I recently got an opportunity to work on migrating Azure SQL Database to Amazon Aurora. Well, it was not technically a migration since our platform needed to support both Azure SQL and Amazon Aurora. The motivation behind this is perhaps a separate post in itself.

For those who have not worked on either of these database engines, here is a little introduction to both.

Azure SQL Database is SQL Server compatible Database as Service on Microsoft’s cloud, Azure.

Amazon Aurora is a MySQL and PostgreSQL-compatible relational database built on Amazon cloud, AWS. We chose to use MySQL flavor for Amazon Aurora.

There was an option to use Amazon RDS for SQL Server. However, there was a significant price difference between SQL Server and Aurora. We realized that the cost and effort for the rewrite would be far less than hosting on Amazon RDS for SQL Server in the longer run.

The migration was a great learning experience for someone like me who has not kept his foot outside Microsoft Technologies or Azure for most of part of his career.

Here are some of the changes we had to do to add support for Amazon Aurora.

The changes required

Docker Image

With Azure SQL, we used docker image mcr.microsoft.com/mssql/server for development since Azure SQL is compatible with SQL Server.

Amazon Aurora, on the other hand, is compatible with MySQL 5.6 and 5.7 using the InnoDB storage engine. The docker image we used for our development was mysql:5.7

SQL IDE/ Editor

The SQL Server comes with SQL Server Management Studio (SSMS) from Microsoft. SSMS provided an easy way to view and manipulate tables/ queries.

For MySQL, we ended up using MySQL Workbench. One interesting issue we had while trying to write SQL queries on MySQL Workbench was that the queries MySQL Workbench by default supported version 8, and the query syntax was not compatible with MySQL 5.7 .

This was a little bit surprising for me since with SQL Server query syntax has mostly been backward compatible. To support the previous version of MySQL I had to update the MySQL default target version by going to Edit -> Preferences -> Modeling -> MySQL and setting the version to 5.7.28 .

Default Target MySQL Version setting

Database Setup – SSDT vs UpDatabase

Our existing DB scripts were written using SSDT for SQL Server. With MySQL, we ended up using the dbup-mysql Nuget package to deploy and upgrade the SQL Server database.

Note that dbup also supports SQL Server. However, there was no motivation for us to move our existing SQL Server implementation to dbup since SSDT works very well for us.

SQL Script differences

Both SQL Server and MySQL are SQL. In reality, their syntax is more different than they are similar. Here are a few differences we came across while converting our queries to MySQL.

We need to use backtick or ` in MySQL to escape keywords instead of [] in SQL Server.

in MySQL to escape keywords instead of in SQL Server. AUTO_INCREMENT keyword to define IDENTITY .

keyword to define . MySQL does not support DateTimeOffset . As a result, we need to save the date in UTC format.

. As a result, we need to save the date in format. MySQL and SQL Server differ in the way we define indexes. For example, MySQL does not support the filtered index.

MySQL does not allow us to DECLARE variables outside of Stored Procedures

variables outside of Stored Procedures MySQL does not have support for syntax like MERGE INTO , WHEN MATCH etc.

, etc. One interesting and less obvious difference between the two is that the data type of COUNT in MySQL is Int64 while in SQL sever it is INT32 . SQL Server COUNT_BIG is the closest to MySQL COUNT .

Note: This list is not exhaustive and it only describes a few of differences I came across during the conversion

Dapper Support

We used Dapper ORM to talk SQL Server from code. This turned out a great choice for us as Dapper also has support for MySQL. This made the porting the queries quite easy. We were able to have a common interface that defined operations and then specific implementation for SQL Server and MySQL.

Conclusion

It was a bit of work but a fun learning exercise for us to add support for Amazon Aurora along with the existing Azure SQL Database.

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