supports conversion of character sets between servers and clients for specific character set
combinations as described in the pg_conversion system catalog.
PostgreSQL includes predefined conversions. For a complete list, see
https://www.postgresql.org/docs/current/static/multibyte.html#MULTIBYTE-TRANSLATION-TABLE
You can create a new conversion using the SQL command CREATE CONVERSION.
Examples
Create a conversion from UTF8 to LATIN1 using the custom myfunc1 function.
CREATE CONVERSION myconv FOR 'UTF8' TO 'LATIN1' FROM myfunc1;
Configure the PostgreSQL client character set.
Method 1
========
psql \encoding SJIS
Method 2
========
SET CLIENT_ENCODING TO 'value';
- 130 -
View the client character set and reset it back to the default value.
SHOW client_encoding;
RESET client_encoding;
Table Level Collation
PostgreSQL supports specifying the sort order and character classification behavior on a per-column
level.
Examples
Specify specific collations for individual table columns.
CREATE TABLE test1 (col1 text COLLATE "de_DE", col2 text COLLATE "es_ES");
Summary
Feature SQL Server Aurora PostgreSQL
View database
character set
SELECT collation_name FROM sys.data-
bases';
select datname, pg_encoding_to_
char(encoding), datcollate, datctype
from pg_database;
Modify the data-
base character set
RECRATE the database • Export the database.
• Drop or rename the database.
• Re-create the database with the
desired new character set.
• Import database data from the
exported file into the new database.
Character set gran-
ularity
Database Database
UTF8 Supported Supported
UTF16 Supported Not Supported
NCHAR/NVARCHAR
data types
Supported Not Supported
For additional details, see https://www.postgresql.org/docs/9.6/static/multibyte.html
- 131 -
Migrate from: SQL Server Cursors
Feature Compatibility SCT Automation Level SCT Action Code Index Key Differences
SCT Action Codes - Cursors Different cursor options
Overview
A set is a fundamental concept of the relation data model from which SQL is derived. SQL is a declar-
ative language that operates on whole sets, unlike most procedural languages that operate on indi-
vidual data elements. A single invocations of an SQL statements can return a whole set or modify
millions of rows.
Many developers are accustom to using procedural or imperative approaches to develop solutions that
are difficult to implement using set-based querying techniques. Also, operating on row data sequen-
tially may be a more appropriate approach is certain situations.
Cursors provide an alternative mechanism for operating on result sets. Instead of receiving a table
object containing rows of data, applications can use cursors to access the data sequentially, row-by-
row. Cursors provide the following capabilities:
l Positioning the cursor at specific rows of the result set using absolute or relative offsets.
l Retrieving a row, or a block of rows, from the current cursor position.
l Modifying data at the current cursor position.
l Isolating data modifications by concurrent transactions that affect the cursor's result.
l T-SQL statements can use cursors in scripts, stored procedures, and triggers.
Syntax
DECLARE <Cursor Name>
CURSOR [LOCAL | GLOBAL]
[FORWARD_ONLY | SCROLL]
[STATIC | KEYSET | DYNAMIC | FAST_FORWARD]
[READ_ONLY | SCROLL_LOCKS | OPTIMISTIC]
[TYPE_WARNING]
FOR <SELECT statement>
[FOR UPDATE [OF <Column List>]][;]
FETCH [NEXT | PRIOR | FIRST | LAST | ABSOLUTE <Value> | RELATIVE <Value>]
FROM <Cursor Name> INTO <Variable List>;
Examples
Process data in a cursor.
- 132 -
DECLARE MyCursor CURSOR FOR
SELECT *
FROM Table1 AS T1
INNER JOIN
Table2 AS T2
ON T1.Col1 = T2.Col1;
OPEN MyCursor;
DECLARE @VarCursor1 VARCHAR(20);
FETCH NEXT
FROM MyCursor INTO @VarCursor1;
WHILE @@FETCH_STATUS = 0
BEGIN
EXEC MyPRocessingProcedure
@InputParameter = @VarCursor1;
FETCH NEXT
FROM product_cursor INTO @VarCursor1;
END
CLOSE MyCursor;
DEALLOCATE MyCursor ;
For more information, see:
l https://docs.microsoft.com/en-us/sql/relational-databases/cursors
l https://docs.microsoft.com/en-us/sql/t-sql/language-elements/cursors-transact-sql
- 133 -
Migrate to: Aurora PostgreSQL Cursors
Feature Compatibility SCT Automation Level SCT Action Code Index Key Differences
SCT Action Codes - Cursors Different cursor options
Overview
Similar to SQL Server's T-SQL Cursors, PostgreSQL has PL/pgSQL cursors that enable you to iterate busi-
ness logic on rows read from the database. They can encapsulate the query and read the query results
a few rows at a time. All access to cursors in PL/pgSQL is performed through cursor variables, which
are always of the refcursor data type.
Examples
Declare a Cursor
DECLARE..CURSOR options that are Transact-SQL extended syntax have no equivalent in PostgreSQL
and they are:
SQL Server's
Option
Use Comments
FORWARD_
ONLY
Defining that FETCH NEXT is the only sup-
ported fetching option
Using FOR LOOP might be a relevant
solution for this option
STATIC Cursor will make a temporary copy of the
data
For small data sets temporary tables
can be created and declare a cursor
that will select these tables
KEYSET Determining that membership and order of
rows in the cursor are fixed
N/A
DYNAMIC Cursor will reflect all data changes made on
the selected rows
Default for PostgreSQL
FAST_
FORWARD
Will use FORWARD_ONLY and READ_ONLY
for optimizing performance
N/A
SCROLL_LOCKS Determine that positioned updates or
deletes made by the cursor are guaranteed
to succeed
N/A
OPTIMISTIC Determine that positioned updates or
deletes made by the cursor will not succeed
if the rows has been updated.
N/A
TYPE_ Will send warning messages to the client if N/A
- 134 -
SQL Server's
Option
Use Comments
WARNING the cursor is implicitly converted from the
requested type
Declare a Cursor in PL/pgSQL to be used with any query. The variable c1 is "unbounded" because it is
not bound to any particular query.
DECLARE c1 refcursor;
Declare a Cursor in PL/pgSQL with a bounded query.
DECLARE c2 CURSOR FOR SELECT * FROM employees;
Declare a Cursor with a parametrized bound query:
l The id variable is replaced by an integer parameter value when the cursor is opened.
l When declaring a Cursor with SCROLL specified, the Cursor can scroll backwards.
l If NO SCROLL is specified, backward fetches are rejected.
DECLARE c3 CURSOR (var1 integer) FOR SELECT * FROM employees where id = var1;
Declare a backward-scrolling compatible Cursor using the SCROLL option.
l SCROLL specifies that rows can be retrieved backwards. NO SCROLL specifies that rows cannot
be retrieved backwards.
l Depending upon the complexity of the execution plan for the query, SCROLL might create per-
formance issues.
l Backward fetches are not allowed when the query includes FOR UPDATE or FOR SHARE.
DECLARE c3 SCROLL CURSOR FOR SELECT id, name FROM employees;
Open a Cursor
The OPEN command is fully compatible between SQL Server and PostgreSQL
Open a Cursor variable that was declared as Unbound and specify the query to execute.
OPEN c1 FOR SELECT * FROM employees WHERE id = emp_id;
Open a Cursor variable that was declared as Unbound and specify the query to execute as a string
expression. This approach provides greater flexibility.
OPEN c1 FOR EXECUTE format('SELECT * FROM %I WHERE col1 = $1',tabname) USING keyvalue;
Parameter values can be inserted into the dynamic command with format() and USING. For example,
the table name is inserted into the query with format(). The comparison value for col1 is inserted with
a USING parameter.
- 135 -
Open a Cursor that was bound to a query when the Cursor was declared and was declared to take argu-
ments.
DO $$
DECLARE
c3 CURSOR (var1 integer) FOR SELECT * FROM employees where id = var1;
BEGIN
OPEN c3(var1 := 42);
END$$;
For the c3 Cursor, supply the argument value expressions.
If the Cursor was not declared to take arguments, the arguments can be specified outside the Cursor.
DO $$
DECLARE
var1 integer;
c3 CURSOR FOR SELECT * FROM employees where id = var1;
BEGIN
var1 := 1;
OPEN c3;
END$$;
Fetch a Cursor
Syntax
FETCH [direction [FROM | IN ] ] cursor_name
PostgreSQL has few more options as a direction for the FETCH command
PostgreSQL's Option Use
ALL Get all remaining rows
FORWARD Same as NEXT
FORWARD (n) Fetch the next n rows
FORWARD ALL Same as ALL
BACKWARD Same as PRIOR
BACKWARD (n) Fetch the prior n rows
BACKWARD ALL Fetch all prior rows
The PL/pgSQL FETCH command retrieves the next row from the Cursor into a variable.
Fetch the values returned from the c3 Cursor into a row variable.
- 136 -
DO $$
DECLARE
c3 CURSOR FOR SELECT * FROM employees;
rowvar employees%ROWTYPE;
BEGIN
OPEN c3;
FETCH c3 INTO rowvar;
END$$;
Fetch the values returned from the c3 Cursor into two scalar data types.
DO $$
DECLARE
c3 CURSOR FOR SELECT id, name FROM employees;
emp_id integer;
emp_name varchar;
BEGIN
OPEN c3;
FETCH FROM c3 INTO emp_id, emp_name;
END$$;
PL/pgSQL supports a special direction clause when fetching data from a Cursor using the NEXT, PRIOR,
FIRST, LAST, ABSOLUTE count, RELATIVE count, FORWARD, or BACKWARD arguments. Omitting dir-
ection is equivalent to specifying NEXT. For example, fetch the last row from the Cursor into the
declared variables.
DO $$
DECLARE
c3 CURSOR FOR SELECT id, name FROM employees;
emp_id integer;
emp_name varchar;
BEGIN
OPEN c3;
FETCH LAST FROM c3 INTO emp_id, emp_name;
END$$;
Summary
Feature SQL Server Aurora PostgreSQL
Cursor options [FORWARD_ONLY | SCROLL] [STATIC |
KEYSET | DYNAMIC | FAST_FORWARD]
[READ_ONLY | SCROLL_LOCKS |
OPTIMISTIC]
[ BINARY ] [ INSENSITIVE ] [ [ NO ] SCROLL
] CURSOR [ { WITH | WITHOUT } HOLD ]
Updateable
cursors
DECLARE CURSOR... FOR UPDATE DECLARE cur_name CURSOR... FOR
UPDATE
Cursor declar-
ation
DECLARE CURSOR DECLARE cur_name CURSOR
Cursor open OPEN OPEN
- 137 -
Feature SQL Server Aurora PostgreSQL
Cursor fetch FETCH NEXT | PRIOR | FIRST | LAST |
ABSOLUTE | RELATIVE
FETCH [ direction [ FROM | IN ] ] cursor_
name where direction can be empty or
one of:
NEXT, PRIOR, FIRST, LAST, ABSOLUTE
count, RELATIVE count, count , ALL
FORWARD, FORWARD count, FORWARD
ALL, BACKWARD, BACKWARD count,
BACKWARD ALL
Cursor close CLOSE CLOSE
Cursor Deal-
locate
DEALLOCATE Same effect as CLOSE (not required)
Cursor end con-
dition
@@FETCH_STATUS system variable Not Supported
For additional details, see https://www.postgresql.org/docs/9.6/static/sql-fetch.html
- 138 -
Migrate from: SQL Server Date and Time Functions
Feature Com-
patibility
SCT Automation
Level
SCT Action Code Index Key Differences
SCT Action Codes - Date Time
Functions
PostgreSQL is using different
function names
Overview
Date and Time Functions are scalar functions that perform operations on temporal or numeric input
and return temporal or numeric values.
System date and time values are derived from the operating system of the server on which SQL Server
is running.
Note: This section does not address timezone considerations and timezone aware functions.
For more information about time zone handling, see Data Types.
Syntax and Examples
The following table lists the most commonly used Date and Time Functions.
Function Purpose Example Result Comments
GETDATE and
GETUTCDATE
Return a datetime
value that contains
the current local or
UTC date and time
SELECT GETDATE
()
2018-04-05
15:53:01.380
DATEPART, DAY,
MONTH, and YEAR
Return an integer
value representing
the specified date-
part of a specified
date
SELECT MONTH
(GETDATE()),
YEAR(GETDATE())
4, 2018
DATEDIFF
Returns an integer
value of datepart
boundaries that are
crossed between
two dates
SELECT DATEDIFF
(DAY, GETDATE
(), EOMONTH
(GETDATE()))
25 How many days
left until end of
the month
DATEADD
Returns a datetime
value that is cal-
culated with an off-
set interval to the
specified datepart of
SELECTDATEADD
(DAY, 25,
GETDATE())
2018-04-30
15:55:52.147
- 139 -
Function Purpose Example Result Comments
a date.
CAST and
CONVERT
Converts datetime
values to and from
string literals and to
and from other dat-
etime formats
SELECT CAST
(GETDATE() AS
DATE)
SELECT CONVERT
(VARCHAR(20),
GETDATE(), 112)
2018-04-05
20180405
Default date
format
Style 112 (ISO)
with no
seprartors
For more information, see
https://docs.microsoft.com/en-us/sql/t-sql/functions/date-and-time-data-types-and-functions-transact-
sql#DateandTimeFunctions
- 140 -
Migrate to: Aurora PostgreSQL Date and Time Func-
tions
Feature Com-
patibility
SCT Automation
Level
SCT Action Code Index Key Differences
SCT Action Codes - Date Time
Functions
PostgreSQL is using different
function names
Overview
Aurora PostgreSQL provides a very rich set of scalar date and time functions; more than SQL Server.
Note: While some of the functionsappear to be similar to those in SQL Server, the func-
tionality can be significantly different. Take extra care when migrating temporal logic to Aur-
ora PostgreSQL paradigms.
Functions and definition
PostgreSQL
Function
Function
Definition
AGE Subtract from current_date
CLOCK_
TIMESTAMP
Current date and time
CURRENT_DATE Current date
CURRENT_TIME Current time of day
CURRENT_
TIMESTAMP
Current date and time (start of current transaction)
DATE_PART Get subfield (equivalent to extract)
DATE_TRUNC Truncate to specified precision
EXTRACT Get subfield
ISFINITE Test for finite interval
JUSTIFY_DAYS Adjust interval so 30-day time periods are represented as months
JUSTIFY_HOURS Adjust interval so 24-hour time periods are represented as days
JUSTIFY_INTERVAL Adjust interval using justify_days and justify_hours, with additional sign adjust-
ments
LOCALTIME Current time of day
- 141 -
PostgreSQL
Function
Function
Definition
MAKE_DATE Create date from year, month and day fields
MAKE_INTERVAL Create interval from years, months, weeks, days, hours, minutes and seconds
fields
MAKE_TIME Create time from hour, minute and seconds fields
MAKE_TIMESTAMP Create timestamp from year, month, day, hour, minute, and seconds fields
MAKE_
TIMESTAMPTZ
Create timestamp with time zone from year, month, day, hour, minute, and
seconds fields. If timezone is not specified, the current time zone is used
NOW Current date and time
STATEMENT_
TIMESTAMP
Current date and time
TIMEOFDAY Current date and time (like clock_timestamp, but as a text string)
TRANSACTION_
TIMESTAMP
Current date and time
TO_TIMESTAMP Convert Unix epoch (seconds since 1970-01-01 00:00:00+00) to timestamp
Summary
SQL Server Function Aurora PostgerSQL Function
GETDATE, CURRENT_TIMESTAMP NOW, CURRENT_DATE, CURRENT_TIME, CURRENT_TIMESTAMP
GETUTCDATE current_timestamp at time zone 'utc'
DAY, MONTH, and YEAR EXTRACT(DAY/MONTH/YEAR FROM TIMESTAMP timestamp_value)
DATEPART EXTRACT, DATE_PART
DATEDIFF DATE_PART
DATEADD + INTERVAL 'X days/months/years'
CAST and CONVERT CAST
For more information, see https://www.postgresql.org/docs/9.6/static/functions-datetime.html
- 142 -
Migrate from: SQL Server String Functions
Feature Com-
patibility
SCT Automation Level
SCT Action Code
Index
Key Differences
N/A Syntax and option dif-
ferences
Overview
String Functions are typically scalar functions that perform an operation on string input and return a
string or a numeric value.
Syntax and Examples
The following table lists the most commonly used string functions.
Function Purpose Example Result Comments
ASCII and
UNICODE
Convert an ASCII or
UNICODE character to
its ASCII or UNICODE
code
SELECTASCII
('A')
65 Returns a
numeric
integer value
CHAR and NCHAR
Convert between ASCII
or UNICODE code to a
string character
SELECT CHAR(65)
'A' Numeric
integer value
as input
CHARINDEX and
PATINDEX
Find the starting pos-
ition of one string
expression (or string
pattern) within another
string expression
SELECT
CHARINDEX('ab',
'xabcdy')
2 Returns a
numeric
integer value
CONCAT and
CONCAT_WS
Combine multiple
string input expres-
sions into a single
string with, or without,
a separator character
(WS)
SELECT CONCAT
('a','b'),
CONCAT_WS
(',','a','b')
'ab', 'a,b'
LEFT, RIGHT,
and SUBSTRING
Return a partial string
from another string
expression based on
position and length
SELECT LEFT
('abs',2),
SUBSTRING
('abcd',2,2)
'ab', 'bc'
LOWER and UPPER
Return a string with all
characters in lower or
SELECT LOWER
('ABcd')
'abcd'
- 143 -
Function Purpose Example Result Comments
upper case. Use for
presentation or to
handle case insensitive
expressions
LTRIM, RTRIM
and TRIM
Remove leading and
trailing spaces
SELECT LTRIM ('
abc d ')
'abc d '
STR
Convert a numeric
value to a string
SELECT STR
(3.1415927,5,3)
3.142 Numeric
expressions
as input
REVERSE
Return a string in
reverse order
SELECT REVERSE
('abcd')
'dcba'
REPLICATE
Return a string that
consists of zero or
more concatenated
copies of another
string expression
SELECT
REPLICATE
('abc', 3)
'abcabcabc'
REPLACE
Replace all occur-
rences of a string
expression with
another
SELECT REPLACE
('abcd', 'bc',
'xy')
'axyd'
STRING_SPLIT
Parse a list of values
with a separator and
return a set of all indi-
vidual elements
SELECT *
FROM STRING_
SPLIT('1,2',
',') AS X(C)
1
2
STRING_
SPLIT is a
table valued
function
STRING_AGG
Return a string that
consists of con-
catenated string values
in row groups
SELECT STRING_
AGG(C, ',')
FROM VALUES(1,
'a'), (1, 'b'),
(2,'c') AS X
(ID,C)
GROUP BY I
1 'ab'
2 'c'
STRING_AGG
is an aggreg-
ate function
For more information, see https://docs.microsoft.com/en-us/sql/t-sql/functions/string-functions-transact-sql
- 144 -
Migrate to: Aurora PostgreSQL String Functions
Feature Com-
patibility
SCT Automation Level
SCT Action Code
Index
Key Differences
N/A Syntax and option dif-
ferences
Overview
Most of SQL Server's String Functions are supported in PostgreSQL, there are few which are not:
l UNICODE- this function will return the integer value of the first character as defined by the
Unicode standard, if you will use UTF8 input ASCII can be used in order to get the same results
l PATINDEX - returns the starting position of the first occurrence of a pattern in a specified expres-
sion, or zeros if the pattern is not found, there is no equivalent function for that but you can cre-
ate the same function with the same name so it will be fully compatible.
Some functions are not supported but they have an equivalent function in PostgreSQL that can be
used in order to get the some functionality.
Some of the functions such as regular expressions do not exist in SQL Server and may be useful for
your application.
Syntax and Examples
The following table lists the most commonly used string functions.
PostgreSQL
Function
Function
Definition
CONCAT Concatenate the text representations of all the arguments:
concat('a', 1) --> a1 Also, can use the (||) operators: select 'a' ||' '|| 'b' --> a b
LOWER / UPPER Returns char, with all letters lowercase or uppercase: lower ('MR. Smith’) --> mr.
smith
LPAD / RPAD Returns expr1, left or right padded to length n characters with the sequence of
characters in expr2: LPAD('Log-1',10,'*') --> *****Log-1
REGEXP_REPLACE Replace substring(s) matching a POSIX regular expression:
regexp_replace('John', '[hn].', '1') --> Jo1
REGEXP_MATCHES
OR SUBSTRING
Return all captured substrings resulting from matching a POSIX regular expres-
sion against the string:
REGEXP_MATCHES ('http://www.aws.com/products', '(http://[[: alnum:]]+.*/)') -->
{http://www.aws.com/}
OR
SUBSTRING ('http://www.aws.com/products', '(http://[[: alnum:]]+.*/)') -->
- 145 -
PostgreSQL
Function
Function
Definition
http://www.aws.com/
REPLACE Returns char with every occurrence of search string replaced with a replacement
string: replace ('abcdef', 'abc', '123') --> 123def
LTRIM / RTRIM Remove the longest string containing only characters from characters (a space by
default) from the start of string:
ltrim('zzzyaws', 'xyz') --> aws
SUBSTRING Extract substring: substring ( 'John Smith', 6 ,1) --> S
TRIM Remove the longest string containing only characters from characters (a space by
default) from the start, end, or both ends:
trim (both from 'yxJohnxx', 'xyz') --> John
ASCII Returns the decimal representation in the database character set of the first char-
acter of char: ascii('a') --> 97
LENGTH Return the length of char: length ('John S.') --> 7
In order to create the PATINDEX function, you should use the code snippet below, note the 0 means
that the expression does not exist so the first position will be 1:
CREATE OR REPLACE FUNCTION "patindex"("pattern" VARCHAR, "expression" VARCHAR )
RETURNS INT AS $BODY$
SELECT COALESCE(STRPOS($2,(
SELECT(REGEXP_MATCHES($2,'(' ||
REPLACE(REPLACE(TRIM($1, '%' ), '%', '.*?' ), '_', '.' )
|| ')','i') )[1 ] LIMIT 1)),0);
$BODY$LANGUAGE 'sql' IMMUTABLE;
SELECT patindex('Lo%', 'Long String' );
patindex |
---------|
1 |
SELECT patindex('%rin%', 'Long String' );
patindex |
---------|
8 |
SELECT patindex('%g_S%', 'Long String' );
patindex |
---------|
4 |
- 146 -
Summary
SQL Server function Aurora PostgreSQL equivalent function
ASCII ASCII
UNICODE For UTF8 inputs only ASCII can be used
CHAR and NCHAR CHR
CHARINDEX POSITION
PATINDEX see examples
CONCAT and CONCAT_WS CONCAT and CONCAT_WS
LEFT, RIGHT, and SUBSTRING LEFT, RIGHT, and SUBSTRING
LOWER and UPPER LOWER and UPPER
LTRIM, RTRIM and TRIM LTRIM, RTRIM and TRIM
STR TO_CHAR
REVERSE REVERSE
REPLICATE LPAD
REPLACE REPLACE
STRING_SPLIT regexp_split_to_array or regexp_split_to_table
STRING_AGG CONCAT_WS
For more information, see https://www.postgresql.org/docs/9.6/static/functions-string.html
- 147 -
Migrate from: SQL Server Databases and Schemas
Feature Compatibility SCT Automation Level SCT Action Code Index Key Differences
N/A
Overview
Databases and Schemas are logical containers for security and access control. Administrators can
grant permissions collectively at both the databases and the schema levels. SQL Server instances
provide security at three levels: Individual Objects, Schemas (collections of objects), and Databases (col-
lections of schemas). For more information, see Data Control Language.
Note: In previous versions of SQL server, the term user was interchangeable with the term
schema. For backward compatibility, each database has several built-in security schemas
including guest, dbo, db_datareaded, sys, INFORMATION_SCHEMA, and others. You most
likely will not need to migrate these schemas.
Each SQL Server instance can host and manage a collection of databases, which consists of SQL Server
processes and the Master, Model, TempDB, and MSDB system databases.
The most common SQL Server administrator tasks at the database level are:
l Managing Physical Files: Add, remove, change file growth settings, and re-size files.
l Managing Filegroups: Partition schemes, object distribution, and read-only protection of tables.
l Managing default options.
l Creating database snapshots.
Unique object identifiers within an instance use three-part identifiers: <Database name>.<Schema
name>.<Object name>.
The recommended way to view database object meta data, including schemas, is to use the
ANSIstandard Information Schema views. In most cases, these views are compatible with other ANSI
compliant Relational Database Management Systems (RDBMS).
To view a list of all databases on the server, use the sys.databases table.
Syntax
Simplified syntax for CREATE DATABASE:
CREATE DATABASE <database name>
[ON [PRIMARY ] <file specifications>[,<filegroup>]
[LOG ON <file specifications>
[WITH <options specification> ] ;
Simplified syntax for CREATE SCHEMA:
- 148 -
CREATE SCHEMA <schema name> | AUTHORIZATION <owner name>;
Examples
Add a file to a database and create a table using the new file.
USE master;
ALTER DATABASE NewDB
ADD FILEGROUP NewGroup;
ALTER DATABASE NewDB
ADD FILE (
NAME = 'NewFile',
FILENAME = 'D:\NewFile.ndf',
SIZE = 2 MB
)
TO FILEGROUP NewGroup;
USE NewDB;
CREATE TABLE NewTable
(
Col1 INT PRIMARY KEY
)
ON NewGroup;
SELECT Name
FROM sys.databases
WHERE database_id > 4;
Create a table within a new schema and database.
USE master
CREATE DATABASE NewDB;
USE NewDB;
CREATE SCHEMA NewSchema;
CREATE TABLE NewSchema.NewTable
(
NewColumn VARCHAR(20) NOT NULL PRIMARY KEY
);
Note: This example uses default settings for the new database and schema.
For more information, see:
l https://docs.microsoft.com/en-us/sql/relational-databases/system-catalog-views/sys-databases-transact-sql
l https://docs.microsoft.com/en-us/sql/t-sql/statements/create-schema-transact-sql
- 149 -
Migrate to: Aurora PostgreSQL Databases and
Schemas
Feature Compatibility SCT Automation Level SCT Action Code Index Key Differences
N/A
Overview
Aurora PostgreSQL supports both the CREATE SCHEMA and CREATE DATABASE statements.
As with SQLServer, Aurora PostgreSQL does have the concept of an instance hosting multiple data-
bases, which in turn contain multiple schemas. Objects in Aurora PostgreSQL are referenced as a three
part name: <database>.<schema>.<object>.
A schema is essentially a namespace that contains named objects.
When database is created, it is cloned from a template.
Syntax
Syntax for CREATE DATABASE:
CREATE DATABASE name
[[WITH ] [OWNER [=] user_name ]
[TEMPLATE [=] template ]
[ENCODING [=] encoding ]
[LC_COLLATE [=] lc_collate ]
[LC_CTYPE [=] lc_ctype ]
[TABLESPACE [=] tablespace_name ]
[ALLOW_CONNECTIONS [=] allowconn ]
[CONNECTION LIMIT [=] connlimit ]
[IS_TEMPLATE [=] istemplate ] ]
Syntax for CREATE SCHEMA:
CREATE SCHEMA schema_name [AUTHORIZATION role_specification ] [schema_element [...
] ]
CREATE SCHEMA AUTHORIZATION role_specification [schema_element [... ] ]
CREATE SCHEMA IF NOT EXISTS schema_name [AUTHORIZATION role_specification ]
CREATE SCHEMA IF NOT EXISTS AUTHORIZATION role_specification
where role_specification can be:
user_name
| CURRENT_USER
| SESSION_USER
- 151 -
Migration Considerations
Unlike SQLServer, Aurora PostgreSQL does not support the USE command to specify the default data-
base (schema)for missing object qualifiers. To use a different database, you must use a new con-
nection, have the required permissions, and refer to the object using the database name.
For applications using a single database and multiple schemas, the migration path is the same and
requires fewer rewrites because two-part names are already being used.
Query the postgres.pg_catalog.pg_database table to view databases in Aurora PostgreSQL.
SELECT datname, datcollate, datistemplate, datallowconn
FROM postgres.pg_catalog.pg_database;
datname |datcollate |datistemplate |datallowconn |
----------|------------|--------------|-------------|
template0 |en_US.UTF-8 |true |false |
rdsadmin |en_US.UTF-8 |false |true |
template1 |en_US.UTF-8 |true |true |
postgres |en_US.UTF-8 |false |true |
Examples
Create a new database.
CREATE DATABASE NewDatabase;
Create a schema for user testing.
CREATE SCHEMA AUTHORIZATION joe;
Create a schema, a table and a view.
CREATE SCHEMA world_flights
CREATE TABLE flights (flight_id VARCHAR(10), departure DATE, airport VARCHAR(30))
CREATE VIEW us_flights AS
SELECT flight_id, departure FROM flights WHERE airport='United States';
For more information, see:
l https://www.postgresql.org/docs/9.6/static/sql-createdatabase.html
l https://www.postgresql.org/docs/9.6/static/sql-createschema.html
- 152 -
Migrate from: SQL Server Dynamic SQL
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A Different paradigm and syntax will require
rewriting the application
Overview
Dynamic SQL is a feature that helps minimize hard-coded SQL. The SQLEngine optimizes code, which
leads to less "hard" parses.
Dynamic SQL allows developers to construct and execute SQL queries at run time as a string, using
some logic in SQL to construct varying query strings, without having to pre-construct them during
development.
There are two options for running Dynamic SQL:use the EXECUTE command or the sp_executesql func-
tion.
EXECUTE Command
This option enables executing a command string within a T-SQL block, procedure, or function. The
EXECUTE command can also be used with linked servers. Meta-data for the result set can be defined by
using the WITH RESULT SETS options.
For parameters, use either the value or @parameter_name=value.
Note: It's important to validate the structure of the string command before running it with the
EXECUTE statement
Syntax
-- Syntax for SQL Server
Execute a stored procedure or function
[{EXEC | EXECUTE } ]
{
[@return_status = ]
{module_name [;number ] | @module_name_var }
[[@parameter = ] {value
| @variable [OUTPUT ]
| [DEFAULT ]
}
]
[,...n ]
[WITH <execute_option> [,...n ] ]
- 153 -
}
[;]
Execute a character string
{EXEC | EXECUTE }
({@string_variable | [N ]'tsql_string' } [+ ...n ] )
[AS {LOGIN | USER } = ' name ' ]
[;]
Execute a pass-through command against a linked server
{EXEC | EXECUTE }
({@string_variable | [N ] 'command_string [? ]' } [+ ...n ]
[{, {value | @variable [OUTPUT ] } } [...n ] ]
)
[AS {LOGIN | USER } = ' name ' ]
[AT linked_server_name ]
[;]
<execute_option>::=
{
RECOMPILE
| {RESULT SETS UNDEFINED }
| {RESULT SETS NONE }
| {RESULT SETS (<result_sets_definition> [,...n ] ) }
}
<result_sets_definition> ::=
{
(
{column_name
data_type
[COLLATE collation_name ]
[NULL | NOT NULL ] }
[,...n ]
)
| AS OBJECT
[db_name . [schema_name ] . | schema_name . ]
{table_name | view_name | table_valued_function_name }
| AS TYPE [schema_name.]table_type_name
| AS FOR XML
}
Example
EXECUTE a 'tsql_string' with a variable.
DECLARE @scm_name sysname;
DECLARE @tbl_name sysname;
EXECUTE ('DROP TABLE ' + @scm_name + '.' + @tbl_name + ';');
- 154 -
Use EXECUTE AS USER to switch context to another user.
DECLARE @scm_name sysname;
DECLARE @tbl_name sysname;
EXECUTE ('DROP TABLE ' + @scm_name + '.' + @tbl_name + ';') AS USER = 'SchemasAdmin';
Use EXECUTE with a result set.
EXEC GetMaxSalByDeptID 23
WITH RESULT SETS
(
([Salary] int NOT NULL)
);
sp_executesql system stored procedure
This option executes a T-SQL command or block that can be executed several times and built dynam-
ically. It also can be used with embedded parameters.
Syntax
-- Syntax for SQL Server, Azure SQL Database, Azure SQL Data Warehouse, Parallel Data
Warehouse
sp_executesql [@stmt = ] statement
[
{, [@params = ] N'@parameter_name data_type [OUT | OUTPUT ][,...n ]' }
{, [@param1 = ] 'value1' [,...n ] }
]
Examples
Executing a SELECT statement.
EXECUTE sp_executesql
N'SELECT * FROM HR.Employees
WHERE DeptID = @DID',
N'@DID int',
@DID = 23;
For more information, see:
l https://docs.microsoft.com/en-us/sql/relational-databases/system-stored-procedures/sp-executesql-transact-
sql?view=sql-server-2017
l https://docs.microsoft.com/en-us/sql/t-sql/language-elements/execute-transact-sql?view=sql-server-2017
- 155 -
Migrate to: Aurora PostgreSQL EXECUTE and
PREPARE
Feature Com-
patibility
SCT Auto-
mation Level
SCT Action
Code Index
Key Differences
N/A Different paradigm and syntax will require
rewriting the application
Overview
EXECUTE
The PostgreSQL EXECUTE command prepares and executes commands dynamically. The EXECUTE com-
mand can also run DDL statements and retrieve data using SQL commands. Similar to SQL Server, the
PostgreSQL EXECUTE command can be used with bind variables.
Note that Converting SQL Server Dynamic SQL to PostgreSQL requires significant effort.
Examples
Execute a SQL SELECT query with the table name as a dynamic variable using bind variables. This query
returns the number of employees under a manager with a specific ID.
DO $$DECLARE
Tabname varchar(30) := 'employees';
num integer := 1;
cnt integer;
BEGIN
EXECUTE format('SELECT count(*) FROM %I WHERE manager = $1', tabname)
INTO cnt USING num;
RAISE NOTICE 'Count is % int table %', cnt, tabname;
END$$;
;
Execute a DML command; first with no variables and then with variables.
DO $$DECLARE
BEGIN
EXECUTE 'INSERT INTO numbers (a) VALUES (1)';
EXECUTE format('INSERT INTO numbers (a) VALUES (%s)', 42);
END$$;
;
- 156 -
Note: %s formats the argument value as a simple string. A null value is treated as an empty
string. %I treats the argument value as an SQL identifier and double-quotes it if necessary. It is
an error for the value to be null.
Execute a DDL command.
DO $$DECLARE
BEGIN
EXECUTE 'CREATE TABLE numbers (num integer)';
END$$;
;
For additional details, seehttps://www.postgresql.org/docs/9.3/static/functions-string.html
PREPARE
Using a PREPARE statement can improve performance of reusable SQL statements.
The PREPARE command can receive a SELECT, INSERT, UPDATE, DELETE, or VALUES statement and
parse it with a user-specified qualifying name so the EXECUTE command can be used later without the
need to re-parse the SQL statement for each execution.
l When using PREPARE to create a prepared statement, it will be viable for the scope of the current
session.
l If a DDL command is executed on a database object referenced by the prepared SQL statement,
the next EXECUTE command requires a hard parse of the SQL statement.
Example
Use PREPARE and EXECUTE commands in tandem: The SQL command is prepared with a user-spe-
cified qualifying name.The SQL command is executed several times, without the need for re-parsing.
PREPARE numplan (int, text, bool) AS
INSERT INTO numbers VALUES($1, $2, $3);
EXECUTE numplan(100, 'New number 100', 't');
EXECUTE numplan(101, 'New number 101', 't');
EXECUTE numplan(102, 'New number 102', 'f');
EXECUTE numplan(103, 'New number 103', 't');
Summary
Functionality SQL Server – Dynamic SQL PostgerSQL- EXECUTE & PREPARE
Execute SQL with
results and bind
variables
DECLARE @sal int;
EXECUTE getSalary @sal OUTPUT;
EXECUTE format('select salary from
employees WHERE %I = $1', col_name)
INTO amount USING col_val;
- 157 -
Functionality SQL Server – Dynamic SQL PostgerSQL- EXECUTE & PREPARE
Execute DML with
variables and bind
variables
DECLARE @amount int
DECLARE @col_val int
DECLARE @col_name carchar(70)
DECLARE @sqlCommand varchar
(1000)
SET @sqlCommand = 'UPDATE
employees SET salary=salary' +
@amount + ' WHERE ' + @col_name +
'=' + @col_val
EXECUTE (@sqlCommand)
EXECUTE format('UPDATE employees SET
salary = salary + $1 WHERE %I = $2', col_
name) USING amount, col_val;
Execute DDL EXECUTE ('CREATE TABLE link_emp
(idemp1 integer, idemp2 integer);');
EXECUTE 'CREATE TABLE link_emp
(idemp1 integer, idemp2 integer)';
Execute Anonym-
ous block
BEGIN ... END; DO $$DECLARE
BEGIN ... END$$;
For additional details, see https://www.postgresql.org/docs/current/static/plpgsql-statements.html
- 158 -
Migrate from: SQL Server Transactions
Feature Com-
patibility
SCT Automation
Level
SCT Action Code Index Key Differences
SCT Action Codes - Trans-
actions
Nested transactions are not sup-
ported and syntax diffrences for
initializing a transaction
Overview
A Transaction is a unit of work performed on a database and typically represents a change in the data-
base. Transactions serve the following purposes:
l Provide units of work that enable recovery from logical or physical system failures while keeping
the database in a consistent state.
l Provide units of work that enable recovery from failures while keeping a database in a consistent
state when a logical or physical system failure occurs.
l Provide isolation between users and programs accessing a database concurrently.
Transactions are an "all-or-nothing" unit of work. Each transactional unit of work must either com-
plete, or it must rollback all data changes. Also, transacions must be isolated from other transactions.
The results of the "view of data" for each transaction must conform to the defined database isolation
level.
Database transactions must comply with ACID properties:
l Atomic: Transactions are "all or nothing". If any part of the transaction fails, the entire trans-
action fails and the database remains unchanged.
Note: There are exceptions to this rule. For example, some constraint violations, per
ANSI definitions, should not cause a transaction rollback.
l Consistent:All transactions must bring the database from one valid state to another valid state.
Data must be valid according to all defined rules, constraints, triggers, etc.
l Isolation: Concurrent execution of transactions must result in a system state that would occur if
transactions were executed sequentially.
Note: There are several exceptions to this rule based on the lenience of the required
isolation level.
l Durable:After a transaction commits successfully and is acknowledged to the client, the engine
must guarantee that its changes are persisted in the event of power loss, system crashes, or any
other errors.
Note: By default, SQL Server uses the "auto commit" (also known as "implicit trans-
actions") mode set to ON. Every statement is treated as a transaction on its own unless a
- 159 -
transaction was explicitly defined. This behavior is different than other engines like
Oracle where, by default, every DML requires an explicit COMMIT statement to be per-
sisted.
Syntax
Simplified syntax for the commands defining transaction boundaries:
Define the beginning of a transaction.
BEGIN TRAN | TRANSACTION [<transaction name>]
Committing work and the end of a transaction.
COMMIT WORK |[TRAN | TRANSACTION [<transaction name>]]
Rollback work at the end of a transaction.
ROLLBACK WORK |[TRAN | TRANSACTION [<transaction name>]]
SQL Server supports the standard ANSI isolation levels defined by the ANSI/ISO SQL standard (SQL92):
Note: Each level provides a different approach for managing the concurrent execution of
transactions. The main purpose of a transaction isolation level is to manage the visibility of
changed data as seen by other running transactions. Additionally, when concurrent trans-
actions access the same data, the level of transaction isolation affects the way they interact
with each other.
l Read Uncommitted: A current transaction can see uncommitted data from other transactions. If
a transaction performs a rollback, all data is restored to its previous state.
l Read Committed: A transaction only sees data changes that were committed. Therefore, dirty
reads are not possible. However, after issuing a commit, it would be visible to the current trans-
action (while it’s still in a running state).
l Repeatable Read:A transaction sees data changes made by the other transactions only after
both transactions issue a commit or are rolled back.
l Serializable:This isolation level is the strictest because it does not permit transaction overwrites
of another transaction's actions. Concurrent execution of a set of serializable transactions is guar-
anteed to produce the same effect as running them sequentially in the same order.
The main difference between isolation levels is the phenomena they prevent from appearing. The
three preventable phenomena are:
l Dirty Reads: A transaction can read data written by another transaction but not yet committed.
l Non-Repeatable (fuzzy) Reads:When reading the same data several times, a transaction can
find the data has been modified by another transaction that has just committed. The same query
executed twice can return different values for the same rows.
- 160 -
l Phantom (ghost) Reads:Similar to a non-repeatable read, but it is related to new data created
by another transaction. The same query executed twice can return different numbers of records.
The following table summarizes the four ANSI/ISO SQL standard (SQL92) isolation levels and indicates
which phenomena are allowed (√) or disallowed (X).
Transaction Isolation Level Dirty Reads Non Repeatable Reads Phantom Reads
Read Uncommitted √ √ √
Read Committed X √ √
Repeatable Read X X √
Serializable X X X
There are two common implementations for transaction isolation:
l Pessimistic Isolation (Locking): Resources accessed by a transaction are locked for the duration
of the transaction. Depending on the operation, resource, and transaction isolation level, other
transactions can "see" changes made by the locking transaction, or they must wait for it to com-
plete. With this mechanism, there is only one copy of the data for all transactions, which min-
imizes memory and disk resource consumption at the expense of transaction lock waits.
l Optimistic Isolation (MVCC):Every transaction owns a set of the versions of the resources (typ-
ically rows) that it accessed. In this mode, transactions don't have to wait for one another at the
expense of increased memory and disk utilization. In this isolation mechanism, there is a chance
that conflicts will arise when transactions attempt to commit. In case of a conflict, the application
needs to be able to handle the rollback, and attempt a retry.
SQL Server implements both mechanisms; they can be used concurrently.
For Optimistic Isolation, SQL Server introduced two additional isolation levels:Read Committed Snap-
shot and Snapshot. For more details see the links at end of this section.
Set the transaction isolation level using SET command. It affects the current execution scope only.
SET TRANSACTION ISOLATION LEVEL {READ UNCOMMITTED | READ COMMITTED | REPEATABLE READ
| SNAPSHOT | SERIALIZABLE }
Examples
Execute two DML statements within a serializable transaction.
SET TRANSACTION ISOLATIONLEVEL SERIALIZABLE;
BEGIN TRANSACTION;
INSERT INTO Table1
VALUES (1, 'A');
UPDATE Table2
SET Column1 = 'Done'
WHERE KeyColumn = 1;
COMMIT TRANSACTION;
- 161 -
Migrate to: Aurora PostgreSQL Transactions
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
SCT Action Codes -
Transactions
Nested transactions are not supported
and syntax diffrences for initializing a
transaction
Overview
As with SQLServer, the same ANSI/ISO SQL (SQL92) isolation levels apply to PostgreSQL, but with sev-
eral similarities and some differences.
Isolation Level Dirty Reads
Non-Repeatable
Reads
Phantom Reads
Read Uncom-
mitted
Permitted but not imple-
mented in PostgreSQL
Permitted Permitted
Read Committed Not permitted Permitted Permitted
Repeatable Read Not permitted Not permitted Permitted but not implemented
in PostgreSQL
Serializable Not permitted Not permitted Not permitted
PostgreSQL technically supports the use of any of the above four transaction isolation levels, but only
three can practically be used. The Read-Uncommitted isolation level serves as Read-Committed.
The way the Repeatable-Read isolation-level is implemented does not allow for phantom reads, which
is similar to the Serializable isolation-level. The primary difference between Repeatable-Read and Seri-
alizable is that Serializable guarantees that the result of concurrent transactions are precisely the same
as if they were executed serially, which is not always true for Repeatable-Reads.
Multiversion Concurrency Control (MVCC)
In PostgreSQL, the MVCC mechanism allows transactions to work with a consistent snapshot of data
ignoring changes made by other transactions that have not yet committed or rolled back. Each trans-
action “sees” a snapshot of accessed data accurate to its execution start time regardless of what other
transactions are doing concurrently.
Isolation Levels
PostgreSQL supports the Read-Committed, Repeatable-Reads, and Serializable isolation levels. Read-
Committed is the default isolation level.
- 163 -
l Read-Committed: The default PostgreSQL transaction isolation level. It prevents sessions from
“seeing” data from concurrent transactions until it is committed. Dirty reads are not permitted.
l Repeatable-Read: Queries can only see rows committed before the first query or DML state-
ment was executed in the transaction.
l Serializable: Provides the strictest transaction isolation level. The Serializable isolation level
assures that the result of the concurrent transactions will be the same as if they were executed
serially. This is not always the case for the Repeatable-Read isolation level.
Setting Isolation Levels in Aurora PostgreSQL
Isolation levels can be configured at several levels:
l Session level
l Transaction level
l Instance level using Aurora “Parameter Groups”.
Syntax
SET TRANSACTION transaction_mode [...]
SET TRANSACTION SNAPSHOT snapshot_id
SET SESSION CHARACTERISTICS AS TRANSACTION transaction_mode [...]
where transaction_mode is one of:
ISOLATION LEVEL {
SERIALIZABLE | REPEATABLE READ | READ COMMITTED | READ UNCOMMITTED
}
READ WRITE | READ ONLY [NOT ] DEFERRABLE
Examples
Configure the isolation level for a specific transaction.
SET TRANSACTION ISOLATION LEVEL READ COMMITTED;
SET TRANSACTION ISOLATION LEVEL REPEATABLE READ;
SET TRANSACTION ISOLATION LEVEL SERIALIZABLE;
Configure the isolation level for a specific session.
SET SESSION CHARACTERISTICS AS TRANSACTION ISOLATION LEVEL READ COMMITTED;
SET SESSION CHARACTERISTICS AS TRANSACTION ISOLATION LEVEL REPEATABLE READ;
View the current isolation level.
SELECT CURRENT_SETTING('TRANSACTION_ISOLATION'); -- Session
SHOW DEFAULT_TRANSACTION_ISOLATION; -- Instance
- 164 -
Modifying instance-level parameters for Aurora PostgreSQL is done using “Parameter Groups”. For
example altering the default_transaction_isolation parameter using the AWS Console or the AWS CLI.
