Convert Microsoft Sql Server To Postgresql

[Rell Jette]

Youcan use the SQL Server to PostgreSQL extension pack in AWS SCT. This extension pack emulates SQL Server database functions in the converted PostgreSQL code. Use the SQL Server to PostgreSQL extension pack to emulate SQL Server Agent and SQL Server Database Mail. For more information about extension packs, see Using extension packs with AWS Schema Conversion Tool.

In PostgreSQL, only the schema owner or a superuser can drop a schema. The owner can drop a schema and all objects that this schema includes even if the owner of the schema doesn't own some of its objects.

When you use different users to convert and apply different schemas to your target database, you can get an error message when AWS SCT can't drop a schema. To avoid this error message, use the superuser role.

For Add comments in the converted code for the action items of selected severity and higher, choose the severity of action items. AWS SCT adds comments in the converted code for action items of the selected severity and higher.

PostgreSQL version 10 and earlier doesn't support procedures. For customers who aren't familiar with using procedures in PostgreSQL, AWS SCT can convert procedures to functions. To do so, select Convert procedures to functions.

Your source SQL Server database can store the output of EXEC in a table. AWS SCT creates temporary tables and an additional procedure to emulate this feature. To use this emulation, select Create additional routines for handling open datasets.

To avoid conversion of object names to lower case, select Avoid casting to lower case for case sensitive operations. This option applies only when you turn on case sensitivity option in your target database.

When you convert a Microsoft SQL Server database to Amazon Aurora PostgreSQL-Compatible Edition (Aurora PostgreSQL) or Amazon Relational Database Service for PostgreSQL (Amazon RDS for PostgreSQL), be aware of the following.

SQL Server allows you to create primary and unique keys for partitioned tables. For PostgreSQL, you create primary or unique keys for each partition directly. Thus, PRIMARY or UNIQUE KEY constraint must be removed from their parent table when migrating to PostgreSQL. The resulting key names take the format _.

SQL Server allows you to create indexes for partitioned tables. For PostgreSQL, an index should be created for each partition directly. Thus, indexes must be removed from their parent tables when migrating to PostgreSQL. The resulting index names take the format _.

A GOTO statement and a label can be used to change the order that statements are run in. Any Transact-SQL statements that follow a GOTO statement are skipped and processing continues at the label. GOTO statements and labels can be used anywhere within a procedure, batch, or statement block. GOTO statements can also be nested.

SQL Server automatically creates and manages deleted and inserted tables. You can use these temporary, memory-resident tables to test the effects of certain data modifications and to set conditions for DML trigger actions. AWS SCT can convert the usage of these tables inside DML trigger statements.

PATINDEX returns the starting position of the first occurrence of a pattern in a specified expression on all valid text and character data types. It returns zeros if the pattern is not found. When converting from SQL Server to Amazon RDS for PostgreSQL, AWS SCT replaces application code that uses PATINDEX with aws_sqlserver_ext.patindex(, ) .

In SQL Server, a user-defined table type is a type that represents the definition of a table structure. You use a user-defined table type to declare table-value parameters for stored procedures or functions. You can also use a user-defined table type to declare table variables that you want to use in a batch or in the body of a stored procedure or function. AWS SCT emulated this type in PostgreSQL by creating a temporary table.

Many organizations are benefiting from the performance gains and ease of administration of the Amazon Aurora database platform. The idea of breaking free from commercial licenses, backup administration, and data center maintenance is always a welcome thought. But where to begin, and how complex is the journey to migrate from SQL Server to Amazon Aurora PostgreSQL-Compatible Edition are common questions. AWS offers different paths for your migration journey, such as the AWS Schema Conversion Tool (AWS SCT), which makes conversions to PostgreSQL easier, and Babelfish, which allows Aurora to understand the SQL Server TDS protocol and language.

In this post, we focus on the approach of re-architecture directly into the PostgreSQL language without using Babelfish. It uses the AWS SCT and AWS Database Migration Service (AWS DMS) to convert code from SQL Server to PostgreSQL. For more information about migrating using Babelfish, visit Using Babelfish to migrate to PostgreSQL.

