![]() ![]() I may connect to the instance using SQL Server Management Studio (SSMS) or other SQL client such as sqlcmd. Once the cluster and instances are ready, I connect to the writer instance to create the database itself. Once the cluster is created, I create an instance using aws rds create-db-instance \ "DBClusterIdentifier": "awsnewblog-cli-demo", db-cluster-parameter-group-name myapp-babelfish db-subnet-group-name default-vpc-1234abcd \ db-cluster-identifier awsnewblog-cli-demo \ Then I create the database cluster (when using the command below, adjust the security group id and the subnet group name) : PG_RDS_LATEST_VERSION=$(aws rds describe-db-engine-versions -engine aurora-postgresql -query 'max(DBEngineVersions.EngineVersion)' -output text) parameters "ParameterName=rds.babelfish_status,ParameterValue=on,ApplyMethod=pending-reboot" \ db-parameter-group-family aurora-postgresql13 \Īws rds modify-db-cluster-parameter-group \ db-cluster-parameter-group-name myapp-babelfish \ I first create a parameter group to activate Babelfish (the console does it automatically): aws rds create-db-cluster-parameter-group \ This option requires additional IAM permissions and preparation that are not relevant for this demo.Īfter a couple of minutes, my cluster is created, it has two instances, one writer and one reader.Īlternatively, I may use the CLI to create a cluster. Under Monitoring section, I also make sure I turn off Enable Enhanced monitoring. Then, lower on the page, I select the option Turn on Babelfish. The updated console has additional filters to help you select the versions that are compatible with Babelfish. In the RDS launch wizard, I first make sure I select an Aurora version compatible with PostgreSQL 13.4, or more recent. The procedure is no different than for the regular Amazon Aurora database. To show you how Babelfish works, let’s first connect to the console and create a new Amazon Aurora PostgreSQL cluster. When you create a table with this datatype through the Babelfish connection, you get this compatible datatype and behaviors that a SQL Server app would expect.Ĭreate a Babelfish Cluster Using the Console In this case, and many others, Babelfish ensures the semantics of SQL Server data types and T-SQL functionality are preserved: we created a MONEY datatype that behaves as SQL Server apps would expect. Such a subtle difference might lead to rounding errors and have a significant impact on downstream processes, such as financial reporting. For example, the MONEY datatype has different characteristics in SQL Server (with four decimals precision) and PostgreSQL (with two decimals precision). Instead, we focused on the most common T-SQL commands and returning the correct response or an error message. ![]() SQL Server has evolved over more than 30 years, and we do not expect to support all functionalities right away. Amazon Aurora provides the security, availability, and reliability of commercial databases at 1/10th the cost. When adopting Babelfish, you save on licensing costs of using SQL Server. Babelfish reduces the risk associated with database migration projects by significantly reducing the number of changes required to the application. Support for T-SQL includes elements such as the SQL dialect, static cursors, data types, triggers, stored procedures, and functions. Babelfish adds the capability to Amazon Aurora PostgreSQL to understand the SQL Server wire protocol Tabular Data Stream (TDS), as well as extending PostgreSQL to understand commonly used T-SQL commands used by SQL Server. Just point the application to an Amazon Aurora PostgreSQL database with Babelfish activated. ![]() You continue to use the existing queries and drivers your application uses today. You can migrate your application in a fraction of the time that a traditional migration would require. It allows you to migrate your SQL Server applications to PostgreSQL cheaper, faster, and with less risks involved with such change. Babelfish allows Amazon Aurora PostgreSQL-Compatible Edition to understand the SQL Server wire protocol. Today, we are making Babelfish for Aurora PostgreSQL available. Motivation is there, but costs and risks are often limiting factors. But there is always more work to do to migrate the application itself, including rewriting application code that interacts with the database. When migrating your databases, you can automate the migration of your database schema and data using the AWS Schema Conversion Tool and AWS Database Migration Service. But migrating away from commercial and legacy databases can be time-consuming and resource-intensive. Many of our customers are telling us they want to move away from proprietary database vendors to avoid expensive costs and burdensome licensing terms. ![]()
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