For additional details, see: http://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_Work-
ingWithParamGroups.html#USER_WorkingWithParamGroups.Modifying
Comparison table of relevant database features related to transactions
Database Feature SQL Server PostgreSQL
AutoCommit Off On (Can be set to OFF)
MVCC Yes Yes
Default Isolation Level Read Committed Read Committed
Supported Isolation Levels REPEATABLE READ | READ COMMITTED |
READ UNCOMMITTED | SERIALIZABLE
Repeatable Reads Seri-
alizable Read-only
Configure Session Isolation
Levels
Yes Yes
Configure Transaction Isol-
ation Levels
Yes Yes
Read-Committed Isolation Level
TX1 TX2 Comment
SELECT employee_id, salary
FROM EMPLOYEES
WHERE employee_id=100;
employee_id | salary
-------------------+----------
100 | 24000.00
select employee_id, salary
from EMPLOYEES
where employee_id=100;
employee_id | salary
------------------+----------
100 | 24000.00
Same results returned from both
sessions
begin;
UPDATE employees
SET salary=27000
WHERE employee_id=100;
begin;
set transaction isolation
level read committed;
TX1 starts a transaction; per-
forms an update.
TX2 starts a transaction with
read-committed isolation level
SELECT employee_id, salary
FROM EMPLOYEES
WHERE employee_id=100;
employee_id | salary
-------------------+----------
100 | 27000.00
SELECT employee_id, salary
FROM EMPLOYEES
WHERE employee_id=100;
employee_id | salary
------------------+----------
100 | 24000.00
TX1 will “see” the modified res-
ults (27000.00) while
TX2 “sees” the original data
(24000.00)
UPDATE employees Waits as TX2 is blocked by TX1
- 165 -
TX1 TX2 Comment
SET salary=29000
WHERE employee_id=100;
Commit; TX1 issues a commit, and the
lock is released
Commit; TX2 issues a commit
SELECT employee_id, salary
FROM EMPLOYEES
WHERE employee_id=100;
employee_id | salary
------------+----------
100 | 29000.00
SELECT employee_id, salary
FROM EMPLOYEES
WHERE employee_id=100;
employee_id | salary
------------------+----------
100 | 29000.00
Both queries return the value -
29000.00
Serializable Isolation Level
TX1 TX2 Comment
SELECT employee_id, salary
FROM EMPLOYEES
WHERE employee_id=100;
employee_id | salary
-------------------+----------
100 | 24000.00
SELECT employee_id, salary
FROM EMPLOYEES
WHERE employee_id=100;
employee_id | salary
-------------------+----------
100 | 24000.00
Same results returned from
both sessions
begin;
UPDATE employees
SET salary=27000
WHERE employee_id=100;
begin;
set transaction isolation level
serializable;
TX1 starts a transaction; per-
forms an update.
TX2 starts a transaction with
isolation level of read com-
mitted
SELECT employee_id, salary
FROM EMPLOYEES
WHERE employee_id=100;
employee_id | salary
------------------+----------
100 | 27000.00
SELECT employee_id, salary
FROM EMPLOYEES
WHERE employee_id=100;
employee_id | salary
------------------+----------
100 | 24000.00
TX1 will “see” the modified
results (27000.00) while
TX2 “sees” the original data
(24000.00)
update employees set salary-
y=29000 where employee_
id=100;
Waits as TX2 is blocked by
TX1
Commit; TX1 issues a commit, and the
lock is released
- 166 -
TX1 TX2 Comment
ERROR: could not serialize
access due to concurrent update
TX2 received an error mes-
sage
Commit;
ROLLBACK
TX2 trying to issue a commit
but receives a rollback mes-
sage, the transaction failed
due to the serializable isol-
ation level
SELECT employee_id, salary
FROM EMPLOYEES
WHERE employee_id=100;
employee_id | salary
-------------------+----------
100 | 27000.00
SELECT employee_id, salary
FROM EMPLOYEES
WHERE employee_id=100;
employee_id | salary
-------------------+----------
100 | 27000.00
Both queries will return the
data updated according to
TX1
Summary
The following table summarizes the key differences in transaction support and syntax when migrating
from SQL Server to Aurora PostgreSQL.
Transaction
Property
SQL Server Aurora PostgreSQL
Default isolation
level
READ COMMITTED READ COMMITED
initialize trans-
action syntax
BEGIN TRAN|TRANSACTION SET TRANSACTION
Default isolation
mechanism
Pessimistic lock based Lock based for writes, consistent read for SELECTs
Commit trans-
action
COMMIT
[WORK|TRAN|TRANSACTION]
COMMIT
[ WORK | TRANSACTION ]
Rollback trans-
action
ROLLBACK [WORK |[ TRAN |
TRANSACTION]
ROLLBACK [ WORK | TRANSACTION ]
Set autocommit
off/on
SET IMPLICIT_TRANSACTIONS
OFF | ON
SET AUTOCOMMIT { = | TO } { ON | OFF }
ANSI Isolation REPEATABLE READ | READ
COMMITTED | READ
UNCOMMITTED |
SERIALIZABLE
REPEATABLE READ | READ COMMITTED | READ
UNCOMMITTED | SERIALIZABLE
MVCC SNAPSHOT and READ
COMMITTED SNAPSHOT
READ COMMITTED SNAPSHOT
- 167 -
Transaction
Property
SQL Server Aurora PostgreSQL
Nested trans-
actions
Supported, view level with
@@trancount
Not Supported
For additional details, see:
l https://www.postgresql.org/docs/9.6/static/tutorial-transactions.html
l https://www.postgresql.org/docs/9.6/static/transaction-iso.html
l https://www.postgresql.org/docs/9.6/static/sql-set-transaction.htm
- 168 -
Migrate from: SQL Server Synonyms
Feature Com-
patibility
SCT Auto-
mation Level
SCT Action
Code Index
Key Differences
N/A PostgreSQL does not support Synony - there is an
available workaround
Overview
Synonyms are database objects that server as alternative identifiers for other database objects. The ref-
erenced database object is called the 'base object' and may reside in the same database, another data-
base on the same instance, or a remote server.
Synonyms provide an abstraction layer to isolate client application code from changes to the name or
location of the base object.
In SQL Server, Synonyms are often used to simplify the use of four-part identifiers when accessing
remote instances.
For Example, table A resides on Server A, and the client application accesses it directly. For scale out
reasons, Table A needs to be moved to server B to offload resource consumption on Server A. Without
synonyms, the client application code must be rewritten to access Server B. Instead, you can create a
synonym called Table A and it will transparently redirect the calling application to Server B without any
code changes.
Synonyms can be created for the following objects:
l Assembly (CLR) stored procedures, table-valued functions, scalar functions, and aggregate func-
tions
l Replication-filter-procedures
l Extended stored procedures
l SQL scalar functions, table-valued functions, inline-tabled-valued functions, views, and stored
procedures
l User defined tables including local and global temporary tables
Syntax
CREATE SYNONYM [<Synonym Schema> ] . <Synonym Name>
FOR [<Server Name> ] . [<Database Name> ] . [Schema Name> ] . <Object Name>
Examples
Create a synonym for a local object in a separate database.
CREATE TABLE DB1.Schema1.MyTable
(
- 169 -
KeyColumn INT IDENTITY PRIMARY KEY,
DataColumn VARCHAR(20) NOT NULL
);
USE DB2;
CREATE SYNONYM Schema2.MyTable
FOR DB1.Schema1.MyTable
Create a synonym for a remote object.
-- On ServerA
CREATE TABLE DB1.Schema1.MyTable
(
KeyColumn INT IDENTITY PRIMARY KEY,
DataColumn VARCHAR(20) NOT NULL
);
-- On Server B
USE DB2;
CREATE SYNONYM Schema2.MyTable
FOR ServerA.DB1.Schema1.MyTable;
Note: This example assumes a linked server named ServerA exists on Server B that points to
Server A.
For more information, see https://docs.microsoft.com/en-us/sql/t-sql/statements/create-synonym-transact-sql
- 170 -
Migrate to: Aurora PostgreSQL Views, Types & Func-
tions
Feature Com-
patibility
SCT Auto-
mation Level
SCT Action
Code Index
Key Differences
N/A PostgreSQL does not support Synonym - there is
an available workaround
Overview
SQL Server's Synonym often being used to give another name for an object, PostgreSQL does not
provide a feature comparable to SQL Server Synonyms. However, you can achieve similar functionality
by using a few PostgreSQL objects.
This lack of functionality will add a manual dimension to migration process wherever SQL Server Syn-
onyms are involved. The user using these objects must have privileges on the base object and relevant
PostgreSQL options should be used.
Example
In order to create a Synonym of a table in PostgreSQL, views should be used.
The first step is to create a table that will be used as the base object, and on top of it, a view that will be
used as Synonym.
CREATE TABLE DB1.Schema1.MyTable
(
KeyColumn NUMERIC PRIMARY KEY,
DataColumn VARCHAR(20) NOT NULL
);
CREATE VIEW DB2.Schema2.MyTable_Syn
AS SELECT * FROM DB1.Schema1.MyTable
Formore information see: Views
In order to create a Synonym of a User Defined Type in PostgreSQL, another User-Defined-Type should
be used to wrap the source Type.
The first step is to create the User-Defined-Type that will be used as the base object, and on top of it, a
User-Defined-Type that will be used as the Synonym.
CREATE TYPE DB1.Schema1.MyType AS (
ID NUMERIC,
name CHARACTER VARYING(100));
- 171 -
CREATE TYPE DB2.Schema2.MyType_Syn AS (
udt DB1.Schema1.MyT);
Formore information see: User Define Types
In order to create a Synonym for a function in PostgreSQL, another Function should be used to wrap
the source Type.
As before, the first step is to create the Function that will the used as the base object, and on top of it,
a Function that will be used as the Synonym.
CREATE OR REPLACE FUNCTION DB1.Schema1.MyFunc (P_NUM NUMERIC)
RETURNS numeric AS $$
begin
RETURN P_NUM * 2;
END; $$
LANGUAGE PLPGSQL;
CREATE OR REPLACE FUNCTION DB2.Schema2.MyFunc_Syn (P_NUM NUMERIC)
RETURNS numeric AS $$
begin
RETURN DB1.Schema1.MyFunc(P_NUM);
END; $$
LANGUAGE PLPGSQL;
Formore information see: User Define Function
- 172 -
Migrate from: SQL Server DELETE and UPDATE FROM
Feature Com-
patibility
SCT Auto-
mation Level
SCT Action
Code Index
Key Differences
N/A DELETE...FROM from_list is not supported -
rewrite to use subqueries
Overview
SQL Server supports an extension to the ANSI standard that allows using an additional FROM clause in
UPDATE and DELETE statements.
This additional FROM clause can be used to limit the number of modified rows by joining the table
being updated, or deleted from, to one or more other tables. This functionality is similar to using a
WHERE clause with a derived table sub-query. For UPDATE, you can use this syntax to set multiple
column values simultaneously without repeating the sub-query for every column.
However, these statements can introduce logical inconsistencies if a row in an updated table is
matched to more than one row in a joined table. The current implementation chooses an arbitrary
value from the set of potential values and is non-deterministic.
Syntax
UPDATE <Table Name>
SET <Column Name> = <Expression> ,...
FROM <Table Source>
WHERE <Filter Predicate>;
DELETE FROM<Table Name>
FROM <Table Source>
WHERE <Filter Predicate>;
Examples
Delete customers with no orders.
CREATE TABLE Customers
(
Customer VARCHAR(20) PRIMARY KEY
);
INSERT INTO Customers
VALUES
('John'),
('Jim'),
('Jack')
- 173 -
CREATE TABLE Orders
(
OrderID INT NOT NULL PRIMARY KEY,
Customer VARCHAR(20) NOT NULL,
OrderDate DATE NOT NULL
);
INSERT INTO Orders (OrderID, Customer, OrderDate)
VALUES
(1, 'Jim', '20180401'),
(2, 'Jack', '20180402');
DELETE FROM Customers
FROM Customers AS C
LEFT OUTER JOIN
Orders AS O
ON O.Customer = C.Customer
WHERE O.OrderID IS NULL;
SELECT *
FROM Customers;
Customer
--------
Jim
Jack
Update multiple columns in Orders based on the values in OrderCorrections.
CREATE TABLE OrderCorrections
(
OrderID INT NOT NULL PRIMARY KEY,
Customer VARCHAR(20) NOT NULL,
OrderDate DATE NOT NULL
);
INSERT INTO OrderCorrections
VALUES (1, 'Jack', '20180324');
UPDATE O
SET Customer = OC.Customer,
OrderDate = OC.OrderDate
FROM Orders AS O
INNER JOIN
OrderCorrections AS OC
ON O.OrderID = OC.OrderID;
SELECT *
FROM Orders;
Customer OrderDate
------------------
Jack 2018-03-24
Jack 2018-04-02
- 174 -
Migrate to: Aurora PostgreSQL DELETE and UPDATE
FROM
Feature Com-
patibility
SCT Auto-
mation Level
SCT Action
Code Index
Key Differences
N/A DELETE...FROM from_list is not supported -
rewrite to use subqueries
Overview
Aurora PostgreSQL does not support DELETE..FROM syntax, but it does support UPDATE FROM syntax.
Syntax
[WITH [RECURSIVE ] with_query [, ...] ]
UPDATE [ONLY ] table_name [* ] [[AS ] alias ]
SET {column_name = {expression | DEFAULT } |
(column_name [, ...] ) = ({expression | DEFAULT } [, ...] ) |
(column_name [, ...] ) = (sub-SELECT )
} [, ...]
[FROM from_list ]
[WHERE condition | WHERE CURRENT OF cursor_name ]
[RETURNING * | output_expression [[AS ] output_name ] [, ...] ]
Migration Considerations
You can easily rewrite the DELETEstatements as subqueries. Place the subqueries in the WHERE
clause. This workaround is simple and, in most cases, easier to read and understand.
Examples
Delete customers with no orders.
CREATE TABLE Customers
(
Customer VARCHAR(20) PRIMARY KEY
);
INSERT INTO Customers
VALUES
('John'),
('Jim'),
('Jack')
CREATE TABLE Orders
- 176 -
(
OrderID INT NOT NULL PRIMARY KEY,
Customer VARCHAR(20) NOT NULL,
OrderDate DATE NOT NULL
);
INSERT INTO Orders (OrderID, Customer, OrderDate)
VALUES
(1, 'Jim', '20180401'),
(2, 'Jack', '20180402');
DELETE FROM Customers
WHERE Customer NOT IN (
SELECT Customer
FROM Orders
);
SELECT *
FROM Customers;
Customer
--------
Jim
Jack
Update.
CREATE TABLE OrderCorrections
(
OrderID INT NOT NULL PRIMARY KEY,
Customer VARCHAR(20) NOT NULL,
OrderDate DATE NOT NULL
);
INSERT INTO OrderCorrections
VALUES (1, 'Jack', '20180324');
UPDATE orders
SET Customer = OC.Customer,
OrderDate = OC.OrderDate
FROM Orders AS O
INNER JOIN
OrderCorrections AS OC
ON O.OrderID = OC.OrderID;
SELECT *
FROM Orders;
Customer OrderDate
------------------
Jack 2018-03-24
Jack 2018-04-02
- 177 -
Summary
The following table identifies similarities, differences, and key migration considerations.
Feature SQL Server Aurora PostgreSQL
Join as part of
DELETE
DELETE FROM ... FROM N/A - Rewrite to use WHERE clause with a sub-query.
Join as part of
UPDATE
UPDATE ... FROM UPDATE ... FROM
For more information, see:
l https://www.postgresql.org/docs/9.6/static/sql-delete.html
l https://www.postgresql.org/docs/9.6/static/sql-update.html
- 178 -
Migrate from: SQL Server Stored Procedures
Feature Com-
patibility
SCT Automation
Level
SCT Action Code Index Key Differences
SCT Action Codes - Stored Pro-
cedures
Syntax and option dif-
ferences
Overview
Stored Procedures are encapsulated, persisted code modules you can execute using the EXECUTE T-
SQL statement. They may have multiple input (IN) and output (OUT) parameters. Table valued user
defined types can be used as input parameters. IN is the default direction for parameters, but OUT
must be explicitly specified. You can specify parameters as both IN and OUT.
SQLServer allows you to run stored procedures in any security context using the EXECUTE AS option.
They can be explicitly recompiled for every execution using the RECOMPILE option and can be encryp-
ted in the database using the ENCRYPTION option to prevent unauthorized access to the source code.
SQL Server provides a unique feature that allows you to use a stored procedure as an input to an
INSERT statement. When using this feature, only the first row in the data set returned by the stored pro-
cedure is evaluated.
Syntax
CREATE [OR ALTER ] {PROC | PROCEDURE } <Procedure Name>
[<Parameter List>
[WITH [ENCRYPTION ]|[RECOMPILE ]|[EXECUTE AS ...]]
AS {
[BEGIN ]
<SQL Code Body>
[END ] }[;]
Examples
Creating and Executing a Stored Procedure
Create a simple parameterized Stored Procedure to validate the basic format of an Email.
CREATE PROCEDURE ValidateEmail
@Email VARCHAR(128), @IsValid BIT = 0 OUT
AS
BEGIN
IF @Email LIKE N'%@%' SET @IsValid = 1
ELSE SET @IsValid = 0
RETURN @IsValid
END;
- 179 -
Execute the procedure.
DECLARE @IsValid BIT
EXECUTE [ValidateEmail]
@Email = '[email protected]', @IsValid = @IsValid OUT;
SELECT @IsValid;
-- Returns 1
EXECUTE [ValidateEmail]
@Email = 'Xy.com', @IsValid = @IsValid OUT;
SELECT @IsValid;
-- Returns 0
Create a stored procedure that uses RETURN to pass the application an error value.
CREATE PROCEDURE ProcessImportBatch
@BatchID INT
AS
BEGIN
BEGIN TRY
EXECUTE Step1 @BatchID
EXECUTE Step2 @BatchID
EXECUTE Step3@BatchID
END TRY
BEGIN CATCH
IF ERROR_NUMBER() = 235
RETURN -1 -- indicate special condition
ELSE
THROW -- handle error normally
END CATCH
END
Using a Table-Valued Input Parameter
Create and populate an OrderItems table.
CREATE TABLE OrderItems(
OrderID INT NOT NULL,
Item VARCHAR(20) NOT NULL,
Quantity SMALLINT NOT NULL,
PRIMARY KEY(OrderID, Item)
);
INSERT INTO OrderItems (OrderID, Item, Quantity)
VALUES
(1, 'M8 Bolt', 100),
(2, 'M8 Nut', 100),
(3, 'M8 Washer', 200),
(3, 'M6 Washer', 100);
Create a tabled valued type for the OrderItem table valued parameter.
- 180 -
CREATE TYPE OrderItems
AS TABLE
(
OrderID INT NOT NULL,
Item VARCHAR(20) NOT NULL,
Quantity SMALLINT NOT NULL,
PRIMARY KEY(OrderID, Item)
);
Create a procedure to process order items.
CREATE PROCEDURE InsertOrderItems
@OrderItems AS OrderItems READONLY
AS
BEGIN
INSERT INTO OrderItems(OrderID, Item, Quantity)
SELECT OrderID,
Item,
Quantity
FROM @OrderItems
END;
Instantiate and populate the table valued variable and pass the data set to the stored procedure.
DECLARE @OrderItems AS OrderItems;
INSERT INTO @OrderItems ([OrderID], [Item], [Quantity])
VALUES
(1, 'M8 Bolt', 100),
(1, 'M8 Nut', 100),
(1, M8 Washer, 200);
EXECUTE [InsertOrderItems]
@OrderItems = @OrderItems;
(3 rows affected)
ItemQuantity
------------------------
1 M8 Bolt 100
2 M8 Nut 100
3 M8 Washer 200
INSERT... EXEC Syntax
INSERT INTO <MyTable>
EXECUTE <MyStoredProcedure>;
For more information, see https://docs.microsoft.com/en-us/sql/t-sql/statements/create-procedure-transact-sql
- 181 -
Migrate to: Aurora PostgreSQL Stored Procedures
Feature Com-
patibility
SCT Automation
Level
SCT Action Code Index Key Differences
SCT Action Codes - Stored Pro-
cedures
Syntax and option dif-
ferences
Overview
PostgreSQL version 9.6 provides support for both stored procedures and stored functions using the
CREATE FUNCTION statement. To emphasize, the procedural statements used by PostgreSQL version
9.6 support the CREATE FUNCTION statement only. The CREATE PROCEDURE statement is not sup-
ported.
PL/pgSQL is the main database programming language used for migrating from SQL Server's T-SQL
code. PostgreSQL supports additional programming languages, also available in Amazon Aurora Post-
greSQL:
l PL/pgSQL
l PL/Tcl
l PL/Perl
Use the psql=> show.rds.extensions command to view all available Amazon Aurora extensions.
PostgreSQL Create Function Privileges
To create a function, a user must have USAGE privilege on the language. When creating a function, a
language parameter can be specified as shown in the examples below.
Examples
Create a new function named FUNC_ALG.
psql=> CREATE OR REPLACE FUNCTION FUNC_ALG(P_NUM NUMERIC)
RETURNS NUMERIC
AS $$
BEGIN
RETURN P_NUM * 2;
END; $$
LANGUAGE PLPGSQL;
l The CREATE OR REPLACE statement creates a new function or replaces an existing function with
these limitations:
- 182 -
l You cannot change the function name or argument types.
l The statement does not allow changing the existing function return type.
l The user must own the function to replace it.
l INPUT parameter (P_NUM) is implemented similar to SQL Server T-SQL INPUT parameter.
l The $$ signs alieviate the need to use single-quoted string escape elements. With the $$ sign,
there is no need to use escape characters in the code when using single quotation marks ( ' ). The
$$ sign appears after the keyword AS and after the function keyword END.
l Use the LANGUAGE PLPGSQL parameter to specify the language for the created function.
Create a function with PostgreSQL PL/pgSQL.
psql=> CREATE OR REPLACE FUNCTION EMP_SAL_RAISE
(IN P_EMP_ID DOUBLE PRECISION, IN SAL_RAISE DOUBLE PRECISION)
RETURNS VOID
AS $$
DECLARE
V_EMP_CURRENT_SAL DOUBLE PRECISION;
BEGIN
SELECT SALARY INTO STRICT V_EMP_CURRENT_SAL
FROM EMPLOYEES WHERE EMPLOYEE_ID = P_EMP_ID;
UPDATE EMPLOYEES SET SALARY = V_EMP_CURRENT_SAL + SAL_RAISE WHERE EMPLOYEE_ID = P_EMP_
ID;
RAISE DEBUG USING MESSAGE := CONCAT_WS('', 'NEW SALARY FOR EMPLOYEE ID: ', P_EMP_ID, '
IS ', (V_EMP_CURRENT_SAL + SAL_RAISE));
EXCEPTION
WHEN OTHERS THEN
RAISE USING ERRCODE := '20001', MESSAGE := CONCAT_WS('', 'AN ERROR WAS ENCOUNTERED -
', SQLSTATE, ' -ERROR-', SQLERRM);
END; $$
LANGUAGE PLPGSQL;
psql=> select emp_sal_raise(200, 1000);
Note: In the example above, the RAISE command can be replaced with RETURN in order to
inform the application that an error occured.
Create a function with PostgreSQL PL/pgSQL.
psql=> CREATE OR REPLACE FUNCTION EMP_PERIOD_OF_SERVICE_YEAR (IN P_EMP_ID DOUBLE
PRECISION)
RETURNS DOUBLE PRECISION
AS $$
DECLARE
V_PERIOD_OF_SERVICE_YEARS DOUBLE PRECISION;
BEGIN
SELECT
EXTRACT (YEAR FROM NOW()) - EXTRACT (YEAR FROM (HIRE_DATE))
INTO STRICT V_PERIOD_OF_SERVICE_YEARS
FROM EMPLOYEES
WHERE EMPLOYEE_ID = P_EMP_ID;
- 183 -
RETURN V_PERIOD_OF_SERVICE_YEARS;
END; $$
LANGUAGE PLPGSQL;
psql=> SELECT EMPLOYEE_ID,FIRST_NAME, EMP_PERIOD_OF_SERVICE_YEAR(EMPLOYEE_ID) AS
PERIOD_OF_SERVICE_YEAR
FROM EMPLOYEES;
Summary
The following table summarizes the differences between SQL Server Stored Procedures and Post-
greSQL Stored Procedures.
SQL Server Aurora PostgreSQL Workaround
General
CREATE Syn-
tax dif-
ferences
CREATE
PROC|PROCEDURE
<Procedure Name>
@Parameter1 <Type>, ...n
AS
<Body>
CREATE [ OR REPLACE ]
FUNCTION <Function
Name> (Parameter1
<Type>, ...n)
AS $$
<body>
Rewrite stored procedure cre-
ation scripts to use FUNCTION
instead of PROC or PROCEDURE.
Rewrite stored procedure cre-
ation scripts to omit the AS $$ pat-
tern.
Rewrite stored procedure para-
meters to not use the @ symbol
in parameter names. Add par-
entheses around the parameter
declaration.
Security
Context
{ EXEC | EXECUTE } AS
{ CALLER | SELF | OWNER
| 'user_name' }
SECURITY INVOKER |
SECURITY DEFINER
For Stored procedures that use
an explicit user name, rewrite the
code from EXECUTE AS 'user' to
SECURITY DEFINER and recreate
the functions with this user
For Stored Procedures that use
the CALLER option, rewrite the
code to include SECURITY
INVOKER.
For Stored procedures that use
the SELF option, rewrite the code
to SECURITY DEFINER.
Encryption Use WITH ENCRYPTION
option
Not supported in Aurora
PostgreSQL
Parameter
direction
IN and OUT|OUTPUT,
by default OUT can be
used as IN as well.
IN, OUT, INOUT, or
VARIADIC
Although the functionality of
these parameters is the same for
SQL Server and PostgreSQL, you
- 184 -
SQL Server Aurora PostgreSQL Workaround
must rewrite the code for syntax
compliance:
Use OUT instead of OUTPUT
USE INOUT instead of OUT for bid-
irectional parameters
Recompile Use WITH RECOMPILE
option
Not supported in Aurora
PostgreSQL
Table Val-
ued Para-
meters
Use declared table type
user defined parameters
Use declared table type
user defined para-
meters
Additional
restrictions
Use BULK INSERT to load
data from text file
Not supported in Aurora
PostgreSQL
For additional details, see:
l https://www.postgresql.org/docs/9.6/static/sql-createfunction.html
l https://www.postgresql.org/docs/9.6/static/plpgsql.html
l https://www.postgresql.org/docs/9.6/static/xplang.html
l https://www.postgresql.org/docs/9.6/static/xfunc-sql.html
- 185 -
Migrate from: SQL Server Error Handling
Feature Com-
patibility
SCT Automation
Level
SCT Action
Code Index
Key Differences
N/A Different paradigm and syntax will
require rewrite of error handling code
Overview
SQL Server error handling capabilities have significantly improved throughout the years. However, pre-
vious features are retained for backward compatibility.
Before SQL Server 2008, only very basic error handling features were available. RAISERROR was the
primary statement used for error handling.
Since SQL 2008, SQL Server has added extensive ".Net like" error handling capabilities including
TRY/CATCH blocks, THROW statements, the FORMATMESSAGE function, and a set of system functions
that return metadata for the current error condition.
TRY/CATCH Blocks
TRY/CATCH blocks implement error handling similar to Microsoft Visual C# and Microsoft Visual C++.
TRY ... END TRY statement blocks can contain T-SQL statements .
If an error is raised by any of the statements within the TRY ... END TRY block, execution stops and is
moved to the nearest set of statements that are bounded by a CATCH ... END CATCH block.
Syntax
BEGIN TRY
<Set of SQL Statements>
END TRY
BEGIN CATCH
<Set of SQL Error Handling Statements>
END CATCH
THROW
The THROW statement raises an exception and transfers execution of the TRY ... END TRY block of
statements to the associated CATCH... END CATCH block of statements.
Throw accepts either constant literals or variables for all parameters.
- 186 -
Syntax
THROW [Error Number>, <Error Message>, < Error State>] [;]
Examples
Use TRY/CATCH error blocks to handle key violations.
CREATE TABLE ErrorTest (Col1 INT NOT NULL PRIMARY KEY);
BEGIN TRY
BEGIN TRANSACTION
INSERT INTO ErrorTest(Col1) VALUES(1);
INSERT INTO ErrorTest(Col1) VALUES(2);
INSERT INTO ErrorTest(Col1) VALUES(1);
COMMIT TRANSACTION;
END TRY
BEGIN CATCH
THROW; -- Throw with no parameters = RETHROW
END CATCH;
(1 row affected)
(1 row affected)
(0 rows affected)
Msg 2627, Level 14, State 1, Line 7
Violation of PRIMARY KEY constraint 'PK__ErrorTes__A259EE54D8676973'.
Cannot insert duplicate key in object 'dbo.ErrorTest'. The duplicate key value is (1).
Note: Contrary to what many SQL developers believe, the values 1 and 2 are indeed inserted
into ErrorTestTable in the above example. This behavior is in accordance with ANSI spe-
cifications stating that a constraint violation should not roll back an entire transaction.
Use THROW with variables
BEGIN TRY
BEGIN TRANSACTION
INSERT INTO ErrorTest(Col1) VALUES(1);
INSERT INTO ErrorTest(Col1) VALUES(2);
INSERT INTO ErrorTest(Col1) VALUES(1);
COMMIT TRANSACTION;
END TRY
BEGIN CATCH
DECLARE @CustomMessage VARCHAR(1000),
@CustomError INT,
@CustomState INT;
SET @CustomMessage = 'My Custom Text ' + ERROR_MESSAGE();
SET @CustomError = 54321;
SET @CustomState = 1;
THROW @CustomError, @CustomMessage, @CustomState;
END CATCH;
- 187 -
(0 rows affected)
Msg 54321, Level 16, State 1, Line 19
My Custom Text Violation of PRIMARY KEY constraint 'PK__ErrorTes__A259EE545CBDBB9A'.
Cannot insert duplicate key in object 'dbo.ErrorTest'. The duplicate key value is (1).
RAISERROR
The RAISERROR statement is used to explicitly raise an error message, similar to THROW. It causes an
error state for the executing session and forwards execution to either the calling scope or, if the error
occurred within a TRY ... END TRY block, to the associated CATCH ... END CATCH block. RAISERROR can
reference a user-defined message stored in the sys.messages system table or can be used with
dynamic message text.
The key differences between THROW and RAISERROR are:
l Message IDs passed to RAISERROR must exist in the sys.messages system table. The error num-
ber parameter passed to THROW does not.
l RAISERRORmessage text may contain printf formatting styles. The message text of THROW may
not.
l RAISERRORuses the severity parameter for the error returned. For THROW, severity is always 16.
Syntax
RAISERROR (<Message ID>|<Message Text> ,<Message Severity> ,<Message State>
[WITH option [<Option List>]])
Examples
Raise a custom error.
RAISERROR (N'This is a custom error message with severity 10 and state 1.', 10, 1)
FORMATMESSAGE
FORMATMESSAGE returns a sting message consisting of an existing error message in the sys.messages
system table, or from a text string, using the optional parameter list replacements. The
FORMATMESSAGE statement is similar to the RAISERROR statement.
Syntax
FORMATMESSAGE (<Message Number> | <Message String>, <Parameter List>)
- 188 -
Error State Functions
SQL Server provides the following error state functions:
l ERROR_LINE
l ERROR_MESSAGE
l ERROR_NUMBER
l ERROR_PROCEDURE
l ERROR_SEVERITY
l ERROR_STATE
l @@ERROR
Examples
Use Error State Functions within a CATCH block.
CREATE TABLE ErrorTest (Col1 INT NOT NULL PRIMARY KEY);
BEGIN TRY;
BEGIN TRANSACTION;
INSERT INTO ErrorTest(Col1) VALUES(1);
INSERT INTO ErrorTest(Col1) VALUES(2);
INSERT INTO ErrorTest(Col1) VALUES(1);
COMMIT TRANSACTION;
END TRY
BEGIN CATCH
SELECT ERROR_LINE(),
ERROR_MESSAGE(),
ERROR_NUMBER(),
ERROR_PROCEDURE(),
ERROR_SEVERITY(),
ERROR_STATE(),
@@Error;
THROW;
END CATCH;
6
Violation of PRIMARY KEY constraint 'PK__ErrorTes__A259EE543C8912D8'. Cannot insert
duplicate key in object 'dbo.ErrorTest'. The duplicate key value is (1).
2627
NULL
14
1
2627
(1 row affected)
(1 row affected)
(0 rows affected)
(1 row affected)
Msg 2627, Level 14, State 1, Line 25
- 189 -
Violation of PRIMARY KEY constraint 'PK__ErrorTes__A259EE543C8912D8'. Cannot insert
duplicate key in object 'dbo.ErrorTest'. The duplicate key value is (1).
For more information, see
l https://docs.microsoft.com/en-us/sql/t-sql/language-elements/raiserror-transact-sql
l https://docs.microsoft.com/en-us/sql/t-sql/language-elements/try-catch-transact-sql
l https://docs.microsoft.com/en-us/sql/t-sql/language-elements/throw-transact-sql
- 190 -
Migrate to: Aurora PostgreSQL Error Handling
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A Different paradigm and syntax will
require rewrite of error handling code
Overview
Aurora PostgreSQL does not provide native replacement for SQL Server error handling features and
options, but it has many comparable options.
To trap the errors, use the BEGIN.. EXCEPTION.. END. By default, any error raised in a PL/pgSQL func-
tion block aborts execution and the surrounding transaction. You can trap and recover from errors
using a BEGIN block with an EXCEPTION clause. The syntax is an extension to the normal syntax for a
BEGIN block.
Syntax
[<<label>> ]
[DECLARE
declarations ]
BEGIN
statements
EXCEPTION
WHEN condition [OR condition ... ] THEN
handler_statements
[WHEN condition [OR condition ... ] THEN
handler_statements
... ]
END;
"condition" is related to the error or the code. For example:
l WHEN interval_field_overflow THEN..
l WHEN SQLSTATE '22015' THEN...
For all error codes, seehttps://www.postgresql.org/docs/9.6/static/errcodes-appendix.html
Throw errors
The PostgreSQL RAISE statement can be used to throw errors. You can combine RAISE with several
levels of severity including:
- 191 -
Severity Usage
DEBUG1..DEBUG5 Provides successively more detailed information for use by developers.
INFO Provides information implicitly requested by the user.
NOTICE Provides information that might be helpful to users.
WARNING Provides warnings of likely problems.
ERROR Reports an error that caused the current command to abort.
LOG Reports information of interest to administrators. For example, checkpoint activ-
ity.
FATAL Reports an error that caused the current session to abort.
PANIC Reports an error that caused all database sessions to abort.
Examples
Use RAISE DEBUG (where DEBUG is the configurable severity level).
psql=> SET CLIENT_MIN_MESSAGES = 'debug';
psql=> DO $$
BEGIN
RAISE DEBUG USING MESSAGE := 'hello world';
END $$;
DEBUG: hello world
DO
Use the client_min_messages parameter to control the level of messages sent to the client. The default
is NOTICE. Use the log_min_messages parameter to control which message levels are written to the
server log. The default is WARNING.
SET CLIENT_MIN_MESSAGES = 'deb
Use EXCEPTION..WHEN...THEN inside BEGIN and END block to handle dividing by zero violations.
CREATE TABLE ErrorTest (Col1 INT NOT NULL PRIMARY KEY);
INSERT INTO employee values ('John',10);
BEGIN
SELECT 5/0;
EXCEPTION
WHEN division_by_zero THEN
RAISE NOTICE 'caught division_by_zero';
return 0;
END;
- 192 -
Summary
The following table identifies similarities, differences, and key migration considerations.
SQL Server Error Handling Feature Aurora PostgreSQL equivalent
TRY ... END TRY and CATCH ... END
CATCH blocks
Inner
BEGIN
...
EXCEPTION WHEN ... THEN
END
THROW and RAISERROR RAISE
FORMATMESSAGE RAISE [ level ] 'format' or ASSERT
Error state functions GET STACKED DIAGNOSTICS
Proprietary error messages in sys.mes-
sages system table
RAISE
For more information, see
l https://www.postgresql.org/docs/9.6/static/ecpg-errors.html
l https://www.postgresql.org/docs/9.6/static/plpgsql-errors-and-messages.html
l https://www.postgresql.org/docs/current/static/runtime-config-logging.html#GUC-LOG-MIN-MESSAGES
- 193 -
Migrate from: SQL Server Flow Control
Feature Com-
patibility
SCT Automation
Level
SCT Action Code Index Key Differences
SCT Action Codes -
Flow Control
Postgres does not support GOTO and
WAITFOR TIME
Overview
Although SQL is a mostly declarative language, it does support flow control commands, which provide
run time dynamic changes in script execution paths.
Note: Before SQL/PSM was introduced in SQL:1999, the ANSI standard did not include flow
control constructs. Therefore, there are significant syntax differences among RDBMS engines.
SQL Server provides the following flow control keywords.
l BEGIN... END:Define boundaries for a block of commands that are executed together.
l RETURN:Exit a server code module (stored procedure, function, etc.) and return control to the
calling scope. RETURN <value> can be used to return an INT value to the calling scope.
l BREAK:Exit WHILE loop execution.
l THROW:Raise errors and potentially return control to the calling stack.
l CONTINUE:Restart a WHILE loop.
l TRY... CATCH:Error handling (see Error Handling).
l GOTO label:Moves the execution point to the location of the specified label.
l WAITFOR:Delay.
l IF... ELSE:Conditional flow control.
l WHILE <condition>:Continue looping while <condition> returns TRUE.
Note: WHILE loops are commonly used with cursors and use the system variable @@FETCH_
STATUS to determine when to exit (see the Cursors section for more details).
For more information about TRY-CATCH and THROW, see Error Handling.
Examples
The following example demonstrates a solution for executing different processes based on the num-
ber of items in an order:
Create and populate an OrderItems table.
CREATE TABLE OrderItems
(
OrderID INT NOT NULL,
- 194 -
Item VARCHAR(20) NOT NULL,
Quantity SMALLINT NOT NULL,
PRIMARY KEY(OrderID, Item)
);
INSERT INTO OrderItems (OrderID, Item, Quantity)
VALUES
(1, 'M8 Bolt', 100),
(2, 'M8 Nut', 100),
(3, 'M8 Washer', 200);
Declare a cursor for looping through all OrderItems and calculating the total quantity per order.
DECLARE OrderItemCursor CURSOR FAST_FORWARD
FOR
SELECT OrderID,
SUM(Quantity) AS NumItems
FROM OrderItems
GROUP BY OrderID
ORDER BY OrderID;
DECLARE @OrderID INT, @NumItems INT;
-- Instantiate the cursor and loop through all orders.
OPEN OrderItemCursor;
FETCH NEXT FROM OrderItemCursor
INTO @OrderID, @NumItems
WHILE @@Fetch_Status = 0
BEGIN;
IF @NumItems > 100
PRINT 'EXECUTING LogLargeOrder - '
+ CAST(@OrderID AS VARCHAR(5))
+ ' ' + CAST(@NumItems AS VARCHAR(5));
ELSE
PRINT 'EXECUTING LogSmallOrder - '
+ CAST(@OrderID AS VARCHAR(5))
+ ' ' + CAST(@NumItems AS VARCHAR(5));
FETCH NEXT FROM OrderItemCursor
INTO @OrderID, @NumItems;
END;
-- Close and deallocate the cursor.
CLOSE OrderItemCursor;
DEALLOCATE OrderItemCursor;
The above code displays the following results:
EXECUTING LogSmallOrder - 1 100
EXECUTING LogSmallOrder - 2 100
EXECUTING LogLargeOrder - 3 200
- 195 -
For more information, see https://docs.microsoft.com/en-us/sql/t-sql/language-elements/control-of-flow
- 196 -
Migrate to: Aurora PostgreSQL Control Structures
Feature Com-
patibility
SCT Automation
Level
SCT Action Code Index Key Differences
SCT Action Codes -
Flow Control
Postgres does not support GOTO and
WAITFOR TIME
Overview
Aurora PostgreSQL provides the following flow control constructs:
l BEGIN... END: Define boundaries for a block of commands executed together.
l CASE: Execute a set of commands based on a predicate (not to be confused with CASE expres-
sions).
l IF... ELSE: Perform conditional flow control.
l ITERATE: Restart a LOOP or WHILE statement.
l LEAVE: Exit a server code module (stored procedure, function etc.) and return control to the call-
ing scope.
l LOOP: Loop indefinitely.
l REPEAT... UNTIL: Loop until the predicate is true.
l RETURN: Terminate execution of the current scope and return to the calling scope.
l WHILE: Continue looping while the condition returns TRUE.
Examples
The following example demonstrates a solution for executing different logic based on the number of
items in an order. It provides the same functionality as the example for SQL Server flow control.
However, unlike the SQL Server example executed as a batch script, Aurora PostgreSQL variables can
only be used in stored routines (procedures and functions).
Create and populate an OrderItems table.
CREATE TABLE OrderItems
(
OrderID INT NOT NULL,
Item VARCHAR(20) NOT NULL,
Quantity SMALLINT NOT NULL,
PRIMARY KEY(OrderID, Item)
);
INSERT INTO OrderItems (OrderID, Item, Quantity)
VALUES
(1, 'M8 Bolt', 100),
- 197 -
(2, 'M8 Nut', 100),
(3, 'M8 Washer', 200);
Create a procedure to declare a cursor and loop through the order items.
CREATE OR REPLACE FUNCTION P()
RETURNS numeric
LANGUAGE plpgsql
AS $function$
DECLARE
done int default false;
var_OrderID int;
var_NumItems int;
OrderItemCursor CURSOR FOR SELECT OrderID,SUM(Quantity) AS NumItems
FROM OrderItems
GROUP BY OrderID
ORDER BY OrderID;
BEGIN
OPEN OrderItemCursor;
LOOP
fetch from OrderItemCursor INTO var_OrderID, var_NumItems;
EXIT WHEN NOT FOUND;
IF var_NumItems > 100 THEN
RAISE NOTICE 'EXECUTING LogLargeOrder - %s',var_OrderID;
RAISE NOTICE 'Num Items: %s', var_NumItems;
ELSE
RAISE NOTICE 'EXECUTING LogSmallOrder - %s',var_OrderID;
RAISE NOTICE 'Num Items: %s', var_NumItems;
END IF;
END LOOP;
done = TRUE;
CLOSE OrderItemCursor;
END; $function$
Summary
While there are some syntax differences between SQL Server and Aurora PostgreSQL flow control state-
ments, most rewrites should be straightforward. The following table summarizes the differences and
identifies how to modify T-SQL code to support similar functionality in Aurora PostgreSQL PL/pgSQL.
COMMAND SQL Server Aurora PostgreSQL
BEGIN...
END
Define command
block boundaries
Define command block boundaries.
RETURN Exit the current scope
and return to caller
Supported for both
Exit a stored function and return to caller.
- 198 -
COMMAND SQL Server Aurora PostgreSQL
scripts and stored
code (procedures and
functions).
BREAK Exit WHILE loop exe-
cution
EXIT WHEN
THROW Raise errors and
potentially return con-
trol to the calling
stack
Raise errors and potentially return control to the calling stack.
TRY - CATCH Error handling Error handling - See Error Handling for more details.
GOTO Move execution to
specified label
Consider rewriting the flow logic using either CASE statements
or nested stored procedures. You can use nested stored pro-
cedures to circumvent this limitation by separating code sec-
tions and encapsulating them in sub-procedures. Use IF
<condition> EXEC <stored procedure> in place of GOTO.
WAITFOR Delay pg_sleep - see:https://www.-
postgresql.org/docs/9.6/static/functions-datetime.html
IF... ELSE Conditional flow con-
trol
Conditional flow control
WHILE Continue execution
while condition is
TRUE
Continue execution while condition is TRUE
For more information, see https://www.postgresql.org/docs/9.6/static/plpgsql-control-structures.html
- 199 -
Migrate from: SQL Server Full-Text Search
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
SCT Action Codes -
Full Text Search
Different paradigm and syntax will
require rewriting the application
Overview
SQL Server supports an optional framework for executing Full-Text search queries against character-
based data in SQL Server tables using an integrated, in-process Full-Text engine and a filter daemon
host process (fdhost.exe).
To run Full-Text queries, a Full-Text catalog must first be created, which in turn may contain one or
more Full-Text indexes. A Full-Text index is comprised of one or more textual columns of a table.
Full-text queries perform smart linguistic searches against Full-Text indexes by identifying words and
phrases based on specific language rules. The searches can be for simple words, complex phrases, or
multiple forms of a word or a phrase. They can return ranking scores for matches (also known as
"hits").
Full-Text Indexes
A Full-Text index can be created on one of more columns of a table or view for any of the following
data types:
l CHAR:Fixed size ASCIIstring column data type
l VARCHAR: Variable size ASCIIstring column data type
l NCHAR: Fixed size UNICODE string column data type
l NVARCHAR: Variable size UNICODE string column data type
l TEXT: ASCII BLOB string column data type (deprecated)
l NTEXT: UNICODE BLOB string column data type (deprecated)
l IMAGE: Binary BLOB data type (deprecated)
l XML: XML structured BLOB data type
l VARBINARY(MAX): Binary BLOB data type
l FILESTREAM: File based storage data type
Note: For more information about data types, see Data Types.
Full-text indexes are created using the CREATE FULLTEXT INDEX statement. A Full-Text index may con-
tain up to 1024 columns from a single table or view.
- 200 -
When creating Full-Text indexes on BINARY type columns, documents such as Microsoft Word can be
stored as a binary stream and parsed correctly by the Full-Text engine.
Full-Text catalogs
Full-text indexes are contained within Full-Text catalog objects. A Full-Text catalog is a logical container
for one or more Full-Text indexes and can be used to collectively administer them as a group for tasks
such as back-up, restore, refresh content, etc.
Full-text catalogs are created using the CREATE FULLTEXT CATALOG statement. A Full-Text catalog
may contain zero or more Full-Text indexes and is limited in scope to a single database.
Full-Text queries
After a Full-Text catalog and index have been create and populated, users can perform Full-Text quer-
ies against these indexes to query for:
l Simple term match for one or more words or phrases
l Prefix term match for words that begin with a set of characters
l Generational term match for inflectional forms of a word
l Proximity term match for words or phrases that are close to another word or phrase
l Thesaurus search for synonymous forms of a word
l Weighted term match for finding words or phrases with weighted proximity values
Full-text queries are integrated into T-SQL and use the following predicates and functions:
l CONTAINS predicate
l FREETEXT predicate
l CONTAINSTABLE table valued function
l FREETEXTTABLE table valued function
Note: Do not confuse Full-Text functionality with the LIKE predicate, which is used for pattern
matching only.