One of the most time-consuming and technically challenging tasks for organizations and DBAs when deciding to migrate from one database platform to another is the planning phase, including both schema conversion and the physical migration of the data. We can break this phase down into five main components:

Database migrations often happen in phases to minimize risk and associated downtime. Typically, companies choose from three common migration strategies when migrating to AWS. We briefly describe each of these in this section.

Rehosting (otherwise known as lift and shift) involves migrating a database to the cloud in its current form. This reduces the time you spend managing database infrastructure such as the data center and physical servers. Other than the physical migration of the data to a cloud-managed virtual instance, all other aspects of the database remain the same.

Replatforming a database allows you to move to a cloud-based fully managed database service while keeping the source database engine unchanged (homogeneous migration). Keeping the database engine the same minimizes complexity and risk of the overall migration while still benefiting from all the features included with a fully managed database service offering.

Re-architecting (otherwise known as rebuilding or refactoring) a database can involve many changes, which may include moving from a relational engine such as on-premises SQL Server to Aurora PostgreSQL. This may also include decomposing a database schema into smaller and more defined subsets that lend well to a microservices model. This may also lead to utilizing several different database platforms and models. Re-architecting provides the right data platform for your workload. This is the most time-consuming and difficult approach, but also yields the most long-term rewards.

The complexities of converting and migrating a database (including constraints, tables, indexes, stored procedures, and views) from one database platform to another can be involved and time-consuming, especially when coupled with strict uptime requirements. Fortunately, when re-architecting SQL Server to Aurora PostgreSQL, you have access to purpose-built tools such as AWS SCT and AWS DMS. In addition, we have developed particular prescriptive guidance through a comprehensive migration playbook. This playbook guides you through the complete journey of converting a SQL Server database schema to Aurora PostgreSQL, implementing the schema changes on the new instance, and finally migrating the data.

AWS SCT provides a project-based user interface to automatically convert the database schema of your source database into a format compatible with your target RDS instance. The following table indicates the level of automation AWS SCT gives for each type of SQL Server database object. This also gives you an idea of how much manual work is required for each object when converting to Aurora PostgreSQL. For example, converting SQL Server tables, views, and stored procedures has an automation rating of 4/5, which implies a higher level of automation versus manual work. In contrast, converting objects like cursors have a conversion automation rating of 3/5, which requires a bit more manual work.

AWS DMS helps you migrate databases to AWS quickly and securely. The source database can remain fully operational during the migration, minimizing downtime to applications when failover occurs. AWS DMS supports homogeneous migrations such as on-premises Microsoft SQL Server to Amazon RDS for SQL Server as well as heterogeneous migrations between different database platforms such as on-premises Oracle to Amazon DynamoDB and Microsoft SQL Server to Amazon DynamoDB.

This customer generated $126 billion in 2017 revenues. The company operates reliable wireless and all-fiber networks, and delivers integrated solutions to businesses worldwide. One of its subsidiaries reaches about one billion people around the world with dynamic media and technology brands. They are currently operating a mission critical application that supports a customer ordering interface used by call representatives (users) to attend to its customers. The application is comprised of 40 Microsoft SQL instances, totaling 97 databases, 47 TB of storage, and a large schema footprint that includes 1,919 stored procedures and 3,021 tables. Given its business criticality, the application has millisecond latency SLAs for all APIs and the customer wanted to preserve similar performance in Aurora PostgreSQL.

The customer re-architected 40 in-house SQL Server database servers to nine Amazon Aurora instances, significantly reducing operational, licensing, and hardware costs. End to end, the customer completed the database schema migration for its over 8,000 database objects and functional code testing on the converted schema in 12 months. Currently, the application is in production meeting the defined SLAs of its business needs.

In this section, we share best practices we uncovered when converting SQL Server to Aurora PostgreSQL. These are lessons learned directly from real database migration engagements performed for customers around the world. Each category includes a table that summarizes each scenario with corresponding links to relevant documentation and further reading.

3a8082e126