In the world of software development, ensuring the quality and reliability of code is paramount. One popular approach to achieving this goal is Test-Driven Development (TDD), where developers write tests before writing the actual code. TDD is also my default way of approaching software development.

However, I’ve started in many environments situations where code changes are made without accompanying automated tests. In such cases, I am using a method I call “Review-Driven Testing”, which helps not only to improve code quality and upskill developers, but also to promote a testing culture within development teams.

What is Review-Driven Testing?

Review-Driven Testing is a practical approach to adding automated tests to code changes that were initially developed without them. While traditional TDD focuses on writing tests before writing code, Review-Driven Testing involves writing tests after the code has been developed and submitted for review. It can be seamlessly used in teams that are not used to writing automated tests by default (yet).

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Code reviews can be very time-consuming. Especially if you are reviewing a lot of code changes. Often, code changes are simple refactorings, such as extracting code into methods/functions or moving code around. These changes are more or less irrelevant for the review, since they usually don’t change the behavior of the code. As a reviewer, one still wants to make sure that code hasn’t been changed during the code move.

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Since AWS is going to sunset / auto-upgrade Aurora Serverless v1 in December 2024, it’s time to upgrade to Aurora Serverless v2. Upgrading to Serverless v2 MySQL instances also means upgrading from MySQL 5.7 to MySQL 8.0 under the hood.

This article describes how to adapt the AWS provided upgrade path to work with a CDK / CloudFormation infrastructure-as-code project, which requires a few more steps which I call the “CDK/CloudFormation retain-and-import dance“.

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This article demonstrates how to upgrade an CDK (or Cloudformation)-managed AWS Aurora cluster from MySQL 5.7 compatible AWS Aurora 2 to MySQL compatible Aurora 3 engine without any - ok, I lied - one minute of downtime. The process utilizes the blue/green deployment feature of Aurora. It also shows how CDK/CloudFormation stacks are brought back in sync with the upgraded Aurora clusters.

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When managing RDS parameter groups with CloudFormation, you might have noticed that changes to a DB cluster or instance parameter group will always cause a reboot of the RDS instance. This happens when the RDS cluster or instance parameter group and the RDS instance are managed by the same CloudFormation stack.

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A recurring task when provisioning databases is to initialize them with initial users, stored procedures, and/or tables.

In the CDK world, there is currently no native support for this, but there are some workarounds. AWS has a blog post about this, but it’s using a Lambda function, and a bunch of custom code which seemed like too much operational overhead for me.

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Having defined what is serverless and what not in the last article, this time let’s have a look at one particular characteristic of serverless systems: they can scale to zero also known as: you don’t pay for idling resources. So let’s discuss why this is actually very important.

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Setting up Amazon Aurora as a replica of an external MySQL primary is a common way of synchronizing and/or migrating self-managed MySQL databases to RDS/Aurora.

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Recently I was trying to restore a Aurora database from an Percona xtrabackup, the de-facto industry standard for backing up self-managed MySQL databases. Luckily, RDS and Aurora natively support restoring a cluster from Percona xtrabackups. This comes very handy for migrations of big databases (For more information, check out the docs and this prescriptive guidance article from AWS).

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For me, serverless has become the default model of developing backend applications.
However, a meeting a few weeks ago was a reminder for me that not the entire world is embracing serverless applications already: I had the task to design an “Energy Data Importer”, an application that collects time series data from different locations, sanitizes and transforms them and stores them in a database for later lookup. So I went ahead and sketched the application with AWS serverless building blocks and patterns.

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