Updating Full-Text Indexes
By default, Full-Text indexes are automatically updated when the underlying data is modified, similar
to a normal B-Tree or Columnstore index. However, large changes to the underlying data may inflict a
performance impact for the Full-Text indexes update because it is a resource intensive operation. In
these cases, you can disable the automatic update of the catalog and update it manually, or on a sched-
ule, to keep the catalog up to date with the underlying tables.
Note: You can monitor the status of Full-Text catalog by using the
FULLTEXTCATALOGPROPERTY(<Full-text Catalog Name>, 'Populatestatus') function.
- 201 -
Examples
Create a ProductReviews table.
CREATE TABLE ProductReviews
(
ReviewID INT NOT NULL
IDENTITY(1,1),
CONSTRAINT PK_ProductReviews PRIMARY KEY(ReviewID),
ProductID INT NOT NULL
/*REFERENCES Products(ProductID)*/,
ReviewText VARCHAR(4000) NOT NULL,
ReviewDate DATE NOT NULL,
UserID INT NOT NULL
/*REFERENCES Users(UserID)*/
);
INSERT INTOProductReviews
(ProductID, ReviewText, ReviewDate, UserID)
VALUES
(1, 'This is a review for product 1, it is excellent and works as expected',
'20180701', 2),
(1, 'This is a review for product 1, it is not that great and failed after two days',
'20180702', 2),
(2, 'This is a review for product 3, it has exceeded my expectations. A+++',
'20180710', 2);
Create a Full-Text catalog for product reviews.
CREATE FULLTEXT CATALOG ProductFTCatalog;
Create a Full-Text index for ProductReviews.
CREATE FULLTEXT INDEX
ON ProductReviews (ReviewText)
KEY INDEX PK_ProductReviews
ON ProductFTCatalog;
Query the Full-Text index for reviews containing the word 'excellent'.
SELECT *
FROM ProductReviews
WHERE CONTAINS(ReviewText, 'excellent');
ReviewIDProductID ReviewText
ReviewDateUserID
---------------------------
----------------
1 1 This is a review for product 1, it is excellent and works as expected
2018-07-01 2
For more information, see https://docs.microsoft.com/en-us/sql/2014/relational-databases/search/Full-Text-search
- 202 -
Migrate to: Aurora PostgreSQL Full-Text Search
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
SCT Action Codes -
Full Text Search
Different paradigm and syntax will
require rewriting the application
Overview
Full-Text indexes are used to speed up textual searches performed against textual data by using the
Full-Text @@ predicate.
Full-Text indexes can be created on almost any column data type, it depends on the operator class
used when the index is created. All classes can be queried from the pg_opclass table and defaults can
be defined.
The default class uses index tsvector data types. The most common use is to create one column with
text or other data type, and use triggers to convert it to a tsvector.
There are two index types for full-text searches: GIN and GiST.
GIN is slowler when building the index because it iscomplete (no false positive results), but it's faster
when querying.
GIN performance on creation can be improved by increasing the maintenance_work_mem parameter.
When creating GIN indexes, they can be combined with these parameters:
l fastupdate: puts updates on the index on a waiting list so they will occur in VACUUM or related
scenarios (default is ON)
l gin_pending_list_limit: the maximum size of a waiting list (in KB, the default is 4MB)
GIN cannot be used as composite index (multi columns) unless you add the btree_gin extension (which
is supported in Aurora).
CREATE EXTENSION btree_gin;
CREATE INDEX reviews_idx ON reviews USING GIN (title, body);
Full-Text Search Functions
Boolean Search
You can use to_tsquery(), which accepts a list of words is checked against the normalized vector cre-
ated with to_tsvector(). To do this, use the @@ operator to check if tsquery matches tsvector. For
example, the following statement returns 't' because the column contains the word 'boy'. This search
also returns 't' for 'boys' but not for 'boyser'.
- 203 -
SELECT to_tsvector('The quick young boy jumped over the fence')
@@ to_tsquery('boy');
Operators Search
The following example shows how to use the AND (&), OR (|), and NOT (!) operators. The example
below returns 't'.
SELECT to_tsvector('The quick young boy jumped over the fence')
@@ to_tsquery('young & (boy | guy) & !girl');
Phase Search
When using to_tsquery, you can also search for a similar term if you replace boy with boys and add the
laungauge to be used.
SELECT to_tsvector('The quick young boy jumped over the fence')
@@ to_tsquery('english', 'young & (boys | guy) & !girl');
Search words within a specific distance ('-' is equal to 1). Theses examples return true.
SELECT to_tsvector('The quick young boy jumped over the fence') @@
to_tsquery('young <-> boy'),
to_tsvector('The quick young boy jumped over the fence') @@
to_tsquery('quick <3> jumped');
Migration Considerations
Migrating Full-Text indexes from SQL Server to Aurora PostgreSQL requires a full rewrite of the code
that addresses creating, managing, and querying of Full-Text searches.
Although the Aurora PostgreSQL full-text engine is significantly less comprehensive than SQL Server, it
is also much simpler to create and manage, and it is sufficiently powerful for most common, basic full-
text requirements.
A text search dictionary can be created. For more information see:
https://www.postgresql.org/docs/9.6/static/sql-createtsdictionary.html
For more complex full-text workloads, Amazon RDS offers CloudSearch, a managed service in the AWS
Cloud that makes it simple and cost-effective to set up, manage, and scale an enterprise grade search
solution. Amazon CloudSearch supports 34 languages and advanced search features such as high-
lighting, autocomplete, and geospatial search.
Currently, there is no direct tooling integration with Aurora PostgreSQL and, therefore, you must cre-
ate a custom application to synchronize the data between RDS instances and the CloudSearch Service.
For more information on CloudSearch, see https://aws.amazon.com/cloudsearch/
- 204 -
Examples
CREATE TABLE ProductReviews
(
ReviewID SERIAL PRIMARY KEY,
ProductID INT NOT NULL
ReviewText TEXT NOT NULL,
ReviewDate DATE NOT NULL,
UserID INT NOT NULL
);
INSERT INTOProductReviews
(ProductID, ReviewText, ReviewDate, UserID)
VALUES
(1, 'This is a review for product 1, it is excellent and works as expected',
'20180701', 2),
(1, 'This is a review for product 1, it is not that great and failed after two days',
'20180702', 2),
(2, 'This is a review for product 3, it has exceeded my expectations. A+++',
'20180710', 2);
Create Full-Text search index.
CREATE INDEX gin_idx ON ProductReviews USING gin (ReviewText gin_trgm_ops);
Note: gin_trgm_ops allows indexing a TEXT data type.
Query the full-text index for reviews containing the word 'excellent'.
SELECT * FROM ProductReviews where ReviewText @@ to_tsquery('excellent');
For more information, see:
l https://www.postgresql.org/docs/9.6/static/textsearch.html
l https://www.postgresql.org/docs/9.6/static/textsearch-features.html
- 205 -
Migrate from: SQL Server JSON and XML
Feature Com-
patibility
SCT Automation
Level
SCT Action
Code Index
Key Differences
XML Syntax and option differences, similar
functionality
Missing FOR XML clause
Overview
Java Script Object Notation (JSON) and eXtensible Markup Language (XML) are the two most common
types of semi-structured data documents used by a variety of data interfaces and NoSQL databases.
Most REST web service APIs support JSON as their native data transfer format. XML is an older, more
mature framework still widely used. It provides many extensions such as XQuery, name spaces,
schemas, and more.
The following example is a JSON document:
[{
"name": "Robert",
"age": "28"
}, {
"name": "James",
"age": "71"
"lastname": "Drapers"
}]
It's XMLcounterpart is:
<?xml version="1.0" encoding="UTF-16" ?>
<root>
<Person>
<name>Robert</name>
<age>28</age>
</Person>
<Person>
<name>James</name>
<age>71</age>
<lastname>Drapers</lastname>
</Person>
</root>
SQL Server provides native support for both XML and JSON in the database using the familiar and con-
venient T-SQL interface.
- 206 -
XML Data
SQL Server provides extensive native support for working with XML data including XML Data Types,
XML Columns, XMLIndexes, and XQuery.
XML Data Types and Columns
XML data can be stored using the following data types:
l The Native XML Data Type uses a BLOB structure but preserves the XML Infoset, which consists
of the containment hierarchy, document order, and element/attribute values. An XML typed doc-
ument may differ from the original text; white space is removed and the order of objects may
change. XML Data stored as a native XML data type has the additional benefit of schema val-
idation.
l An Annotated Schema (AXSD) can be used to distribute XML documents to one or more tables.
Hierarchical structure is maintained, but element order is not.
l CLOB or BLOB such as VARCHAR(MAX) and VARBINARY(MAX) can be used to store the original
XML document.
XML Indexes
SQL Server allows creation of PRIMARY and SECONDARY XML indexes on columns with a native XML
data type. Secondary indexes can be created for PATH, VALUE, or PROPERTY, which are helpful for vari-
ous types of workload queries.
XQuery
SQL Server supports a subset of the W3C XQUERY language specification. It allows executing queries
directly against XML data and using them as expressions or sets in standard T-SQL statements.
For example:
DECLARE @XMLVar XML = '<Root><Data>My XML Data</Data></Root>';
SELECT @XMLVar.query('/Root/Data');
Result: <Data>My XML Data</Data>
JSON Data
SQL Server does not support a dedicated JSON data type. However, you can store JSON documents in
an NVARCHAR column. For more information about BLOBS, see Data Types.
SQL Server provides a set of JSONfunctions that can be used for the following tasks:
- 207 -
l Retrieve and modify values in JSON documents.
l Convert JSON objects to a set (table) format.
l Use standard T-SQL queries with converted JSON objects.
l Convert tabular results of T-SQL queries to JSON format.
The functions are:
l ISJSON:Tests if a string contains a valid JSON string. Use in WHEREclause to avoid errors.
l JSON_VALUE: Retrieves a scalar value from a JSON document.
l JSON_QUERY: Retrieves a whole object or array from a JSON document.
l JSON_MODIFY: Modifies values in a JSON document.
l OPENJSON: Converts a JSON document to a SET that can be used in the FROM clause of a T-SQL
query.
The FOR JSON clause of SELECT queries can be used to convert a tabular set to a JSON document.
Examples
Create a table with a native typed XML column.
CREATE TABLE MyTable
(
XMLIdentifier INT NOT NULL PRIMARY KEY,
XMLDocument XML NULL
);
Query a JSON document.
DECLARE @JSONVar NVARCHAR(MAX);
SET @JSONVar = '{"Data":{"Person":[{"Name":"John"},{"Name":"Jane"},
{"Name":"Maria"}]}}';
SELECT JSON_QUERY(@JSONVar, '$.Data');
For more information, see:
l https://docs.microsoft.com/en-us/sql/relational-databases/json/json-data-sql-server
l https://docs.microsoft.com/en-us/sql/relational-databases/xml/xml-data-sql-server
- 208 -
Migrate to: Aurora PostgreSQL JSON and XML
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
XML Syntax and option differences, similar
functionality
Missing FOR XML clause
Overview
PostgreSQL provides native JSON Document support using the JSON data types JSON and JSONB.
JSON stores an exact copy of the input text that processing functions must reparse on each execution.
It also preserves semantically-insignificant white space between tokens and the order of keys within
JSON objects.
JSONB stores data in a decomposed binary format causing slightly slower input performance due to
added conversion to binary overhead. But, it is significantly faster to process since no reparsing is
needed on reads.
l Does not preserve white space.
l Does not preserve the order of object keys.
l Does not keep duplicate object keys. If duplicate keys are specified in the input, only the last
value is retained.
Most applications store JSON data as JSONB unless there are specialized needs. For additional inform-
ation about the differences between JSON and JSOB datatypes, see
https://www.postgresql.org/docs/9.6/static/datatype-json.html
In order to adhere to the full JSON specification, database encoding must be set to UTF8. If the data-
base code page is set to non-UTF8, characters that can be represented in the database encoding, but
not in UTF8, are allowed. This condition is not desirable.
Examples
Querying JSON data in PostgreSQL uses different syntax than SQLServer.
Return the JSON document stored in the emp_data column associated with emp_id=1.
SELECT emp_data FROM employees WHERE emp_id = 1;
Return all JSON documents stored in the emp_data column having a key named address.
SELECT emp_data FROM employees WHERE emp_data ? ' address';
Return all JSON items that have an address key or a hobbies key.
- 209 -
SELECT * FROM employees WHERE emp_data ?| array['address', 'hobbies'];
Return all JSON items that have both an address key and a hobbies key.
SELECT * FROM employees WHERE emp_data ?& array['a', 'b'];
Return the value of home key in the phone numbers array.
SELECT emp_data ->'phone numbers'->>'home' FROM employees;
Return all JSON documents where the address key is equal to a specified value and return all JSON doc-
uments where address key contains a specific string (using like).
SELECT * FROM employees WHERE emp_data->>'address' = '1234 First Street, Capital
City';
SELECT * FROM employees WHERE emp_data->>'address' like '%Capital City%';
For additional details, see https://www.postgresql.org/docs/9.6/static/functions-json.html
Indexing and Constraints with JSONB Columns
You can use the CREATE UNIQUE INDEX statement to enforce constraints on values inside JSON doc-
uments. For example, you can create a unique index that forces values of the address key to be
unique.
CREATE UNIQUE INDEX employee_address_uq ON employees((emp_data->>'address') ) ;
This index allows the first SQL insert statement to work and causes the second to fail.
INSERT INTO employees VALUES
(2, 'Second Employee','{"address": "1234 Second Street, Capital City"}');
INSERT INTO employees VALUES
(3, 'Third Employee', '{"address": "1234 Second Street, Capital City"}');
ERROR: duplicate key value violates unique constraint "employee_address_uq" SQL state:
23505 Detail: Key ((emp_data ->> 'address'::text))=(1234 Second Street, Capital City)
already exists.
For JSON data, PostgreSQL Supports B-Tree, HASH, and Generalized Inverted Indexs (GIN). A GIN index
is a special inverted index structure that is useful when an index must map many values to a row (such
as indexing JSON documents).
When using GIN indexes, you can efficiently and quickly query data using only the following JSON oper-
ators: @>, ?, ?&, ?|
Without indexes, PostgreSQL is forced to perform a full table scan when filtering data. This condition
applies to JSON data and will most likely have a negative impact on performance since Postgres has to
step into each JSON document.
Create an index on the address key of emp_data.
- 210 -
CREATE idx1_employees ON employees ((emp_data->>'address'));
Create a GIN index on a specific key or the entire emp_data column.
CREATE INDEX idx2_employees ON cards USING gin ((emp_data->'tags'));
CREATE INDEX idx3_employees ON employees USING gin (emp_data);
XML
PostgreSQL provides an XML data type for table columns. The primary advantage of using XML
columns, rather than placing XML data in text columns, is that the XML data is type checked when inser-
ted. Additionally, there are support functions to perform type-safe operations.
Xml can store well-formed “documents” as defined by XML standard or “content” fragments that
defined by the production XMLDecl. Content fragments can have more than one top-level element or
character node.
IS DOCUMENT can be used to evaluate whether a particular XML value is a full document or only a con-
tent fragment.
Examples
The following example demonstrates how to create XML data and insert it into a table:
Insert a document, and then insert a content fragment. Both types of XML data can be inserted into the
same column. If the XML is incorrect (such as a missing tag), the insert fails with the relevant error. The
query retrieves only document records.
CREATE TABLE test (a xml);
insert into test values (XMLPARSE (DOCUMENT '<?xml vesion-
n="1.0"?><Series><title>Simpsons</title><chapter>...</chapter></Series>'));
insert into test values (XMLPARSE (CONTENT 'note<tag>value</tag><tag>value</tag>'));
select * from test where a IS DOCUMENT;
Summary
The following table identifies similarities, differences, and key migration considerations.
Feature SQL Server Aurora PostgreSQL
XML and JSON nat-
ive data types
XML with schema col-
lections
JSON
JSON functions IS_JSON, JSON_VALUE,
JSON_QUERY, JSON_
MODFIY, OPEN_JSON,
A set of more than 20 dedicated JSON functions.
See: https://www.-
postgresql.org/docs/9.6/static/functions-json.html
- 211 -
Feature SQL Server Aurora PostgreSQL
FOR JSON
XML functions XQUERY and XPATH,
OPEN_XML, FOR XML
Many XML functions, see:https://www.-
postgresql.org/docs/9.6/static/functions-xml.html
Missing the FOR XML cluase, can use string_agg instead.
XML and JSON
Indexes
Primary and Sec-
ondary PATH, VALUE
and PROPERTY
indexes
Supported
For additional information on PostgresSQL XML Types & Functions, see:
l https://www.postgresql.org/docs/9.6/static/datatype-xml.html
l https://www.postgresql.org/docs/9.6/static/functions-xml.html
For additional details for the JSON data type & functions, see:
l https://www.postgresql.org/docs/9.6/static/datatype-json.html
l https://www.postgresql.org/docs/9.6/static/functions-json.html
- 212 -
Migrate from: SQL Server MERGE
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
SCT Action Codes -
MERGE
Rewrite to use INSERT… ON
CONFLICT
Overview
MERGEis a complex , hybrid DML/DQL statement for performing INSERT, UPDATE, or DELETE oper-
ations on a target table based on the results of a logical join of the target table and a source data set.
MERGEcan also return row sets similar to SELECT using the OUTPUT clause, which gives the calling
scope access to the actual data modifications of the MERGE statement.
The MERGE statement is most efficient for non-trivial conditional DML. For example, inserting data if a
row key value does not exist and updating the existing row if the key value already exists.
You can easily manage additional logic such as deleting rows from the target that don't appear in the
source. For simple, straightforward updates of data in one table based on data in another, it is typically
more efficient to use simple INSERT, DELETE, and UPDATE statements. All MERGE functionality can be
replicated using INSERT, DELETE, and UPDATE statements, but not necessarily less efficiently.
The SQL Server MERGE statement provides a wide range of functionality and flexibility and is com-
patible with ANSI standard SQL:2008. SQLServer has many extensions to MERGE that provide efficient
T-SQLsolutions for synchronizing data.
Syntax
MERGE [INTO] <Target Table> [AS] <Table Alias>]
USING <Source Table>
ON <Merge Predicate>
[WHEN MATCHED [AND <Predicate>]
THEN UPDATE SET <Column Assignments...> | DELETE]
[WHEN NOT MATCHED [BY TARGET] [AND <Predicate>]
THEN INSERT [(<Column List>)]
VALUES (<Values List>) | DEFAULT VALUES]
[WHEN NOT MATCHED BY SOURCE [AND <Predicate>]
THEN UPDATE SET <Column Assignments...> | DELETE]
OUTPUT [<Output Clause>]
Examples
Perform a simple one-way synchronization of two tables.
CREATE TABLE SourceTable
(
- 213 -
Col1 INT NOT NULL PRIMARY KEY,
Col2 VARCHAR(20) NOT NULL
);
CREATE TABLE TargetTable
(
Col1 INT NOT NULL PRIMARY KEY,
Col2 VARCHAR(20) NOT NULL
);
INSERT INTO SourceTable (Col1, Col2)
VALUES
(2, 'Source2'),
(3, 'Source3'),
(4, 'Source4');
INSERT INTO TargetTable (Col1, Col2)
VALUES
(1, 'Target1'),
(2, 'Target2'),
(3, 'Target3');
MERGE INTO TargetTable AS TGT
USING SourceTable AS SRC ON TGT.Col1 = SRC.Col1
WHEN MATCHED
THEN UPDATE SET TGT.Col2 = SRC.Col2
WHEN NOT MATCHED
THEN INSERT (Col1, Col2)
VALUES (SRC.Col1, SRC.Col2);
SELECT * FROM TargetTable;
Col1 Col2
----------
1 Target1
2 Source2
3 Source3
4 Source4
Perform a conditional two-way synchronization using NULL for "no change" and DELETE from the tar-
get when the data is not found in the source.
TRUNCATE TABLESourceTable;
INSERT INTO SourceTable (Col1, Col2) VALUES (3, NULL), (4, 'NewSource4'), (5,
'Source5');
MERGE INTO TargetTable AS TGT
USING SourceTable AS SRC ON TGT.Col1 = SRC.Col1
WHEN MATCHED AND SRC.Col2 IS NOT NULL
THEN UPDATE SET TGT.Col2 = SRC.Col2
WHEN NOT MATCHED
THEN INSERT (Col1, Col2)
VALUES (SRC.Col1, SRC.Col2)
- 214 -
Migrate to: Aurora PostgreSQL MERGE
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
SCT Action Codes -
MERGE
Rewrite to use INSERT… ON
CONFLICT
Overview
Currently, PostgreSQL version 9.6 does not support the use of the MERGE SQL command. As an altern-
ative, consider using the INSERT… ON CONFLICT clause, which can handle cases where insert clauses
might cause a conflict, and then redirect the operation as an update.
Examples
Using the ON ONFLICT clause:
CREATE TABLE EMP_BONUS (
EMPLOYEE_ID NUMERIC,
BONUS_YEAR VARCHAR(4),
SALARY NUMERIC,
BONUS NUMERIC,
PRIMARY KEY (EMPLOYEE_ID, BONUS_YEAR));
INSERT INTO EMP_BONUS (EMPLOYEE_ID, BONUS_YEAR, SALARY)
SELECT EMPLOYEE_ID, EXTRACT(YEAR FROM NOW()), SALARY
FROM EMPLOYEES
WHERE SALARY < 10000
ON CONFLICT (EMPLOYEE_ID, BONUS_YEAR)
DO UPDATE SET BONUS = EMP_BONUS.SALARY * 0.5;
SELECT * FROM EMP_BONUS;
employee_id | bonus_year | salary | bonus
-------------+------------+---------+----------
103 | 2017 | 9000.00 | 4500.000
104 | 2017 | 6000.00 | 3000.000
105 | 2017 | 4800.00 | 2400.000
106 | 2017 | 4800.00 | 2400.000
107 | 2017 | 4200.00 | 2100.000
109 | 2017 | 9000.00 | 4500.000
110 | 2017 | 8200.00 | 4100.000
111 | 2017 | 7700.00 | 3850.000
112 | 2017 | 7800.00 | 3900.000
113 | 2017 | 6900.00 | 3450.000
115 | 2017 | 3100.00 | 1550.000
116 | 2017 | 2900.00 | 1450.000
117 | 2017 | 2800.00 | 1400.000
118 | 2017 | 2600.00 | 1300.000
- 216 -
Running the same operation multiple times using the ON CONFLICT clause does not generate an error
because the existing records are redirected to the update clause.
For more information, see:
https://www.postgresql.org/docs/9.6/static/sql-insert.html
https://www.postgresql.org/docs/9.6/static/unsupported-features-sql-standard.htm
- 217 -
Migrate from: SQL Server PIVOT and UNPIVOT
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
SCT Action Codes -
PIVOT
Straight forward rewrite to use traditional
SQL syntax
Overview
PIVOT and UNPIVOT are relational operations used to transform a set by rotating rows into columns
and columns into rows.
PIVOT
The PIVOT operator consists of several clauses and implied expressions.
The "Anchor" column is the column that is not be pivoted and results in a single row per unique value,
similar to GROUP BY.
The pivoted columns are derived from the PIVOT clause and are the row values transformed into
columns. The values for these columns are derived from the source column defined in the PIVOT
clause.
Syntax
SELECT <Anchor column>,
[Pivoted Column 1] AS <Alias>,
[Pivoted column 2] AS <Alias>
...n
FROM
(<SELECT Statement of Set to be Pivoted>)
AS <Set Alias>
PIVOT
(
<Aggregate Function>(<Aggregated Column>)
FOR
[<Column With the Values for the Pivoted Columns Names>]
IN ([Pivoted Column 1], [Pivoted column 2] ...)
) AS <Pivot Table Alias>;
PIVOTExamples
Create and populate the Orders Table.
- 218 -
CREATE TABLE Orders
(
OrderID INT NOT NULL
IDENTITY(1,1) PRIMARY KEY,
OrderDate DATE NOT NULL,
Customer VARCHAR(20) NOT NULL
);
INSERT INTO Orders (OrderDate, Customer)
VALUES
('20180101', 'John'),
('20180201', 'Mitch'),
('20180102', 'John'),
('20180104', 'Kevin'),
('20180104', 'Larry'),
('20180104', 'Kevin'),
('20180104', 'Kevin');
Create a simple PIVOT for the number of orders per day (days of month 5-31 omitted for example sim-
plicity).
SELECT 'Number of Orders Per Day' AS DayOfMonth,
[1], [2], [3], [4] /*...[31]*/
FROM (
SELECT OrderID,
DAY(OrderDate) AS OrderDay
FROM Orders
) AS SourceSet
PIVOT
(
COUNT(OrderID)
FOR OrderDay IN ([1], [2], [3], [4] /*...[31]*/)
) AS PivotSet;
DayOfMonth 1 2 3 4 /*...[31]*/
----------
Number of Orders Per Day 2 1 0 4
Note: The result set is now oriented in rows vs. columns. The first column is the description
of the columns to follow.
PIVOT for number of orders per day per customer.
SELECT Customer,
[1], [2], [3], [4] /*...[31]*/
FROM (
SELECT OrderID,
Customer,
DAY(OrderDate) AS OrderDay
FROM Orders
) AS SourceSet
PIVOT
(
COUNT(OrderID)
- 219 -
FOR OrderDay IN ([1], [2], [3], [4] /*...[31]*/)
) AS PivotSet;
Customer 1 2 3 4
------------
John 1 1 0 0
Kevin 0 0 0 3
Larry 0 0 0 1
Mitch 1 0 0 0
UNPIVOT
UNPIVOT is similar to PIVOT in reverse, but spreads existing column values into rows.
The source set is similar to the result of the PIVOT with values pertaining to particular entities listed in
columns. Since the result set has more rows than the source, aggregations aren't required.
It is less commonly used than PIVOT because most data in relational databases have attributes in
columns; not the other way around.
UNPIVOTExamples
Create an populate the "pivot like" EmployeeSales table (in a actual scenario, this is most likely a view
or a set from an external source).
CREATE TABLE EmployeeSales
(
SaleDate DATE NOT NULL PRIMARY KEY,
John INT,
Kevin INT,
Mary INT
);
INSERT INTO EmployeeSales
VALUES
('20180101', 150, 0, 300),
('20180102', 0, 0, 0),
('20180103', 250, 50, 0),
('20180104', 500, 400, 100);
Unpivot employee sales per date into individual rows per employee.
SELECT SaleDate,
Employee,
SaleAmount
FROM
(
SELECT SaleDate, John, Kevin, Mary
FROM EmployeeSales
) AS SourceSet
UNPIVOT (
SaleAmount
- 220 -
FOR Employee IN (John, Kevin, Mary)
)AS UnpivotSet;
SaleDate Employee SaleAmount
--------------------------
2018-01-01 John 150
2018-01-01 Kevin 0
2018-01-01 Mary 300
2018-01-02 John 0
2018-01-02 Kevin 0
2018-01-02 Mary 0
2018-01-03 John 250
2018-01-03 Kevin 50
2018-01-03 Mary 0
2018-01-04 John 500
2018-01-04 Kevin 400
2018-01-04 Mary 100
For more information, see https://docs.microsoft.com/en-us/sql/t-sql/queries/from-using-pivot-and-unpivot
- 221 -
Migrate to: Aurora PostgreSQL PIVOT and UNPIVOT
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
SCT Action Codes -
PIVOT
Straight forward rewrite to use traditional
SQL syntax
Overview
Aurora PostgreSQL does not support the PIVOT and UNPIVOT relational operators.
Functionality of both operators can be rewritten to use standard SQL syntax, as shown in the examples
below.
Examples
PIVOT
Create and populate the Orders Table
CREATE TABLE Orders
(
OrderID SERIAL PRIMARY KEY,
OrderDate DATE NOT NULL,
Customer VARCHAR(20) NOT NULL
);
INSERT INTO Orders (OrderDate, Customer)
VALUES
('20180101', 'John'),
('20180201', 'Mitch'),
('20180102', 'John'),
('20180104', 'Kevin'),
('20180104', 'Larry'),
('20180104', 'Kevin'),
('20180104', 'Kevin');
Simple PIVOT for number of orders per day (days of month 5-31 omitted for example simplicity)
SELECT 'Number of Orders Per Day' AS DayOfMonth,
COUNT(CASE WHEN date_part('day', OrderDate) = 1 THEN 'OrderDate' ELSE NULL END) AS
"1",
COUNT(CASE WHEN date_part('day', OrderDate) = 2 THEN 'OrderDate' ELSE NULL END) AS
"2",
COUNT(CASE WHEN date_part('day', OrderDate) = 3 THEN 'OrderDate' ELSE NULL END) AS
"3",
COUNT(CASE WHEN date_part('day', OrderDate) = 4 THEN 'OrderDate' ELSE NULL END) AS
- 222 -
"4" /*...[31]*/
FROM Orders AS O;
DayOfMonth 1 2 3 4 /*...[31]*/
----------
Number of Orders Per Day 2 1 0 4
PIVOT for number of order per day, per customer
SELECT Customer,
COUNT(CASE WHEN date_part('day', OrderDate) = 1 THEN 'OrderDate' ELSE NULL END) AS
"1",
COUNT(CASE WHEN date_part('day', OrderDate) = 2 THEN 'OrderDate' ELSE NULL END) AS
"2",
COUNT(CASE WHEN date_part('day', OrderDate) = 3 THEN 'OrderDate' ELSE NULL END) AS
"3",
COUNT(CASE WHEN date_part('day', OrderDate) = 4 THEN 'OrderDate' ELSE NULL END) AS "4"
/*...[31]*/
FROM Orders AS O
GROUP BY Customer;
Customer 1 2 3 4
------------
John 1 1 0 0
Kevin 0 0 0 3
Larry 0 0 0 1
Mitch 1 0 0 0
UNPIVOT
Create an populate the 'pivot like' EmployeeSales table.
Note: in real life this will most likely be a view, or a set from an external source.
CREATE TABLE EmplyeeSales
(
SaleDate DATE NOT NULL PRIMARY KEY,
John INT,
Kevin INT,
Mary INT
);
INSERT INTO EmplyeeSales
VALUES
('20180101', 150, 0, 300),
('20180102', 0, 0, 0),
('20180103', 250, 50, 0),
('20180104', 500, 400, 100);
Unpivot employee sales per date into individual rows per employee
SELECT SaleDate, Employee, SaleAmount
FROM (
- 223 -
SELECT SaleDate,
Employee,
CASE
WHEN Employee = 'John' THEN 'John'
WHEN Employee = 'Kevin' THEN 'Kevin'
WHEN Employee = 'Mary' THEN 'Mary'
END AS SaleAmount
FROM EmplyeeSales as emp
CROSS JOIN
(
SELECT 'John' AS Employee
UNION ALL
SELECT 'Kevin'
UNION ALL
SELECT 'Mary'
) AS Employees
) AS UnpivotedSet;
SaleDate Employee SaleAmount
--------------------------
2018-01-01 John 150
2018-01-01 Kevin 0
2018-01-01 Mary 300
2018-01-02 John 0
2018-01-02 Kevin 0
2018-01-02 Mary 0
2018-01-03 John 250
2018-01-03 Kevin 50
2018-01-03 Mary 0
2018-01-04 John 500
2018-01-04 Kevin 400
2018-01-04 Mary 100
- 224 -
Migrate from: SQL Server Triggers
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
SCT Action Codes
- Triggers
Syntax and option differences, similar func-
tionality - PostgreSQL trigger calling a func-
tion
Overview
Triggers are special types of stored procedures that execute automatically in response to events and
are most commonly used for Data Manipulation Language (DML).
SQL Server supports AFTER/FOR and INSTEAD OF triggers, which can be created on tables and views
(AFTER and FOR are synonymous). SQLServer also provides an event trigger framework at the server
and database levels that includes Data Definition Language (DDL), Data Control Language (DCL), and
general system events such as login.
Note: SQLSever does not support FOR EACH ROW triggers in which the trigger code is
executed once for each row of modified data.
Trigger Execution
AFTER triggers execute after DML statements complete execution. INSTEAD OF triggers execute code in
place of the original DML statement. AFTER triggers can be created on tables only. INSTEAD OF Triggers
can be created on tables and Views.
Only a single INSTEAD OF trigger can be created for any given object and event. When multiple AFTER
triggers exist for the same event and object, you can partially set the trigger order by using the sp_
settriggerorder system stored procedure. It allows setting the first and last triggers to be executed, but
not the order of others.
Trigger Scope
SQL Server supports only statement level triggers. The trigger code is executed only once per state-
ment. The data modified by the DML statement is available to the trigger scope and is saved in two vir-
tual tables: INSERTED and DELETED. These tables contain the entire set of changes performed by the
DML statement that caused trigger execution.
SQL triggers always execute within the transaction of the statement that triggered the execution. If the
trigger code issues an explicit ROLLBACK, or causes an exception that mandates a rollback, the DML
statement is also rolled back (for INSTEAD OF triggers, the DML statement is not executed and, there-
fore, does not require a rollback).
- 225 -
Examples
Usea DML Trigger to Audit Invoice Deletions
The following example demonstrates how to use a trigger to log rows deleted from a table.
Create and populate an Invoices table.
CREATE TABLE Invoices
(
InvoiceID INT NOT NULL PRIMARY KEY,
Customer VARCHAR(20) NOT NULL,
TotalAmount DECIMAL(9,2) NOT NULL
);
INSERT INTO Invoices (InvoiceID,Customer,TotalAmount)
VALUES
(1, 'John', 1400.23),
(2, 'Jeff', 245.00),
(3, 'James', 677.22);
Create an InvoiceAuditLog table.
CREATE TABLE InvoiceAuditLog
(
InvoiceID INT NOT NULL PRIMARY KEY,
Customer VARCHAR(20) NOT NULL,
TotalAmount DECIMAL(9,2) NOT NULL,
DeleteDateDATETIME NOT NULL DEFAULT (GETDATE()),
DeletedByVARCHAR(128) NOT NULL DEFAULT (CURRENT_USER)
);
Create an AFTER DELETE trigger to log deletions from the Invoices table to the audit log.
CREATE TRIGGER LogInvoiceDeletes
ON Invoices
AFTER DELETE
AS
BEGIN
INSERT INTO InvoiceAuditLog (InvoiceID, Customer, TotalAmount)
SELECT InvoiceID,
Customer,
TotalAmount
FROM Deleted
END;
Delete an invoice.
DELETE FROM Invoices
WHERE InvoiceID = 3;
Query the content of both tables.
- 226 -
SELECT *
FROM Invoices AS I
FULL OUTER JOIN
InvoiceAuditLog AS IAG
ONI.InvoiceID = IAG.InvoiceID;
The code above displays the following results.
InvoiceID Customer TotalAmount InvoiceID Customer
TotalAmountDeleteDateDeletedBy
---------------------------------------------------------------
-------------
1 John 1400.23 NULL NULL NULLNULL
NULL
2 Jeff 245.00 NULL NULL NULLNULL
NULL
NULL NULL NULL 3 James
677.2220180224 13:02Domain/JohnCortney
Create a DDL Trigger
Create a trigger to protect all tables in the database from accidental deletion.
CREATE TRIGGER PreventTableDrop
ON DATABASE FOR DROP_TABLE
AS
BEGIN
RAISERROR ('Tables Can''t be dropped in this database', 16, 1)
ROLLBACK TRANSACTION
END;
Test the trigger by attempting to drop a table.
DROP TABLE [Invoices];
GO
The system displays the follow message indicating the Invoices table cannot be dropped.
Msg 50000, Level 16, State 1, Procedure PreventTableDrop, Line 5 [Batch Start Line 56]
Tables Can't be dropped in this database
Msg 3609, Level 16, State 2, Line 57
The transaction ended in the trigger. The batch has been aborted.
For more information, see
l https://docs.microsoft.com/en-us/sql/relational-databases/triggers/dml-triggers
l https://docs.microsoft.com/en-us/sql/relational-databases/triggers/ddl-triggers
- 227 -
Migrate to: Aurora PostgreSQL Triggers
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
SCT Action Codes -
Triggers
Syntax and option differences, similar
functionality - PostgreSQL trigger call-
ing a function
Overview
Triggers provide much of the same functionality as SQLServer:
l DML Triggers fire based on table related events such as DML.
l Event Triggers fire after certain database events such as running DDL commands.
Unlike SQL Server triggers, PostgreSQL triggers must call a function. They do not support anonymous
blocks of PL/pgSQL code as part of the trigger body. The user-supplied function is declared with no
arguments and has a return type of trigger.
PostgreSQL DML Triggers
l PostgreSQL triggers can run BEFORE or AFTER a DML operation.
l They fire before the operation is attempted on a row.
l Before constraints are checked and the INSERT, UPDATE, or DELETE is attempted.
l If the trigger fires before or instead of the event, the trigger can skip the operation
for the current row or change the row being inserted (for INSERT and UPDATE oper-
ations only).
l Triggers can fire after the operation was completed, after constraints are checked and the
INSERT, UPDATE, or DELETE command completed. If the trigger fires after the event, all
changes, including the effects of other triggers, are "visible" to the trigger.
l PostgreSQL triggers can run INSTEAD OF a DML command when created on views.
l PostgreSQL triggers can run FOR EACH ROW affected by the DML statement or FOR EACH
STATEMENT running only once as part of a DML statement.
When
Fired
Database Event
Row-Level Trigger (FOR
EACH ROW)
Statement-Level Trigger (FOR
EACH STATEMENT)
BEFORE INSERT, UPDATE, DELETE Tables and foreign
tables
Tables, views, and foreign tables
TRUNCATE — Tables
AFTER INSERT, UPDATE, DELETE Tables and foreign Tables, views, and foreign tables
- 228 -
When
Fired
Database Event
Row-Level Trigger (FOR
EACH ROW)
Statement-Level Trigger (FOR
EACH STATEMENT)
tables
TRUNCATE — Tables
INSTEAD
OF
INSERT, UPDATE, DELETE Views —
TRUNCATETRUNCATE — —
PostgreSQL Event Triggers
An event trigger executes when a specific event associated with the trigger occurs in the database. Sup-
ported events include ddl_command_start, ddl_command_end, table_rewrite, and sql_drop.
l ddl_command_start occurs before the execution of a CREATE, ALTER, DROP, SECURITY LABEL,
COMMENT, GRANT, REVOKE or SELECT INTO command.
l ddl_command_end occurs after the command completed and before the transaction commits.
l sql_drop fires only for the DROP DDL command, before ddl_command_end trigger fire.
For a full list of supported PostgreSQL event trigger types, see
https://www.postgresql.org/docs/9.6/static/event-trigger-matrix.html
Example
Create a Trigger
In order to create an equivalent version of the SQL Server DML trigger in PostgreSQL, first create a func-
tion trigger that stores the execution logic.
psql=> CREATE OR REPLACE FUNCTION PROJECTS_SET_NULL()
RETURNS TRIGGER
AS $$
BEGIN
IF TG_OP = 'UPDATE' AND OLD.PROJECTNO != NEW.PROJECTNO OR
TG_OP = 'DELETE' THEN
UPDATE EMP
SET PROJECTNO = NULL
WHERE EMP.PROJECTNO = OLD.PROJECTNO;
END IF;
IF TG_OP = 'UPDATE' THEN RETURN NULL;
ELSIF TG_OP = 'DELETE' THEN RETURN NULL;
END IF;
END; $$
LANGUAGE PLPGSQL;
CREATE FUNCTION
Create the trigger.
- 229 -
psql=> CREATE TRIGGER TRG_PROJECTS_SET_NULL
AFTER UPDATE OF PROJECTNO OR DELETE
ON PROJECTS
FOR EACH ROW
EXECUTE PROCEDURE PROJECTS_SET_NULL();
CREATE TRIGGER
Test the trigger by deleting a row from the PROJECTS table.
psql=> DELETE FROM PROJECTS WHERE PROJECTNO=123;
psql=> SELECT PROJECTNO FROM EMP WHERE PROJECTNO=123;
projectno
-----------
(0 rows)
Create a DDL Trigger
In order to create an equivalent version of the SQL Server DDL System/Schema level triggers, such as a
trigger that prevents running a DDL DROP on objects in the HR schema, first create an event trigger
function.
Note that trigger functions are created with no arguments and must have a return type of TRIGGER or
EVENT_TRIGGER.
psql=> CREATE OR REPLACE FUNCTION ABORT_DROP_COMMAND()
RETURNS EVENT_TRIGGER
AS $$
BEGIN
RAISE EXCEPTION 'The % Command is Disabled', tg_tag;
END; $$
LANGUAGE PLPGSQL;
CREATE FUNCTION
Create the event trigger, which fires before the start of a DDL DROP command.
psql=> CREATE EVENT TRIGGER trg_abort_drop_command
ON DDL_COMMAND_START
WHEN TAG IN ('DROP TABLE', 'DROP VIEW', 'DROP FUNCTION', 'DROP
SEQUENCE', 'DROP MATERIALIZED VIEW', 'DROP TYPE')
EXECUTE PROCEDURE abort_drop_command();
Test the trigger by attempting to drop the EMPLOYEES table.
psql=> DROP TABLE EMPLOYEES;
ERROR: The DROP TABLE Command is Disabled
CONTEXT: PL/pgSQL function abort_drop_command() line 3 at RAISE
- 230 -
Summary
Feature SQL Server Aurora PostgreSQL
DML Triggers Scope Statement level only FOR EACH ROW and FOR EACH
STATMENT
Access to change set INSERTED and DELETED Virtual
multi-row tables
OLD and NEW virtual one-row tables
System event triggers DDL, DCL and other event types event triggers
Trigger execution
phase
AFTER and INSTEAD OF AFTER, BEFORE, and INSTEAD OF
Multi-trigger execution
order
Can only set first and last using
sp_settriggerorder
Call function within a function
Drop a trigger DROP TRIGGER <trigger name>; DROP TRIGGER <trigger name>;
Modify trigger code Use the ALTER TRIGGER statement Modify function code
Enable/Disable a trig-
ger
Use the ALTER TRIGGER <trigger
name> ENABLE;
and
ALTER TRIGGER <trigger name>
DISABLE;
ALTER TABLE
Triggers on views INSTEAD OF TRIGGERS only INSTEAD OF TRIGGERS only
For additional details, see https://www.postgresql.org/docs/9.6/static/plpgsql-trigger.html
- 231 -
Migrate from: SQL Server TOP and FETCH
Feature Compatibility SCT Automation Level SCT Action Code Index Key Differences
SCT Action Codes - TOP
and FETCH
TOP is not supportd
Overview
SQL Server supports two options for limiting and paging result sets returned to the client. TOP is a leg-
acy, proprietary T-SQL keyword that is still supported due to its wide usage. The ANSI compliant syntax
of FETCH and OFFSET were introduced in SQL Server 2012 and are recommended for paginating res-
ults sets.
TOP
The TOP (n) operator is used in the SELECT list and limits the number of rows returned to the client
based on the ORDER BY clause.
Note: When TOPis used with no ORDER BY clause, the query is non-deterministic and may
return any rows up to the number specified by the TOP operator.
TOP (n) can be used with two modifier options:
l TOP (n) PERCENT is used to designate a percentage of the rows to be returned instead of a fixed
maximal row number limit (n). When using PERCENT, n can be any value from 1-100.
l TOP (n) WITH TIES is used to allow overriding the n maximal number (or percentage) of rows spe-
cified in case there are additional rows with the same ordering values as the last row.
Note: If TOP (n) is used without WITH TIES and there are additional rows that have the same
ordering value as the last row in the group of n rows, the query is also non-deterministic
because the last row may be any of the rows that share the same ordering value.
Syntax
SELECT TOP (<Limit Expression>) [PERCENT] [WITH TIES ] <Select Expressions List>
FROM...
OFFSET... FETCH
OFFSET... FETCH as part of the ORDER BY clause is the ANSI compatible syntax for limiting and pagin-
ating result sets. It allows specification of the starting position and limits the number of rows returned,
which enables easy pagination of result sets.
- 232 -
Similar to TOP, OFFSET... FETCH relies on the presentation order defined by the ORDER BY clause.
Unlike TOP, it is part of the ORDER BY clause and can't be used without it.
Note: Queries using FETCH... OFFSET can still be non-deterministic if there is more than one
row that has the same ordering value as the last row.
Syntax
ORDER BY <Ordering Expression> [ASC | DESC ] [,...n ]
OFFSET <Offset Expression> {ROW | ROWS }
[FETCH {FIRST | NEXT } <Page Size Expression> {ROW | ROWS } ONLY ]
Examples
Create the OrderItems table.
CREATE TABLE OrderItems
(
OrderID INT NOT NULL,
Item VARCHAR(20) NOT NULL,
Quantity SMALLINT NOT NULL,
PRIMARY KEY(OrderID, Item)
);
INSERT INTO OrderItems (OrderID, Item, Quantity)
VALUES
(1, 'M8 Bolt', 100),
(2, 'M8 Nut', 100),
(3, 'M8 Washer', 200),
(3, 'M6 Locking Nut', 300);
Retrieve the 3 most ordered items by quantity.
-- Using TOP
SELECT TOP (3) *
FROM OrderItems
ORDER BYQuantity DESC;
-- USING FETCH
SELECT *
FROM OrderItems
ORDER BYQuantity DESC
OFFSET 0 ROWS FETCH NEXT 3 ROWS ONLY;
OrderID Item Quantity
-------------------
3 M6 Locking Nut 300
3 M8 Washer 200
2 M8 Nut 100
Include rows with ties.
- 233 -
SELECT TOP (3) WITH TIES *
FROM OrderItems
ORDER BYQuantity DESC;
OrderID Item Quantity
-------------------
3 M6 Locking Nut 300
3 M8 Washer 200
2 M8 Nut 100
1M8 Bolt100
Retrieve half the rows based on quantity.
SELECT TOP (50) PERCENT *
FROM OrderItems
ORDER BYQuantity DESC;
OrderID Item Quantity
-------------------
3 M6 Locking Nut 300
3 M8 Washer 200
For more information, see
l https://docs.microsoft.com/en-us/sql/t-sql/queries/select-order-by-clause-transact-sql
l https://docs.microsoft.com/en-us/sql/t-sql/queries/top-transact-sql
- 234 -
Migrate to: Aurora PostgreSQL LIMIT and OFFSET
(TOP and FETCH Equivalent)
Feature Compatibility SCT Automation Level SCT Action Code Index Key Differences
SCT Action Codes - TOP and
FETCH
TOP is not sup-
portd
Overview
Aurora PostgreSQL supports the non-ANSI compliant (but popular with other engines) LIMIT... OFFSET
operator for paging results sets.
The LIMIT clause limits the number of rows returned and does not require an ORDER BY clause,
although that would make the query non-deterministic.
