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Amazon Databse
The database upgrade process involves recreating the target database using a regular copy of the database database, usually done for testing and development purposes. Fully managed database solutions like Amazon Relational Database Service (Amazon RDS) or Amazon Aurora make this easy. However, database administrators may need to perform some post-upgrade operations, such as hiding data or changing passwords, or they may need to schedule multiple upgrades because they are managing multiple databases, each with more than one environment. In some cases, frequent, even daily updates are required.
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In this post, we describe serverless solution features that you can use to perform database update operations at scale, with a greater degree of automation. This solution can be used and tested using the instructions available in the GitHub repo. In the next section, we’ll look at what you’re going to build.
The solution deployed focused on a time-consuming administrative task that DBAs had to deal with: updating the database.
This process includes rebuilding the existing database. Usually this is a clone used for testing and development and its data needs to be “updated”. You can use the last available backup or snapshot of the linked production environment to update the database. The solution can be applied in situations when you are creating a new environment from scratch.
The backup can be logical (partial or complete export of the source database) or physical (a binary copy of the database, which can be full, incremental, full or partial). The solution described in this post allows you to use physical backups (Amazon RDS or Aurora snapshots) during restore or Aurora replication to replicate your database.
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Before describing in more detail what the solution looks like and how it works, it is important to understand at a high level the key steps that are part of a database update:
The Step Functions State Machine implemented for this solution includes several states; most of them involve specific steps of the database update operation. Specifically, some states are required only for Amazon RDS, others are only for Aurora, and others are required for both.
The following table lists the main steps involved in updating the RDS DB version performed by our solution.
The following table lists the main steps involved in the Aurora cluster upgrade performed by our solution.
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A graphical representation of the State Function State machine containing the above states is available on the GitHub repo. You can use it on RDS DB instances, Aurora clusters, or both.
One of the main challenges in coordinating deployments with serverless services is managing their stateless nature. When a particular task is performed by a database-driven Lambda function, how do we know when the task is complete? Job search strategy is a good solution. The following image is an excerpt from the solution showing this mechanism:
The steps to update the database are ordered by the Step Functions state machine based on a given input file – the “update file”. It’s a JSON document containing all the input parameters to the state machine (especially Lambda functions related to state machine states) that define the properties of the update.
The update file contains information about a specific update, so ideally for a production database with two different non-production environments (one for development and one for testing), a DBA should prepare two update files. Once these files are identified, they are ready to use and the associated update can be scheduled or automated.
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The file contains one element for each state machine state that requires input. For more information on how to identify it, see the GitHub repository.
In this post, we show you three examples of the elements involved in the three different steps of the database update process.
The previous section of the update file indicates that the RDS for the MySQL database instance “mysqlinstp” should be used as the source for updating to the latest point of the database instance “mysqlinstd”. This section includes other information about the new database to be restored, including security group ID, storage type, and DB instance class. The state machine checks every 60 seconds whether the refresh operation is complete. In the “controls” section, you can see that the database is always restored with a name ending with the “%temp” suffix. This suffix will be removed later in another step.
The previous section of the upgrade file indicates that the newly upgraded RDS Database instance “mysqlinstdtemp” needs to be renamed to “mysqlinstd”. The state machine checks every 10 seconds if the rename operation is complete.
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The previous section of the update file indicates that the “00test.sql” and “01test.sql” scripts hosted in Amazon S3 in the “olproj” bucket will be run using an RDS-based Lambda for the “mysqlinstd” MySQL DB instance. Database credentials are retrieved using Secret Manager and job status is checked every 10 seconds.
At the end of the restore process, the new database has the same password for all users in the database, including the primary user. This situation can represent a problem from a security point of view, and so the Step Function’s State Machine consists of the following two states:
, the master user’s password is automatically changed with the new password specified in the update file. If a Privacy Manager secret is set for that database, the secret can also be synced. See the following code:
, if the Secret Manager secret is set and the distribution options are enabled, the secret containing the user’s master password will be converted:
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The first option is more suitable if you are more familiar with Lambda and want to maintain the infrastructure of a completely serverless solution. On the other hand, DBAs who are used to managing SQL scripts directly on the server can easily manage them using System Manager: the script is downloaded from Amazon S3 to an EC2 instance as part of the solution and run from there.
In an SQL script you can run SELECT, DDL (Data Definition Language), DML (Data Manipulation Language) and DCL (Data Control Language). At the time of this writing, this feature is only available for MySQL relational databases (Amazon RDS for MySQL, Amazon RDS for MariaDB, and Aurora MySQL)
Updating databases is especially important when you have to manage hundreds of related production and non-production databases. This solution uses encrypted DynamoDB tables to record information about database updates, allowing you to quickly answer questions like:
As of this writing, the solution does not provide a local secondary index (LSI) or a global secondary index (GSI) for the table, but you can easily add a new GSI to increase the number of access patterns that can be accommodated according to your needs.
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You can easily monitor the operation of your state machines using the Step Functions API or its dashboard.
The graphical checker can immediately tell you which state is faulty or what state the machine is in. If you select a state, you can also see the associated input and output.
Lambda records information about its runs in Amazon CloudWatch Logs, from which you can get more information about what happened during a particular activity.
The solution uses Amazon SNS to send an email about the success or failure of the database update being performed. If successful, the message sent contains some details about the newly updated database.
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The solution could be improved in several respects, especially when it comes to including database updates. As of this writing, the input has to be prepared manually, but in the future we are thinking of providing a user interface where you can generate linked JSON files and immediately perform some preliminary checks that can verify the information provided.
Users are notified via Amazon SNS, but another option might be to use Amazon Simple Email Service (Amazon SES) to get more detailed information about updates made by sending a formatted email with additional information about the newly updated database.
At the time of this writing, the solution does not support Amazon RDS for SQL Server, and running SQL scripts after the upgrade is only available for MySQL-related tools. We are working to bring these features to the rest of the tools.
In this post, we have shown how to automate database update operations using serverless technology. The solution described can help you increase the level of automation in your infrastructure; especially it can help reduce
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