The OFFSET clause is zero-based, similar to SQL Server and used for pagination.
OFFSET 0 is the same as omitting the OFFSET clause, as is OFFSET with a NULL argument.
Syntax
SELECT select_list
FROM table_expression
[ORDER BY ... ]
[LIMIT {number | ALL } ] [OFFSET number ]
Migration Considerations
LIMIT... OFFSET syntax can be used to replace the functionality of both TOP(n) and FETCH... OFFSET in
SQL Server. It is automatically converted by the Schema Conversion Tool (SCT) except for the WITH TIES
and PERCENT modifiers.
To replace the PERCENT option, you must first calculate how many rows the query returns and then cal-
culate the fixed number of rows to be returned based on that number (see the example below).
Note: Since this technique involves added complexity and accessing the table twice, consider
changing the logic to use a fixed number instead of percentage.
To replace the WITH TIES option, you must rewrite the logic to add another query that checks for the
existence of additional rows that have the same ordering value as the last row returned from the LIMIT
clause.
Note: Since this technique introduces significant added complexity and three accesses to the
source table, consider changing the logic to introduce a tie-breaker into the ORDER BY clause
(see the example below).
- 235 -
Examples
Create the OrderItems table.
CREATE TABLE OrderItems
(
OrderID INT NOT NULL,
Item VARCHAR(20) NOT NULL,
Quantity SMALLINT NOT NULL,
PRIMARY KEY(OrderID, Item)
);
INSERT INTO OrderItems (OrderID, Item, Quantity)
VALUES
(1, 'M8 Bolt', 100),
(2, 'M8 Nut', 100),
(3, 'M8 Washer', 200),
(3, 'M6 Locking Nut', 300);
Retrieve the three most ordered items by quantity.
SELECT *
FROM OrderItems
ORDER BYQuantity DESC
LIMIT 3 OFFSET 0;
OrderID Item Quantity
-------------------
3 M6 Locking Nut 300
3 M8 Washer 200
1 M8 Bolt100
Include rows with ties.
SELECT *
FROM
(
SELECT *
FROM OrderItems
ORDER BY Quantity DESC
LIMIT 3 OFFSET 0
) AS X
UNION
SELECT *
FROM OrderItems
WHERE Quantity = (
SELECT Quantity
FROM OrderItems
ORDER BY Quantity DESC
LIMIT 1 OFFSET 2
)
ORDER BY Quantity DESC
- 236 -
OrderID Item Quantity
-------------------
3 M6 Locking Nut 300
3 M8 Washer 200
2 M8 Nut 100
1M8 Bolt100
Retrieve half the rows based on quantity.
CREATE or replace FUNCTION getOrdersPct(int) RETURNS SETOF OrderItems AS $$
SELECT * FROM OrderItems
ORDER BY Quantity desc LIMIT (SELECT COUNT(*)*$1/100 FROM OrderItems) oFFSET 0;
$$LANGUAGE SQL;
SELECT * from getOrdersPct(50);
or
SELECT getOrdersPct(50);
OrderID Item Quantity
-------------------
3 M6 Locking Nut 300
3 M8 Washer 200
Summary
SQL Server Aurora PostgreSQL Comments
TOP (n) LIMIT n
TOP (n) WITH TIES Not supported See examples for work-
around
TOP (n) PERCENT Not supported See examples for work-
around
OFFSET... FETCH LIMIT... OFFSET
For more information, see https://www.postgresql.org/docs/9.6/static/queries-limit.html
- 237 -
Migrate from: SQL Server User Defined Functions
Feature Com-
patibility
SCT Automation Level
SCT Action Code
Index
Key Differences
N/A Syntax and option dif-
ferences
Overview
User Defined Functions (UDF) are code objects that accept input parameters and return either a scalar
value or a set consisting of rows and columns. SQL Server UDFs can be implemented using T-SQL or
Common Language Runtime (CLR) code.
Note: This section does not cover CLR code objects.
Function invocations can not have any lasting impact on the database. They must be contained and
can only modify objects and data local to their scope (for example, data in local variables). Functions
are not allowed to modify data or the structure of a database.
Functions may be deterministic or non-deterministic. Deterministic functions always return the same
result when executed with the same data. Non-deterministic functions may return different results
each time they execute. For example, a function that returns the current date or time.
SQL Server supports three types of T-SQL UDFs: Scalar Functions, Inline Table-Valued Functions, and
Multi-Statement Table-Valued Functions.
Scalar User Defined Functions
Scalar UDFs accept zero or more parameters and return a scalar value. They can be used in T-SQL
expressions.
Syntax
CREATE FUNCTION <Function Name> ([{<Parameter Name> [AS] <Data Type> [= <Default
Value>] [READONLY]} [,...n]])
RETURNS <Return Data Type>
[AS]
BEGIN
<Function Body Code>
RETURN <Scalar Expression>
END[;]
Examples
Create a scalar function to change the first character of a string to upper case.
- 238 -
CREATE FUNCTION dbo.UpperCaseFirstChar (@String VARCHAR(20))
RETURNS VARCHAR(20)
AS
BEGIN
RETURN UPPER(LEFT(@String, 1)) + LOWER(SUBSTRING(@String, 2, 19))
END;
SELECT dbo.UpperCaseFirstChar ('mIxEdCasE');
---------
Mixedcase
Inline User Defined Table-Valued Functions
Inline table-valued UDFs are similar to views or a Common Table Expressions (CTE) with the added
benefit of parameters. They can be used in FROM clauses as subqueries and can be joined to other
source table rows using the APPLY and OUTER APPLY operators. In-line table valued UDFs have many
associated internal optimizer optimizations due to their simple, view-like characteristics.
Syntax
CREATE FUNCTION <Function Name> ([{<Parameter Name> [AS] <Data Type> [= <Default
Value>] [READONLY]} [,...n]])
RETURNS TABLE
[AS]
RETURN (<SELECT Query>)[;]
Examples
Create a table valued function to aggregate employee orders
CREATE TABLE Orders
(
OrderID INT NOT NULL PRIMARY KEY,
EmployeeID INT NOT NULL,
OrderDate DATETIME NOT NULL
);
INSERT INTO Orders (OrderID, EmployeeID, OrderDate)
VALUES
(1, 1, '20180101 13:00:05'),
(2, 1, '20180201 11:33:12'),
(3, 2, '20180112 10:22:35');
CREATE FUNCTION dbo.EmployeeMonthlyOrders
(@EmployeeID INT)
RETURNS TABLE AS
RETURN
(
SELECT EmployeeID,
- 239 -
YEAR(OrderDate) AS OrderYear,
MONTH(OrderDate) AS OrderMonth,
COUNT(*) AS NumOrders
FROM Orders AS O
WHERE EmployeeID = @EmployeeID
GROUP BY EmployeeID,
YEAR(OrderDate),
MONTH(OrderDate)
);
SELECT *
FROM dbo.EmployeeMonthlyOrders (1)
EmployeeID OrderYear OrderMonth NumOrders
---------- ----------------------------
1 2018 1 1
1 2018 2 1
Multi-Statment User Defined Table-Valued Functions
Multi-statement table-valued UDFs , like In-line UDFs, are also similar to views or CTEs with the added
benefit of parameters. They can be used in FROM clauses as sub queries and can be joined to other
source table rows using the APPLY and OUTER APPLY operators.
The difference between multi-statement UDFs and the inline UDFs is that multi-statement UDFs are
not restricted to a single SELECT statement. They can consist of multiple statements including logic
implemented with flow control, complex data processing, security checks, etc.
The downside of using multi-statement UDFs is that there are far less optimizations possible and per-
formance may suffer.
Syntax
CREATE FUNCTION <Function Name> ([{<Parameter Name> [AS] <Data Type> [= <Default
Value>] [READONLY]} [,...n]])
RETURNS <@Return Variable> TABLE <Table Definition>
[AS]
BEGIN
<Function Body Code>
RETURN
END[;]
For more information, see https://docs.microsoft.com/en-us/sql/t-sql/statements/create-function-transact-sql
- 240 -
Migrate to: Aurora PostgreSQL User Defined Func-
tions
Feature Com-
patibility
SCT Automation Level
SCT Action Code
Index
Key Differences
N/A Syntax and option dif-
ferences
See Stored Procedures.
Syntax
CREATE [OR REPLACE ] FUNCTION
name ([[argmode ] [argname ] argtype [{DEFAULT | = } default_expr ] [, ...]
] )
[RETURNS rettype
| RETURNS TABLE (column_name column_type [, ...] ) ]
{LANGUAGE lang_name
| TRANSFORM {FOR TYPE type_name } [, ... ]
| WINDOW
| IMMUTABLE | STABLE | VOLATILE | [NOT ] LEAKPROOF
| CALLED ON NULL INPUT | RETURNS NULL ON NULL INPUT | STRICT
| [EXTERNAL ] SECURITY INVOKER | [EXTERNAL ] SECURITY DEFINER
| PARALLEL {UNSAFE | RESTRICTED | SAFE }
| COST execution_cost
| ROWS result_rows
| SET configuration_parameter {TO value | = value | FROM CURRENT }
| AS 'definition'
| AS 'obj_file', 'link_symbol'
} ...
[WITH (attribute [, ...] ) ]
- 241 -
Migrate from: SQL Server User Defined Types
Feature Compatibility SCT Automation Level
SCT Action Code
Index
Key Differences
Syntax and option dif-
ferences
Overview
SQL Server User defined Types provide a mechanism for encapsulating custom data types and for
adding NULL constraints.
SQL Server also supports table-valued user defined types, which you can use to pass a set of values to
a stored procedure.
User defined types can also be associated to CLR code assemblies. Beginning with SQL Server 2014,
memory optimized types support memory optimized tables and code.
Note: If your code uses custom rules bound to data types, Microsoft recommends dis-
continuing the use of this deprecated feature.
All user defined types are based on an existing system data types. They allow developers to reuse the
definition, making the code and schema more readable.
Syntax
The simplified syntax for the CREATE TYPE statement is specified below.
CREATE TYPE <type name> {
FROM <base type> [NULL | NOT NULL ] | AS TABLE (<Table Definition>)}
Examples
User Defined Types
Create a ZipCode Scalar User Defined Type.
CREATE TYPE ZipCode
FROM CHAR(5)
NOT NULL
Use the ZipCode type in a table.
CREATE TABLE UserLocations
(UserID INT NOT NULL PRIMARY KEY, ZipCode ZipCode);
- 242 -
INSERT INTO [UserLocations] ([UserID],[ZipCode]) VALUES (1, '94324');
INSERT INTO [UserLocations] ([UserID],[ZipCode]) VALUES (2, NULL);
The above code displays the following error message indicating NULL values for ZipCode are not
allowed.
Msg 515, Level 16, State 2, Line 78
Cannot insert the value NULL into column 'ZipCode', table 'tempdb.dbo.UserLocations';
column does not allow nulls. INSERT fails.
The statement has been terminated.
Table-Valued types
The following example demonstrates how to create and use a table valued types to pass a set of values
to a stored procedure:
Create the OrderItems table.
CREATE TABLE OrderItems
(
OrderID INT NOT NULL,
Item VARCHAR(20) NOT NULL,
Quantity SMALLINT NOT NULL,
PRIMARY KEY(OrderID, Item)
);
Create a table valued type for the OrderItems table.
CREATE TYPE OrderItems
AS TABLE
(
OrderID INT NOT NULL,
Item VARCHAR(20) NOT NULL,
Quantity SMALLINT NOT NULL,
PRIMARY KEY(OrderID, Item)
);
Create the InsertOrderItems procedure. Note that the entire set of rows from the table valued para-
meter is handled with one statement.
CREATE PROCEDURE InsertOrderItems
@OrderItems AS OrderItems READONLY
AS
BEGIN
INSERT INTO OrderItems(OrderID, Item, Quantity)
SELECT OrderID,
Item,
Quantity
FROM @OrderItems;
END
Instantiate the OrderItems type, insert the values, and pass it to a stored procedure.
- 243 -
DECLARE @OrderItems AS OrderItems;
INSERT INTO @OrderItems ([OrderID], [Item], [Quantity])
VALUES
(1, 'M8 Bolt', 100),
(1, 'M8 Nut', 100),
(1, M8 Washer, 200);
EXECUTE [InsertOrderItems] @OrderItems = @OrderItems;
(3 rows affected)
Select all rows from the OrderItems table.
SELECT * FROM OrderItems;
OrderID Item Quantity
-------------------
1 M8 Bolt 100
1 M8 Nut 100
1 M8 Washer 200
For more information, see https://docs.microsoft.com/en-us/sql/t-sql/statements/create-type-transact-sql
- 244 -
Migrate to: Aurora PostgreSQL User Defined Types
Feature Com-
patibility
SCT Automation Level
SCT Action Code
Index
Key Differences
Syntax and option dif-
ferences
Overview
Similar to SQL Server, PostgreSQL enables creation of User Defined Types using the CREATE TYPE state-
ment. A User Defined Type is owned by the user who creates it. If a schema name is specified, the type
is created under that schema.
PostgreSQL supports the creation of several different User Defined Types:
l Composite Typesstore a single named attribute attached to a data type or multiple attributes as
an attribute collection. In PostgreSQL, you can also use the CREATE TYPE statement standalone
with an association to a table.
l Enumerated Types (enum) store a static ordered set of values. For example, product cat-
egories.
CREATE TYPE PRODUCT_CATEGORT AS ENUM
('Hardware', 'Software', 'Document');
l Range Types store a range of values, for example, a range of timestamps used to represent the
ranges of time of when a course is scheduled.
CREATE TYPE float8_range AS RANGE
(subtype = float8, subtype_diff = float8mi);
For more information see https://www.postgresql.org/docs/9.6/static/rangetypes.html
l Base Types are the system core types (abstract types) and are implemented in a low-level lan-
guage such as C.
l Array Types support definition of columns as multidimensional arrays. An array column can be
created with a built-in type or a user-defined base type, enum type, or composite.
CREATE TABLE COURSE_SCHEDULE (
COURSE_ID NUMERIC PRIMARY KEY,
COURSE_NAME VARCHAR(60),
COURSE_SCHEDULES text[]);
For additional details, see https://www.postgresql.org/docs/9.1/static/arrays.html
- 245 -
Syntax
CREATE TYPE name AS RANGE (
SUBTYPE = subtype
[, SUBTYPE_OPCLASS = subtype_operator_class ]
[, COLLATION = collation ]
[, CANONICAL = canonical_function ]
[, SUBTYPE_DIFF = subtype_diff_function ]
)
CREATE TYPE name (
INPUT = input_function,
OUTPUT = output_function
[, RECEIVE = receive_function ]
[, SEND = send_function ]
[, TYPMOD_IN = type_modifier_input_function ]
[, TYPMOD_OUT = type_modifier_output_function ]
[, ANALYZE = analyze_function ]
[, INTERNALLENGTH = {internallength | VARIABLE } ]
[, PASSEDBYVALUE ]
[, ALIGNMENT = alignment ]
[, STORAGE = storage ]
[, LIKE = like_type ]
[, CATEGORY = category ]
[, PREFERRED = preferred ]
[, DEFAULT = default ]
[, ELEMENT = element ]
[, DELIMITER = delimiter ]
[, COLLATABLE = collatable ]
)
Examples
Create a User Define Type for storing an employee phone numbers.
CREATE TYPE EMP_PHONE_NUM AS (
PHONE_NUM VARCHAR(11));
CREATE TABLE EMPLOYEES (
EMP_ID NUMERIC PRIMARY KEY,
EMP_PHONE EMP_PHONE_NUM NOT NULL);
INSERT INTO EMPLOYEES VALUES(1, ROW('111-222-333'));
SELECT a.EMP_ID, (a.EMP_PHONE).PHONE_NUM FROM EMPLOYEES a;
emp_id | phone_num
--------+-------------
1 | 111-222-333
(1 row)
Create a PostgreSQL Object Type as a collection of Attributes for the employees table.
- 246 -
CREATE OR REPLACE TYPE EMP_ADDRESS AS OBJECT (
STATE VARCHAR(2),
CITY VARCHAR(20),
STREET VARCHAR(20),
ZIP_CODE NUMERIC);
CREATE TABLE EMPLOYEES (
EMP_ID NUMERIC PRIMARY KEY,
EMP_NAME VARCHAR(10) NOT NULL,
EMP_ADDRESS EMP_ADDRESS NOT NULL);
INSERT INTO EMPLOYEES
VALUES(1, 'John Smith',
('AL', 'Gulf Shores', '3033 Joyce Street', '36542'));
SELECT a.EMP_NAME,
(a.EMP_ADDRESS).STATE,
(a.EMP_ADDRESS).CITY,
(a.EMP_ADDRESS).STREET,
(a.EMP_ADDRESS).ZIP_CODE
FROM EMPLOYEES a;
emp_name | state | city | street | zip_code
------------+-------+-------------+-------------------+----------
John Smith | AL | Gulf Shores | 3033 Joyce Street | 36542
For additional details, see:
l https://www.postgresql.org/docs/9.6/static/sql-createtype.html
l https://www.postgresql.org/docs/9.6/static/rowtypes.htm
- 247 -
Migrate from: SQL Server Sequences and Identity
Feature Com-
patibility
SCT Automation
Level
SCT Action Code Index Key Differences
SCT Action Codes - Sequences and
Identity
Less options with SERIAL
Reseeding need to be
rewrited
Overview
Automatic enumeration functions and columns are common with relational database management sys-
tems and are often used for generating surrogate keys.
SQL Server provides several features that support automatic generation of monotonously increasing
value generators.
l IDENTITY property of a table column
l SEQUENCE objects framework
l Numeric functions such as IDENTITY, and NEWSEQUENTIALID
Identity
The IDENTITY property is probably the most widely used means of generating surrogate primary keys
in SQL Server applications. Each table may have a single numeric column assigned as an IDENTITY
using the CREATE TABLE or ALTER TABLE DDL statements. You can explicitly specify a starting value
and increment.
Note: The identity property does not enforce uniqueness of column values, indexing, or any
other property. Additional constraints such as Primary or Unique keys, explicit index spe-
cifications, or other properties must be specified in addition to the IDENTITY property.
The IDENTITY value is generated as part of the transaction that inserts table rows. Applications can
obtain IDENTITY values using the @@IDENTITY, SCOPE_IDENTITY, and IDENT_CURRENT functions.
You can manage IDENTITY columns using the DBCC CHECKIDENT command, which provides func-
tionality for reseeding and altering properties.
Syntax
IDENTITY [(<Seed Value>, <Increment Value>)]
- 248 -
Examples
Create a table with an IDENTITY column.
CREATE TABLE MyTABLE
(
Col1 INT NOT NULL
PRIMARY KEY NONCLUSTERED IDENTITY(1,1),
Col2 VARCHAR(20) NOT NULL
);
Insert a row and retrieve the generated IDENTITY value .
DECLARE@LastIdent INT;
INSERT INTO MyTable(Col2)
VALUES('SomeString');
SET @LastIdent = SCOPE_IDENTITY()
Create a table with a non-key IDENTITY column and an increment of 10.
CREATE TABLE MyTABLE
(
Col1 VARCHAR(20) NOT NULL
PRIMARY KEY,
Col2 INT NOT NULL
IDENTITY(1,10),
);
Create a table with a compound PK including an IDENTITY column.
CREATE TABLE MyTABLE
(
Col1 VARCHAR(20) NOT NULL,
Col2 INT NOT NULL
IDENTITY(1,10),
PRIMARY KEY (Col1, Col2)
);
SEQUENCE
Sequences are objects that are independent of a particular table or column and are defined using the
CREATE SEQUENCE DDL statement. You can manage sequences using the ALTER SEQUENCE statement.
Multiple tables and multiple columns from the same table may use the values from one or more
SEQUENCE objects.
You can retrieve a value from a SEQUENCE object using the NEXT VALUEFOR function. For example, a
SEQUENCE value can be used as a default value for a surrogate key column.
SEQUENCE objects provide several advantages over IDENTITY columns:
- 249 -
l Can be used to obtain a value before the actual INSERT takes place.
l Value series can be shared among columns and tables.
l Easier management, restart, and modification of sequence properties.
l Allows assignment of value ranges using sp_sequence_get_range and not just per-row values.
Syntax
CREATE SEQUENCE <Sequence Name> [AS <Integer Data Type> ]
START WITH <Seed Value>
INCREMENT BY <Increment Value>;
ALTER SEQUENCE <Sequence Name>
RESTART [WITH <Reseed Value>]
INCREMENT BY <New Increment Value>;
Examples
Create a sequence and use it for a primary key default.
CREATE SEQUENCE MySequence AS INT START WITH1 INCREMENT BY 1;
CREATE TABLE MyTable
(
Col1 INT NOT NULL
PRIMARY KEY NONCLUSTERED DEFAULT (NEXT VALUE FOR MySequence),
Col2 VARCHAR(20) NULL
);
INSERT MyTable (Col1, Col2) VALUES (DEFAULT, 'cde'), (DEFAULT, 'xyz');
SELECT * FROM MyTable;
Col1 Col2
--------
1 cde
2 xyz
Sequential Enumeration Functions
SQL Server provides two sequential generation functions: IDENTITY and NEWSEQUENTIALID.
Note: The IDENTITY function should not be confused with the IDENTITY property of a column.
The IDENTITY function can be used only in a SELECT ... INTOstatement to insert IDENTITY column val-
ues into a new table.
The NEWSEQUNTIALID function generates a hexadecimal GUID, which is an integer. While the NEWID
function generates a random GUID, the NEWSEQUENTIALID function guarantees that every GUID
- 250 -
created is greater (in numeric value) than any other GUID previously generated by the same function
on the same server since the operating system restart.
Note: NEWSEQUENTIALID can be used only with DEFAULT constraints associated with
columns having a UNIQUEIDENTIFIER data type.
Syntax
IDENTITY (<Data Type> [, <Seed Value>, <Increment Value>]) [AS <Alias>]
NEWSEQUENTIALID()
Examples
Use the IDENTITY function as surrogate key for a new table based on an existing table.
CREATE TABLE MySourceTable
(
Col1 INTNOT NULL PRIMARY KEY,
Col2 VARCHAR(10) NOT NULL,
Col3 VARCHAR(10) NOT NULL
);
INSERT INTO MySourceTable
VALUES
(12, 'String12', 'String12'),
(25, 'String25', 'String25'),
(95, 'String95', 'String95');
SELECT IDENTITY(INT, 100, 1) AS SurrogateKey,
Col1,
Col2,
Col3
INTO MyNewTable
FROM MySourceTable
ORDER BYCol1 DESC;
SELECT *
FROM MyNewTable;
SurrogateKeyCol1Col2 Col3
------------------------
100 95 String95String95
101 25 String25String25
102 12 String12String12
Use NEWSEQUENTIALIDas a surrogate key for a new table.
CREATE TABLE MyTable
(
Col1 UNIQUEIDENTIFIER NOT NULL
- 251 -
PRIMARY KEY NONCLUSTEREDDEFAULT NEWSEQUENTIALID()
);
INSERT INTO MyTable
DEFAULT VALUES;
SELECT*
FROM MyTable;
Col1
----
9CC01320-C5AA-E811-8440-305B3A017068
For more information, see
l https://docs.microsoft.com/en-us/sql/relational-databases/sequence-numbers/sequence-numbers
l https://docs.microsoft.com/en-us/sql/t-sql/statements/create-table-transact-sql-identity-property
- 252 -
Migrate to: Aurora PostgreSQL Sequences and
SERIAL
Feature Com-
patibility
SCT Automation
Level
SCT Action Code Index Key Differences
SCT Action Codes - Sequences and
Identity
Less options with SERIAL
Reseeding need to be
rewrited
Sequences Usage
The PostgreSQL CREATE SEQUENCE command is mostly compatible with the SQL Server CREATE
SEQUENCE command. Sequences in PostgreSQL serve the same purpose as in SQL Server; they gen-
erate numeric identifiers automatically. A sequence object is owned by the user that created it.
Sequence Parameters
l TEMPORARY or TEMP:PostgreSQL can create a temporary sequence within a session. Once the
session ends, the sequence is automatically dropped.
l IF NOT EXISTS: Creates a sequence. If a sequence with an identical name already exists, it is
replaced.
l INCREMENT BY: An optional parameter with a default value of 1. Positive values generate
sequence values in ascending order. Negative values generate sequence values in descending
sequence.
l START WITH: An optional parameter having a default of 1. It uses the MINVALUE for ascending
sequences and the MAXVALUE for descending sequences.
l MAXVALUE | NO MAXVALUE: Defaults are between 263 for ascending sequences and -1 for des-
cending sequences.
l MINVALUE | NO MINVALUE: Defaults are between 1 for ascending sequences and -263 for des-
cending sequences.
l CYCLE | NO CYCLE: If the sequence value reaches MAXVALUE or MINVALUE, the CYCLE para-
meter instructs the sequence to return to the initial value (MINVALUE or MAXVALUE). The default
is NO CYCLE.
l CACHE: In PostgreSQL, the NOCACHE is not supported. By default, when the CACHE parameter is
not specified, no sequence values are pre-cached into memory (equivalent to the SQL Server
NOCACHE parameter). The minimum value is 1.
l OWNED BY | OWNBY NON: Specifies that the sequence object is to be associated with a specific
column in a table. When dropping this type of sequence, an error is returned due to the
sequence/table association.
- 253 -
Syntax
CREATE [TEMPORARY | TEMP ] SEQUENCE [IF NOT EXISTS ] name
[INCREMENT [BY ] increment ]
[MINVALUE minvalue | NO MINVALUE ] [MAXVALUE maxvalue | NO MAXVALUE ]
[START [WITH ] start ] [CACHE cache ] [[NO ] CYCLE ]
[OWNED BY {table_name.column_name | NONE } ]
Most SQL Server CREATE SEQUENCE parameters are compatible with PostgreSQL.
Examples
Create a sequence.
CREATE SEQUENCE SEQ_1 START WITH 100
INCREMENT BY 1 MAXVALUE 99999999999 CACHE 20 NO CYCLE;
Drop a sequence.
DROP SEQUENCE SEQ_1;
View sequences created in the current schema and sequence specifications.
SELECT * FROM INFORMATION_SCHEMA.SEQUENCES;
OR
\ds
Use a PostgreSQL sequence as part of a CREATE TABLE and an INSERT statement.
CREATE TABLE SEQ_TST
(COL1 NUMERIC DEFAULT NEXTVAL('SEQ_1') PRIMARY KEY, COL2 VARCHAR(30));
INSERT INTO SEQ_TST (COL2) VALUES('A');
SELECT * FROM SEQ_TST;
col1 | col2
------+------
100 | A
Use the OWNED BY parameter to associate the sequence with a table.
CREATE SEQUENCE SEQ_1 START WITH 100 INCREMENT BY 1 OWNED BY SEQ_TST.COL1;
Query the current value of a sequence.
SELECT CURRVAL('SEQ_1);
Manually increment a sequence value according to the INCREMENT BY value.
SELECT NEXTVAL('SEQ_1');
OR
SELECT SETVAL('SEQ_1', 200);
- 254 -
Alter an existing sequence.
ALTER SEQUENCE SEQ_1 MAXVALUE 1000000;
SERIAL Usage
PostgreSQL enables you to create a sequence similar to the IDENTITY property supported by identity
columns. When creating a new table, the sequence is created through the SERIAL pseudo-type. Other
types from the same family are SMALLSERIAL and BIGSERIAL.
By assigning a SERIAL type to a column on table creation, PostgreSQL creates a sequence using the
default configuration and adds a NOT NULL constraint to the column. The newly created sequence
behaves as a regular sequence (incremented by 1) and no composite SERIAL option.
Use a SERIAL Sequence.
CREATE TABLE SERIAL_SEQ_TST(COL1 SERIAL PRIMARY KEY, COL2 VARCHAR(10));
INSERT INTO SERIAL_SEQ_TST(COL2) VALUES('A');
SELECT * FROM SERIAL_SEQ_TST;
col1 | col2
------+------
1 | A
\ds
Schema | Name | Type | Owner
--------+-------------------------+----------+-------
public | serial_seq_tst_col1_seq | sequence | pg_tst_db
Use the PostgreSQL SERIAL pseudo-type (with a Sequence that is created implicitly).
psql=> CREATE TABLE SERIAL_SEQ_TST(COL1 SERIAL PRIMARY KEY, COL2 VARCHAR(10));
psql=> \ds
Schema | Name | Type | Owner
--------+-------------------------+----------+-------
public | serial_seq_tst_col1_seq | sequence | pg_tst_db
psql=> ALTER SEQUENCE SERIAL_SEQ_TST_COL1_SEQ RESTART WITH 100 INCREMENT BY 10;
psql=> INSERT INTO SERIAL_SEQ_TST(COL2) VALUES('A');
psql=> INSERT INTO SERIAL_SEQ_TST(COL1, COL2) VALUES(DEFAULT, 'B');
psql=> SELECT * FROM SERIAL_SEQ_TST;
col1 | col2
------+------
100 | A
110 | B
Regarding the SQL Server's IDENTITY examples, but using SERIAL column will create a sequence, you
can use ALTER SEQUENCE command to implement some IDENTITY features.
- 255 -
To create a table with a SERIAL column and an increment of 10, you should use:
CREATE TABLE SERIAL_SEQ_TST(COL1 SERIAL PRIMARY KEY, COL2 VARCHAR(10));
ALTER SEQUENCE serial_seq_tst_col1_seq INCREMENT BY 10;
Note: The auto generated sequence's name should be created with the following format:
TABLENAME_COLUMNNAME_seq
Create a table with a compound PK including an SERIAL column.
CREATE TABLE SERIAL_SEQ_TST
(COL1 SERIAL, COL2 VARCHAR(10), PRIMARY key (COL1,COL2));
Summary
The following table identifies similarities, differences, and key migration considerations.
Feature SQLServer Aurora PostgreSQL
independent
SEQUENCE object
CREATE
SEQUENCE
CREATE SEQUENCE
Automatic enu-
merator column
property
IDENTITY SERIAL
Reseed sequence
value
DBCC
CHECKIDENT
1. Find sequence name:
pg_get_serial_sequence('[table_name]', '[serial_field_name]')
2. SELECT SETVAL((SELECT pg_get_serial_sequence('table_
name', 'person_id')), 1, false);
Column restrictions Numeric Numeric
Controlling seed
and interval values
CREATE/ALTER
SEQUENCE
CREATE/ALTER SEQUENCE
Sequence setting ini-
tialization
Maintained
through service
restarts
ALTER SEQUENCE
Explicit values to
column
not allowed by
default, SET
IDENTITY_INSERT
ON required
Allowed
- 256 -
Configuration
- 258 -
Migrate from: SQL Server Session Options
Feature Com-
patibility
SCT Auto-
mation Level
SCT Action
Code Index
Key Differences
N/A N/A SET options are significantly different, except for
transaction isolation control
Overview
Session Options in SQL Server is a collection of run-time settings that control certain aspects of how
the server handles data for individual sessions. A session is the period between a login event and a dis-
connect event (or an exec sp_reset_connection command for connection pooling).
Each session may have multiple execution scopes, which are all the statements before the GO keyword
used in SQL Server management Studio scripts, or any set of commands sent as a single execution
batch by a client application. Each execution scope may contain additional sub-scopes. For example,
scripts calling stored procedures or functions.
You can set the global session options, which all execution scopes use by default, using the SET T-SQL
command. Server code modules such as stored procedures and functions may have their own exe-
cution context settings, which are saved along with the code to guarantee the validity of results.
Developers can explicitly use SET commands to change the default settings for any session or for an
execution scope within the session. Typically, client applications send explicit SET commands upon con-
nection initiation.
You can view the metadata for current sessions using the sp_who_system stored procedure and the
sysprocesses system table.
Note: To change the default setting for SQL Server Management Studio, click Tools >Options
> Query Execution > SQL Server > Advanced.
Syntax
Syntax for the SET command:
SET
Category Setting
-----------------------
Date and time DATEFIRST | DATEFORMAT
Locking DEADLOCK_PRIORITY | SET LOCK_TIMEOUT
Miscellaneous CONCAT_NULL_YIELDS_NULL | CURSOR_CLOSE_ON_COMMIT | FIPS_FLAGGER |SET
IDENTITY_INSERT
LANGUAGE | OFFSETS | QUOTED_IDENTIFIER
Query Execution ARITHABORT | ARITHIGNORE | FMTONLY | NOCOUNT |NOEXEC | NUMERIC_
ROUNDABORT | PARSEONLY
QUERY_GOVERNOR_COST_LIMIT |ROWCOUNT | TEXTSIZE
ANSI ANSI_DEFAULTS | ANSI_NULL_DFLT_OFF | ANSI_NULL_DFLT_ON | ANSI_NULLS |
- 259 -
ANSI_PADDING
ANSI_WARNINGS
Execution Stats FORCEPLAN | SHOWPLAN_ALL |SHOWPLAN_TEXT | SHOWPLAN_XML | STATISTICS
IO | STATISTICS XML
STATISTICS PROFILE | STATISTICS TIME
Transactions IMPLICIT_TRANSACTIONS |REMOTE_PROC_TRANSACTIONS |TRANSACTION ISOLATION
LEVEL | XACT_ABORT
Note: For more details about individual settings, see the link at the end of this section.
SET ROWCOUNT for DML Deprecated Setting
The SET ROWCOUNT for DML statements has been deprecated as of SQL Server 2008 in accordance
with https://docs.microsoft.com/en-us/previous-versions/sql/sql-server-2008-r2/ms143729(v=sql.105).
Up to and including SQL Server 2008 R2, you could limit the amount of rows affected by INSERT,
UPDATE, and DELETE operations using SET ROWCOUNT. For example, it is a common practice in SQL
Server to batch large DELETE or UPDATE operations to avoid transaction logging issues. The following
example loops and deletes rows having 'ForDelete' set to 1, but only 5000 rows at a time in separate
transactions (assuming the loop is not within an explicit transaction).
SET ROWCOUNT 5000;
WHILE @@ROWCOUNT > 0
BEGIN
DELETE FROM MyTable
WHERE ForDelete = 1;
END
Begining with SQL Server 2012, SET ROWCOUNT is ignored for INSERT, UPDATE and DELETE state-
ments. The same functionality can be achieved by using TOP, which can be converted to the Aurora
PostgreSQL LIMIT.
For example, the previous code could be rewritten as:
WHILE @@ROWCOUNT > 0
BEGIN
DELETE TOP (5000)
FROM MyTable
WHERE ForDelete = 1;
END
The latter syntax can be converted automatically by SCT to Aurora PostgeSQL. See the code example in
Aurora PostgreSQL Session Options.
Examples
Use SET within a stored procedure.
CREATE PROCEDURE <ProcedureName>
AS
BEGIN
- 260 -
<Some non critical transaction code>
SET TRANSACTION_ISOLATION_LEVELSERIALIZABLE;
SET XACT_ABORT ON;
<Some critical transaction code>
END
Note: Explicit SET commands affect their execution scope and sub scopes.
After the scope terminates and the procedure code exits, the calling scope resumes its ori-
ginal settings used before the calling the stored procedure.
For more information, see https://docs.microsoft.com/en-us/sql/t-sql/statements/set-statements-transact-sql
- 261 -
Migrate to: Aurora PostgreSQL Session Options
Feature Com-
patibility
SCT Auto-
mation Level
SCT Action
Code Index
Key Differences
N/A N/A SET options are significantly different, except for
transaction isolation control
Overview
Aurora PostgreSQL supports hundreds of Server System Variables to control server behavior and the
global and session levels.
PostgreSQL provides session-modifiable parameters that are configured using the SET SESSION com-
mand. Configuration of parameters using SET SESSION will only be applicable in the current session.
To view the list of parameters that can be set with SET SESSION , you can query pg_settings:
SELECT * FROM pg_settings where context = 'user';
Examples of commonly used session parameters:
l client_encoding - configures the connected client character set.
l force_parallel_mode - forces use of parallel query for the session.
l lock_timeout - sets the maximum allowed duration of time to wait for a database lock to
release.
l search_path - sets the schema search order for object names that are not schema-qualified.
l transaction_isolation - sets the current Transaction Isolation Level for the session.
You can view Aurora PostgreSQL variables using the PostgreSQL command line utility, Aurora database
cluster parameters, Aurora database instance parameters, or SQL interface system variables.
Converting from SQL Server 2008 SET ROWCOUNT for DML oper-
ations
As mentioned in SQL Server Sessions Options, the use of SET ROWCOUNT for DML operations is
deprecated as of SQL Server 2008 R2. Code that uses the SET ROWCOUNT syntax can not be converted
automatically. Either rewrite to use TOP before running SCT, or manually change it afterward.
The example used to batch DELETE operations in SQL Server using TOP:
WHILE @@ROWCOUNT > 0
BEGIN
DELETE TOP (5000)
FROM MyTable
WHERE ForDelete = 1;
END
can be easily rewritten to use Aurora PostgreSQL LIMIT clause :
- 262 -
WHILE row_count() > 0 LOOP
DELETE FROM num_test
WHERE ctid IN (
SELECT ctid
FROM num_test
LIMIT 10)
END LOOP;
Examples
Change the time zone of the connected session.
SET SESSION DateStyle to POSTGRES, DMY;
SET
SELECT NOW();
now
-------------------------------------
Sat 09 Sep 11:03:43.597202 2017 UTC
(1 row)
SET SESSION DateStyle to ISO, MDY;
SET
SELECT NOW();
now
-----------------------------
2017-09-09 11:04:01.3859+00
(1 row)
Summary
The following table summarizes commonly used SQL Server session options and their corresponding
Aurora PostgreSQL system variables.
Category SQL Server Aurora PostgreSQL
Date and time DATEFIRST
DATEFORMAT
Use DOW in queries
DateStyle
Locking LOCK_TIMEOUT lock_timeout
Transactions IMPLICIT_TRANSACTIONS
TRANSACTION ISOLATION LEVEL
SET TRANSACTION
BEGIN TRANSACTION ISOLATION LEVEL
Query exe-
cution
IDENTITY_INSERT
LANGUAGE
QUOTED_IDENTIFIER
See Identity and sequences
lc_monetary / lc_numeric / lc_time
N/A
- 263 -
Category SQL Server Aurora PostgreSQL
NOCOUNT N/A and not needed
Execution stats SHOWPLAN_ALL, TEXT, and XML
STATISTICS IO, XML, PROFILE, and TIME
See Execution Plans
Miscellaneous CONCAT_NULL_YIELDS_NULL
ROWCOUNT
N/A
Use LIMIT within SELECT
For more information, see: For more details, see https://www.postgresql.org/docs/9.6/static/sql-set.html
- 264 -
Migrate from: SQL Server Database Options
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A N/A Use Cluster and Database/Cluster
Parameter
Overview
SQL Server provides database level options that can be set using the ALTER DATABASE...
SETcommand. These settings enable you to:
l Set default session options. For more information, see Session Options.
l Enable or disable database features such as SNAPSHOT_ISOLATION, CHANGE_TRANCKING, and
ENABLE_BROKER.
l Configure High availability and disaster recovery options such as always on availability groups
l Configure security access control such as restricting access to a single user, setting the database
offline, or setting the database to read-only.
Syntax
Syntax for setting database options:
ALTER DATABASE {<database name> } SET {<option> [,...n ] };
Examples
Set a database to read-only and use ARITHABORT by default.
ALTER DATABASE Demo SET READ_ONLY, ARITHABORT ON;
Set a database to use automatic statistic creation.
ALTERDATABASE Demo SET AUTO_CREATE_STATISTICS ON;
Set a database offline immediately.
ALTER DATABASE DEMO SET OFFLINE WITH ROLLBACK IMMEDIATE;
For more information, see https://docs.microsoft.com/en-us/sql/t-sql/statements/alter-database-transact-sql-set-options
- 265 -
Migrate to: Aurora PostgreSQL Database Options
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A N/A Use Cluster and Database/Cluster
Parameter
Overview
Aurora PostgreSQL supports both the CREATE SCHEMA and CREATE DATABASE statements.
As with SQLServer, Aurora PostgreSQL does have the concept of an instance hosting multiple data-
bases, which in turn contain multiple schemas. Objects in Aurora PostgreSQL are referenced as a three
part name: <database>.<schema>.<object>.
Database options are related to the cluster level parameters which are managed by the AWS Cluster
Parameter Groups but some MSSQL equivalent parameters can be found at the instance level in the
AWS Database Parameter Group.
Datable Options are being compared to "AWS Database Parameter Group" and Server Options are
being compared to "AWS Cluster Parameter Group", for more information, see Server Options.
- 266 -
Migrate from: SQL Server Server Options
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A N/A Use Cluster and Database/Cluster
Parameter
Overview
SQLServer provides server-level settings that affect all databases and all sessions. You can modify
these settings using the sp_configure system stored procedure.
You can use Server Options to perform the following configuration tasks:
l Define hardware utilization such as memory management, affinity mask, priority boost, network
packet size, and soft Non-Uniform Memory Access (NUMA) .
l Alter run time global values such as recovery interval, remote login timeout, optimization for ad-
hoc workloads, and cost threshold for parallelism.
l Enable and disable global features such as C2 Audit, OLE, procedures, CLR procedures, and allow
trigger recursion.
l Configure global security settings such as server authentication mode, remote access, shell
access with xp_cmdshell, CLR access level, and database chaining.
l Set default values for sessions such as user options, default language, backup compression, and
fill factor.
Some settings require an explicit RECONFIGURE command to apply the changes to the server. High
risk settings require RECONFIGUREWITH OVERRIDE for the changes to be applied. Some advanced
options are hidden by default. To view and modify these settings, set show advanced options to 1 and
re-execute sp_configure.
Note: Server audits are managed with the T-SQL commands CREATE and ALTER SERVER
AUDIT.
Syntax
EXECUTE sp_configure <option>, <value>;
Examples
Limit server memory usage to 4GB.
EXECUTE sp_configure 'show advanced options', 1;
RECONFIGURE;
- 267 -
sp_configure 'max server memory', 4096;
RECONFIGURE;
Allow command shell access from T-SQL.
EXEC sp_configure 'show advanced options', 1;
RECONFIGURE;
EXEC sp_configure 'xp_cmdshell', 1;
RECONFIGURE;
Viewing current values.
EXECUTE sp_configure
For more information, see
https://docs.microsoft.com/en-us/sql/database-engine/configure-windows/server-configuration-options-sql-server
- 268 -
Migrate to: Aurora PostgreSQL Aurora Parameter
Groups
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A N/A Use Cluster and Database/Cluster
Parameter
Overview
When running PostgreSQL databases as Amazon Aurora Clusters, Parameter Groups are used to
change to cluster-level and database-level parameters.
Most of the PostgreSQL parameters are configurable in an Amazon Aurora PostgreSQL cluster, but
some are disabled and cannot be modified. Since Amazon Aurora clusters restrict access to the under-
lying operating system, modification to PostgreSQL parameters must be made using Parameter
Groups.
Amazon Aurora is a cluster of database instances and, as a direct result, some of the PostgreSQL para-
meters apply to the entire cluster while other parameters apply only to a particular database instance.
Aurora PostgreSQL Parameter Class Controlled Via
Cluster-level parameters
Single cluster parameter group per Amazon
Aurora Cluster
Managed via cluster parameter groups
For example:
• The PostgreSQL wal_buffers parameter is controlled
via a cluster parameter group.
• The PostgreSQL autovacuum parameter is controlled
via a cluster parameter group.
• The client_encoding parameter is controlled via a
cluster parameter group.
Database Instance-Level parameters
Every instance in an Amazon Aurora cluster
can be associated with a unique database
parameter group
Managed via database parameter groups For example:
• The PostgreSQL shared_buffers memory cache con-
figuration parameter is controlled via a database para-
meter group with an AWS-optimized default value
based on the configured database class: {DBIn-
stanceClassMemory/10922}.
• The PostgreSQL max_connections parameter, which
controls maximum number of client connections
- 269 -
Aurora PostgreSQL Parameter Class Controlled Via
allowed to the PostgreSQL instance, is controlled via a
database parameter group. Default value is optimized
by AWS based on the configured database class: LEAST
({DBInstanceClassMemory/9531392},5000).
• The authentication_timeout parameter, which con-
trols the maximum time to complete client authen-
tication (in seconds), is controlled via a database
parameter group.
• The superuser_reserved_connections parameter,
which determines the number of reserved connection
"slots" for PostgreSQL superusers, is configured via a
database parameter group.
• The PostgreSQL effective_cache_size, which informs
the query optimizer how much cache is present in the
kernel and helps control how expensive large index
scans will be, is controlled via a database level para-
meter group. The default value is optimized by AWS
based on database class (RAM): {DBIn-
stanceClassMemory/10922}.
Examples
Create and Configure a New Parameter Group
Follow the steps below to create and configure Amazon Aurora database and cluster parameter
groups:
1. Navigate to the "Parameter group" section in the RDS Service of the AWS Console.
2. Click Create Parameter Group.
Note: You cannot edit the default parameter group. You must create a custom para-
meter group to apply changes to your Amazon Aurora cluster and its database
instances.
- 270 -
3. Select the DB family from the Parameter group family drop-down list. Select DB Parameter
Group from the Type drop-down list (another option is to select Cluster Parameter Group for
modifying cluster parameters). Click Create.
Modify an Existing Parameter Group
1. Navigate to the "Parameter group" section in the RDS Service of the AWS Console.
2. Click the name of the parameter to edit.
- 271 -
3. Click the Edit parameters button.
4. Change parameter values and click Save changes.
For more details, see https://www.postgresql.org/docs/9.6/static/sql-set.html
Create and Configure a New Parameter Group
Follow the steps below to create and configure Amazon Aurora database and cluster parameter
groups:
1. Navigate to the "Parameter group" section in the RDS Service of the AWS Console.
2. Click Create Parameter Group.
Note: You cannot edit the default parameter group. You must create a custom para-
meter group to apply changes to your Amazon Aurora cluster and its database
instances.
3. Select from the Parameter group family drop-down list. Select DB Parameter Group from the
Type drop-down list (another option is to select Cluster Parameter Group for modifying cluster
- 272 -
parameters). Click Create.
Modify an Existing Parameter Group
1. Navigate to the "Parameter group" section in the RDS Service of the AWS Console.
2. Click the name of the parameter to edit.
3. Click the Edit parameters button.
4. Change parameter values and click Save changes.
For more details, see https://www.postgresql.org/docs/9.6/static/sql-set.html
- 273 -
Syntax
Server-level options are set with the SET GLOBAL command.
SET GLOBAL <option> = <Value>;
Examples
Modify Compression Level
Decrease compression level to reduce CPU usage.
SET GLOBAL innodb_compression_level = 5;
Create Parameter Groups
The following walkthrough demonstrates how to create and configure the Amazon Aurora database
and cluster parameter groups:
Navigate to Parameter Group in the RDS Service of the AWS Console.
Click Create Parameter Group.
Note: You cannot edit the default parameter group. Create a custom parameter group to
apply changes to your Amazon Aurora cluster and its database instances.
On the new page:
l Select aurora-PostgreSQL5.7from the Parameter group familydropdown list.
l Select DB Parameter Group from the Type dropdown list. Another option is to select Cluster
Parameter Group to modify cluster parameters.
l Click Create.
- 274 -
Modify a Parameter Group
The following walkthrough demonstrates how to modify an existing parameter group:
Navigate to the Parameter group section in the RDS Service of the AWS Console.
Click the name of the parameter group to edit.
On the new page, click the Edit parameters button.
Change parameter values and click Save changes.
For more information, see:
l https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_WorkingWithParamGroups.html
- 275 -
High Availability and Disaster Recovery (HADR)
- 276 -
Migrate from: SQL Server Backup and Restore
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A SCT Action Codes -
Backup
Storage level backup managed by
Amazon RDS
Overview
The term Backup refers to both the process of copying data and to the resulting set of data created by
the processes that copy data for safekeeping and disaster recovery. Backup processes copy SQLServer
data and transaction logs to media such as tapes, network shares, cloud storage, or local files. These
"backups" can then be copied back to the database using a restore process.
SQLServer uses files, or filegroups, to create backups for an individual database or subset of a data-
base. Table backups are not supported.
When a database uses the FULL recovery model, transaction logs also need to be backed up. Trans-
action logs allow backing up only database changes since the last full backup and provide a mech-
anism for point-in-time restore operations.
Recovery Model is a database-level setting that controls transaction log management. The three avail-
able recovery models are SIMPLE, FULL, and BULK LOGGED. For more information, see https://-
docs.microsoft.com/en-us/sql/relational-databases/backup-restore/recovery-models-sql-server.
The SQLServer RESTORE process copies data and log pages from a previously created backup back to
the database. It then triggers a recovery process that rolls forward all committed transactions not yet
flushed to the data pages when the backup took place. It also rolls back all uncommitted transactions
written to the data files.
SQL Server supports the following types of backups:
l Copy-Only Backups are independent of the standard chain of SQL Server backups. They are typ-
ically used as "one-off" backups for special use cases and do not interrupt normal backup oper-
ations.
l Data Backups copy data files and the transaction log section of the activity during the backup. A
Data Backup may contain the whole database (Database Backup) or part of the database. The
parts can be a Partial Backup or a file/filegroup.
l A Database Backup is a Data Backup representing the entire database at the point in time when
the backup process finished.
l A Differential Backup is a data backup containing only the data structures (extents) modified
since the last full backup. A differential backup is dependent on the previous full backup and can
not be used alone.
l A Full Backup is a data backup containing a Database Backup and the transaction log records of
the activity during the backup process.
- 277 -
l Transaction Log Backups do not contain data pages. They contain the log pages for all trans-
action activity since the last Full Backup or the previous Transaction Log Backup.
l File Backups consist of one or more files or filegroups.
SQL Server also supports Media Families and Media Sets that can be used to mirror and stripe backup
devices. For more information, see https://docs.microsoft.com/en-us/sql/relational-databases/backup-
restore/media-sets-media-families-and-backup-sets-sql-server
SQL Server 2008 Enterprise edition and later versions, support Backup Compression. Backup Com-
pression provides the benefit of a smaller backup file footprint, less I/O consumption, and less net-
work traffic at the expense of increased CPU utilization for executing the compression algorithm. For
more information, see https://docs.microsoft.com/en-us/sql/relational-databases/backup-restore/-
backup-compression-sql-server
A database backed up in the SIMPLE recovery mode can only be restored from a full or differential
backup. For FULL and BULK LOGGED recovery models, transaction log backups can be restored also
to minimize potential data loss.
Restoring a database involves maintaining a correct sequence of individual backup restores. For
example, a typical restore operation may include the following steps:
1. Restore the most recent Full Backup.
2. Restore the most recent Differential Backup.
3. Restore a set of uninterrupted Transaction Log Backups, in order.
4. Recover the database.
For large databases, a full restore, or a complete database restore, from a full database backup is not
always a practical solution. SQL Server supports Data File Restore that restores and recovers a set of
files and a single Data Page Restore, except for databases using the SIMPLE recovery model.
Syntax
Backup syntax:
Backing Up a Whole Database
BACKUP DATABASE <Database Name> [<Files / Filegroups> ] [READ_WRITE_FILEGROUPS ]
TO <Backup Devices>
[<MIRROR TO Clause> ]
[WITH [DIFFERENTIAL ]
[<Option List> ][;]
BACKUP LOG <Database Name>
TO <Backup Devices>
[<MIRROR TO clause> ]
[WITH <Option List> ][;]
<Option List> =
COPY_ONLY | {COMPRESSION | NO_COMPRESSION } | DESCRIPTION = <Description>
| NAME = <Backup Set Name> | CREDENTIAL | ENCRYPTION | FILE_SNAPSHOT | {EXPIREDATE =
<Expiration Date> | RETAINDAYS = <Retention> }
{NOINIT | INIT } | {NOSKIP | SKIP } | {NOFORMAT | FORMAT } |
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{NO_CHECKSUM | CHECKSUM } | {STOP_ON_ERROR | CONTINUE_AFTER_ERROR }
{NORECOVERY | STANDBY = <Undo File for Log Shipping> } | NO_TRUNCATE
ENCRYPTION (ALGORITHM = <Algorithm> | SERVER CERTIFICATE = <Certificate> | SERVER
ASYMMETRIC KEY = <Key> );
Restore Syntax:
RESTORE DATABASE <Database Name> [<Files / Filegroups> ] | PAGE = <Page ID>
FROM <Backup Devices>
[WITH [RECOVERY | NORECOVERY | STANDBY = <Undo File for Log Shipping> } ]
[, <Option List>]
[;]
RESTORE LOG <Database Name> [<Files / Filegroups> ] | PAGE = <Page ID>
[FROM <Backup Devices>
[WITH [RECOVERY | NORECOVERY | STANDBY = <Undo File for Log Shipping> } ]
[, <Option List>]
[;]
<Option List> =
MOVE <File to Location>
| REPLACE | RESTART | RESTRICTED_USER | CREDENTIAL
| FILE = <File Number> | PASSWORD = <Passord>
| {CHECKSUM | NO_CHECKSUM } | {STOP_ON_ERROR | CONTINUE_AFTER_ERROR }
| KEEP_REPLICATION | KEEP_CDC
| {STOPAT = <Stop Time>
| STOPATMARK = <Log Sequence Number>
| STOPBEFOREMARK = <Log Sequence Number>
Examples
Perform a full compressed database backup.
BACKUP DATABASE MyDatabase TO DISK='C:\Backups\MyDatabase\FullBackup.bak'
WITH COMPRESSION;
Perform a log backup.
BACKUP DATABASE MyDatabase TO DISK='C:\Backups\MyDatabase\LogBackup.bak'
WITH COMPRESSION;
Perform a partial differential backup.
BACKUP DATABASE MyDatabase
FILEGROUP = 'FileGroup1',
FILEGROUP = 'FileGroup2'
TO DISK='C:\Backups\MyDatabase\DB1.bak'
WITH DIFFERENTIAL;
Restore a database to a point in time.
RESTORE DATABASE MyDatabase
FROM DISK='C:\Backups\MyDatabase\FullBackup.bak'
- 279 -
WITH NORECOVERY;
RESTORE LOG AdventureWorks2012
FROM DISK='C:\Backups\MyDatabase\LogBackup.bak'
WITH NORECOVERY, STOPAT = '20180401 10:35:00';
RESTORE DATABASE AdventureWorks2012 WITH RECOVERY;
For more information, see
l https://docs.microsoft.com/en-us/sql/relational-databases/backup-restore/backup-overview-sql-server
l https://docs.microsoft.com/en-us/sql/relational-databases/backup-restore/restore-and-recovery-overview-sql-
server
- 280 -
Migrate to: Aurora PostgreSQL Backup and Restore
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A SCT Action Codes -
Backup
Storage level backup managed by
Amazon RDS
Overview
Aurora PostgreSQL continuously backs up all cluster volumes and retains restore data for the duration
of the backup retention period. The backups are incremental and can be used to restore the cluster to
any point in time within the backup retention period. You can specify a backup retention period from
one to 35 days when creating or modifying a database cluster. Backups incur no performance impact
and do not cause service interruptions.
Additionally, you can manually trigger data snapshots in a cluster volume that can be saved beyond the
retention period. You can use Snapshots to create new database clusters.
Note: Manual snapshots incur storage charges for Amazon RDS.
Restoring Data
You can recover databases from Aurora's automatically retained data or from a manually saved snap-
shot. Using the automatically retained data significantly reduces the need to take frequent snapshots
and maintain Recovery Point Objective (RPO) policies.
The RDS console displays the available time frame for restoring database instances in the Latest Restor-
able Time and Earliest Restorable Time fields. The Latest Restorable Time is typically within the last five
minutes. The Earliest Restorable Time is the end of the backup retention period.
Note: The Latest Restorable Time and Earliest Restorable Time fields display when a database
cluster restore has been completed. Both display NULL until the restore process completes.
Database Cloning
Database cloning is a fast and cost-effective way to create copies of a database. You can create mul-
tiple clones from a single DB cluster and additional clones can be created from existing clones. When
first created, a cloned database requires only minimal additional storage space.
Database cloning uses a copy-on-write protocol. Data is copied only when it changes either on the
source or cloned database.
Data cloning is useful for avoiding impacts on production databases. For example:
- 281 -
l Testing schema or parameter group modifications.
l Isolating intensive workloads. For example, exporting large amounts of data and running high
resource-consuming queries.
l Development and Testing with a copy of a production database.
Copying and sharing snapshots
Database snapshots can be copied and shared within the same AWS Region, across AWS Regions, and
across AWS accounts. Snapshot sharing allows an authorized AWS account to access and copy snap-
shots. Authorized users can restore a snapshot from its current location without first copying it.
Copying an automated snapshot to another AWS account requires two steps:
l Create a manual snapshot from the automated snapshot.
l Copy the manual snapshot to another account.
Backup Storage
In all RDS regions, Backup Storage is the collection of both automated and manual snapshots for all
database instances and clusters. The size of this storage is the sum of all individual instance snap-
shots.
When an Aurora PostgreSQL database instance is deleted, all automated backups of that database
instance are also deleted. However, Amazon RDS provides the option to create a final snapshot before
deleting a database instance. This final snapshot is retained as a manual snapshot. Manual snapshots
are not automatically deleted.
The Backup Retention Period
Retention periods for Aurora PostgreSQL DB cluster backups are configured when creating a cluster. If
not explicitly set, the default retention is one day when using the Amazon RDS API or the AWS CLI. The
retention period is seven days if using the AWS Console. You can modify the backup retention period
at any time with values of one to 35 days.
Disabling automated backups
You cannot disable automated backups on Aurora PostgreSQL. The backup retention period for Aurora
PostgreSQL is managed by the database cluster.
Migration Considerations
Migrating from a self managed backup policy to a Platform as a Service (PaaS) environment such as
Aurora PostgreSQL is a complete paradigm shift. You no longer need to worry about transaction logs,
file groups, disks running out of space, and purging old backups.
- 282 -
Amazon RDS provides guaranteed continuous backup with point-in-time restore up to 35 days.
Managing a SQL Server backup policy with similar RTO and RPO is a challenging task. With Aurora Post-
greSQL, all you need to set is the retention period and take some manual snapshots for special use
cases.
Examples
The following walk-through describes how to change Aurora PostgreSQL DB cluster retention settings
from one day to seven days using the RDS console.
Login to the RDS Console and click Clusters.
Click the DBcluster identifier.
Validate the current automatic backup settings.
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Scroll down to the DB Cluster Members section and click the database instance with the writer role.
On the top left, click Instance Actions >Modify.
Scroll down to the Backup section. Select 7 Days from the drop-down list.
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Summary
Feature SQL Server Aurora PostgreSQL Comments
Recovery
Model
SIMPLE, BULK LOGGED,
FULL
N/A The functionality of Aurora
PostgreSQL backups is equi-
valent to the FULL recovery
model.
Backup Data-
base
BACKUP DATABASE aws rds create-db-cluster-
snapshot --db-cluster-
snapshot-identifier Snap-
shot_name --db-cluster-
identifier Cluster_Name
Partial Backup BACKUP DATABASE ...
FILE= ... | FILEGROUP = ...
N/A Can use export utils
Log Backup BACKUP LOG N/A Backup is at the storage
level.
Differential
Backups
BACKUP DATABASE ...
WITH DIFFERENTIAL
N/A Can be done manually using
export tools
Database Snap-
shots
BACKUP DATABASE ...
WITH COPY_ONLY
RDS console or API The terminology is incon-
sistent between SQL Server
and Aurora PostgreSQL. A
database snapshot in SQL
Server is similar to database
cloning in Aurora Post-
greSQL. Aurora PostgreSQL
database snapshots are sim-
ilar to a COPY_ONLY backup
in SQL Server.
Database
Clones
CREATE DATABASE...
AS SNAPSHOT OF...
Create new cluster from a
cluster snapshot:
aws rds restore-db-
cluster-from-snapshot --
db-cluster-identifier
NewCluster --snapshot-
identifier Snap-
shotToRestore --engine
aurora-postgresql
Add a new instance to the
new/restored cluster:
The terminology is incon-
sistent between SQL Server
and Aurora PostgreSQL. A
database snapshot in SQL
Server is similar to database
cloning in Aurora Post-
greSQL. Aurora PostgreSQL
database snapshots are sim-
ilar to a COPY_ONLY backup
in SQL Server.
- 286 -
Feature SQL Server Aurora PostgreSQL Comments
aws rds create-db-
instance --region us-east-
1 --db-subnet-group
default --engine aurora-
postgresql --db-cluster-
identifier clustername-
restore --db-instance-iden-
tifier newinstance-nodeA
--db-instance-class
db.r4.large
Point in time
restore
RESTORE DATABASE |LOG
... WITH STOPAT...
Create new cluster from a
cluster snapshot by given
custom time to restore:
aws rds restore-db-
cluster-to-point-in-time --
db-cluster-identifier
clustername-restore --
source-db-cluster-iden-
tifier clustername --
restore-to-time 2017-09-
19T23:45:00.000Z
Add a new instance to the
new/restored cluster:
aws rds create-db-
instance --region us-east-
1 --db-subnet-group
default --engine aurora-
postgresql --db-cluster-
identifier clustername-
restore --db-instance-iden-
tifier newinstance-nodeA
--db-instance-class
db.r4.large
Partial Restore RESTORE DATABASE...
FILE= ... | FILEGROUP = ...
N/A The cluster can be restored
to a new cluster and the
needed data can be copied
to the primary cluster
- 287 -
Migrate from: SQL Server High Availability Essentials
Feature Com-
patibility
SCT Auto-
mation Level
SCT Action
Code Index
Key Differences
N/A N/A Multi replica, scale out solution using Amazon
Aurora clusters and Availability Zones
Overview
SQLServer provides several solutions to support high availability and disaster recovery requirements
including Always On Failover Cluster Instances (FCI), Always On Availability Groups, Database Mirroring,
and Log Shipping. The following sections describe each solution.
Note: This section does not cover backup and restore. See Backup and Restore.
Always On Failover Cluster Instances (FCI)
Always On Failover Cluster Instances use the Windows Server Failover Clustering (WSFC) operating sys-
tem framework to deliver redundancy at the server instance level.
An FCI is an instance of SQL Server installed across two or more WSFC nodes. For client applications,
the FCI is transparent and appears to be a normal instance of SQL Server running on a single server.
The FCI provides failover protection by moving the services from one WSFC node Windows server to
another WSFC node windows server in the event the current "active" node becomes unavailable or
degraded.
FCIs target scenarios where a server fails due to a hardware malfunction or a software hangup.
Without FCI, a significant hardware or software failure would render the service unavailable until the
malfunction is corrected. With FCI, another server can be configured as a "stand by" to replace the ori-
ginal serverif it stops servicing requests.
For each service or cluster resource, there is only one node that actively services client requests
(known as "owning a resource group"). A monitoring agent constantly monitors the resource owners
and can transfer ownership to another node in the event of a failure or planned maintenance such as
installing service packs or security patches. This process is completely transparent to the client applic-
ation, which may continue to submit requests as normal when the failover or ownership transfer pro-
cess completes.
FCI can significantly minimize downtime due to hardware or software general failures. The main bene-
fits of FCI are:
l Full instance level protection.
l Automatic failover of resources from one node to another.
l Supports a wide range of storage solutions. WSFC cluster disks can be iSCSI, Fiber Channel, SMB
file shares, and others.
- 289 -
l Supports multi-subnet.
l No need client application configuration after a failover.
l Configurable failover policies.
l Automatic health detection and monitoring.
For more information, see
https://docs.microsoft.com/en-us/sql/sql-server/failover-clusters/windows/always-on-failover-cluster-instances-sql-
server
Always On Availability Groups
Always On Availability Groups is the most recent high availability and disaster recovery solution for SQL
Server. It was introduced in SQL Server 2012 and supports high availability for one or more user data-
bases. Because it can be configured and managed at the database level rather than the entire server, it
provides much more control and functionality. As with FCI, Always On Availability Groups relies on the
framework services of WSFC nodes.
Always On Availability Groups utilize real-time log record delivery and apply mechanism to maintain
near real-time, readable copies of one or more databases.
These copies can also be used as redundant copies for resource usage distribution between servers (a
scale-out read solution).
The main characteristics of Always On Availability Groups are:
l Supports up to nine availability replicas: One primary replica and up to eight secondary readable
replicas.
l Supports both asynchronous-commit and synchronous-commit availability modes.
l Supports automatic failover, manual failover, and a forced failover. Only the latter can result in
data loss.
l Secondary replicas allow both read-only access and offloading of backups.
l Availability Group Listener may be configured for each availability group. It acts as a virtual
server address where applications can submit queries. The listener may route requests to a read-
only replica or to the primary replica for read-write operations. This configuration also facilitates
fast failover as client applications do not need to be reconfigured post failover.
l Flexible failover policies.
l The automatic page repair feature protects against page corruption.
l Log transport framework uses encrypted and compressed channels.
l Rich tooling and APIs including Transact-SQL DDL statements, management studio wizards,
Always On Dashboard Monitor, and Powershell scripting.
For more information, see
https://docs.microsoft.com/en-us/sql/database-engine/availability-groups/windows/always-on-availability-groups-sql-
server
- 290 -
Database Mirroring.
Note: Microsoft recommends avoiding Database Mirroring for new development. This feature
is deprecated and will be removed in a future release. It is recommended to use Always On
Availability Groups instead.
Database mirroring is a legacy solution to increase database availability by supporting near instant-
aneous failover. It is similar in concept to Always On Availability Groups, but can only be configured for
one database at a time and with only one "standby"replica.
For more information, see
https://docs.microsoft.com/en-us/sql/database-engine/database-mirroring/database-mirroring-sql-server
Log Shipping
Log shipping is one of the oldest and well tested high availability solutions. It is configured at the data-
base level similar to Always On Availability Groups and Database Mirroring. Log shipping can be used
to maintain one or more standby (secondary) databases for a single master (primary) database.
The Log shipping process involves three steps:
1. Backing up the transaction log of the primary database instance.
2. Copying the transaction log backup file to a secondary server.
3. Restoring the transaction log backup to apply changes to the secondary database.
Log shipping can be configured to create multiple secondary database replicas by repeating steps 2
and 3 above for each secondary server. Unlike FCI and Always On Availability Groups, log shipping solu-
tions do not provide automatic failover.
In the event the primary database becomes unavailable or unusable for any reason, an administrator
must configure the secondary database to serve as the primary and potentially reconfigure all client
applications to connect to the new database.
Note: Secondary databases can be used for read-only access, but require special handling.
For more information, see
https://docs.microsoft.com/en-us/sql/database-engine/log-shipping/configure-log-shipping-
sql-server
The main characteristics of Log Shipping solutions are:
l Provides sredundancy for a single primary database and one or more secondary databases. Log
Shipping is considered less of a high availability solution due to the lack of automatic failover.
l Supports limited read-only access to secondary databases.
l Administrators have control over the timing and delays of the primary server log backup and sec-
ondary server restoration.
l Longer delays can be useful if data is accidentally modified or deleted in the primary database.
- 291 -
For more information about log shipping, see
https://docs.microsoft.com/en-us/sql/database-engine/log-shipping/about-log-shipping-sql-server
Examples
Configure an Always On Availability Group.
CREATE DATABASE DB1;
ALTER DATABASE DB1 SET RECOVERY FULL;
BACKUP DATABASE DB1 TO DISK = N'\\MyBackupShare\DB1\DB1.bak' WITH FORMAT;
CREATE ENDPOINT DBHA STATE=STARTED
AS TCP (LISTENER_PORT=7022) FOR DATABASE_MIRRORING (ROLE=ALL);
CREATE AVAILABILITY GROUP AG_DB1
FOR
DATABASE DB1
REPLICA ON
'SecondarySQL' WITH
(
ENDPOINT_URL = 'TCP://SecondarySQL.MyDomain.com:7022',
AVAILABILITY_MODE = ASYNCHRONOUS_COMMIT,
FAILOVER_MODE = MANUAL
);
-- On SecondarySQL
ALTER AVAILABILITY GROUP AG_DB1 JOIN;
RESTORE DATABASE DB1 FROM DISK = N'\\MyBackupShare\DB1\DB1.bak'
WITH NORECOVERY;
-- On Primary
BACKUP LOG DB1
TO DISK = N'\\MyBackupShare\DB1\DB1_Tran.bak'
WITH NOFORMAT
-- On SecondarySQL
RESTORE LOG DB1
FROM DISK = N'\\MyBackupShare\DB1\DB1_Tran.bak'
WITH NORECOVERY
ALTER DATABASE MyDb1 SET HADR AVAILABILITY GROUP = MyAG;
For more information, see
https://docs.microsoft.com/en-us/sql/sql-server/failover-clusters/high-availability-solutions-sql-server
- 292 -
Migrate to: Aurora PostgreSQL High Availability
Essentials
Feature Com-
patibility
SCT Automation
Level
SCT Action
Code Index
Key Differences
N/A N/A Multi replica, scale out solution using Amazon
Aurora clusters and Availability Zones
Overview
Aurora PostgreSQL is a fully managed Platform as a Service (PaaS) providing high availability cap-
abilities. Amazon RDS provides database and instance administration functionality for provisioning,
patching, backup, recovery, failure detection, and repair.
New Aurora PostgreSQL database instances are always created as part of a cluster. If you don't specify
replicas at creation time, a single-node cluster is created. You can add database instances to clusters
later.
Regions and Availability Zones
Amazon RDS is hosted in multiple global locations. Each location is composed of Regions and Avail-
ability Zones. Each Region is a separate geographic area having multiple, isolated Availability Zones.
Amazon RDS supports placement of resources such as database instances and data storage in multiple
locations. By default, resources are not replicated across regions.
Each Region is completely independent and each Availability Zone is isolated from all others. However,
the main benefit of Availability Zones within a Region is that they are connected through low-latency,
high bandwidth local network links.
- 293 -
Resources may have different scopes. A resource may be global, associated with a specific region
(region level) , or associated with a specific Availability Zone within a region. For more information, see
https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/resources.html
When creating a database instance, you can specify an availability zone or use the default "No Prefer-
ence", in which case Amazon chooses the availability zone for you.
Aurora PostgreSQL instances can be distributed across multiple availability zones. Applications can be
designed to take advantage of failover such that in the event of an instance in one availability zone fail-
ing, another instance in different availability zone will take over and handle requests.
Elastic IP addresses can be used to abstract the failure of an instance by remapping the virtual IP
address to one of the available database instances in another Availability Zone. For more information,
see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/elastic-ip-addresses-eip.html
An Availability Zone is represented by a region code followed by a letter identifier. For example, us-
east-1a.
Note: To guarantee even resource distribution across Availability Zones for a region, Amazon
RDS independently maps Availability Zones to identifiers for each account. For example, the
Availability Zone us-east-1a for one account might not be in the same location as us-east-1a
for another account. Users cannot coordinate Availability Zones between accounts.
Aurora PostgreSQL DB Cluster
A DB cluster consists of one or more DB instances and a cluster volume that manages the data for
those instances. A cluster volume is a virtual database storage volume that may span multiple Avail-
ability Zones with each holding a copy of the database cluster data.
An Aurora database cluster is made up of one of more of the following types of instances:
l A Primary instance that supports both read and write workloads. This instance is used for all
DML transactions. Every Aurora DB cluster has one, and only, one primary instance.
l An Aurora Replica that supports read-only workloads. Every Aurora PostgreSQL database
cluster may contain from zero to 15 Aurora Replicas in addition to the primary instance for a
total maximum of 16 instances. Aurora Replicas enable scale-out of read operations by off-
loading reporting or other read-only processes to multiple replicas. Place aurora replicas in mul-
tiple availability Zones to increase availability of the databases.
- 294 -
Endpoints
Endpoints are used to connect to Aurora PostgreSQL databases. An endpoint is a Universal Resource
Locator (URL) comprised of a host address and port number.
l A Cluster Endpoint is an endpoint for an Aurora database cluster that connects to the current
primary instance for that database cluster regardless of the availability zone in which the primary
resides. Every Aurora PostgreSQL DB cluster has one cluster endpoint and one primary instance.
The cluster endpoint should be used for transparent failover for either read or write workloads.
Note: Use the cluster endpoint for all write operations including all DML and DDL state-
ments.
If the primary instance of a DB cluster fails for any reason, Aurora automatically fails over server
requests to a new primary instance. An example of a typical Aurora PostgreSQL DB Cluster end-
point is: mydbcluster.cluster-123456789012.us-east-1.rds.amazonaws.com:3306
l A Reader Endpoint is an endpoint that is used to connect to one of the Aurora read-only rep-
licas in the database cluster. Each Aurora PostgreSQL database cluster has one reader endpoint.
If there are more than one Aurora Replicas in the cluster, the reader endpoint redirects the con-
nection to one of the available replicas. Use the Reader Endpoint to support load balancing for
read-only connections. If the DB cluster contains no replicas, the reader endpoint redirects the
connection to the primary instance. If an Aurora Replica is created later, the Reader Endpoint
starts directing connections to the new Aurora Replica with minimal interruption in service. An
example of a typical Aurora PostgreSQL DB Reader Endpoint is: mydbcluster.cluster-ro-
123456789012.us-east-1.rds.amazonaws.com:3306
l An Instance Endpoint is a specific endpoint for every database instance in an Aurora DB cluster.
Every Aurora PostgreSQL DB instance regardless of its role has its own unique instance endpoint.
Use the Instance Endpoints only when the application handles failover and read workload scale-
out on its own. For example, you can have certain clients connect to one replica and others to
- 295 -
another. An example of a typical Aurora PostgreSQL DB Reader Endpoint is: pgs-
dbinstance.123456789012.us-east-1.rds.amazonaws.com:3306
Some general considerations for using endpoints:
l Consider using the cluster endpoint instead of individual instance endpoints because it supports
high-availability scenarios. In the event that the primary instance fails, Aurora PostgreSQL auto-
matically fails over to a new primary instance. This configuration can be accomplished by either
promoting an existing Aurora Replica to be the new primary or by creating a new primary
instance.
l If you use the cluster endpoint instead of the instance endpoint, the connection is automatically
redirected to the new primary.
l If you choose to use the instance endpoint, you must use the RDS cosole or the API to discover
which database instances in the database cluster are available and their current roles. Then, con-
nect using that instance endpoint.
l Be aware that the reader endpoint load balances connections to Aurora Replicas in an Aurora
database cluster, but it does not load balance specific queries or workloads. If your application
requires custom rules for distributing read workloads, use instance endpoints.
l The reader endpoint may redirect connection to a primary instance during the promotion of an
Aurora Replica to a new primary instance.
Amazon Aurora Storage
Aurora PostgreSQL data is stored in a cluster volume. The Cluster volume is a single, virtual volume
that uses fast solid state disk (SSD) drives.The cluster volume is comprised of multiple copies of the
data distributed between availability zones in a region. This configuration minimizes the chances of
data loss and allows for the failover scenarios mentioned above.
Aurora cluster volumes automatically grow to accommodate the growth in size of your databases. An
Aurora cluster volume has a maximum size of 64 tebibytes (TiB). Since table size is theoretically limited
to the size of the cluster volume, the maximum table size in an Aurora DB cluster is 64 TiB.
Storage Auto-Repair
The chance of data loss due to disk failure is greatly minimize due to the fact that Aurora PostgreSQL
maintains multiple copies of the data in three Availability Zones. Aurora PostgreSQL detects failures in
the disks that make up the cluster volume. If a disk segment fails, Aurora repairs the segment auto-
matically. Repairs to the disk segments are made using data from the other cluster volumes to ensure
correctness. This process allows Aurora to significantly minimize the potential for data loss and the
subsequent need to restore a database.
Survivable Cache Warming
When a database instance starts, Aurora PostgreSQL performs a "warming" process for the buffer
pool. Aurora PostgreSQL pre-loads the buffer pool with pages that have been frequently used in the
- 296 -
past. This approach improves performance and shortens the natural cache filling process for the initial
period when the database instance starts servicing requests. Aurora PostgreSQL maintains a separate
process to manage the cache, which can stay alive even when the database process restarts. The buffer
pool entries remain in memory regardless of the database restart providing the database instance with
a fully "warm" buffer pool.
Crash Recovery
Aurora PostgreSQL can instantaneously recover from a crash and continue to serve requests. Crash
recovery is performed asynchronously using parallel threads enabling the database to remain open
and available immediately after a crash.
For more information about crash recovery, see https://-
docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Aurora.Managing.html#Aurora.Managing.FaultTolerance.
Examples
The following walkthrough describes how to configure a read-only replica using the AWS RDS console:
Login to the RDS Console and click Clusters.
Click the cluster name.
Scroll down to the DB Cluster Members section and click the current primary instance (writer role).
- 297 -
Click Instance actions and select Create Aurora Replica.
On the Create Aurora Replica page, select the Availability zone, or leave it blank to let AWS to select
the availability zone. For Publicly accessible, select No if you don't want to assign a public IP address
to this instance. For Encryption, select Enable Encryption if you want the instance to be encrypted. For
Instance specifications, select the Instance size.
- 298 -
Scroll down to continue specifying the Aurora Replica settings. Leave the Aurora replica
sourcedefault setting. For Failover, select the failover priority setting for this instance (1 to 15). If the
primary instance fails and multiple Aurora Replicas are available, the next-in-line primary is the Aurora
Replicas that will have the highest priority. You can also select a specific parameter group for this rep-
lica.
Note: The Aurora Replica can be a different size than the Primary Instance and can use a dif-
ferent parameter group.
- 299 -
Scroll down and select values for Monitoring and Maintenance.
- 300 -
Click Create Aurora Replica and return to the dashboard to view the creation progress.
Summary
Feature SQL Server Aurora PostgreSQL Comments
Server level failure
protection
Failover Cluster
Instances
N/A Not applicable. Clustering is handled
by Aurora PostgreSQL.
Database level fail-
ure protection
Always On Avail-
ability Groups
Aurora Replicas
Log replication Log Shipping N/A Not applicable. Aurora PostgreSQL
handles data replication at the stor-
age level.
Disk error pro-
tection
RESTORE... PAGE= Automatically
Maximum Read
Onlyreplicas
8 + Primary 15 + Primary
- 301 -
Feature SQL Server Aurora PostgreSQL Comments
Failover address Availability Group
Listener
Cluster Endpoint
Read Only work-
loads
READ INTENT con-
nection
Read Endpoint
For more information, see:
l https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Aurora.Overview.html
l https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using-regions-availability-zones.html
- 302 -
Indexes
- 303 -
Migrate from: SQL Server Clustered and Non
Clustered Indexes
Feature Com-
patibility
SCT Automation
Level
SCT Action Code Index Key Differences
SCT Action Codes - Indexes CLUSTERED INDEX is not sup-
ported
Few missing options
Overview
Indexes are physical disk structures used to optimize data access. They are associated with tables or
materialized views and allow the query optimizer to access rows and individual column values without
scanning an entire table.
An index consists of index keys, which are columns from a table or view. They are sorted in ascending
or descending order providing quick access to individual values for queries that use equality or range
predicates. Database indexes are similar to book indexes that list page numbers for common terms.
Indexes created on multiple columns are called Composite Indexes.
SQL Server implements indexes using the Balanced Tree algorithm (B-tree).
Note: SQL Server supports additional index types such as hash indexes (for memory-optim-
ized tables), spatial indexes, full text indexes, and XML indexes.
Indexes are created automatically to support table primary keys and unique constraints. They are
required to efficiently enforce uniqueness. Up to 250 indexes can be created on a table to support com-
mon queries.
SQL Server provides two types of B-Tree indexes:Clustered Indexes and Non-Clustered Indexes.
Clustered Indexes
Clustered indexes include all the table's column data in their leaf level. The entire table data is sorted
and logically stored in order on disk. A Clustered Index is similar to a phone directory index where the
entire data is contained for every index entry. Clustered indexes are created by default for Primary Key
constraints. However, a primary key doesn't necessarily need to use a clustered index if it is explicitly
specified as non-clustered.
Clustered indexes are created using the CREATE CLUSTEREDINDEX statement. Only one clustered
index can be created for each table because the index itself is the table's data. A table having a
clustered index is called a "clustered table" (also known as an "index organized table" in other rela-
tional database management systems). A table with no clustered index is called a "heap".
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Examples
Create a Clustered Index as part of table definition.
CREATE TABLE MyTable
(
Col1 INT NOT NULL
PRIMARY KEY,
Col2 VARCHAR(20) NOT NULL
);
Create an explicit clustered index using CREATE INDEX.
CREATE TABLE MyTable
(
Col1 INT NOT NULL
PRIMARY KEY NONCLUSTERED,
Col2 VARCHAR(20) NOT NULL
);
CREATE CLUSTEREDINDEX IDX1
ON MyTable(Col2);
Non-Clustered Indexes
Non clustered indexes also use the B-Tree algorithm but consist of a data structure separate from the
table itself. They are also sorted by the index keys, but the leaf level of a non-clustered index contains
pointers to the table rows; not the entire row as with a clustered index.
Up to 999 non-clustered indexes can be created on a SQL Server table. The type of pointer used at the
lead level of a non-clustered index (also known as a row locator) depends on whether the table has a
clustered index (clustered table) or not (heap). For heaps, the row locators use a physical pointer (RID).
For clustered tables, row locators use the clustering key plus a potential uniquifier. This approach min-
imizes non-clustered index updates when rows move around, or the clustered index key value
changes.
Both clustered and non clustered indexes may be defined as UNIQUE using the CREATEUNIQUE INDEX
statement. SQL Server maintains indexes automatically for a table or view and updates the relevant
keys when table data is modified.
Examples
Create a unique non-clustered index as part of table definition.
CREATE TABLE MyTable
(
Col1 INT NOTNULL
PRIMARY KEY,
Col2 VARCHAR(20) NOT NULL
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UNIQUE
);
Create a unique non-clustered index using CREATE INDEX.
CREATE TABLE MyTable
(
Col1 INT NOT NULL
PRIMARY KEY CLUSTERED,
Col2 VARCHAR(20) NOT NULL
);
CREATE UNIQUE NONCLUSTEREDINDEX IDX1 ON MyTable(Col2);
Filtered Indexes and Covering Indexes
SQL Server also supports two special options for non clustered indexes. Filtered indexes can be cre-
ated to index only a subset of a table's data. They are useful when it is known that the application will
not need to search for specific values such as NULLs.
For queries that typically require searching on particular columns but also need additional column
data from the table, non-clustered indexes can be configured to include additional column data in the
index leaf level in addition to the row locator. This may prevent expensive lookup operations, which fol-
low the pointers to either the physical row location (in a heap) or traverse the clustered index key in
order to fetch the rest of the data not part of the index. If a query can get all the data it needs from the
non-clustered index leaf level, that index is considered a "covering" index.
Examples
Create a filtered index to exclude NULL values.
CREATENONCLUSTEREDINDEX IDX1
ON MyTable(Col2)
WHERE Col2 IS NOT NULL;
Create a covering index for queries that search on col2 but also need data from col3.
CREATE NONCLUSTEREDINDEXIDX1
ON MyTable (Col2)
INCLUDE (Col3);
Indexes On Computed Columns
SQL Server allows creating indexes on persisted computed columns. Computed columns are table or
view columns that derive their value from an expression based on other columns in the table. They are
not explicitly specified when data is inserted or updated. This feature is useful when a query’s filter pre-
dicates are not based on the column table data as-is, but on a function or expression.
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Examples
For example, consider the following table that stores phone numbers for customers, but the format is
not consistent for all rows; some include country code and some do not:
CREATE TABLE PhoneNumbers
(
PhoneNumber VARCHAR(15) NOT NULL
PRIMARY KEY,
Customer VARCHAR(20) NOT NULL
);
INSERT INTO PhoneNumbers
VALUES
('+1-510-444-3422','Dan'),
('644-2442-3119','John'),
('1-402-343-1991','Jane');
The following query to look up the owner of a specific phone number must scan the entire table
because the index cannot be used due to the preceding % wild card.
SELECT Customer
FROM PhoneNumbers
WHERE PhoneNumber LIKE '%510-444-3422';
A potential solution would be to add a computed column that holds the phone number in reverse
order.
ALTER TABLE PhoneNumbers
ADD ReversePhone AS REVERSE(PhoneNumber)
PERSISTED;
CREATE NONCLUSTERED INDEX IDX1
ON PhoneNumbers (ReversePhone)
INCLUDE (Customer);
Now, the following query can be used to search for the customer based on the reverse string, which
places the wild card at the end of the LIKE predicate. This approach provides an efficient index seek to
retrieve the customer based on the phone number value.
DECLARE @ReversePhone VARCHAR(15) = REVERSE('510-444-3422');
SELECT Customer
FROM PhoneNumbers
WHERE ReversePhone LIKE @ReversePhone + '%';
For more information, see:
l https://docs.microsoft.com/en-us/sql/relational-databases/indexes/clustered-and-nonclustered-indexes-
described
l https://docs.microsoft.com/en-us/sql/t-sql/statements/create-index-transact-sql
- 307 -
Migrate to: Aurora PostgreSQL Clustered and Non
Clustered Indexes
Feature Com-
patibility
SCT Automation
Level
SCT Action Code Index Key Differences
SCT Action Codes - Indexes CLUSTERED INDEX is not sup-
ported
Few missing options
Overview
Aurora PostgreSQL supports Balanced Tree (b-tree) indexes similar to SQL Server. However, the ter-
minology, use, and options for these indexes are different.
Aurora PostgreSQL is missing the CLUSTERED INDEX feature but has other options which SQL Server
doesn't have, Index Prefix and Blob indexing.
Cluster Table
PostgreSQL does not support cluster tables directly, but provides similar functionality using the
CLUSTER feature. The PostgreSQL CLUSTER statement specifies table sorting based on an index
already associated with the table. When using the PostgreSQL CLUSTER command, the data in the
table is physically sorted based on the index, possibly using a primary key column.
The CLUSTER statement can be used as needed to re-cluster the table.
Example
Create a new table with a simple primary key. Because the storage engine is InnoDB, the table is cre-
ated as a clustered table that sorts data based on the primary key itself.
CREATE TABLE SYSTEM_EVENTS (
EVENT_ID NUMERIC,
EVENT_CODE VARCHAR(10) NOT NULL,
EVENT_DESCIPTION VARCHAR(200),
EVENT_TIME DATE NOT NULL,
CONSTRAINT PK_EVENT_ID PRIMARY KEY(EVENT_ID));
INSERT INTO SYSTEM_EVENTS VALUES(9, 'EV-A1-10', 'Critical', '01-JAN-2017');
INSERT INTO SYSTEM_EVENTS VALUES(1, 'EV-C1-09', 'Warning', '01-JAN-2017');
INSERT INTO SYSTEM_EVENTS VALUES(7, 'EV-E1-14', 'Critical', '01-JAN-2017');
CLUSTER SYSTEM_EVENTS USING PK_EVENT_ID;
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SELECT * FROM SYSTEM_EVENTS;
event_id | event_code | event_desciption | event_time
----------+------------+------------------+------------
1 | EVNT-C1-09 | Warning | 2017-01-01
7 | EVNT-E1-14 | Critical | 2017-01-01
9 | EVNT-A1-10 | Critical | 2017-01-01
INSERT INTO SYSTEM_EVENTS VALUES(2, 'EV-E2-02', 'Warning', '01-JAN-2017');
SELECT * FROM SYSTEM_EVENTS;
event_id | event_code | event_desciption | event_time
----------+------------+------------------+------------
1 | EVNT-C1-09 | Warning | 2017-01-01
7 | EVNT-E1-14 | Critical | 2017-01-01
9 | EVNT-A1-10 | Critical | 2017-01-01
2 | EVNT-E2-02 | Warning | 2017-01-01
CLUSTER SYSTEM_EVENTS USING PK_EVENT_ID; -- Run CLUSTER again to re-cluster
SELECT * FROM SYSTEM_EVENTS;
event_id | event_code | event_desciption | event_time
----------+------------+------------------+------------
1 | EVNT-C1-09 | Warning | 2017-01-01
2 | EVNT-E2-02 | Warning | 2017-01-01
7 | EVNT-E1-14 | Critical | 2017-01-01
9 | EVNT-A1-10 | Critical | 2017-01-01
Btree Indexes
When creating an Index in PostgreSQL, a B-Tree Index is created by default, similar to the behavior in
SQLServer. PostgreSQL B-Tree indexes have the same characteristics as SQLServer and can handle
equality and range queries on data. The PostgreSQL optimizer considers using B-Tree indexes espe-
cially for one or more of the following operators in queries: >, >=, <, <=, =
In addition, performance improvements can be achieved when using IN, BETWEEN, IS NULL, or IS NOT
NULL.
Example
Create a PostgreSQL B-Tree Index.
CREATE INDEX IDX_EVENT_ID ON SYSTEM_LOG(EVENT_ID);
OR
CREATE INDEX IDX_EVENT_ID1 ON SYSTEM_LOG USING BTREE (EVENT_ID);
For more details, see https://www.postgresql.org/docs/9.6/static/sql-createindex.html
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Column and Multiple Column Secondary Indexes
Currently, only B-tree, GiST, GIN, and BRIN support Multi-Column Indexes. 32 columns can be spe-
cified when creating a Multi-Column Index.
PostgreSQL uses the exact same syntax as SQLServer to create Multi-Column Indexes.
Example
Create a multi-column index on the EMPLOYEES table.
CREATE INDEX IDX_EMP_COMPI
ON EMPLOYEES (FIRST_NAME, EMAIL, PHONE_NUMBER);
Drop a multiple-column Index.
DROP INDEX IDX_EMP_COMPI;
For additional details: https://www.postgresql.org/docs/9.6/static/indexes-multicolumn.html
Expression Indexes and Partial Indexes
Example
Create an Expression Index in PostgreSQL.
CREATE TABLE SYSTEM_EVENTS(
EVENT_ID NUMERIC PRIMARY KEY,
EVENT_CODE VARCHAR(10) NOT NULL,
EVENT_DESCIPTION VARCHAR(200),
EVENT_TIME TIMESTAMP NOT NULL);
CREATE INDEX EVNT_BY_DAY ON SYSTEM_EVENTS(EXTRACT(DAY FROM EVENT_TIME));
Insert records into the SYSTEM_EVENTS table, gathering table statistics using the ANALYZE statement
and verifying that the Expression Index (“EVNT_BY_DAY”) is being used for data access.
INSERT INTO SYSTEM_EVENTS
SELECT ID AS event_id,
'EVNT-A'||ID+9||'-'||ID AS event_code,
CASE WHEN mod(ID,2) = 0 THEN 'Warning' ELSE 'Critical' END AS event_desc,
now() + INTERVAL '1 minute' * ID AS event_time
FROM
(SELECT generate_series(1,1000000) AS ID) A;
INSERT 0 1000000
ANALYZE SYSTEM_EVENTS;
ANALYZE
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EXPLAIN
SELECT * FROM SYSTEM_EVENTS
WHERE EXTRACT(DAY FROM EVENT_TIME) = '22';
QUERY PLAN
-----------------------------------------------------------------------------------
Bitmap Heap Scan on system_events (cost=729.08..10569.58 rows=33633 width=41)
Recheck Cond: (date_part('day'::text, event_time) = '22'::double precision)
-> Bitmap Index Scan on evnt_by_day (cost=0.00..720.67 rows=33633 width=0)
Index Cond: (date_part('day'::text, event_time) = '22'::double precision)
Partial Indexes
PostgreSQL also provides Partial Indexes, which are indexes that use a WHERE clause when created.
The most significant benefit of using partial indexes is a reduction of the overall subset of indexed
data, allowing users to index relevant table data only. Partial indexes can be used to increase efficiency
and reduce the size of the index.
Example
Create a PostgreSQL partial Index.
CREATE TABLE SYSTEM_EVENTS(
EVENT_ID NUMERIC PRIMARY KEY,
EVENT_CODE VARCHAR(10) NOT NULL,
EVENT_DESCIPTION VARCHAR(200),
EVENT_TIME DATE NOT NULL);
CREATE INDEX IDX_TIME_CODE ON SYSTEM_EVENTS(EVENT_TIME)
WHERE EVENT_CODE like '01-A%';
For additional details, see
https://www.postgresql.org/docs/9.6/static/sql-createindex.html#SQL-CREATEINDEX-CONCURRENTLY
BRIN Indexes
PostgreSQL does not provide native support for BITMAP indexes. However, a BRIN index, which splits
table records into block ranges with MIN/MAX summaries, can be used as a partial alternative for cer-
tain analytic workloads. For example, BRIN indexes are suited for queries that rely heavily on aggreg-
ations to analyze large numbers of records.
Example
Create a PostgreSQL BRIN Index.
CREATE INDEX IDX_BRIN_EMP ON EMPLOYEES USING BRIN(salary);
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Summary
The following table summarizes the key differences to consider when migrating b-tree indexes from
SQL Server to Aurora PostgreSQL
Index Feature SQL Server Aurora PostgreSQL
Clustered indexes
supported for
Table keys, composite or single
column, unique and non-unique, null
or not null
On indexes
Non clustered index
supported for
Table keys, composite or single
column, unique and non unique, null
or not null
Table keys, composite or single
column, unique and non unique, null
or not null
Max number of non
clustered indexes
999 N/A
Max total index key
size
900 bytes N/A
Max columns per
index
32 32
Index Prefix N/A Supported
Filtered Indexes Supported Supported (Partial Indexes)
Indexes on BLOBS N/A Supported
For additional details, see:
l https://www.postgresql.org/docs/9.6/static/indexes-types.html
l https://www.postgresql.org/docs/9.6/static/sql-createindex.html#SQL-CREATEI
l https://www.postgresql.org/docs/current/static/sql-cluster.html
l https://www.postgresql.org/docs/9.6/static/sql-createindex.html#SQL-CREATEINDEX-CONCURRENTLY
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Management
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Migrate from: SQL Server Agent
Overview
SQL Server Agent provides two main functions: Scheduling automated maintenance jobs, and alerting.
Note: Other SQL built-in frameworks such as replication, also use SQL Agent jobs.
See Maintenance Plans and Alerting.
For more information about SQL Server Agent, see https://docs.microsoft.com/en-us/sql/ssms/agent/sql-server-agent
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Migrate to: Aurora PostgreSQL Scheduled Lambda
SQL Server Agent provides two main functions: Scheduling automated maintenance jobs and alerting.
Note: Other SQL built-in frameworks such as replication also use SQL Agent jobs.
Maintenance Plans and Alerting are covered in separate sections:
l Maintenance Plans
l Alerting
Currently, there is no equivalent in Aurora PostgreSQL for scheduling tasks but you can create sched-
uled AWS Lambda that will execute a stored procedure, find an example in DB Mail
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Migrate from: SQL Server Alerting
Feature Com-
patibility
SCT Auto-
mation Level
SCT Action
Code Index
Key Differences
N/A N/A Use Event Notifications Subscription with Amazon
Simple Notification Service (SNS)
Overview
SQLServer provides SQLServer Agent to generate alerts. When running, SQLServer Agent constantly
monitors SQL Server windows application log messages, performance counters, and Windows Man-
agement Instrumentation (WMI) objects. When a new error event is detected, the agent checks the
MSDB database for configured alerts and executes the specified action.
You can define SQL Server Agent alerts for the following categories:
l SQL Server events
l SQL Server performance conditions
l WMI events
For SQL Server events, the alert options include the following settings:
l Error Number:Alert when a specific error is logged.
l Severity Level:Alert when any error in the specified severity level is logged.
l Database:Filter the database list for which the event will generate an alert.
l Event Text:Filter specific text in the event message.
Note: SQL Server agent is pre-configured with several high severity alerts. It is highly recom-
mended to enable these alerts.
To generate an alert in response to a specific performance condition, specify the performance counter
to be monitored, the threshold values for the alert, and the predicate for the alert to occur. The fol-
lowing list identifies the performance alert settings:
l Object: The Performance counter category or the monitoring area of performance.
l Counter:A counter is a specific attribute value of the object.
l Instance: Filter by SQL Server instance (multiple instances can share logs).
l Alert if counter and Value: The threshold for the alert and the predicate. The threshold is a
number. Predicates are Falls below, becomes equal to, or rises above the threshold.
WMI events require the WMI namespace and the WMIQuery Language (WQL) query for specific events.
Alerts can be assigned to specific operators with schedule limitations and multiple response types
including:
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l Execute an SQL Server Agent Job.
l Send Email, Net Send command, or a pager notification.
You can configure Alerts and responses with SQL Server Management Studio or system stored pro-
cedures.
Examples
Configure an alert for all errors with severity 20.
EXEC msdb.dbo.sp_add_alert
@name = N'Severity 20 Error Alert',
@severity = 20,
@notification_message = N'A severity 20 Error has occurred. Initiating emergency pro-
cedure',
@job_name = N'Error 20 emergency response';
For more information, see https://docs.microsoft.com/en-us/sql/ssms/agent/alerts
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Migrate to: Aurora PostgreSQL Alerting
Feature Com-
patibility
SCT Auto-
mation Level
SCT Action
Code Index
Key Differences
N/A N/A Use Event Notifications Subscription with
Amazon Simple Notification Service (SNS)
Overview
Aurora PostgreSQL does not support direct configuration of engine alerts. Use the Event Notifications
Infrastructure to collect history logs or receive event notifications in near real-time.
Amazon Relational Database Service (RDS) uses Amazon Simple Notification Service (SNS) to provide
notifications for events. SNS can send notifications in any form supported by the region including
email, text messages, or calls to HTTP endpoints for response automation.
Events are grouped into categories. You can only subscribe to event categories, not individual events.
SNS sends notifications when any event in a category occurs.
You can subscribe to alerts for database instances, database clusters, database snapshots, database
cluster snapshots, database security groups, and database parameter groups. For example, a sub-
scription to the Backup category for a specific database instance sends notifications when backup
related events occur on that instance. A subscription to a Configuration Change category for a database
security group sends notifications when the security group changes.
Note: For Amazon Aurora, some events occur at the cluster rather than instance level. You
will not receive those events if you subscribe to an Aurora DB instance.
SNSsends event notifications to the address specified when the subscription was created. Typically,
administrators create several subscriptions. For example, one subscription to receive logging events
and another to receive only critical events for a production environment requiring immediate
responses.
You can disable notifications without deleting a subscription by setting the Enabled radio button to No
in the Amazon RDS console. Alternatively, use the Command Line Interface (CLI) or RDS API to change
the Enabled setting.
Subscriptions are identified by the Amazon Resource Name (ARN) of an Amazon SNS topic. The
Amazon RDS console creates ARNs when subscriptions are created. When using the CLI or API, you
must create the ARN using the Amazon SNS console or the Amazon SNS API.
Examples
The following walkthrough demonstrates how to create an Event Notification Subscription:
Sign into an Amazon AWS account, open the AWS Console, and navigate to the Amazon RDS page.
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Click Events on the left navigation pane.
If you have not previously subscribed to events, the screen displays zero events.
Click Event Subscriptions and then click CREATE EVENT SUBSCRIPTION on the top right side.
Enter the Name of the subscription and select a Target of ARN or Email. For email subscriptions,
enter values for Topic name and With these recipients.
- 319 -
Select the event source and choose specific event categories. Click the drop-down menu to view the list
of available categories.
Choose the event categories to be monitored and click Create.
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Raising Errors from Within the Database
Error Log Types
PostgreSQLsupports the following log severity levels:
Log Type Information Written to Log
DEBUG1…DEBUG5 Provides successively-more-detailed information for use by developers.
INFO Provides information implicitly requested by the user.
NOTICE Provides information that might be helpful to users.
WARNING Provides warnings of likely problems.
ERROR Reports the error that caused the current command to abort.
LOG Reports information of interest to administrators.
FATAL Reports the error that caused the current session to abort.
PANIC Reports the error that caused all database sessions to abort.
Several parameters control how and where PostgreSQL log and errors files are placed:
Parameter Description
log_filename Sets the file name pattern for log files.
Modifiable via an Aurora Database Parameter Group.
log_rotation_age (min) Automatic log file rotation will occur after N minutes.
Modifiable via an Aurora Database Parameter Group.
log_rotation_size (kB) Automatic log file rotation will occur after N kilobytes.
Modifiable via an Aurora Database Parameter Group.
log_min_messages Sets the message levels that are logged (DEBUG, ERROR, INFO, etc.…).
Modifiable via an Aurora Database Parameter Group.
log_min_error_state-
ment
Causes all statements generating errors at or above this level to be logged
(DEBUG, ERROR, INFO, etc.…).
Modifiable via an Aurora Database Parameter Group.
log_min_duration_
statement
Sets the minimum execution time above which statements will be logged (ms).
Modifiable via an Aurora Database Parameter Group.
Note: Modifications to certain parameters such as log_directory (which sets the destination
directory for log files) or logging_collector (which starts a subprocess to capture stderr output
and/or csvlogs into log files) are disabled for an Aurora PostgreSQL instance.
For more information, see https://www.postgresql.org/docs/9.6/static/runtime-config-logging.html
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Migrate from: SQL Server Database Mail
Feature Compatibility SCT Automation Level SCT Action Code Index Key Differences
N/A SCT Action Codes - Mail Use Lambda Integration
Overview
The Database Mail framework is an email client solution for sending messages directly from SQL
Server. Email capabilities and APIs within the database server provide easy management of the fol-
lowing messages:
l Server administration messages such as alerts, logs, status reports, and process confirmations.
l Application messages such as user registration confirmation and action verifications.
Note: Database Mail is turned off by default.
The main features of the Database Mail framework are:
l Database Mail sends messages using the standard and secure Simple Mail Transfer Protocol
(SMTP) .
l The email client engine runs asynchronously and sends messages in a separate process to min-
imize dependencies.
l Database Mail supports multiple SMTP Servers for redundancy.
l Full support and awareness of Windows Server Failover Cluster for high availability envir-
onments.
l Multi-profile support with multiple failover accounts in each profile.
l Enhanced security management with separate roles in MSDB.
l Security is enforced for mail profiles.
l Attachment sizes are monitored and can be capped by the administrator.
l Attachment file types can be blacklisted.
l Email activity can be logged to SQL Server, the Windows application event log, and a set of sys-
tem tables in MSDB.
l Supports full auditing capabilities with configurable retention policies.
l Supports both plain text and HTML messages.
Architecture
Database Mail is built on top of the Microsoft SQL Server Service Broker queue management frame-
work.
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The system stored procedure sp_send_dbmail sends email messages. When this stored procedure is
executed, it inserts an row to the mail queue and records the Email message.
The queue insert operation triggers execution of the Database Mail process (DatabaseMail.exe). The
Database Mail process then reads the Email information and sends the message to the SMTP servers.
When the SMTP servers acknowledge or reject the message, the Database Mail process inserts a status
row into the status queue, including the result of the send attempt. This insert operation triggers the
execution of a system stored procedure that updates the status of the Email message send attempt.
Database Mail records all Email attachments in the system tables. SQL Server provides a set of system
views and stored procedures for troubleshooting and administration of the Database Mail queue.
Deprecated SQL Mail framework
The previous SQL Mail framework using xp_sendmail has been deprecated as of SQL Server 2008R2 in
accordance with https://docs.microsoft.com/en-us/previous-versions/sql/sql-server-2008-r2/ms143729
(v=sql.105).
The legacy mail system has been completely replaced by the greatly enhanced DB mail framework
described here. The previous system has been out of use for many years because it was prone to syn-
chronous execution issues and windows mail profile quirks.
Syntax
EXECUTEsp_send_dbmail
[[,@profile_name =] '<Profile Name>']
[,[,@recipients =] '<Recipients>']
[,[,@copy_recipients =] '<CC Recipients>']
[,[,@blind_copy_recipients =] '<BCC Recipients>']
[,[,@from_address =] '<From Address>']
[,[,@reply_to =] '<Reply-to Address>']
[,[,@subject =] '<Subject>']
[,[,@body =] '<Message Body>']
[,[,@body_format =] '<Message Body Format>']
[,[,@importance =] '<Importance>']
[,[,@sensitivity =] '<Sensitivity>']
[,[,@file_attachments =] '<Attachments>']
[,[,@query =] '<SQL Query>']
[,[,@execute_query_database =] '<Execute Query Database>']
[,[,@attach_query_result_as_file =] <Attach Query Result as File>]
[,[,@query_attachment_filename =] <Query Attachment Filename>]
[,[,@query_result_header =] <Query Result Header>]
[,[,@query_result_width =] <Query Result Width>]
[,[,@query_result_separator =] '<Query Result Separator>']
[,[,@exclude_query_output =] <Exclude Query Output>]
[,[,@append_query_error =] <Append Query Error>]
[,[,@query_no_truncate =] <Query No Truncate>]
[,[,@query_result_no_padding =] @<Parameter for Query Result No Padding>]
[,[,@mailitem_id =] <Mail item id>] [,OUTPUT]
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Examples
Create a Database Mail account.
EXECUTE msdb.dbo.sysmail_add_account_sp
@account_name = 'MailAccount1',
@description = 'Mail account for testing DB Mail',
@email_address = '[email protected]',
@replyto_address = '[email protected]',
@display_name = 'Mailer for registration messages',
@mailserver_name = 'smtp.MyDomain.com' ;
Create a Database Mail profile.
EXECUTE msdb.dbo.sysmail_add_profile_sp
@profile_name = 'MailAccount1 Profile',
@description = 'Mail Profile for testing DB Mail' ;
Associate the account with the profile.
EXECUTE msdb.dbo.sysmail_add_profileaccount_sp
@profile_name = 'MailAccount1 Profile',
@account_name = 'MailAccount1',
@sequence_number =1 ;
Grant the profile access to DBMailUsers role.
EXECUTE msdb.dbo.sysmail_add_principalprofile_sp
@profile_name = 'MailAccount1 Profile',
@principal_name = 'ApplicationUser',
@is_default = 1 ;
Send a message with sp_db_sendmail.
EXEC msdb.dbo.sp_send_dbmail
@profile_name = 'MailAccount1 Profile',
@recipients = '[email protected]',
@query = 'SELECT * FROM fn_WeeklySalesReport(GETDATE())',
@subject = 'Weekly Sales Report',
@attach_query_result_as_file = 1 ;
For more information, see https://docs.microsoft.com/en-us/sql/relational-databases/database-mail/database-mail
- 325 -
Migrate to: Aurora PostgreSQL Database Mail
Feature Compatibility SCT Automation Level SCT Action Code Index Key Differences
N/A SCT Action Codes - Mail Use Lambda Integration
Overview
Aurora PostgreSQL does not provide native support for sending email message from the database. For
alerting purposes, use the Event Notification Subscription feature to send email notifications to oper-
ators. For more information, see Alerting.
The only way to sent Email from the database is to use the LAMBDA integration. For more information
about Lambda, see https://aws.amazon.com/lambda.
Examples
Sending an Email from Aurora PostgreSQL via LambdaIntegration
First, configure AWS SES.
In the AWS console, navigate to SES > SMTP Settings and click Create My SMtp Credentials. Note the
SMTP server name; you will use it in the Lambda function.
Enter a name for IAMUser Name (SMTP user) and click Create.
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Note the credentials; you will use them to authenticate with the SMTP server.
Note: After leaving this page, the credentials cannot be retrieved.
Navigate back to the SES page, click Email Addresses on the left, and click Verify a New Email
Address. Before sending email, they must be verified.
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The next page indicates that the email is pending verification.
After the email is verified, create a table to store messages to be sent by the Lambda fuction.
CREATE TABLE emails (title varchar(600), body varchar(600), recipients varchar(600));
To create the Lambda function, navigate to the Lambda page and click Create function.
Select Author from Scratch, enter a name for your project, and select Python 2.7 as the runtime. Be
sure to use a role with the correct permissions. Click Create function.
Download this Github project.
In your local environment, create two files: main.py and db_util.py. Cut and paste the content below
into main.py and db_util.py respectively. Be sure to replace the code highlighted in red with values for
your environment.
main.py:
#!/usr/bin/python
import sys
import logging
import psycopg2
from db_util import make_conn, fetch_data
def lambda_handler(event, context):
query_cmd = "select * from mails"
print query_cmd
# get a connection, if a connect cannot be made an exception will be raised here
conn = make_conn()
result = fetch_data(conn, query_cmd)
conn.close()
return result
db_util.py:
#!/usr/bin/python
import psycopg2
import smtplib
import email.utils
- 328 -
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
db_host = 'YOUR_RDS_HOST'
db_port = 'YOUR_RDS_PORT'
db_name = 'YOUR_RDS_DBNAME'
db_user = 'YOUR_RDS_USER'
db_pass = 'YOUR_RDS_PASSWORD'
def sendEmail(recp, sub, message):
# Replace [email protected] with your "From" address.
# This address must be verified.
SENDER = 'PUT HERE THE VERIFIED EMAIL'
SENDERNAME = 'AWS Lambda'
# Replace [email protected] with a "To" address. If your account
# is still in the sandbox, this address must be verified.
RECIPIENT = recp
# Replace smtp_username with your Amazon SES SMTP user name.
USERNAME_SMTP = "YOUR_SMTP_USERNAME"
# Replace smtp_password with your Amazon SES SMTP password.
PASSWORD_SMTP = "YOUR_SMTP PASSWORD"
# (Optional) the name of a configuration set to use for this message.
# If you comment out this line, you also need to remove or comment out
# the "X-SES-CONFIGURATION-SET:" header below.
CONFIGURATION_SET = "ConfigSet"
# If you're using Amazon SES in an AWS Region other than US West (Oregon),
# replace email-smtp.us-west-2.amazonaws.com with the Amazon SES SMTP
# endpoint in the appropriate region.
HOST = "YOUR_SMTP_SERVERNAME"
PORT = 587
# The subject line of the email.
SUBJECT = sub
# The email body for recipients with non-HTML email clients.
BODY_TEXT = ("Amazon SES Test\r\n"
"This email was sent through the Amazon SES SMTP "
"Interface using the Python smtplib package."
)
# The HTML body of the email.
BODY_HTML = """<html>
<head></head>
<body>
<h1>Amazon SES SMTP Email Test</h1>""" + message + """</body>
</html>
"""
# Create message container - the correct MIME type is multipart/alternative.
msg = MIMEMultipart('alternative')
- 329 -
msg['Subject'] = SUBJECT
msg['From'] = email.utils.formataddr((SENDERNAME, SENDER))
msg['To'] = RECIPIENT
# Comment or delete the next line if you are not using a configuration set
#msg.add_header('X-SES-CONFIGURATION-SET',CONFIGURATION_SET)
# Record the MIME types of both parts - text/plain and text/html.
part1 = MIMEText(BODY_TEXT, 'plain')
part2 = MIMEText(BODY_HTML, 'html')
# Attach parts into message container.
# According to RFC 2046, the last part of a multipart message, in this case
# the HTML message, is best and preferred.
msg.attach(part1)
msg.attach(part2)
# Try to send the message.
try:
server = smtplib.SMTP(HOST, PORT)
server.ehlo()
server.starttls()
#stmplib docs recommend calling ehlo() before & after starttls()
server.ehlo()
server.login(USERNAME_SMTP, PASSWORD_SMTP)
server.sendmail(SENDER, RECIPIENT, msg.as_string())
server.close()
# Display an error message if something goes wrong.
except Exception as e:
print ("Error: ", e)
else:
print ("Email sent!")
def make_conn():
conn = None
try:
conn = psycopg2.connect("dbname='%s' user='%s' host='%s' password='%s'" % (db_
name, db_user, db_host, db_pass))
except:
print "I am unable to connect to the database"
return conn
def fetch_data(conn, query):
result = []
print "Now executing: %s" % (query)
cursor = conn.cursor()
cursor.execute(query)
print("Number of new mails to be sent: ", cursor.rowcount)
raw = cursor.fetchall()
for line in raw:
print(line[0])
sendEmail(line[2],line[0],line[1])
- 330 -
result.append(line)
cursor.execute('delete from mails')
cursor.execute('commit')
return result
Note: In the body of db_util.py, Lambda deletes the content of the mails table.
Place the main.py and db_util.py files inside the Github extracted folder and create a new zipfile that
includes your two new files.
Return to your Lambda project and change the Code entry type to Upload a .ZIP file, change the
Handler to mail.lambda_handler, and upload the file. Click Save.
To test the lambda function, click Test and enter the Event name.
- 331 -
Note: The Lambda function can be triggered by multiple options. This walkthrough demon-
strates how to schedule it to run every minute. Remember, you are paying for each Lambda
execution.
To create a scheduled trigger, use CloudWatch, enter all details, and click Add.
- 332 -
Migrate from: SQL Server ETL
Feature Compatibility SCT Automation Level SCT Action Code Index Key Differences
N/A N/A Use Amazon Glue for ETL
Overview
SQL Server offers a native Extract, Transform, and Load (ETL) framework of tools and services to sup-
port enterprise ETL requirements. The legacy Data Transformation Services (DTS) has been deprecated
as of SQL Server 2008 (see https://docs.microsoft.com/en-us/previous-versions/sql/sql-server-2008-
r2/cc707786(v=sql.105)) and replaced with SQL Server Integration Services (SSIS), which was introduced
with SQLServer 2005.
DTS
DTS was introduced in SQLServer version 7 in 1998. It was significantly expanded in SQL Server 2000
with features such as FTP, database level operations, and Microsoft Message Queuing (MSMQ) integ-
ration. It included a set of objects, utilities, and services that enabled easy, visual construction of com-
plex ETL operations across heterogeneous data sources and targets.
DTS supported OLE DB, ODBC, and text file drivers. It allowed transformations to be scheduled using
SQL Server Agent. DTS also provided version control and backup capabilities with version control sys-
tems such as Microsoft Visual SourceSafe.
The fundamental entity in DTS was the DTS Package. Packages were the logical containers for DTS
objects such as connections, data transfers, transformations, and notifications. The DTS framework
also included the following tools:
l DTS Wizards
l DTS Package Designers
l DTS Query Designer
l DTS Run Utility
SSIS
The SSIS framework was introduced in SQL Server 2005, but was limited to the top-tier editions only,
unlike DTS which was available with all editions.
SSIS has evolved over DTS to offer a true modern, enterprise class, heterogeneous platform for a
broad range of data migration and processing tasks. It provides a rich workflow oriented design with
features for all types of enterprise data warehousing. It also supports scheduling capabilities for multi-
dimensional cubes management.
SSIS Provides the following tools:
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l SSIS Import/Export Wizard is an SQL Server Management Studio extension that enables quick
creation of packages for moving data between a wide array of sources and destinations.
However, it has limited transformation capabilities.
l SQL Server Business Intelligence Development Studio (BIDS) is a developer tool for creating
complex packages and transformations. It provides the ability to integrate procedural code into
package transformations and provides a scripting environment. Recently, BIDS has been
replaced by SQL Server Data Tools - Business intelligence (SSDT-BI).
SSIS objects include:
l Connections
l Event handlers
l Workflows
l Error handlers
l Parameters (Beginning with SQL Server 2012)
l Precedence constraints
l Tasks
l Variables
SSIS packages are constructed as XML documents and can be saved to the file system or stored within
a SQL Server instance using a hierarchical name space.
For more information, see
l https://docs.microsoft.com/en-us/sql/integration-services/sql-server-integration-services
l https://en.wikipedia.org/wiki/Data_Transformation_Services
- 335 -
Migrate to: Aurora PostgreSQL ETL
Feature Compatibility SCT Automation Level SCT Action Code Index Key Differences
N/A N/A Use Amazon Glue for ETL
Overview
Aurora PostgreSQL provides Amazon Glue for enterprise class Extract, Transform, and Load(ETL). It is
a fully managed service that performs data cataloging, cleansing, enriching, and movement between
heterogeneous data sources and destinations. Being a fully managed service, the user does not need
to be concerned with infrastructure management.
Amazon Glue Key Features
Integrated Data Catalog
The Amazon Glue Data Catalog is a persistent metadata store, that can be used to store all data assets,
whether in the cloud or on-premises. It stores table schemas, job steps, and additional meta data
information for managing these processes. Amazon Glue can automatically calculate statistics and
register partitions in order to make queries more efficient. It maintains a comprehensive schema ver-
sion history for tracking changes over time.
Automatic Schema Dfiscovery
Amazon Glue provides automatic crawlers that can connect to source or target data providers. The
crawler uses a prioritized list of classifiers to determine the schema for your data and then generates
and stores the metadata in the Amazon Glue Data Catalog. Crawlers can be scheduled or executed on-
demand. You can also trigger a crawler when an event occurs to keep metadata current.
Code Generation
Amazon Glue automatically generates the code to extract, transform, and load data. All you need to do
is point Glue to your data source and target. The ETL scripts to transform, flatten, and enrich data are
created automatically. Amazon Glue scripts can be generated in Scala or Python and are written for
Apache Spark.
Developer Endpoints
When interactively developing Glue ETL code, Amazon Glue provides development endpoints for edit-
ing, debugging, and testing. You can use any IDE or text editor for ETL development. Custom readers,
writers, and transformations can be imported into Glue ETL jobs as libraries. You can also use and
- 336 -
share code with other developers in the Amazon Glue GitHub repository (see
https://github.com/awslabs/aws-glue-libs).
Flexible Job Scheduler
Amazon Glue jobs can be triggered for execution either on a pre-defined schedule, on-demand, or as a
response to an event.
Multiple jobs can be started in parallel and dependencies can be explicitly defined across jobs to build
complex ETL pipelines. Glue handles all inter-job dependencies, filters bad data, and retries failed
jobs. All logs and notifications are pushed to Amazon CloudWatch; you can monitor and get alerts
from a central service.
Migration Considerations
Currently, there are no automatic tools for migrating ETL packages from DTS or SSIS into Amazon
Glue. Migration from SQL Server to Aurora PostgreSQL requires rewriting ETL processes to use
Amazon Glue.
Alternatively, consider using an EC2 SQL Server instance to run the SSIS service as an interim solution.
The connectors and tasks must be revised to support Aurora PostgreSQL instead of SQL Server, but
this approach allows gradual migration to Amazon Glue.
Examples
The following walkthrough describes how to create an Amazon Glue job to upload a CSV file from S3 to
Aurora PostgreSQL.
The source file for this walkthrough is a simple Visits table in CSV format. The objective is to upload
this file to an S3 bucket and create a Glue job to discover and copy it into an Aurora PostgreSQL data-
base.
- 337 -
Step 1 - Create a Bucket in Amazon S3 and Upload the CSV File
Navigate to the S3 management console page https://s3.console.aws.amazon.com/s3/home and click
Create Bucket.
Note: This walkthrough demonstrates how to create the buckets and upload the files manu-
ally, which is automated using the S3 API for production ETLs. Using the console to manually
execute all the settings will help you get familiar with the terminology, concepts, and work
flow.
- 338 -
In the create bucket wizard, enter a unique name for the bucket, select a region and click Next.
Configure the options. For this walkthrough, skip this phase and click Next.
- 339 -
On the Set Permissions page, configure access permissions to allow public access to the visits file and
click Next.
- 340 -
Review your settings and click Create Bucket.
- 341 -
On the S3 Management Console, click the newly created bucket.
On the bucket page, click Upload.
- 342 -
On the upload page, either "drag and drop" or use the Add Files button to select the upload files. Click
Next.
Set the required permissions and click Next. For this walk-through, allow public access to the visits
file. Click Next.
- 343 -
On the Set Properties page, select the required options. For this walk-through, skip this step. Click
Next.
- 344 -
Review the settings and click Upload.
- 345 -
Click Add tables using a crawler. Alternatively, click the Crawlers navigation link on the left and then
click Add Crawler.
Provide a descriptive name for the crawler and click Next.
Leave the default S3 data store and choose whether the file is in a path in your account or another
account. For this example, the path is in my account and specified in the Include path text box. Click
Next.
Note: Click the small folder icon to the right of the Include path text box to open a visual
folder hierarchy navigation window.
- 347 -
Select whether the crawler accesses another data store or not. For this example, only uses the visits
file. Click Next.
The IAM role window allows selection of the security context the crawler uses to execute. You can
choose an existing role, update an existing policy, or create a new role. For this example, create a new
role. Click Next.
- 348 -
Choose the crawler schedule and frequency. For this example, use Run on demand. Click Next.
Click Add database and provide a name for the new catalog database. Enter an optional table prefix
for easy reference. Click Next.
- 349 -
Review your entries and click Finish to create the crawler.
- 350 -
Step 3 - Run the Crawler
Navigate to the Crawlers page on the glue management console
https://console.aws.amazon.com/glue/home?catalog:tab=crawlers.
Since you just created a new crawler, a message box asks if you want to run it now. You can click the
link or check the check-box near the crawler's name and click the Run crawler button.
- 351 -
After the crawler completes, the Visits table should be discovered and recorded in the catalog in the
table specified.
The following message box appears on the page:
Click the link to get to the table that was just discovered and then click the table name.
Verify the crawler identified the table's properties and schema correctly.
Note: You can manually adjust the properties and schema JSON files using the buttons on the
top right.
- 352 -
The process is similar the one used for the crawler.
Step 4 - Create an ETL Job to Copy the Visits Table to an Aurora Post-
greSQL Database.
Navigate to the Amazon Glue ETL Jobs page at https://-
console.aws.amazon.com/glue/home?etl:tab=jobs. Since this is the first job, the list is empty. Click Add
Job.
Enter a name for the ETL job and pick a role for the security context. For this example, use the same
role created for the crawler. The job may consist of a pre-existing ETL script, a manually-authored
script, or an automatic script generated by Amazon Glue. For this example, use Amazon Glue. Enter a
name for the script file or accept the default, which is also the job's name. Configure advanced prop-
erties and parameters if needed and click Next.
- 354 -
Select the data source for the job (in this example, there is only one). Click Next.
On the Data Target page, select Create tables in your data target, use the JDBC Data store, and the
gluerds connection type. Click Add Connection.
- 355 -
On the Add connection page, enter the access details for the Aurora Instance and lick Add.
Click Next to display the column mapping between the source and target. For this example, leave the
default mapping and data types. Click Next.
- 356 -
Review the job properties and click Save job and edit script.
- 357 -
Review the generated script and make manual changes as needed. You can use the built-in templates
for source, target, target location, transform, and spigot using the buttons at the top right section of
the screen.
For this example, run the script as-is. Click Run Job.
The optional parameters window displays. Click Run Job.
Navigate back to the glue management console jobs page at
https://console.aws.amazon.com/glue/home?etl:tab=jobs.
- 358 -
On the history tab, verify the job status as Succeeded and view the logs if needed.
Now open your query IDE, connect to the Aurora PostgreSQL cluster, and query the visits database to
make sure the data has been transferred successfully.
For more information, see
- 359 -
Migrate from: SQL Server Export and Import with
Text files
Feature Compatibility SCT Automation Level SCT Action Code Index Key Differences
N/A N/A Non-compatible tool
Overview
SQLServer provides many options for exporting and importing text files. These operations are com-
monly used for data migration, scripting, and backup.
l Save results to a file in SQL Server Management Studio (SSMS): https://sup-
port.microsoft.com/en-us/help/860545/how-to-create-csv-or-rpt-files-from-an-sql-statement-in-
microsoft-sql
l SQLCMD: https://docs.microsoft.com/en-us/sql/relational-databases/scripting/sqlcmd-run-trans-
act-sql-script-files?view=sql-server-2017#save-the-output-to-a-text-file
l PowerShell wrapper for SQLCMD
l SSMSImport/Export Wizard: https://docs.microsoft.com/en-us/sql/integration-services/import-
export-data/start-the-sql-server-import-and-export-wizard?view=sql-server-2017
l SQLServer Reporting Services (SSRS)
l Bulk Copy Program (BCP): https://docs.microsoft.com/en-us/sql/relational-databases/import-
export/import-and-export-bulk-data-by-using-the-bcp-utility-sql-server?view=sql-server-2017
All of the options described above required additional tools to export data. Most of the tools are open
source and provide support for a variety of databases.
SQLCMD is a command line utility for executing T-SQL statements, system procedures, and script files.
It uses ODBC to execute T-SQL batches. For example:
SQLCMD -i C:\sql\myquery.sql -o C:\sql\output.txt
SQLCMD utility syntax:
sqlcmd
-a packet_size
-A (dedicated administrator connection)
-b (terminate batch job if there is an error)
-c batch_terminator
-C (trust the server certificate)
-d db_name
-e (echo input)
-E (use trusted connection)
-f codepage | i:codepage[,o:codepage] | o:codepage[,i:codepage]
-g (enable column encryption)
- 361 -
-G (use Azure Active Directory for authentication)
-h rows_per_header
-H workstation_name
-i input_file
-I (enable quoted identifiers)
-j (Print raw error messages)
-k[1 | 2] (remove or replace control characters)
-K application_intent
-l login_timeout
-L[c] (list servers, optional clean output)
-m error_level
-M multisubnet_failover
-N (encrypt connection)
-o output_file
-p[1] (print statistics, optional colon format)
-P password
-q "cmdline query"
-Q "cmdline query" (and exit)
-r[0 | 1] (msgs to stderr)
-R (use client regional settings)
-s col_separator
-S [protocol:]server[instance_name][,port]
-t query_timeout
-u (unicode output file)
-U login_id
-v var = "value"
-V error_severity_level
-w column_width
-W (remove trailing spaces)
-x (disable variable substitution)
-X[1] (disable commands, startup script, environment variables, optional exit)
-y variable_length_type_display_width
-Y fixed_length_type_display_width
-z new_password
-Z new_password (and exit)
-? (usage)
Examples
Connect to a named instance using Windows Authentication and specify input and output files.
sqlcmd -S MyMSSQLServer\MyMSSQLInstance -i query.sql -o outputfile.txt
If the file is needed for import to another database, query the data as INSERT commands and CREATE
for the object.
You can export data with SQLCMD and import with Export/Import wizard.
For more information, see: https://docs.microsoft.com/en-us/sql/tools/sqlcmd-utility?view=sql-server-2017
- 362 -
Migrate to: Aurora PostgreSQL pg_dump and pg_
restore
Feature Compatibility SCT Automation Level SCT Action Code Index Key Differences
N/A N/A Non-compatible tool
Overview
PostgreSQL provides the native utilities pg_dump and pg_restore to perform logical database exports
and imports with comparable functionality to the SQlServer SQLCMD utility. For example, moving data
between two databases and creating logical database backups.
l pg_dump:Export data
l pg_restore: Import data
The binaries for both utilities must be installed on your local workstation or on an Amazon EC2 server
as part of the PostgreSQL client binaries.
PostgreSQL dump files created using pg_dump can be copied, after export, to an Amazon S3 bucket as
cloud backup storage or for maintaining the desired backup retention policy. Later, when dump files
are needed for database restore, the dump files can be copied back to a desktop or server that has a
PostgreSQL client (such as your workstation or an Amazon EC2 server) to issue the pg_restore com-
mand.
Notes:
l pg_dump creates consistent backups even if the database is being used concurrently.
l pg_dump does not block other users accessing the database (readers or writers).
l pg_dump only exports a single database. To backup global objects common to all databases in a
cluster (such as roles and tablespaces), use pg_dumpall.
l Unlike Data Pump, PostgreSQL dump files are plain-text files.
Examples
Export data using pg_dump. Use a workstation or server with the PostgreSQL client installed to con-
nect to the Aurora PostgreSQL instance in AWS. Issue the pg_dump command providing the hostname
(-h), database user name (-U), and database name (-d).
$pg_dump -h hostname.rds.amazonaws.com -U username -d db_name
-f dump_file_name.sql
- 363 -
Note: The output file, dump_file_name.sql, is stored on the server where the pg_dump com-
mand executes. You can later copy the outfile to an S3 Bucket if needed.
Run pg_dump and copy the backup file to an Amazon S3 bucket using a pipe and the AWS CLI.
$pg_dump -h hostname.rds.amazonaws.com -U username -d db_name -f dump_file_name.sql |
aws s3 cp - s3://pg-backup/pg_bck-$(date"+%Y-%m-%d-%H-%M-%S")
Restore data using pg_restore. Use a workstation or server with the PostgreSQL client installed to con-
nect to the Aurora PostgreSQL instance. Issue the pg_restore command providing the hostname (-h),
database user name (-U), database name (-d), and the dump file.
$pg_restore -h hostname.rds.amazonaws.com -U username -d dbname_restore dump_file_
name.sql
Copy the output file from the local server to an Amazon S3 Bucket using the AWS CLI. Upload the
dump file to an S3 bucket.
$aws s3 cp /usr/Exports/hr.dmp s3://my-bucket/backup-$(date "+%Y-
%m-%d-%H-%M-%S")
Note: The {-$(date "+%Y-%m-%d-%H-%M-%S")} format is valid on Linux servers only.
Download the output file from the S3 bucket.
$aws s3 cp s3://my-bucket/backup-2017-09-10-01-10-10 /usr/Exports/hr.dmp
Note: You can create a copy of an existing database without having to use pg_dump or pg_
restore. Instead, use the template keyword to specify the source database.
psql> CREATE DATABASE mydb_copy TEPLATE mydb;
Summary
Description SQL Server export / import PostgreSQL Dump
Export data to
a file
using SQLCMD or Export/Import Wiz-
ard
SQLCMD -i C:\sql\myquery.sql -o
C:\sql\output.txt
pgdump -F c -h host-
name.rds.amazonaws.com -U username -d
hr -p 5432 > c:\Export\hr.dmp
Import data to
a new database
with a new
name
Run SQLCMD with objects and data cre-
ation script
SQLCMD -i C:\sql\myquery.sql
pg_restore -h host-
name.rds.amazonaws.com -U hr -d hr_
restore -p 5432 c:\Expor\hr.dmp
- 364 -
Migrate from: SQL Server Viewing Server Logs
Feature Com-
patibility
SCT Auto-
mation Level
SCT Action
Code Index
Key Differences
N/A N/A View logs from the Amazon RDS console, the
Amazon RDS API, the AWS CLI, or the AWS SDKs
Overview
SQL Server logs system and user generated events to the SQL Server Error Log and to the Windows Applic-
ation Log. It logs recovery messages, kernel messages, security events, maintenance events, and other
general server level error and informational messages. The Windows Application Log contains events
from all windows applications including SQL Server and SQL Server agent.
SQL Server Management Studio Log Viewer unifies all logs into a single consolidated view. You can also
view the logs with any text editor.
Administrators typically use the SQL Server Error Log to confirm successful completion of processes,
such as backup or batches, and to investigate the cause of run time errors. These logs can help detect
current risks or potential future problem areas.
To view the log for SQL Server, SQL Server Agent, Database Mail, and Windows applications, open the
SQL Server Management Studio Object Explorer pane, navigate to Management > SQL Server Logs
,and double-click the current log.
The following table identifies some common error codes database administrators typically look for in
the error logs:
Error Code Error Message
1105 Could not allocate space
3041 Backup Failed
9002 Transaction Log Full
14151 Replication agent failed
17053 Operating System Error
18452 Login Failed
9003 Possible database corruption
Examples
The following screenshot shows typical Log File Viewer content:
- 366 -
Migrate to: Aurora PostgreSQL Viewing Server Logs
Feature Com-
patibility
SCT Auto-
mation Level
SCT Action
Code Index
Key Differences
N/A N/A View logs from the Amazon RDS console, the
Amazon RDS API, the AWS CLI, or the AWS SDKs
Overview
Aurora PostgreSQL provides administrators with access to the PostgreSQL error log.
The PostgreSQL Error Log is generated by default. To generate the slow query and general logs, set the
corresponding parameters in the database parameter group. For more details about parameter
groups, see Server Options.
You can view Aurora PostgreSQL logs directly from the Amazon RDS console, the Amazon RDS API, the
AWS CLI, or the AWS SDKs. You can also direct the logs to a database table in the main database and
use SQLqueries to view the data. To download a binary log, use the AWS Console.
Several parameters control how and where PostgreSQL log and errors files are placed:
Parameter Description
log_filename Sets the file name pattern for log files.
Modifiable via an Aurora Database Parameter Group.
log_rotation_age (min) Automatic log file rotation will occur after N minutes.
Modifiable via an Aurora Database Parameter Group.
log_rotation_size (kB) Automatic log file rotation will occur after N kilobytes.
Modifiable via an Aurora Database Parameter Group.
log_min_messages Sets the message levels that are logged (DEBUG, ERROR, INFO, etc.…).
Modifiable via an Aurora Database Parameter Group.
log_min_error_state-
ment
Causes all statements generating errors at or above this level to be logged
(DEBUG, ERROR, INFO, etc.…).
Modifiable via an Aurora Database Parameter Group.
log_min_duration_
statement
Sets the minimum execution time above which statements will be logged (ms).
Modifiable via an Aurora Database Parameter Group.
Note: Modifications to certain parameters, such as log_directory (which sets the destination
directory for log files) or logging_collector (which starts a sub-process to capture stderr output
and/or csvlogs into log files) are disabled for Aurora PostgreSQL instances.
For more information, see https://www.postgresql.org/docs/9.6/static/runtime-config-logging.html
- 368 -
Examples
The following walkthrough demonstrates how to view the Aurora PostgreSQL error logs in the RDS con-
sole.
Using a web browser, navigate to https://console.aws.amazon.com/rds/home and click Instances.
Click the instance for which you want to view the error log.
Scroll down to the logs section and click the log name.
- 369 -
Migrate from: SQL Server Maintenance Plans
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A N/A Backups via the RDS services Table main-
tenance via SQL
Overview
A Maintenance plan is a set of automated tasks used to optimize a database, performs regular
backups, and ensure it is free of inconsistencies. Maintenance plans are implemented as SQLServer
Integration Services (SSIS) packages and are executed by SQL Server Agent jobs. They can be run manu-
ally or automatically at scheduled time intervals.
SQLServer provides a variety of pre-configured maintenance tasks. You can create custom tasks using
T-SQLscripts or operating system batch files.
Maintenance plans are typically used for the following tasks:
l Backing up database and transaction log files.
l Performing cleanup of database backup files in accordance with retention policies.
l Performing database consistency checks.
l Rebuilding or reorganizing indexes.
l Decreasing data file size by removing empty pages (shrink a database).
l Updating statistics to help the query optimizer obtain updated data distributions.
l Running SQL Server Agent jobs for custom actions.
l Executing a T-SQL task.
Maintenance plans can include tasks for operator notifications and history/maintenance cleanup. They
can also generate reports and output the contents to a text file or the maintenance plan tables in
msdb.
Maintenance plans can be created and managed using the maintenance plan wizard in SQL Server Man-
agement Studio, Maintenance Plan Design Surface (provides enhanced functionality over the wizard),
Management Studio Object Explorer, and T-SQL system stored procedures.
For more information about SQL Server Agent migration, see SQL Server Agent.
Deprecated DBCC Index and Table Maintenance Commands
The DBCC DBREINDEX, INDEXDEFRAG, and SHOWCONTIG commands have been deprecated as of SQL
Server 2008R2 in accordance with
https://docs.microsoft.com/en-us/previous-versions/sql/sql-server-2008-r2/ms143729(v=sql.105).
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In place of the deprecated DBCC, SQL Server provides newer syntax alternatives as detailed in the fol-
lowing table.
Deprecated DBCC Command Use Instead
DBCC DBREINDEX ALTER INDEX ... REBUILD
DBCC INDEXDEFRAG ALTER INDEX ... REORGANIZE
DBCC SHOWCONTIG sys.dm_db_index_physical_stats
For the Aurora PostgreSQL alternatives to these maintenance commands, see Aurora PostgreSQL Main-
tenance Plans.
Examples
Enable Agent XPs, which are disabled by default.
EXEC [sys].[sp_configure] @configname = 'show advanced options', @configvalue = 1
RECONFIGURE ;
EXEC [sys].[sp_configure] @configname = 'agent xps', @configvalue = 1
RECONFIGURE;
Create a T-SQL maintenance plan for a single index rebuild.
USE msdb;
Add the Index Maintenance IDX1 job to SQL Server Agent.
EXEC dbo.sp_add_job @job_name = N'Index Maintenance IDX1', @enabled = 1, @description
= N'Optimize IDX1 for INSERT' ;
Add the T-SQL job step "Rebuild IDX1 to 50 percent fill".
EXEC dbo.sp_add_jobstep @job_name = N'Index Maintenance IDX1', @step_name = N'Rebuild
IDX1 to 50 percent fill', @subsystem = N'TSQL',
@command = N'Use MyDatabase; ALTER INDEX IDX1 ON Shcema.Table REBUILD WITH (FILL_
FACTOR = 50), @retry_attempts = 5, @retry_interval = 5;
Add a schedule to run every day at 01:00 AM.
EXEC dbo.sp_add_schedule @schedule_name = N'Daily0100', @freq_type = 4, @freq_interval
= 1, @active_start_time = 010000;
Associate the schedule Daily0100 with the job Index Maintenance IDX1.
EXEC sp_attach_schedule @job_name = N'Index Maintenance IDX1' @schedule_name =
N'Daily0100' ;
For more information, see
https://docs.microsoft.com/en-us/sql/relational-databases/maintenance-plans/maintenance-plans
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Migrate to: Aurora PostgreSQL Maintenance Plans
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A N/A Backups via the RDS services Table main-
tenance via SQL
Overview
Amazon RDS performs automated database backups by creating storage volume snapshots that back
up entire instances, not individual databases.
RDS creates snapshots during the backup window for individual database instances and retains snap-
shots in accordance with the backup retention period. You can use the snapshots to restore a database
to any point in time within the backup retention period.
Note: The state of a database instance must be ACTIVE for automated backups to occur.
You can backup database instances manually by creating an explicit database snapshot. Use the AWS
console, the AWS CLI, or the AWS API to take manual snapshots.
Examples
Create a Manual Database Snapshot Using the RDS Console
Login to the RDS console and click DB Instances.
Click the Instance.
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Click Instance Actions and select Take Snapshot.
Enter a Snapshot name and click the Take Snapshot button.
Viewing and Restoring Snapshots on the RDS Console
Login to the RDS console and click Snapshots.
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Click the snapshot to restore.
Click Actions and then click Restore Snapshot. A snapshot restore operation does not overwrite the
database instance; it creates a new snapshot.
Enter the Instance specifications and click the Restore DB instance button at the bottom of the
page.
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You can also restore a database instance to a point-in-time. For more details see Backup and Restore.
For all other tasks, use a third-party or a custom application scheduler.
Rebuild and Reorganize a table
Aurora PostgreSQL supports the VACUUM, ANALYZE and REINDEX commands, which are similar to the
REORGANIZE option of SQL Server indexes.
VACUUM MyTable;
ANALYZE MyTable;
REINDEX TABLE MyTable;
l VACUUM: Reclaims storage
l ANALYZE: Collect statistics
l REINDEX: Recreate all indexes
For more information see
l https://www.postgresql.org/docs/9.6/static/sql-analyze.html
l https://www.postgresql.org/docs/9.6/static/sql-vacuum.html
l https://www.postgresql.org/docs/9.6/static/sql-reindex.html
- 376 -
Converting Deprecated DBCC Index and Table Maintenance Com-
mands
Deprecated DBCC Command Aurora PostgreSQL Equivalent
DBCC DBREINDEX REINDEX INDEX or REINDEX TABLE
DBCC INDEXDEFRAG VACUUM table_name or VACUUM table_name column_name
Update Statistics to Help the Query Optimizer Get Updated Data Dis-
tribution
For more information, see Managing Statistics.
Summary
The following table summarizes the key tasks that use SQL Server maintenance Plans and a com-
parable Aurora PostgreSQL solutions.
Task SQL Server Aurora PostgreSQL
Rebuild or reorganize
indexes
ALTERINDEX / ALTER TABLE REINDEX INDEX / REINDEX TABLE
Decrease data file size by
removing empty pages
DBCC SHRINKDATABASE / DBCC
SHRINKFILE
VACUUM
Update statistics to help
the query optimizer get
updated data distribution
UPDATE STATISTICS / sp_updatest-
ats
ANALYZE
Perform database con-
sistency checks
DBCC CHECKDB / DBCC
CHECKTABLE
N/A
Back up the database and
transaction log files
BACKUP DATABASE / BACKUP LOG Automatically (can be use with CLI)
Run SQL Server Agent jobs
for custom actions
sp_start_job, scheduled N/A
For more information, see
https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_WorkingWithAutomatedBackups.html
- 377 -
Migrate from: SQL Server Monitoring
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A N/A Use Amazon Cloud Watch ser-
vice
Overview
Monitoring server performance and behavior is a critical aspect of maintaining service quality and
includes ad hoc data collection, ongoing data collection, root cause analysis, preventative actions, and
reactive actions. SQL Server provides an array of interfaces to monitor and collect server data.
Windows Operating System Level Tools
The Windows Scheduler can be used to trigger execution of script files to collect, store, and process
performance data.
System Monitor is a graphical tool for measuring and recording performance of SQL Server and other
windows related metrics using the Windows Management Interface (WMI) performance objects.
Note: Performance objects can also be accessed directly from T-SQL using the system func-
tion sys.dm_os_performance_counters.
Performance counters exist for both real time measurements such as CPU Utilization and for aggreg-
ated history such as average active transactions.
For a full list of the object hierarchy, see:
https://docs.microsoft.com/en-us/sql/relational-databases/performance-monitor/use-sql-server-
objects
SQL Server Extended Events
SQL Server's latest tracing framework provides very lightweight and robust event collection and stor-
age. SQL Server Management Studio features the New Session Wizard and New Session graphic user
interfaces for managing and analyzing captured data. SQL Server Extended Events consists of the fol-
lowing items:
l SQL Server Extended Events Package is a logical container for Extended Events objects.
l SQL Server Extended Events Targets are consumers of events. Targets include Event File, which
writes data to the file Ring Buffer for retention in memory, or for processing aggregates such as
Event Counters and Histograms.
l SQL Server Extended Events Engine is a collection of services and tools that comprise the
framework.
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l SQL Server Extended Events Sessions are logical containers mapped many-to-many with pack-
ages, events, and filters.
The following example creates a session that logs lock escalations and lock timeouts to a file.
CREATE EVENT SESSION Locking_Demo
ON SERVER
ADD EVENT sqlserver.lock_escalation,
ADD EVENT sqlserver.lock_timeout,
ADD TARGET package0.etw_classic_sync_target
(SET default_etw_session_logfile_path = N'C:\ExtendedEvents\Locking\Demo_
20180502.etl')
WITH (MAX_MEMORY=8MB, MAX_EVENT_SIZE=2MB);
GO
SQL Server Tracing Framework and the SQL Server Profiler Tool
The SQL Server trace framework is the predecessor to the Extended Events framework and remains
popular among database administrators. The lighter and more flexible Extended Events Framework is
recommended for development of new monitoring functionality.
SQL Server Management Studio
SQL Server management studio provides several monitoring extensions:
l SQL Server Activity Monitor is an in-process, real-time, basic high-level information graphical
tool.
l Query Graphical Show Plan provides easy exploration of estimated and actual query execution
plans.
l Query Live Statistics displays query execution progress in real time.
l Replication Monitor and Log Shipping Monitor
l Standard Performance Reports
T-SQL
From the T-SQL interface, SQL Server provides many system stored procedures, system views, and
functions for monitoring data.
System stored procedures such as sp_who and sp_lock provide real-time information. sp_monitor
provides aggregated data.
Built in functions such as @@CONNECTIONS, @@IO_BUSY, @@TOTAL_ERRORS, and others provide
high level server information.
A rich set of System Dynamic Management functions and views are provided for monitoring almost
every aspect of the server. These functions reside in the sys schema and are prefixed with dm_string.
- 379 -
For more information about Dynamic Management Views, see https://docs.microsoft.com/en-us/sql/re-
lational-databases/system-dynamic-management-views/system-dynamic-management-views
Trace Flags
Trace flags can be set to log events. For example, set trace flag 1204 to log deadlock information.
SQL Server Query Store
Query Store is a database level framework supporting automatic collection of queries, execution plans,
and run time statistics. This data is stored in system tables and can be used to diagnose performance
issues, understand patterns, and understand trends. It can also be set to automatically revert plans
when a performance regression is detected.
In addition, it is common for SQL Server administrators to use third-party tools—most of which build
on the existing native monitoring framework—and add historical, analytical, exploratory features, and
automatic advisers.
For more information, see
https://docs.microsoft.com/en-us/sql/relational-databases/performance/monitor-and-tune-for-performance
- 380 -
Migrate to: Aurora PostgreSQL Monitoring
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A N/A Use Amazon Cloud Watch ser-
vice
Overview
Amazon RDS provide a rich monitoring infrastructure for Aurora PostgreSQL clusters and instances
with the native Cloud Watch service. See the following up-to-date articles that include examples and
walkthroughs for monitoring Aurora PostgreSQLclusters and instances:
l https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/CHAP_Monitoring.html
l https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_Monitoring.OS.html
You can also use the Insight AWS tool to monitor PostgeSQL.
Example
The following walkthrough demonstrates how to access the Amazon Aurora Performance Insights Con-
sole:
Navigate to the RDS section of the AWS Console.
Select Performance Insights.
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The web page displays a dashboard containing current and past database performance metrics. You
can choose the period of the displayed performance data (5m, 1h, 6h or 24h) as well as different cri-
teria to filter and slice the information (waits, SQL, Hosts or Users, etc.).
Enabling Performance Insights
Performance Insights is enabled by default for Amazon Aurora clusters. If you have more than one
database in your Aurora cluster, performance data for all databases is aggregated. Database per-
formance data is retained for 24 hours.
For additional details, see http://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_PerfInsights.html
- 382 -
Migrate from: SQL Server Resource Governor
Feature Compatibility SCT Automation Level SCT Action Code Index Key Differences
N/A N/A Divid between instances
Overview
SQLServer Resource Governor provides the capability to control and manage resource consumption.
Administrators can specify and enforce workload limits on CPU, physical I/O, and Memory. Resource
configurations are dynamic and can be changed in real time.
Use Cases
The following list identifies typical Resource Governor use cases:
l Minimize performance bottlenecks and inconsistencies to better support Service Level Agree-
ments (SLA) for multiple workloads and users.
l Protect against runaway queries that consume a large amount of resources or explicitly
throttle I/O intensive operations. For example, consistency checks with DBCC that may bot-
tleneck the I/O subsystem and negatively impact concurrent workloads.
l Allow tracking and control for resource-based pricing scenarios to improve predictability of
user charges.
Concepts
The three basic concepts in Resource Governor are Resource Pools, Workload Groups, and Classification.
l Resource Pools represent physical resources. Two built-in resource pools, internal and default,
are created when SQL Server is installed. You can create custom user-defined resource pools for
specific workload types.
l Workload Groups are logical containers for session requests with similar characteristics. Work-
load Groups allow aggregate resource monitoring of multiple sessions. Resource limit policies
are defined for a Workload Group. Each Workload Group belongs to a Resource Pool.
l Classification is a process that inspects incoming connections and assigns them to a specific
Workload Group based on the common attributes. User-defined functions are used to imple-
ment Classification. For more information, see User Defined Functions.
Examples
Enable the Resource Governor.
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ALTER RESOURCE GOVERNOR RECONFIGURE;
Create a Resource Pool.
CREATE RESOURCE POOL ReportingWorkloadPool
WITH (MAX_CPU_PERCENT = 20);
ALTER RESOURCE GOVERNOR RECONFIGURE;
Create a Workload Group.
CREATE WORKLOAD GROUP ReportingWorkloadGroup USING poolAdhoc;
ALTER RESOURCE GOVERNOR RECONFIGURE;
Create a classifier function.
CREATE FUNCTION dbo.WorkloadClassifier()
RETURNS sysname WITH SCHEMABINDING
AS
BEGIN
RETURN (CASE
WHEN HOST_NAME()= 'ReportServer'
THEN 'ReportingWorkloadGroup'
ELSE 'Default'
END)
END;
Register the classifier function.
ALTER RESOURCE GOVERNOR with (CLASSIFIER_FUNCTION = dbo.WorkloadClassifier);
ALTER RESOURCE GOVERNOR RECONFIGURE;
For more information, see
https://docs.microsoft.com/en-us/sql/relational-databases/resource-governor/resource-governor
- 384 -
Migrate to: Aurora PostgreSQL Dedicated Amazon
Aurora Clusters
Feature Compatibility SCT Automation Level SCT Action Code Index Key Differences
N/A N/A Divid between instances
Overview
PostgreSQL does not have built-in resource management capabilities equivalent to the functionality
provided by SQL Server's Resource Governor. However, due to the elasticity and flexibility provided by
“cloud economics”, workarounds could be applicable and such capabilities might not be as of similar
importance to monolithic on-premises databases.
The SQL Server's Resource Governor primarily exists because traditionally, SQL Server instances were
installed on very powerful monolithic servers that powered multiple applications simultaneously. The
monolithic model made the most sense in an environment where the licensing for the SQL Server data-
base was per-CPU and where SQL Server instances were deployed on physical hardware. In these scen-
arios, it made sense to consolidate as many workloads as possible into fewer servers. With cloud
databases, the strict requirement to maximize the usage of each individual “server” is often not as
important and a different approach can be employed:
Individual Amazon Aurora clusters can be deployed, with varying sizes, each dedicated to a specific
application or workload. Additional read-only Aurora Replica servers can be used to offload any report-
ing workloads from the master instance.
With Amazon Aurora, separate and dedicated database clusters can be deployed, each dedicated to a
specific application/workload creating isolation between multiple connected sessions and applications.
Each Amazon Aurora instance (Primary/Replica) can scale independently in terms of CPU and memory
resources using different instance types. Because multiple Amazon Aurora Instances can be instantly
deployed and much less overhead is associated with the deployment and management of Aurora
instances when compared to physical servers, separating different workloads to different instance
classes could be a suitable solution for controlling resource management.
For more information about instance types and resources,see
https://aws.amazon.com/ec2/instance-types/
In addition, each Amazon Aurora primary/replica instance can also be directly accessed from your
applications using its own endpoint. This capability is especially useful if you have multiple Aurora
- 385 -
read-replicas for a given cluster and you want to use different Aurora replicas to segment your work-
load.
Examples
Suppose that you were using a single SQL Server instance for multiple separate applications and used
SQL Server's Resource Governor to enforce a workload separation, allocating a specific amount of
server resources for each application. With Amazon Aurora, you might want to create multiple sep-
arate databases for each individual application.
Follow these steps to add additional replica instances to an existing Amazon Aurora cluster:
Navigate to the instances section under RDS.
Select the Amazon Aurora cluster that you want to scale-out by adding an additional read Replica, click
the Instance Actions button, and click Create Aurora Replica.
Select the instance class depending on the amount of compute resources your application requires.
Click Create Aurora Replica.
Dedicated Aurora PostgreSQL Instances
Feature Amazon Aurora Instances
Set the maximum CPU
usage for a resource group
Create a dedicated Aurora Instance for a specific application.
Limit the degree of par-
allelism for specific queries
SET max_parallel_workers_per_gather TO x;
Setting the PostgreSQL
max_parallel_workers_per_gather parameter should be done as part of
your application database connection.
Limit parallel execution SET max_parallel_workers_per_gather TO 0;
Limit the number of active
sessions
Manually detect the number of connections that are open from a spe-
cific application and restrict connectivity either via database procedures
or within the application DAL itself.
select pid from pg_stat_activity where usename in( select usename from
pg_stat_activity where state = 'active' group by usename having count(*)
> 10) and state = 'active' order by query_Start;
Restrict maximum runtime
of queries
Manually terminate sessions that exceed the required threshold. You
can detect the length of running queries using SQL commands and
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Feature Amazon Aurora Instances
restrict max execution duration using either database procedures or
within the application DAL itself.
SELECT pg_terminate_backend(pid) FROM pg_stat_activity WHERE now()-
pg_stat_activity.query_start > interval '5 minutes';
Limit the maximum idle
time for sessions
Manually terminate sessions that exceed the required threshold. You
can detect the length of your idle sessions using SQL queries and
restrict maximum execution using either database procedures or within
the application DAL itself.
SELECT pg_terminate_backend(pid) FROM pg_stat_activity WHERE dat-
name = 'regress' AND pid <> pg_backend_pid() AND state = 'idle' AND
state_change < current_timestamp - INTERVAL '5' MINUTE;
Limit the time that an idle
session holding open locks
can block other sessions
Manually terminate sessions that exceed the required threshold. You
can detect the length of blocking idle sessions using SQL queries and
restrict max execution duration using either database procedures or
within the application DAL itself.
SELECT pg_terminate_backend(blocking_locks.pid)
FROM pg_catalog.pg_locks AS blocked_locks
JOIN pg_catalog.pg_stat_activity AS blocked_activity ON blocked_activ-
ity.pid = blocked_locks.pid
JOIN pg_catalog.pg_locks AS blocking_locks ON blocking_locks.locktype
= blocked_locks.locktype AND blocking_locks.DATABASE IS NOT
DISTINCT FROM blocked_locks.DATABASE AND blocking_locks.relation
IS NOT DISTINCT FROM blocked_locks.relation AND blocking_locks.page
IS NOT DISTINCT FROM blocked_locks.page AND blocking_locks.tuple IS
NOT DISTINCT FROM blocked_locks.tuple AND blocking_locks.virtualxid
IS NOT DISTINCT FROM blocked_locks.virtualxid AND blocking_lock-
s.transactionid IS NOT DISTINCT FROM blocked_locks.transactionid AND
blocking_locks.classid IS NOT DISTINCT FROM blocked_locks.classid
AND blocking_locks.objid IS NOT DISTINCT FROM blocked_locks.objid
AND blocking_locks.objsubid IS NOT DISTINCT FROM blocked_lock-
s.objsubid AND blocking_locks.pid != blocked_locks.pid JOIN pg_cata-
log.pg_stat_activity AS blocking_activity ON blocking_activity.pid =
blocking_locks.pid WHERE NOT blocked_locks.granted and blocked_
activity.state_change < current_timestamp - INTERVAL '5' minute;
For additional details, see: https://www.postgresql.org/docs/9.6/static/runtime-config-resource.html
- 387 -
Migrate from: SQL Server Linked Servers
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A Linked Servers Syntax and option differences, similar
functionality
Overview
Linked Servers enable the database engine to connect to external Object Linking and Embedding for
Data Bases (OLE-DB) sources. They are typically used to execute T-SQL commands and include tables
in other instances of SQL Server, or other RDBMS engines such as Oracle. SQL Server supports mul-
tiple types of OLE-DB sources as linked servers, including Microsoft Access, Microsoft Excel, text files
and others.
The main benefits of using linked servers are:
l Reading external data for import or processing.
l Executing distributed queries, data modifications, and transactions for enterprise-wide data
sources.
l Querying heterogeneous data source using the familiar T-SQL API.
Linked servers can be configured using either SQL Server Management Studio, or the system stored
procedure sp_addlinkedserver. The available functionality and the specific requirements vary sig-
nificantly between the various OLE-DB sources. Some sources may allow read only access, others may
require specific security context settings, etc.
The Linked Server Definition contains the linked server alias, the OLE DB provider, and all the para-
meters needed to connect to a specific OLE-DB data source.
The OLE-DB provider is a .Net Dynamic Link Library (DLL) that handles the interaction of SQL Server
with all data sources of its type. For example, OLE-DB Provider for Oracle. The OLE-DB data source is
the specific data source to be accessed, using the specified OLE-DB provider.
Note: SQL Server distributed queries can be used with any custom OLE DB provider as long as
the required interfaces are implemented correctly.
SQL Server parses the T-SQL commands that access the linked server and sends the appropriate
requests to the OLE-DB provider. There are several access methods for remote data, including opening
the base table for read or issuing SQLqueries against the remote data source.
Linked servers can be managed using SQL Server Management Studio graphical user interface or T-
SQL system stored procedres.
l EXECUTEsp_addlinkedserver to add new server definitions.
l EXECUTEsp_addlinkedserverlogin to define security context.
- 388 -
l EXECUTEsp_linkedservers or SELECT * FROM sys.servers system catalog view to
retrieve meta data.
l EXECUTE sp_dropserver to delete a linked server.
Linked server data sources are accessed from T-SQL using a fully qualified, four part naming
scheme:<Server Name>.<Database Name>.<Schema Name>.<Object Name>.
Additionally, the OPENQUERY row set function can be used to explicitly invoke pass-through queries
on the remote linked server, and the OPENROWSET and OPENDATASOURCErow set functions can be
used for ad-hoc remote data access without defining the linked server in advance.
Syntax
EXECUTEsp_addlinkedserver
[@server= ] <Linked Server Name>
[, [@srvproduct= ] <Product Name>]
[, [@provider= ] <OLE DB Provider>]
[, [@datasrc= ] <Data Source>]
[, [@location= ] <Data Source Address>]
[, [@provstr= ] <Provider Connection String>]
[, [@catalog= ] <Database>];
Examples
Create a linked server to a local text file.
EXECUTE sp_addlinkedserver MyTextLinkedServer, N'Jet 4.0',
N'Microsoft.Jet.OLEDB.4.0',
N'D:\TextFiles\MyFolder',
NULL,
N'Text';
Define security context.
EXECUTE sp_addlinkedsrvlogin MyTextLinkedServer, FALSE, Admin, NULL;
Use sp_tables_ex to list tables in folder.
EXEC sp_tables_ex MyTextLinkedServer;
Issue a SELECT query using 4 part name.
SELECT *
FROM MyTextLinkedServer...[FileName#text];
For more information, see
- 389 -
Migrate to: Aurora PostgreSQL DBLink and FDWrap-
per
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A Linked Servers Syntax and option differences, similar
functionality
Overview
Querying data in remote databases is available via two primary options:
l dblink database link function
l postgresql_fdw (Foreign Data Wrapper, FDW) extension
The Postgres foreign data wrapper extension is new to PostgreSQL and provides functionality similar
to dblink. However, the Postgres foreign data wrapper aligns closer with the SQL standard and can
provide improved performance.
Examples
Load the dblink extension into PostgreSQL.
CREATE EXTENSION dblink;
Create a persistent connection to a remote PostgreSQL database using the dblink_connect function
specifying a connection name (myconn), database name (postgresql), port (5432), host (hostname),
user (username), and password (password).
SELECT dblink_connect
('myconn', 'dbname=postgres port=5432 host=hostname user=username password=password');
The connection can be used to execute queries against the remote database.
Execute a query using the previously created connection (myconn) via the dblink function. The query
returns the id and name columns from the employees table. On the remote database, you must spe-
cify the connection name and the SQL query to execute as well as parameters and datatypes for selec-
ted columns (id and name in this example).
SELECT * from dblink
('myconn', 'SELECT id, name FROM EMPLOYEES') AS p(id int,fullname text);
Close the connection using the dblink_disconnect function.
SELECT dblink_disconnect('myconn');
- 391 -
Alternatively, you can use the dblink function specifying the full connection string to the remote Post-
greSQL database including the database name, port, hostname, username, and password. This can be
done instead of using a previously defined connection. You must also specify the SQL query to execute
as well as parameters and datatypes for the selected columns (id and name, in this example).
SELECT * from dblink
('dbname=postgres port=5432 host=hostname user=username password=password',
'SELECT id, name FROM EMPLOYEES') AS p(id int,fullname text);
DML commands are supported on tables referenced via the dblink function. For example, you can
insert a new row and then delete it from the remote table.
SELECT * FROM dblink('myconn',$$INSERT into employees VALUES (3,'New Employees No.
3!')$$) AS t(message text);
SELECT * FROM dblink('myconn',$$DELETE FROM employees WHERE id=3$$) AS t(message
text);
Create a new local table (new_employees_table) by querying data from a remote table.
SELECT emps.* INTO new_employees_table FROM dblink('myconn','SELECT * FROM employees')
AS emps(id int, name varchar);
Join remote data with local data.
SELECT local_emps.id , local_emps.name, s.sale_year, s.sale_amount
FROM local_emps INNER JOIN
dblink('myconn','SELECT * FROM working_hours') AS s(id int, hours worked int)
ON local_emps.id = s.id;
Execute DDL statements in the remote database.
SELECT * FROM dblink('myconn',$$CREATE table new_remote_tbl (a int, b text)$$) AS t(a
text);
For additional details, see https://www.postgresql.org/docs/9.6/static/dblink.html
- 392 -
Migrate from: SQL Server Scripting
Feature Com-
patibility
SCT Auto-
mation Level
SCT Action
Code Index
Key Differences
N/A N/A Non-compatible tool sets and scripting languages
Use PostgreSQL pgAdmin, Amazon RDS API, AWS
Management Console, and Amazon CLI
Overview
SQLServer supports T-SQL and XQuery scripting within multiple execution frameworks such as
SQLServer Agent, and stored procedures.
The SQLCMD command line utility can also be used to execute T-SQL scripts. However, the most
extensive and feature-rich scripting environment is PowerShell.
SQL Server provides two PowerShell snap-ins that implement a provider exposing the entire SQL
Server Management Object Model (SMO) as PowerShell paths. Additionally, a SQL Server cmd can be
used to execute specific SQL Server commands.
Note: Invoke-Sqlcmd can be used to execute scripts using the SQLCMD utility.
The sqlps utility launches the PowerShell scripting environment and automatically loads the SQL Server
modules. sqlps can be launched from a command prompt or from the Object Explorer pane of SQL
Server Management Studio. You can execute ad-hoc PowerShell commands and script files (for
example, .\SomeFolder\SomeScript.ps1).
Note: SQL Server Agent supports executing PowerShell scripts in job steps. For more inform-
ation, see SQL Server Agent.
SQL Server also supports three types of direct database engine queries:T-SQL, XQuery, and the
SQLCMD utility. T-SQL and XQuery can be called from stored procedures, SQL Server Management Stu-
dio (or other IDE), and SQL Server agent Jobs. The SQLCMD utility also supports commands and vari-
ables.
Examples
Backup a database with PowerShell using the default backup options.
PS C:\> Backup-SqlDatabase -ServerInstance "MyServer\SQLServerInstance" -Database
"MyDB"
Get all rows from the MyTable table in they MyDB database
PS C:\> Read-SqlTableData -ServerInstance MyServer\SQLServerInstance" -DatabaseName
"MyDB" -TableName "MyTable"
- 393 -
Migrate to: Aurora PostgreSQL Scripting
Feature Com-
patibility
SCT Auto-
mation Level
SCT Action
Code Index
Key Differences
N/A N/A Non-compatible tool sets and scripting languages
Use PostgreSQL pgAdmin, Amazon RDS API, AWS
Management Console, and Amazon CLI
Overview
As a Platform as a Service (PaaS), Aurora PostgreSQL accepts connections from any compatible client,
but you cannot access the PostgreSQL command line utility typically used for database administration.
However, you can use PostgreSQL tools installed on a network host and the Amazon RDS API. The
most common tools for Aurora PostgreSQL scripting and automation include PostgreSQL pgAdmin,
PostgreSQL Utilities, and the Amazon RDS API. The following sections describe each tool.
PostgreSQL pgAdmin
PostgreSQL pgAdmin is the most commonly used tool for development and administration of Post-
greSQL servers. It is available as a free Community Edition and paid support is available.
The PostgreSQL pgAdmin also supports a Python scripting shell that you can use interactively and pro-
grammatically. For more information see: https://www.pgadmin.org/
Amazon RDS API
The Amazon RDS API is a web service for managing and maintaining Aurora PostgreSQL (and other)
relational databases. It can be used to setup, operate, scale, backup, and perform many common
administration tasks. The RDSAPI supports multiple database platforms and can integrate admin-
istration seamlessly for heterogeneous environments.
Note: The Amazon RDS APIis asynchronous. Some interfaces may require polling or callback
functions to receive command status and results.
You can access Amazon RDS using the AWS Management Console, the AWSCommand Line Interface
(CLI), and the Amazon RDS Progammatic API as described in the following sections.
AWS Management Console
The AWS Management Console is a simple web-based set of tools for interactive management of Aur-
ora PostgreSQL and other RDS services. It can be accessed at https://console.aws.amazon.com/rds/
- 395 -
AWS Command Line Interface (CLI)
The Amazon AWS Command Line Interface is an open source tool that runs on Linux, Windows, or
MacOS having Python 2 version 2.6.5 and higher or Python 3 version 3.3 and higher.
The AWS CLI is built on top of the AWS SDK for Python (Boto), which provides commands for inter-
acting with AWS services. With minimal configuration, you can start using all AWS Management Con-
sole functionality from your favorite terminal application.
l Linux shells: Use common shell programs such as Bash, Zsh, or tsch.
l Windows command line: Run commands in PowerShell or the Windows Command Processor.
l Remotely: Run commands on Amazon EC2 instances through a remote terminal such as PuTTY
or SSH.
The AWS Tools for Windows PowerShell and AWS Tools for PowerShell Core are PowerShell modules
built on the functionality exposed by the AWS SDK for .NET. These Tools enable scripting operations
for AWS resources using the PowerShell command line.
Note: You cannot use SQL Server cmdlets in PowerShell.
Amazon RDS Programmatic API
The Amazon RDS API can be used to automate management of DB instances and other Amazon RDS
objects.
For more information about Amazon RDS API, see:
l API actions: http://docs.aws.amazon.com/AmazonRDS/latest/APIReference/API_Operations.html
l Data Types: http://docs.aws.amazon.com/AmazonRDS/latest/APIReference/API_Types.html
l Common query parameters: http://-
docs.aws.amazon.com/AmazonRDS/latest/APIReference/CommonParameters.html
l Error codes: http://docs.aws.amazon.com/AmazonRDS/latest/APIReference/CommonErrors.html
Examples
The following walkthrough describes how to connect to an Aurora PostgreSQL DBinstance using the
PostgreSQL Utility:
Login to the Amazon RDS Console and click Clusters.
- 396 -
From a command shell, type the following:
psql --host=mypostgresql.c6c8mwvfdgv0.us-west-2.rds.amazonaws.com --port=5432 --user-
name=awsuser --password --dbname=mypgdb
l The --host parameter is the endpoint DNS name of the Aurora PostgreSQL DB cluster.
l The --port parameter is the port number .
For more information, see
l https://docs.aws.amazon.com/cli/latest/reference/
l https://docs.aws.amazon.com/AmazonRDS/latest/APIReference/Welcome.html
- 398 -
Performance Tuning
- 399 -
Migrate from: SQL Server Execution Plans
Feature Com-
patibility
SCT Automation
Level
SCT Action
Code Index
Key Differences
N/A N/A Syntax differences
Completely different optimizer with different
operators and rules
Overview
Execution plans provide users detailed information about the data access and processing methods
chosen by the SQL Server Query Optimizer. They also provide estimated or actual costs of each oper-
ator and sub tree. Execution plans provide critical data for troubleshooting query performance issues.
SQLServer creates execution plans for most queries and returns them to client applications as plain
text or XML documents. SQLServer produces an execution plan when a query executes, but it can also
generate estimated plans without executing a query.
SQL Server Management Studio provides a graphical view of the underlying XML plan document using
icons and arrows instead of textual information. This graphical view is extremely helpful when invest-
igating the performance aspects of a query.
To request an estimated execution plan, use the SET SHOWPLAN_XML, SHOWPLAN_ALL, or
SHOWPLAN_TEXT statements.
Examples
Show the estimated execution plan for a query.
SET SHOWPLAN_XML ON;
SELECT *
FROM MyTable
WHERE SomeColumn = 3;
SET SHOWPLAN_XML OFF;
Actual execution plans return after execution of the query or batch of queries completes and include
run-time statistics about resource usage and warnings. To request the actual execution plan, use the
SET STATISTICS XML statement to return the XML document object. Alternatively, use the STATISTICS
PROFILE statement, which returns an additional result set containing the query execution plan.
Show the actual execution plan for a query.
SET STATISTICS XML ON;
SELECT *
FROM MyTable
WHERE SomeColumn = 3;
SET STATISTICS XML OFF;
- 400 -
Migrate to: Aurora PostgreSQL Execution Plans
Feature Com-
patibility
SCT Automation
Level
SCT Action
Code Index
Key Differences
N/A N/A Syntax differences
Completely different optimizer with different
operators and rules
Overview
When using EXPLAIN, PostgreSQL will generate the estimated execution plan for actions such as:
SELECT, INSERT, UPDATE and DELETE and will build a structured tree of plan nodes representing the
different actions taken (the sign “->” represent a root line in the PostgreSQL execution plan). In addi-
tion, the EXPLAIN statement will provide statistical information regarding each action such as: cost,
rows, time and loops.
When using the EXPLAIN command as part of a SQL statement, the statement will not execute, and the
execution plan would be an estimation. However, by using the EXPLAIN ANALYZE command, the state-
ment will actually be executed in addition to displaying the execution plan itself.
PostgreSQL EXPLAIN Synopsis:
EXPLAIN [(option [, ...] ) ] statement
EXPLAIN [ANALYZE ] [VERBOSE ] statement
where option can be one of:
ANALYZE [boolean ]
VERBOSE [boolean ]
COSTS [boolean ]
BUFFERS [boolean ]
TIMING [boolean ]
FORMAT {TEXT | XML | JSON | YAML }
Examples
1. Displaying the execution plan of a SQL statement using the EXPLAIN command:
psql=> EXPLAIN
SELECT EMPLOYEE_ID, LAST_NAME, FIRST_NAME FROM EMPLOYEES
WHERE LAST_NAME='King' AND FIRST_NAME='Steven';
--------------------------------------------------------------------------------------
----
Index Scan using idx_emp_name on employees (cost=0.14..8.16 rows=1 width=18)
Index Cond: (((last_name)::text = 'King'::text) AND ((first_name)::text =
- 402 -
'Steven'::text))
(2 rows)
2. Running the same statement with the ANALYZE keyword:
psql=> EXPLAIN ANALYZE
SELECT EMPLOYEE_ID, LAST_NAME, FIRST_NAME FROM EMPLOYEES
WHERE LAST_NAME='King' AND FIRST_NAME='Steven';
--------------------------------------------------------------------------------------
----
Seq Scan on employees (cost=0.00..3.60 rows=1 width=18) (actual
time=0.012..0.024 rows=1 loops=1)
Filter: (((last_name)::text = 'King'::text) AND ((first_name)::text =
'Steven'::text))
Rows Removed by Filter: 106
Planning time: 0.073 ms
Execution time: 0.037 ms
(5 rows)
By adding the ANALYZE keyword and executing the statement, we get additional information in addi-
tion to the execution plan.
3. Viewing a PostgreSQL execution plan showing a FULL TABLE SCAN:
psql=> EXPLAIN ANALYZE
SELECT EMPLOYEE_ID, LAST_NAME, FIRST_NAME FROM EMPLOYEES
WHERE SALARY > 10000;
--------------------------------------------------------------------------------------
------
Seq Scan on employees (cost=0.00..3.34 rows=15 width=18) (actual time=0.012..0.036
rows=15 loops=1)
Filter: (salary > '10000'::numeric)
Rows Removed by Filter: 92
Planning time: 0.069 ms
Execution time: 0.052 ms
(5 rows)
PostgreSQL can perform several scan types for processing and retrieving data from tables including:
sequential scans, index scans, and bitmap index scans. The sequential scan (“Seq Scan”) is PostgreSQL
equivalent for SQL Server “Table Scan” (full table scan).
For additional information see: https://www.postgresql.org/docs/9.6/static/sql-explain.html
- 403 -
Migrate from: SQL Server Query Hints and Plan
Guides
Feature Com-
patibility
SCT Auto-
mation Level
SCT Action
Code Index
Key Differences
N/A N/A Very limited set of hints - Index hints and
optimizer hints as comments
Syntax differences
Overview
SQLServer hints are instructions that override automatic choices made by the query processor for DML
and DQLstatements. The term hint is misleading because, in reality, it forces an override to any other
choice of execution plan.
JOIN Hints
LOOP, HASH, MERGE, and REMOTE hints can be explicitly added to a JOIN. For example, ... Table1
INNERLOOP JOIN Table2 ON .... These hints force the optimizer to use Nested Loops, Hash Match, or
Merge physical join algorithms. REMOTE enables processing a join with a remote table on the local
server.
Table Hints
Table hints override the default behavior of the query optimizer. Table hints are used to explicitly force
a particular locking strategy or access method for a table operation clause. These hints do not modify
the defaults and apply only for the duration of the DML or DQL statement.
Some common table hints are INDEX = <Index value>, FORCESEEK, NOLOCK, and TABLOCKX.
Query Hints
Query hints affect the entire set of query operators, not just the individual clause in which they appear.
Query hints may be JOIN Hints, Table Hints, or from a set of hints that are only relevant for Query
Hints.
Some common table hints include OPTIMIZE FOR, RECOMPILE, FORCE ORDER, FAST <rows>.
Query hints are specified after the query itself following the WITH options clause.
- 404 -
Plan Guides
Plan guides provide similar functionality to query hints in the sense they allow explicit user inter-
vention and control over query optimizer plan choices. Plan guides can use either query hints or a full
fixed, pre-generated plan attached to a query. The difference between query hints and plan guides is
the way they are associated with a query.
While query or table hints need to be explicitly stated in the query text, they are not an option if you
have no control over the source code generating these queries. If an application uses ad-hoc queries
instead of stored procedures, views, and functions, the only way to affect query plans is to use plan
guides. They are often used to mitigate performance issues with third-party software
A plan guide consists of the statement whose execution plan needs to be adjusted and either an
OPTIONclause that lists the desired query hints or a full XML query plan that is enforced as long it is
valid.
At run time, SQL Server matches the text of the query specified by the guide and attaches the OPTION
hints. Alternatively, it assigns the provided plan for execution.
SQL Server supports three types of Plan Guides:
l Object Plan Guides target statements that run within the scope of a code object such as a
stored procedure, function, or trigger. If the same statement is found in another context, the
plan guide is not be applied.
l SQL Plan Guides are used for matching general ad-hoc statements not within the scope of code
objects. In this case, any instance of the statement regardless of the originating client is assigned
the plan guide.
l Template Plan Guides can be used to abstract statement templates that differ only in parameter
values. It can be used to override the PARAMETERIZATIONdatabase option setting for a family of
queries.
Syntax
Query Hints:
Note: The following syntax is for SELECT. Query hints can be used in all DQL and DML state-
ments.
SELECT <statement>
OPTION
(
{{HASH|ORDER} GROUP
|{CONCAT |HASH|MERGE} UNION
|{LOOP|MERGE|HASH} JOIN
|EXPAND VIEWS
|FAST <Rows>
|FORCE ORDER
|{FORCE|DISABLE} EXTERNALPUSHDOWN
- 405 -
|IGNORE_NONCLUSTERED_COLUMNSTORE_INDEX
|KEEP PLAN
|KEEPFIXED PLAN
|MAX_GRANT_PERCENT = <Percent>
|MIN_GRANT_PERCENT = <Percent>
|MAXDOP <Number of Processors>
|MAXRECURSION <Number>
|NO_PERFORMANCE_SPOOL
|OPTIMIZE FOR (@<Variable> {UNKNOWN|= <Value>}[,...])
|OPTIMIZE FOR UNKNOWN
|PARAMETERIZATION {SIMPLE|FORCED}
|RECOMPILE
|ROBUST PLAN
|USE HINT ('<Hint>' [,...])
|USE PLAN N'<XML Plan>'
|TABLE HINT (<Object Name> [,<Table Hint>[[,...]])
});
Create a Plan Guide:
EXECUTEsp_create_plan_guide @name = '<Plan Guide Name>'
,@stmt = '<Statement>'
,@type = '<OBJECT|SQL|TEMPLATE>'
,@module_or_batch = 'Object Name>'|'<Batch Text>'| NULL
,@params = '<Parameter List>'|NULL }
,@hints = 'OPTION(<Query Hints>'|'<XML Plan>'|NULL;
Examples
Limit parallelism for a sales report query.
EXEC sp_create_plan_guide
@name = N'SalesReportPlanGuideMAXDOP',
@stmt = N'SELECT *
FROM dbo.fn_SalesReport(GETDATE())
@type = N'SQL',
@module_or_batch = NULL,
@params = NULL,
@hints = N'OPTION (MAXDOP 1)';
Use table and query hints.
SELECT *
FROM MyTable1 AS T1
WITH (FORCESCAN)
INNER LOOPJOIN
MyTable2 AS T2
WITH (TABLOCK, HOLDLOCK)
ON T1.Col1 = T2.Col1
WHERE T1.Date BETWEEN DATEADD(DAY, -7, GETDATE()) AND GETDATE()
- 406 -
Migrate to: Aurora PostgreSQL DB Query Planning
Feature Com-
patibility
SCT Auto-
mation Level
SCT Action
Code Index
Key Differences
N/A N/A Very limited set of hints - Index hints and
optimizer hints as comments
Syntax differences
Overview
PostgreSQL does not support database hints to influence the behavior of the query planner, and you
cannot influence how execution plans are generated from within SQL queries. Although database hints
are not directly supported, session parameters (also known as Query Planning Parameters) can influ-
ence the behavior of the query optimizer at the session level.
Example
Configure the query planner to use indexes instead of full table scans (disable SEQSCAN).
psql=> SET ENABLE_SEQSCAN=FALSE;
Set the query planner’s estimated “cost” of a disk page fetch that is part of a series of sequential
fetches (SEQ_PAGE_COST) and set the planner's estimate of the cost of a non-sequentially-fetched disk
page (RANDOM_PAGE_COST). Reducing the value of RANDOM_PAGE_COST relative to SEQ_PAGE_COST
causes the query planner to prefer index scans, while raising the value makes index scans more
“expensive”.
psql=> SET SEQ_PAGE_COST to 4;
psql=> SET RANDOM_PAGE_COST to 1;
Enable or disable the query planner's use of nested-loops when performing joins. While it is
impossible to completely disable the usage of nested-loop joins, setting the ENABLE_NESTLOOP to OFF
discourages the query planner from choosing nested-loop joins compared to alternative join methods.
psql=> SET ENABLE_NESTLOOP to FALSE;
For additional details, see https://www.postgresql.org/docs/9.6/static/runtime-config-query.html
- 408 -
Migrate from: SQL Server Managing Statistics
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A N/A Syntax and option differences, similar
functionality
Overview
Statistics objects in SQL Server are designed to support SQL Server's cost-based query optimizer. It
uses statistics to evaluate the various plan options and choose an optimal plan for optimal query per-
formance.
Statistics are stored as BLOBs in system tables and contain histograms and other statistical inform-
ation about the distribution of values in one or more columns. A histogram is created for the first
column only and samples the occurrence frequency of distinct values. Statistics and histograms are col-
lected by either scanning the entire table or by sampling only a percentage of the rows.
You can view Statistics manually using the DBCC SHOW_STATISTICS statement or the more recent
sys.dm_db_stats_properties and sys.dm_db_stats_histogram system views.
SQL Server provides the capability to create filtered statistics containing a WHERE predicate. Filtered
statistics are useful for optimizing histogram granularity by eliminating rows whose values are of less
interest, for example NULLs.
SQL Server can manage the collection and refresh of statistics automatically (the default). Use the
AUTO_CREATE_STATISTICS and AUTO_UPDATE_STATISTICS database options to change the defaults.
When a query is submitted with AUTO_CREATE_STATISTICS on and the query optimizer may benefit
from a statistics that do not yet exist, SQLServer creates the statistics automatically. You can use the
AUTO_UPDATE_STATISTICS_ASYNC database property to set new statistics creation to occur imme-
diately (causing queries to wait) or to run asynchronously. When run asynchronously, the triggering
execution cannot benefit from optimizations the optimizer may derive from it.
After creation of a new statistics object, either automatically or explicitly using the CREATE STATISTICS
statement, the refresh of the statistics is controlled by the AUTO_UPDATE_STATISTICS database option.
When set to ON, statistics are recalculated when they are stale, which happens when significant data
modifications have occurred since the last refresh.
Syntax
CREATE STATISTICS <Statistics Name>
ON <Table Name> (<Column> [,...])
[WHERE <Filter Predicate>]
[WITH <Statistics Options>;
- 409 -
Examples
Create new statistics on multiple columns. Set to use a full scan and to not refresh.
CREATE STATISTICS MyStatistics
ON MyTable (Col1, Col2)
WITH FULLSCAN, NORECOMPUTE;
Update statistics with a 50% sampling rate.
UPDATE STATISTICS MyTable(MyStatistics)
WITH SAMPLE 50 PERCENT;
View the statistics histogram and data.
DBCC SHOW_STATISTICS ('MyTable','MyStatistics');
Turn off automatic statistics creation for a database.
ALTER DATABASE MyDB SET AUTO_CREATE_STATS OFF;
For more information, see:
l https://docs.microsoft.com/en-us/sql/relational-databases/statistics/statistics
l https://docs.microsoft.com/en-us/sql/t-sql/statements/create-statistics-transact-sql
l https://docs.microsoft.com/en-us/sql/t-sql/database-console-commands/dbcc-show-statistics-transact-sql
- 410 -
Migrate to: Aurora PostgreSQL Table Statistics
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A N/A Syntax and option differences, similar
functionality
Overview
Use the ANALYZE command to collect statistics about a database, a table, or a specific table column.
The PostgreSQL ANALYZE command collects table statistics that support the generation of efficient
query execution plans by the query planner.
l Histograms: ANALYZE collects statistics on table column values and creates a histogram of the
approximate data distribution in each column.
l Pages and Rows: ANALYZE collects statistics on the number of database pages and rows from
which each table is comprised.
l Data Sampling: For large tables, the ANALYZE command takes random samples of values rather
than examining each row. This allows the ANALYZE command to scan very large tables in a rel-
atively small amount of time.
l Statistic Collection Granularity: Executing the ANALYZE command without parameters instructs
PostgreSQL to examine every table in the current schema. Supplying the table name or column
name to ANALYZE instructs the database to examine a specific table or table column.
Automatic Statistics Collection
By default, PostgreSQL is configured with an autovacuum daemon which automates the execution of
statistics collection via the ANALYZE commands (in addition to automation of the VACUUM command).
The autovacuum daemon scans for tables that show signs of large modifications in data to collect the
current statistics. Autovacuum is controlled by several parameters. For additional details, see
https://www.postgresql.org/docs/9.6/static/runtime-config-autovacuum.html
Manual Statistics Collection
PostgreSQL allows collecting statistics on-demand using the ANALYZE command at the database level,
table-level, or table column-level.
l ANALYZE on indexes is not currently supported.
l ANALYZE requires only a read-lock on the target table. It can run in parallel with other activity on
the table.
- 411 -
l For large tables, ANALYZE takes a random sample of the table contents. It is configured via the
show default_statistics_target parameter. The default value is 100 entries. Raising the limit might
allow more accurate planner estimates to be made at the price of consuming more space in the
pg_statistic table.
Examples
Gather statistics for the entire database.
psql=> ANALYZE;
Gather statistics for a specific table. The VERBOSE keyword displays progress.
psql=> ANALYZE VERBOSE EMPLOYEES;
Gather statistics for a specific column.
psql=> ANALYZE EMPLOYEES (HIRE_DATE);
Specify the default_statistics_target parameter for an individual table column and reset it back to
default.
psql=> ALTER TABLE EMPLOYEES ALTER COLUMN SALARY SET STATISTICS 150;
psql=> ALTER TABLE EMPLOYEES ALTER COLUMN SALARY SET STATISTICS -1;
Larger values increase the time needed to complete an ANALYZE, but improve the quality of the col-
lected planner's statistics, which can potentially lead to better execution plans.
View the current (session / global) default_statistics_target, modify it to 150, and analyze the
EMPLOYEES table:
psql=> SHOW default_statistics_target ;
psql=> SET default_statistics_target to 150;
psql=> ANALYZE EMPLOYEES ;
View the last time statistics were collected for a table.
select relname, last_analyze from pg_stat_all_tables;
- 412 -
Summary
Feature SQL Server PostgreSQL
Analyze a specific
database table
CREATE STATISTICS MyStatistics
ON MyTable (Col1, Col2)
ANALYZE EMPLOYEES;
Analyze a database
table while only
sampling certain
rows
UPDATE STATISTICS MyTable(MyStatistics)
WITH SAMPLE 50 PERCENT;
Configure via number of entries
for the table:
SET default_statistics_target to
150;
ANALYZE EMPLOYEES ;
View last time stat-
istics were collected
DBCC SHOW_STATISTICS ('MyT-
able','MyStatistics');
select relname, last_analyze
from pg_stat_all_tables;
For additional information, see:
l https://www.postgresql.org/docs/9.6/static/sql-analyze.html
l https://www.postgresql.org/docs/9.6/static/routine-vacuuming.html#AUTOVACUUM
- 413 -
Physical Storage
- 414 -
Migrate from: SQL Server Columnstore Index
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A Aurora PostgreSQL offers no com-
parable feature
Overview
SQLServer provides Columnstore Indexes that use column-based data storage to compress data and
improve query performance in data warehouses. Columnstore indexes are the preferred data storage
format for data warehousing and analytic workloads. As a best practice, use Columnstore indexes with
fact tables and large dimension workloads.
Examples
Create a table with a columnar store index.
CREATE TABLE products(ID [int] NOT NULL, OrderDate [int] NOT NULL, ShipDate [int] NOT
NULL);
GO
CREATE CLUSTERED COLUMNSTORE INDEX cci_T1 ON products;
GO
For more information, see :
https://docs.microsoft.com/en-us/sql/relational-databases/indexes/columnstore-indexes-overview?view=sql-server-2017
- 415 -
Migrate to: Aurora PostgreSQL
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A Aurora PostgreSQL offers no com-
parable feature
Amazon Aurora PostgreSQL does not currently provide a directly comparable alternative for SQL
Server's Columstore Index.
- 416 -
Migrate from: SQL Server Indexed Views
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A Different paradigm and syntax will require
rewriting the application
Overview
The first index created on a view must be a clustered index. Subsequent indexes can be non-clustered
indexes. For more information about clustered and non-clustered indexes, see:
https://docs.microsoft.com/en-us/sql/relational-databases/indexes/clustered-and-nonclustered-
indexes-described?view=sql-server-2017
Before creating an index on a view, the following requirements must be met:
l The WITH SCHEMABINDING option must be used when creating the view.
l Verify the SET options are correct for all existing tables referenced in the view and for the session
(the the link at the end of this section for required values).
l Ensure that a clustered index on the view is exists.
Note: Indexed views cannot be used with temporal queries (FOR SYSTEM_TIME).
Examples
Set the required SET options, create a view (with the WITH SCHEMABINDING option), and create an
index on this view.
SET NUMERIC_ROUNDABORT OFF;
SET ANSI_PADDING, ANSI_WARNINGS, CONCAT_NULL_YIELDS_NULL, ARITHABORT,
QUOTED_IDENTIFIER, ANSI_NULLS ON;
GO
CREATE VIEW Sales.Ord_view
WITH SCHEMABINDING
AS
SELECT SUM(Price*Qty*(1.00-Discount)) AS Revenue,
OrdTime, ID, COUNT_BIG(*) AS COUNT
FROM Sales.OrderDetail AS ordet, Sales.OrderHeader AS ordhead
WHERE ordet.SalesOrderID = ordhead.SalesOrderID
GROUP BY OrdTime, ID;
GO
CREATE UNIQUE CLUSTERED INDEX IDX_V1
ON Sales.Ord_view (OrdTime, ID);
GO
- 417 -
Migrate to: Aurora PostgreSQL Materialized Views
Feature Com-
patibility
SCT Automation
Level
SCT Action
Code Index
Key Differences
N/A Different paradigm and syntax will require
rewriting the application
Overview
PostgreSQL does not support view indexes, but does provide similar functionality with Materialized
Views. Queries associated with Materialized Views are executed and the view data is populated when
the REFRESH command is issued.
The PostgreSQL implementation of Materialized Views has three primary limitations:
l PostgreSQL Materialized Views may be refreshed either manually or using a job running the
REFRESH MATERIALIZED VIEW command. Automatic refresh of Materialized Views require the cre-
ation of a trigger.
l PostgreSQL Materialized Views only support complete (full) refresh.
l DML on Materialized Views is not supported.
In some cases, when the tables are big, full REFRESH can cause performance issues, in this case, trig-
gers can be used to sync between one table to the new table (the new table can be used as a view)that
can indexed.
Examples
Create a Materialized View named sales_summary using the sales table as the source.
CREATE MATERIALIZED VIEW sales_summary AS
SELECT seller_no,sale_date,sum(sale_amt)::numeric(10,2) as sales_amt
FROM sales
WHERE sale_date < CURRENT_DATE
GROUP BY seller_no, sale_date
ORDER BY seller_no, sale_date;
Execute a manual refresh of the Materialized View:
REFRESH MATERIALIZED VIEW sales_summary;
Note: The Materialized View data is not refreshed automatically if changes occur to its under-
lying tables. For automatic refresh of materialized view data, a trigger on the underlying tables
must be created.
- 419 -
Creating a Materialized View
When you create a Materialized View in PostgreSQL, it uses a regular database table underneath. You
can create database indexes on the Materialized View directly and improve performance of queries
that access the Materialized View.
Example
Create an index on the sellerno and sale_date columns of the sales_summary Materialized View.
CREATE UNIQUE INDEX sales_summary_seller
ON sales_summary (seller_no, sale_date);
Summary
Indexed views Materialized view
Create
Materialized
View
SET NUMERIC_ROUNDABORT OFF;
SET ANSI_PADDING, ANSI_WARNINGS,
CONCAT_NULL_YIELDS_NULL,
ARITHABORT, QUOTED_IDENTIFIER,
ANSI_NULLS ON; GO
CREATE VIEW Sales.Ord_view WITH
SCHEMABINDING AS SELECT SUM
(Price*Qty*(1.00-Discount)) AS Rev-
enue, OrdTime, ID, COUNT_BIG(*) AS
COUNT FROM Sales.OrderDetail AS
ordet, Sales.OrderHeader AS ordhead
WHERE ordet.SalesOrderID = ord-
head.SalesOrderID GROUP BY
OrdTime, ID; GO
CREATE UNIQUE CLUSTERED INDEX
IDX_V1 ON Sales.Ord_view (OrdTime,
ID); GO
CREATE MATERIALIZED VIEW mv1 AS SELECT *
FROM employees;
Indexed
refreshed
Automatic Manual. You can automate refreshes using trig-
gers:
Create a trigger that initiates a refresh after
every DML command on the underlying tables:
CREATE OR REPLACE FUNCTION
refresh_mv1()
- 420 -
Indexed views Materialized view
returns trigger language plpgsql as
$$ begin
refresh materialized view mv1;
return null;
end $$;
Create trigger refresh_ mv1 after insert, update,
delete or truncate on employees for each state-
ment execute procedure refresh_mv1();
DML Supported Not Supported
For more details, see: https://www.postgresql.org/docs/current/static/rules-materializedviews.htm
- 421 -
Migrate from: SQL Server Partitioning
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
SCT Action Codes
- Partitions
Does not support LEFT partition or foreign
keys referencing partitioned tables
Overview
SQL Server provides a logical and physical framework for partitioning table and index data. Each table
and index are partitioned, but may have only one partition. SQL Server 2017 supports up to 15,000 par-
titions.
Partitioning separates data into logical units that can be stored in more than one file group. SQL Server
partitioning is horizontal, where data sets of rows are mapped to individual partitions. A partitioned
table or index is a single object and must reside in a single schema within a single database. Objects
composed of disjointed partitions is not allowed.
All DQL and DML operations are partition agnostic except for the special predicate $partition, which
can be used for explicit partition elimination.
Partitioning is typically needed for very large tables to address the following management and per-
formance challenges:
l Deleting or inserting large amounts of data in a single operation with partition switching instead
of individual row processing while maintaining logical consistency.
l Maintenance operations can be split and customized per partition. For example, older data par-
titions can be compressed and more active partitions can be rebuilt or reorganized more fre-
quently.
l Partitioned tables may use internal query optimization techniques such as collocated and par-
allel partitioned joins.
l Physical storage performance can be optimized by distributing IO across partitions and physical
storage channels.
l Concurrency improvements due to the engine's ability to escalate locks to the partition level
rather than the whole table.
Partitioning in SQL Server uses the following three objects:
l Partitioning Column: A Partitioning column is the column (or columns) used by the partition
function to partition the table or index. The value of this column determines the logical partition
to which it belongs. You can use computed columns in a partition function as long as they are
explicitly PERSISTED. Partitioning columns may be any data type that is a valid index column with
less than 900 bytes per key except timestamp and LOBdata types.
- 422 -
l Partition Function:A Partition function is a database object that defines how the values of the
partitioning columns for individual tables or index rows are mapped to a logical partition. The
partition function describes the partitions for the table or index and their boundaries.
l Partition Scheme: A partition scheme is a database object that maps individual logical partitions
of a table or an index to a set of file groups, which in turn consist of physical operating system
files. Placing individual partitions on individual file groups enables backup operations for indi-
vidual partitions (by backing their associated file groups).
Syntax
CREATE PARTITION FUNCTION <Partition Function>(<Data Type>)
AS RANGE [LEFT | RIGHT ]
FOR VALUES (<Boundary Value 1>,...)[;]
CREATE PARTITION SCHEME <Partition Scheme>
AS PARTITION <Partition Function>
[ALL] TO (<File Group> | [PRIMARY ] [,...])[;]
CREATE TABLE <Table Name> (<Table Definition>)
ON <Partition Schema> (<Partitioning Column>);
Examples
Create a partitioned table.
CREATE PARTITION FUNCTION PartitionFunction1 (INT)
AS RANGE LEFT FOR VALUES (1, 1000, 100000);
CREATE PARTITION SCHEME PartitionScheme1
AS PARTITION PartitionFunction1
ALLTO (PRIMARY);
CREATE TABLE PartitionTable (
Col1 INT NOT NULLPRIMARY KEY,
Col2 VARCHAR(20)
)
ON PartitionScheme1 (Col1);
For more information, see
l https://docs.microsoft.com/en-us/sql/relational-databases/partitions/partitioned-tables-and-indexes
l https://docs.microsoft.com/en-us/sql/t-sql/statements/create-table-transact-sql
l https://docs.microsoft.com/en-us/sql/t-sql/statements/create-partition-scheme-transact-sql
l https://docs.microsoft.com/en-us/sql/t-sql/statements/create-partition-function-transact-sql
- 423 -
Migrate to: Aurora PostgreSQL Table Inheritance
Feature Com-
patibility
SCT Automation
Level
SCT Action
Code Index
Key Differences
SCT Action
Codes - Par-
titions
Does not support LEFT partition or foreign
keys referencing partitioned tables
Overview
The table partitioning mechanism in PostgreSQL differs from SQL Server. Partitioning in PostgreSQL is
implemented using “table inheritance”. Each table partition is represented by a child table referenced
to a single parent table. The parent table should be empty and is only used to represent the entire
table data set (as a meta-data dictionary and as a query source).
Partition management operations are performed directly on the child tables. Querying is performed
directly on the parent table.
For more information, see:https://www.postgresql.org/docs/9.6/static/ddl-inherit.html
Implementing List Table Partitioning
Follow these steps to implement list table partitioning:
1. Create a parent table (“master table”) from which all child tables (“partitions”) will inherit.
2. Create child tables (which act similar to Table Partitions) that inherit from the parent table. The
child tables should have an identical structure to the parent table.
3. Create Indexes on each child table. Optionally, add constraints (for example, primary keys or
check constraints) to define allowed values in each table.
4. Create a database trigger to redirect data inserted into the parent table to the appropriate child
table.
5. Ensure the PostgreSQL constraint_exclusion parameter is enabled and set to partition. This para-
meter insures the queries are optimized for working with table partitions.
show constraint_exclusion;
constraint_exclusion
----------------------
partition
For additional information on PostgreSQL constraint_exclusion parameter:
https://www.postgresql.org/docs/9.6/static/runtime-config-query.html#GUC-CONSTRAINT-EXCLUSION
- 424 -
PostgreSQL 9.6 does not support “declarative partitioning” nor several of the table partitioning features
available in SQL Server.
Note: PostgreSQL 9.6 Table Partitioning does not support the creation of foreign keys on the
parent table. Alternative solutions include application-centric methods such as trig-
gers/functions.
Note: PostgreSQL 9.6 does not support sub-partitions and does not support SPLIT and
EXCHANGE of table partitions.
Examples
Create a PostgreSQL “list-partitioned table”:
Create the parent table.
CREATE TABLE SYSTEM_LOGS
(EVENT_NO NUMERIC NOT NULL,
EVENT_DATE DATE NOT NULL,
EVENT_STR VARCHAR(500),
ERROR_CODE VARCHAR(10));
Create child tables (“partitions”) with check constraints.
CREATE TABLE SYSTEM_LOGS_WARNING (
CHECK (ERROR_CODE IN('err1', 'err2', 'err3')))INHERITS (SYSTEM_LOGS);
CREATE TABLE SYSTEM_LOGS_CRITICAL (
CHECK (ERROR_CODE IN('err4', 'err5', 'err6'))) INHERITS (SYSTEM_LOGS);
Create indexes on each of the child tables (“partitions”).
CREATE INDEX IDX_SYSTEM_LOGS_WARNING ON SYSTEM_LOGS_WARNING(ERROR_CODE);
CREATE INDEX IDX_SYSTEM_LOGS_CRITICAL ON SYSTEM_LOGS_CRITICAL(ERROR_CODE);
Create a function to redirect data inserted into the Parent Table.
CREATE OR REPLACE FUNCTION SYSTEM_LOGS_ERR_CODE_INS()
RETURNS TRIGGER AS
$$
BEGIN
IF (NEW.ERROR_CODE IN('err1', 'err2', 'err3')) THEN
INSERT INTO SYSTEM_LOGS_WARNING VALUES (NEW.*);
ELSIF (NEW.ERROR_CODE IN('err4', 'err5', 'err6')) THEN
INSERT INTO SYSTEM_LOGS_CRITICAL VALUES (NEW.*);
ELSE
RAISE EXCEPTION 'Value out of range, check SYSTEM_LOGS_ERR_CODE_INS () Function!';
END IF;
RETURN NULL;
END;
$$
LANGUAGE plpgsql;
- 425 -
Attach the trigger function created above to log to the table.
CREATE TRIGGER SYSTEM_LOGS_ERR_TRIG
BEFORE INSERT ON SYSTEM_LOGS
FOR EACH ROW EXECUTE PROCEDURE SYSTEM_LOGS_ERR_CODE_INS();
Insert data directly into the parent table.
INSERT INTO SYSTEM_LOGS VALUES(1, '2015-05-15', 'a...', 'err1');
INSERT INTO SYSTEM_LOGS VALUES(2, '2016-06-16', 'b...', 'err3');
INSERT INTO SYSTEM_LOGS VALUES(3, '2017-07-17', 'c...', 'err6');
View results from across all the different child tables.
SELECT * FROM SYSTEM_LOGS;
event_no | event_date | event_str
----------+------------+-----------
1 | 2015-05-15 | a...
2 | 2016-06-16 | b...
3 | 2017-07-17 | c...
SELECT * FROM SYSTEM_LOGS_WARNING;
event_no | event_date | event_str | error_code
----------+------------+-----------+------------
1 | 2015-05-15 | a... | err1
2 | 2016-06-16 | b... | err3
SELECT * FROM SYSTEM_LOGS_CRITICAL;
event_no | event_date | event_str | error_cod
----------+------------+-----------+------------
3 | 2017-07-17 | c... | err6
Create a PostgreSQL “range-partitioned table”:
Create the parent table.
CREATE TABLE SYSTEM_LOGS
(EVENT_NO NUMERIC NOT NULL,
EVENT_DATE DATE NOT NULL,
EVENT_STR VARCHAR(500));
Create the child tables (“partitions”) with check constraints.
CREATE TABLE SYSTEM_LOGS_2015 (CHECK (EVENT_DATE >= DATE '2015-01-01' AND EVENT_DATE <
DATE '2016- 01-01')) INHERITS (SYSTEM_LOGS);
CREATE TABLE SYSTEM_LOGS_2016 (CHECK (EVENT_DATE >= DATE '2016-01-01' AND EVENT_DATE <
DATE '2017-01-01')) INHERITS (SYSTEM_LOGS);
CREATE TABLE SYSTEM_LOGS_2017 (CHECK (EVENT_DATE >= DATE '2017-01-01' AND EVENT_DATE
<= DATE '2017-12-31')) INHERITS (SYSTEM_LOGS);
Create indexes on each child table (“partitions”).
- 426 -
CREATE INDEX IDX_SYSTEM_LOGS_2015 ON SYSTEM_LOGS_2015(EVENT_DATE);
CREATE INDEX IDX_SYSTEM_LOGS_2016 ON SYSTEM_LOGS_2016(EVENT_DATE);
CREATE INDEX IDX_SYSTEM_LOGS_2017 ON SYSTEM_LOGS_2017(EVENT_DATE);
Create a function to redirect data inserted into the parent table.
CREATE OR REPLACE FUNCTION SYSTEM_LOGS_INS ()
RETURNS TRIGGER AS
$$
BEGIN
IF (NEW.EVENT_DATE >= DATE '2015-01-01' AND
NEW.EVENT_DATE <DATE '2016-01-01') THEN
INSERT INTO SYSTEM_LOGS_2015 VALUES (NEW.*);
ELSIF (NEW.EVENT_DATE >= DATE '2016-01-01' AND
NEW.EVENT_DATE < DATE '2017-01-01') THEN
INSERT INTO SYSTEM_LOGS_2016 VALUES (NEW.*);
ELSIF (NEW.EVENT_DATE >= DATE '2017-01-01' AND
NEW.EVENT_DATE <=DATE '2017-12-31') THEN
INSERT INTO SYSTEM_LOGS_2017 VALUES (NEW.*);
ELSE
RAISE EXCEPTION 'Date out of range. check SYSTEM_LOGS_INS () function!';
END IF;
RETURN NULL;
END;
$$
LANGUAGE plpgsql;
Attach the trigger function created above to log to the SYSTEM_LOGS table.
CREATE TRIGGER SYSTEM_LOGS_TRIG BEFORE INSERT ON SYSTEM_LOGS
FOR EACH ROW EXECUTE PROCEDURE SYSTEM_LOGS_INS ();
Insert data directly to the parent table.
INSERT INTO SYSTEM_LOGS VALUES (1, '2015-05-15', 'a...');
INSERT INTO SYSTEM_LOGS VALUES (2, '2016-06-16', 'b...');
INSERT INTO SYSTEM_LOGS VALUES (3, '2017-07-17', 'c...');
Test the solution by selecting data from the parent and child tables.
SELECT * FROM SYSTEM_LOGS;
event_no | event_date | event_str
----------+------------+-----------
1 | 2015-05-15 | a...
2 | 2016-06-16 | b...
3 | 2017-07-17 | c...
SELECT * FROM SYSTEM_LOGS_2015;
event_no | event_date | event_str
----------+------------+-----------
1 | 2015-05-15 | a...
- 427 -
Summary
The following table identifies similarities, differences, and key migration considerations.
Feature SQL Server Aurora PostgreSQL
Partition types RANGE only RANGE, LIST
Partitioned tables scope All tables are partitioned, some have
more than one partition
All tables are not partitioned
Partition boundary direction LEFT or RIGHT RIGHT
Exchange partition Any partition to any partition N/A
Partition function Abstract function object, Independent
of individual column
N/A
Partition scheme Abstract partition storage mapping
object
N/A
Limitations on partitioned
tables
None — all tables are partitioned Not all commands are com-
patible with table inheritance
For more information, see https://www.postgresql.org/docs/9.6/static/ddl-partitioning.html
- 428 -
Security
- 429 -
Migrate from: SQL Server Column Encryption
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A N/A Syntax and option differences, similar
functionality
Overview
SQL Server provides encryption and decryption functions to secure the content of individual columns.
The following list identifies common encryption functions:
l EncryptByKey and DecryptByKey
l EncryptByCert and DecruptByCert
l EncryptByPassPhrase and DecruptByPassPhrase
l EncryptByAsymKey and DecryptByAsymKey
You can use these functions anywhere in your code; they are not limited to encrypting table columns. A
common use case is to increase run time security by encrypting of application user security tokens
passed as parameters.
These functions follow the general SQL Server encryption hierarchy, which in turn use the Windows
Server Data Protection API.
Symmetric encryption and decryption consume minimal resources and can be used for large data sets.
Note: This section does not cover Transparent Data Encryption (TDE) or AlwaysEncrypted end-
to-end encryption.
Syntax
General syntax for EncryptByKey and DecryptByKey:
EncryptByKey (<key GUID> , {'text to be encrypted' }, {<use authenticator flag>}, {
<authenticator> } );
DecryptByKey ('Encrypted Text' , <use authenticator flag>, {<authenticator> )
Examples
The following example demonstrates how to encrypt an employee Social Security Number:
Create a database master key.
- 430 -
USE MyDatabase;
CREATE MASTER KEY
ENCRYPTION BY PASSWORD = '<MyPassword>';
Create a certificate and a key.
CREATE CERTIFICATE Cert01
WITH SUBJECT = 'SSN';
CREATE SYMMETRIC KEY SSN_Key
WITH ALGORITHM = AES_256
ENCRYPTION BY CERTIFICATE Cert01;
Create an employees table.
CREATE TABLE Employees
(
EmployeeID INT PRIMARYKEY,
SSN_encrypted VARBINARY(128) NOT NULL
);
Open the symmetric key for encryption.
OPEN SYMMETRIC KEY SSN_Key
DECRYPTION BY CERTIFICATE Cert01;
Insert the encrypted data.
INSERTINTOEmployees (EmployeeID, SSN_encrypted)
VALUES
(1, EncryptByKey(Key_GUID('SSN_Key') , '1112223333', 1, HashBytes('SHA1', CONVERT
(VARBINARY, 1)));
SELECT EmployeeID,
CONVERT(CHAR(10),DecryptByKey(SSN, 1 , HashBytes('SHA1', CONVERT(VARBINARY,
EmployeeID)))) AS SSN
FROM Employees;
EmployeeID SSN_EncryptedSSN
-----------------------------------------
1 0x00F983FF436E32418132...1112223333
For more information, see:
l https://docs.microsoft.com/en-us/sql/relational-databases/security/encryption/encrypt-a-column-of-data
l https://docs.microsoft.com/en-us/sql/relational-databases/security/encryption/encryption-hierarchy
- 431 -
Migrate to: Aurora PostgreSQL Column Encryption
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A N/A Syntax and option differences, similar
functionality
Overview
Aurora PostgreSQL provides encryption and decryption functions similar to SQL Server using the
pgcrypto extension. To use this feature, you must first install the pgcrypto extension.
CREATE EXTENSION pgcrypto;
Aurora PostgreSQL supports manyencryption algorithms:
l MD5
l SHA1
l SHA224/256/384/512
l Blowfish
l AES
l Raw encryption
l PGP Symmetric encryption
l PGP Public-Key encryption
This section describes the use of PGP_SYM_ENCRYPT and PGP_SYM_DECRYPT, but there are many
more options available (see at the link and the end of this section).
Syntax
Encrypt columns using PGP_SYM_ENCRYPT.
pgp_sym_encrypt(data text, psw text [, options text ]) returns bytea
pgp_sym_decrypt(msg bytea, psw text [, options text ]) returns text
Examples
The following example demonstrates how to encrypt an employee's Social Security Number:
Create a users table.
CREATE TABLE users (id SERIAL, name VARCHAR(60), pass TEXT);
Insert the encrypted data.
- 432 -
INSERT INTO users (name, pass) VALUES ('John',PGP_SYM_ENCRYPT('123456', 'AES_KEY'));
Verify the data is encrypted.
SELECT * FROM users;
id |name |pass
|
---|-----|----------------------------------------------------------------------------
---------------------------------------------------------------|
2 |John |\x-
c30d04070302c30d07ff8b3b12f26ad233015a72bab4d3b-
b73f5a80d5187b1b043149dd961da58e76440ca9eb4a5f7483cc8ce957b47e39b143cf4b1192bb39 |
Query using the encryption key.
SELECT name, PGP_SYM_DECRYPT(pass::bytea, 'AES_KEY') as pass
FROM users WHERE (name LIKE '%John%');
name |pass |
-----|-------|
John |123465 |
Update the data.
UPDATE users SET (name, pass) = ('John',PGP_SYM_ENCRYPT('0000', 'AES_KEY')) WHERE
id='2';
SELECT name, PGP_SYM_DECRYPT(pass::bytea, 'AES_KEY') as pass
FROM users WHERE (name LIKE '%John%');
name |pass |
-----|-----|
John |0000 |
For more information, see https://www.postgresql.org/docs/9.6/static/pgcrypto.html
- 433 -
Migrate from: SQL Server Data Control Language
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A N/A Similar syntax and similar func-
tionality
Overview
The ANSI standard specifies, and most Relational Database Management Systems (RDBMS) use, GRANT
and REVOKE commands to control permissions.
However, SQL Server also provides a DENY command to explicitly restrict access to a resource. DENY
takes precedence over GRANT and is needed to avoid potentially conflicting permissions for users hav-
ing multiple logins. For example, if a user has DENY for a resource through group membership but
GRANT access for a personal login, the user is denied access to that resource.
SQL Server allows granting permissions at multiple levels from lower-level objects such as columns to
higher level objects such as servers. Permissions are categorized for specific services and features such
as the service broker.
Permissions are used in conjunction with database users and roles. See Users and Roles for more
details.
Syntax
Simplified syntax for SQL Server DCL commands:
GRANT {ALL [PRIVILEGES ] } | <permission> [ON <securable> ] TO <principal>
DENY {ALL [PRIVILEGES ] } | <permission> [ON<securable> ] TO <principal>
REVOKE [GRANT OPTION FOR ] {[ALL [PRIVILEGES ] ]|<permission>} [ON<securable> ] {
TO | FROM } <principal>
For more information, see
https://docs.microsoft.com/en-us/sql/relational-databases/security/permissions-hierarchy-database-engine
- 434 -
Migrate to: Aurora PostgreSQL Data Control Lan-
guage
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A N/A Similar syntax and similar func-
tionality
Overview
Aurora PostgreSQL supports the ANSI Data Control Language (DCL) commands GRANT and REVOKE.
Administrators can grant or revoke permissions for individual objects such as a column, a stored func-
tion, or a table. Permissions can be granted to multiple objects using ALL % IN SCHEMA. % can be
TABLES, SEQUENCES or FUNCTIONS.
Use the following command to grant select on all tables in schema to a specific user.
GRANT SELECT ON ALL TABLES IN SCHEMA <Schema Name> TO <Role Name>;
Aurora PostgreSQL provides a GRANT permission option that is similar to SQL Server's WITH GRANT
OPTION clause. This permission grants a user permission to further grant the same permission to
other users.
GRANT EXECUTE
ON FUNCTION demo.Procedure1
TO UserY
WITH GRANT OPTION;
The following table identifies Aurora MyPostgreSQL privileges.
Permissions Use to
SELECT Allows to query rows from table.
INSERT Allows to insert rows into a table.
UPDATE Allows to update rows in table.
DELETE Allows to delete rows from table
TRUNCATE Allows to truncate a table.
REFERENCES Allows to create a foreign key constraint.
TRIGGER Allows the creation of a trigger on the specified table
CREATE The purpose of this permission depends on the target object. For more
information, see the links at the end of this section.
CONNECT Allows the role to connect to the specified database.
- 435 -
Permissions Use to
TEMPORARY / TEMP Allows creation of temporary tables.
EXECUTE Allow the user to execute a function.
USAGE The purpose of this permission depends on the target object. For more
information, see the links at the end of this section.
ALL / ALL PRIVILEGES Grant all available privileges
Syntax
GRANT {{SELECT | INSERT | UPDATE | DELETE | TRUNCATE | REFERENCES | TRIGGER }
[, ...] | ALL [PRIVILEGES ] }
ON {[TABLE ] table_name [, ...]
| ALL TABLES IN SCHEMA schema_name [, ...] }
TO role_specification [, ...] [WITH GRANT OPTION ]
GRANT {{SELECT | INSERT | UPDATE | REFERENCES } (column_name [, ...] )
[, ...] | ALL [PRIVILEGES ] (column_name [, ...] ) }
ON [TABLE ] table_name [, ...]
TO role_specification [, ...] [WITH GRANT OPTION ]
GRANT {{USAGE | SELECT | UPDATE }
[, ...] | ALL [PRIVILEGES ] }
ON {SEQUENCE sequence_name [, ...]
| ALL SEQUENCES IN SCHEMA schema_name [, ...] }
TO role_specification [, ...] [WITH GRANT OPTION ]
GRANT {{CREATE | CONNECT | TEMPORARY | TEMP } [, ...] | ALL [PRIVILEGES ] }
ON DATABASE database_name [, ...]
TO role_specification [, ...] [WITH GRANT OPTION ]
GRANT {USAGE | ALL [PRIVILEGES ] }
ON DOMAIN domain_name [, ...]
TO role_specification [, ...] [WITH GRANT OPTION ]
GRANT {USAGE | ALL [PRIVILEGES ] }
ON FOREIGN DATA WRAPPER fdw_name [, ...]
TO role_specification [, ...] [WITH GRANT OPTION ]
GRANT {USAGE | ALL [PRIVILEGES ] }
ON FOREIGN SERVER server_name [, ...]
TO role_specification [, ...] [WITH GRANT OPTION ]
GRANT {EXECUTE | ALL [PRIVILEGES ] }
ON {FUNCTION function_name ([[argmode ] [arg_name ] arg_type [, ...] ] ) [,
...]
| ALL FUNCTIONS IN SCHEMA schema_name [, ...] }
TO role_specification [, ...] [WITH GRANT OPTION ]
GRANT {USAGE | ALL [PRIVILEGES ] }
ON LANGUAGE lang_name [, ...]
TO role_specification [, ...] [WITH GRANT OPTION ]
- 436 -
GRANT {{SELECT | UPDATE } [, ...] | ALL [PRIVILEGES ] }
ON LARGE OBJECT loid [, ...]
TO role_specification [, ...] [WITH GRANT OPTION ]
GRANT {{CREATE | USAGE } [, ...] | ALL [PRIVILEGES ] }
ON SCHEMA schema_name [, ...]
TO role_specification [, ...] [WITH GRANT OPTION ]
GRANT {CREATE | ALL [PRIVILEGES ] }
ON TABLESPACE tablespace_name [, ...]
TO role_specification [, ...] [WITH GRANT OPTION ]
GRANT {USAGE | ALL [PRIVILEGES ] }
ON TYPE type_name [, ...]
TO role_specification [, ...] [WITH GRANT OPTION ]
where role_specification can be:
[GROUP ] role_name
| PUBLIC
| CURRENT_USER
| SESSION_USER
GRANT role_name [, ...] TO role_name [, ...] [WITH ADMIN OPTION ]
Examples
Grant SELECT Permission to a user on all tables in the demo database.
GRANT SELECT ON ALL TABLES IN SCHEMA emps TO John;
Revoke EXECUTE permissions from a user on the EmployeeReport stored procedure.
REVOKE EXECUTE ON FUNCTION EmployeeReport FROM John;
For more information, see https://www.postgresql.org/docs/9.6/static/sql-grant.html
- 437 -
Migrate from: SQL Server Transparent Data Encryp-
tion
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A N/A Storage level encryption managed by
Amazon RDS
Overview
Transparent Data Encryption (TDE) is an SQL Server feature designed to protect "data at rest" in the
event an attacker obtains the physical media containing database files.
TDE does not require application changes and is completely transparent to users. The storage engine
encrypts and decrypts data on-the-fly. Data is not encrypted while in memory or on the network. TDE
can be turned on or off individually for each database.
TDE encryption uses a Database Encryption Key (DEK) stored in the database boot record, making it
available during database recovery. The DEK is a symmetric key signed with a server certificate from
the master system database.
In many instances, security compliance laws require TDE for data at rest.
Examples
The following example demonstrates how to enable TDE for a database:
Create a master key and certificate.
USE master;
CREATE MASTER KEY ENCRYPTION BY PASSWORD = 'MyPassword';
CREATE CERTIFICATE TDECert WITH SUBJECT = 'TDE Certificate';
Create a database encryption key.
USE MyDatabase;
CREATE DATABASE ENCRYPTION KEY
WITH ALGORITHM = AES_128
ENCRYPTION BY SERVER CERTIFICATE TDECert;
Enable TDE.
ALTER DATABASE MyDatabase SET ENCRYPTION ON;
For more information, see
https://docs.microsoft.com/en-us/sql/relational-databases/security/encryption/transparent-data-encryption
- 438 -
Migrate to: Aurora PostgreSQL Transparent Data
Encryption
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A N/A Storage level encryption managed by
Amazon RDS
Overview
Amazon Aurora PostgreSQL provides the ability to encrypt data at rest (data stored in persistent stor-
age) for new database instances. When data encryption is enabled, Amazon Relational Database Ser-
vice (RDS) automatically encrypts the database server storage, automated backups, read replicas, and
snapshots using the AES-256 encryption algorithm.
You can manage the keys used for RDS encrypted instances from the Identity and Access Management
(IAM) console using the AWS Key Management Service (AWS KMS). If you require full control of a key,
you must manage it yourself. You cannot delete, revoke, or rotate default keys provisioned by AWS
KMS.
The following limitations exist for Amazon RDS encrypted instances:
l You can only enable encryption for an Amazon RDS database instance when you create it, not
afterward. It is possible to encrypt an existing database by creating a snapshot of the database
instance and then creating an encrypted copy of the snapshot. You can restore the database
from the encrypted snapshot, see:
https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_CopySnapshot.html
l Encrypted database instances cannot be modified to disable encryption.
l Encrypted Read Replicas must be encrypted with the same key as the source database instance.
l An unencrypted backup or snapshot can not be restored to an encrypted database instance.
l KMS encryption keys are specific to the region where they are created. Copying an encrypted
snapshot from one region to another requires the KMS key identifier of the destination region.
Note: Disabling the key for an encrypted database instance prevents reading from, or writing
to, that instance. When Amazon RDS encounters a database instance encrypted by a key to
which Amazon RDS does not have access, it puts the database instance into a terminal state.
In this state, the database instance is no longer available and the current state of the database
can't be recovered. To restore the database instance, you must re-enable access to the encryp-
tion key for Amazon RDS and then restore the database instance from a backup.
Examples
The following walk-through demonstrates how to enable TDE.
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Enable Encryption
In the database settings, enable encryption and choose a master key. You can choose the default key
provided for the account or define a specific key based on an IAM KMS ARN from your account or a dif-
ferent account.
Create an Encryption Key
Navigate to the IAM and click Encryption keys and then CREATEKEY.
Enter the Alias and Description. Under Advanced Options, Select KMS. Click Next.
Add a tag specifying the key’s name. Click Next.
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Click Finish. Click the new key to display the ARN.
Set the master encryption key using the newly created ARN.
Launch the instance.
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Migrate from: SQL Server Users and Roles
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A N/A Syntax and option differences, similar
functionality
There are no users - only roles
Overview
SQL Server provides two layers of security principals:Logins at the server level and Users at the data-
base level. Logins are mapped to users in one or more databases. Administrators can grant logins
server-level permissions that are not mapped to particular databases such as Database Creator, Sys-
tem Administrator, and Security Administrator.
SQL Server also supports Roles for both the server and the database levels. At the database level,
administrators can create custom roles in addition to the general purpose built-in roles.
For each database, administrators can create users and associate them with logins. At the database
level, the built-in roles include db_owner, db_datareader, db_securityadmin and others. A database user
can belong to one or more roles (users are assigned to the public role by default and can't be
removed). Administrators can grant permissions to roles and then assign individual users to the roles
to simplify security management.
Logins are authenticated using either Windows Authentication, which uses the Windows Server Active
Directory framework for integrated single sign-on, or SQL authentication, which is managed by the SQL
Server service and requires a password, certificate, or asymmetric key for identification. Logins using
windows authentication can be created for individual users and domain groups.
In previous versions of SQL server, the concepts of user and schema were interchangeable. For back-
ward compatibility, each database has several existing schemas, including a default schema named
dbo which is owned by the db_owner role. Logins with system administrator privileges are automatically
mapped to the dbo user in each database. Typically, you do not need to migrate these schemas.
Examples
Create a login.
CREATE LOGIN MyLogin WITH PASSWORD = 'MyPassword'
Create a database user for MyLogin.
USE MyDatabase; CREATE USER MyUser FOR LOGIN MyLogin;
Assign MyLogin to a server role.
ALTER SERVER ROLE dbcreator ADD MEMBER 'MyLogin'
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Migrate to: Aurora PostgreSQL Users and Roles
Feature Com-
patibility
SCT Automation
Level
SCT Action Code
Index
Key Differences
N/A N/A Syntax and option differences, similar
functionality
There are no users - only roles
Overview
PostgreSQL supports only roles; there are no users. However, there is a CREATE USER command, which
is an alias for CREATE ROLE that automatically includes the LOGIN permission.
Roles are defined at the database cluster level and are valid in all databases in the PostgreSQL cluster.
Syntax
Simplified syntax for CREATE ROLE in Aurora PostgreSQL:
CREATE ROLE name [[WITH ] option [... ] ]
where option can be:
SUPERUSER | NOSUPERUSER
| CREATEDB | NOCREATEDB
| CREATEROLE | NOCREATEROLE
| INHERIT | NOINHERIT
| LOGIN | NOLOGIN
| REPLICATION | NOREPLICATION
| BYPASSRLS | NOBYPASSRLS
| CONNECTION LIMIT connlimit
| [ENCRYPTED | UNENCRYPTED ] PASSWORD 'password'
| VALID UNTIL 'timestamp'
| IN ROLE role_name [, ...]
| IN GROUP role_name [, ...]
| ROLE role_name [, ...]
| ADMIN role_name [, ...]
| USER role_name [, ...]
| SYSID uid
Example
Create a new database role called "hr_role" that allows users to create new databases in the Post-
greSQL cluster. Note that this role is not able to login to the database and act as a “database user”. In
addition, grant SELECT, INSERT and DELETE privileges on the hr.employees table to the role.
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CREATE ROLE hr_role;
GRANT SELECT, INSERT,DELETE on hr.employees to hr_role;
Summary
The following table summarizes common security tasks and the differences between SQL Server and
Aurora PostgerSQL
Task SQL Server Aurora PostgreSQL
View database
users
SELECT Name FROM sys.sysusers SELECT * FROM pg_roles where
rolcanlogin = true;
Create a user
and password
CREATE USER <User Name> WITH
PASSWORD = <PassWord>;
CREATE USER <User Name> WITH
PASSWORD '<PassWord>';
Create a role
CREATE ROLE <Role Name> CREATE ROLE <Role Name>
Change a user's
password
ALTER LOGIN <SQL Login> WITH
PASSWORD = <PassWord>;
ALTER USER <SQL Login> WITH
PASSWORD '<PassWord>';
External authen-
tication
Windows Authentication N/A
Add a user to a
role
ALTER ROLE <Role Name> ADD
MEMBER <User Name>
ALTER ROLE <Role Name> SET
<property and value>
Lock a user
ALTER LOGIN <Login Name>
DISABLE
REVOKE CONNECT ON DATABASE
<database_name> from <Role
Name>;
Grant SELECT on
a schema
GRANT SELECT ON SCHEMA::<S-
chema Name> to <User Name>
GRANT SELECT ON ALL TABLES IN
SCHEMA <Schema Name> TO <User
Name>;
For more details, see: https://www.postgresql.org/docs/9.6/static/sql-createrole.html
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Appendix A:SQL Server 2018 Deprecated
Feature List
SQL Server 2018 Deprecated
Feature
Section
TEXT, NTEXT, and IMAGE data
types
SQLServer Data Types topic and Aurora PostgreSQL Data Types
topic
SET ROWCOUNT for DML SQL Server Session Options topic and Aurora PostgreSQL Session
Options topic
TIMESTAMP syntax for CREATE
TABLE
SQL Server Creating Tables topic and Aurora PostgreSQL Creating
Tables topic
DBCC DBREINDEX,
INDEXDEFRAG, and
SHOWCONTIG
SQL Server Maintenance Plans topic
Old SQL Mail SQL Server Database Mail
IDENTITY Seed, Increment, non
PK, and compound
SQL Server Sequences and Identity and Aurora PostgreSQL
Sequences and Identity
Stored Procedures RETURN Val-
ues
SQL Server Stored Procedures and Aurora PostgreSQL Stored Pro-
cedures
GROUP BY ALL, Cube, and Com-
pute By
SQL Server GROUP BY and Aurora PostgreSQL GROUP BY
DTS SQL Server ETL and Aurora PostgreSQL ETL
Old outer join syntax *= and =* SQL Server Table JOIN and Aurora PostgreSQL Table JOIN
'String Alias' = Expression Migration Tips
DEFAULT keyword for INSERT
statements
Migration Tips
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Migration Quick Tips
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Migration Quick Tips
This section provides migration tips that can help save time as you transition from SQL Server to Aur-
ora PostgreSQL. They address many of the challenges faced by administrators new to Aurora Post-
greSQL. Some of these tips describe functional differences in similar features between SQL Server and
Aurora PostgreSQL.
Management
l The equivalent of SQL Server's CREATE DATABASE... AS SNAPSHOT OF... resembles Aurora Post-
greSQL Database cloning. However, unlike SQL Server snapshots, which are read only, Aurora
PostgreSQL cloned databases are updatable.
l In Aurora PostgreSQL, the term "Database Snapshot" is equivalent to SQL Server's BACKUP
DATABASE... WITH COPY_ONLY.
l Partitioning in Aurora PostgreSQL is called "INHERITS" tables and act completely different in
terms of management
l Unlike SQL Server's statistics, Aurora PostgreSQL does not collect detailed key value distribution;
it relies on selectivity only. When troubleshooting execution, be aware that parameter values are
insignificant to plan choices.
l Many missing features such as sending emails can be achieved with quick implementations of
Amazon's services (like Lambda).
l Parameters and backups are managed by Amazon's RDS. It is very useful in terms of checking
parameter's value against its default and comparing them to another parameter group.
l High Availability can be implemented in few clicks to create Replicas.
l With Database Links, there are two options. The db_link extension is similar to SQL Server.
SQL
l Triggers work differently in Aurora PostgreSQL. Triggers can be also executed for each row (not
just once). The syntax for inserted and deleted is new and old.
l Aurora PostgreSQL does not support the @@FETCH_STATUS system parameter for cursors.
When declaring cursors in Aurora PostgreSQL, you must create an explicit HANDLER object.
l To execute a stored procedure (functions), use SELECT instead of EXECUTE.
l To execute a string as a query, use Aurora PostgreSQL Prepared Statements instead of either sp_
executesql, or EXECUTE(<String>) syntax.
l In Aurora PostgreSQL, IF blocks must be terminated with END IF. WHILE..LOOP loops must be ter-
minated with END LOOP.
l Aurora PostgreSQL syntax for opening a transaction is START TRANSACTION as opposed to
BEGIN TRANSACTION. COMMIT and ROLLBACK are used without the TRANSACTION keyword.
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l Aurora PostgreSQL does not use special data types for UNICODE data. All string types may use
any character set and any relevant collation.
l Collations can be defined at the server, database, and column level, similar to SQL Server. They
cannot be defined at the table level.
l SQL Server's DELETE <Table Name> syntax, which allows omitting the FROM keyword, is invalid in
Aurora PostgreSQL. Add the FROM keyword to all delete statements.
l Aurora PostgreSQL allows multiple rows with NULL for a UNIQUEconstraint; SQL Server allows
only one. Aurora PostgreSQL follows the behavior specified in the ANSI standard.
l Aurora PostgreSQL SERIAL column property is similar to IDENTITY in SQL Server. However, there
is a major difference in the way sequences are maintained. While SQL Server caches a set of val-
ues in memory, the last allocation is recorded on disk. When the service restarts, some values
may be lost, but the sequence continues from where it left off. In Aurora PostgreSQL, each time
the service is restarted, the seed value to SERIAL is reset to one increment interval larger than the
largest existing value. Sequence position is not maintained across service restarts.
l Parameter names in Aurora PostgreSQL do not require a preceding "@". You can declare local
variables such as SET schema.test = 'value' and get the value by SELECT current_setting('user-
name.test');
l Local parameter scope is not limited to an execution scope. You can define or set a parameter in
one statement, execute it, and then query it in the following batch.
l Error handling in Aurora PostgreSQL has less features, but for special requirements, you can log
or send alerts by inserting into tables or catching errors.
l Aurora PostgreSQL does not support the MERGE statement. Use the REPLACE statement and the
INSERT... ON DUPLICATE KEY UPDATE statement as alternatives.
l You cannot concatenate strings in Aurora PostgreSQL using the "+" operator. 'A' + 'B' is not a
valid expression. Use the CONCAT function instead. For example, CONCAT('A', 'B').
l Aurora PostgreSQL does not support aliasing in the select list using the 'String Alias' = Expres-
sion. Aurora PostgreSQL treats it as a logical predicate, returns 0 or FALSE, and will alias the
column with the full expression. USE the AS syntax instead. Also note that this syntax has been
deprecated as of SQL Server 2008 R2.
l Aurora PostgreSQL has a large set of string functions that is much more diverse than SQL Server.
Some of the more useful string functions are:
l TRIM is not limited to full trim or spaces. The syntax is TRIM([{BOTH | LEADING |
TRAILING} [<remove string>] FROM] <source string>)).
l LENGTH in PostgreSQL is equivalent to DATALENGTH in T-SQL. CHAR_LENGTH is the equi-
valent of T-SQL LENGTH.
l SUBSTRING_INDEXreturns a substring from a string before the specified number of occur-
rences of the delimiter.
l FIELD returns the index (position) of the first argument in the subsequent arguments.
l POSITION returns the index position of the first argument within the second argument.
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l REGEXP_MATCHES provides support for regular expressions.
l For more string functions, see
https://www.postgresql.org/docs/9.6/static/functions-string.html
l The Aurora PostgreSQL CAST function is for casting between collation and not other data types.
Use CONVERT for casting data types.
l Aurora PostgreSQL is much stricter than SQL Server in terms of statement terminators. Be sure
to always use a semicolon at the end of statements.
l There is no CREATE PROCEDURE syntax; only CREATE FUNCTION. You can create a function that
returns void.
l Beware of control characters when copying and pasting a script to Aurora PostgreSQL clients.
Aurora PostgreSQL is much more sensitive to control characters than SQL Server and they result
in frustrating syntax errors that are hard to find.
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Glossary
ACID
Atomicity, Consistency, Isolation, Durability
AES
Advanced Encryption Standard
ANSI
American National Standards Institute
API
Application Programming Interface
ARN
Amazon Resource Name
AWS
Amazon Web Services
BLOB
Binary Large Object
CDATA
Character Data
CLI
Command Line Interface
CLOB
Character Large Object
CLR
Common Language Runtime
CPU
Central Processing Unit
CRI
Cascading Referential Integrity
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CSV
Comma Separated Values
CTE
Common Table Expression
DB
Database
DBCC
Database Console Commands
DDL
Data Definition Language
DEK
Database Encryption Key
DES
Data Encryption Standard
DML
Data Manipulation Language
DQL
Data Query Language
FCI
Failover Cluster Instances
HADR
High Availability and Disaster Recovery
IAM
Identity and Access Management
IP
Internet Protocol
ISO
International Organization for Standardization
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JSON
JavaScript Object Notation
KMS
Key Management Service
NUMA
Non-Uniform Memory Access
OLE
Object Linking and Embedding
OLTP
Online Transaction Processing
PaaS
Platform as a Service
PDF
Portable Document Format
QA
Quality Assurance
RDMS
Relational Database Management System
RDS
Amazon Relational Database Service
REGEXP
Regular Expression
SCT
Schema Conversion Tool
SHA
Secure Hash Algorithm
SLA
Service Level Agreement
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SMB
Server Message Block
SQL
Structured Query Language
SQL/PSM
SQL/Persistent Stored Modules
SSD
Solid State Disk
SSH
Secure Shell
T-SQL
Transact-SQL
TDE
Transparent Data Encryption
UDF
User Defined Function
UDT
User Defined Type
UTC
Universal Time Coordinated
WMI
Windows Management Instumentation
WQL
Windows Management Instrumentation Query Language
WSFC
Windows Server Failover Clustering
XML
Extensible Markup Language
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