AWS

For an updated ready-to-use CloudFormation template of this code, see newer post: Complete code: cross-region RDS recovery .

Amazon RDS is a great database-as-a-service, which takes care of almost all database-related maintenance tasks for you - everything from automated backups and patching to replication and fail-overs into another availability zones.

Unfortunately all of this fails if the region where your RDS is hosted fails. Region-wide failures are very rare, but they do happen! RDS does not support cross-region replication at the moment, so you cannot simply create a replica of your database in another region (unless you host the database on an EC2 instance and set up the replication yourself). The second-best option, to make sure you can restore your service quickly in another region, is to always have a copy of your latest database backup in that region. In case of RDS, that can mean copying automated snapshots. There is no option for AWS to do it automatically, but it can be easily scripted with AWS Lambda functions.

The purpose of every deployment pipeline is… a deployment. So this final part of the series, will focus on just that. If you missed the intro, check out the video where I describe a typical pipeline here. The other parts of this series are part 1: test phase and part 2: build phase.

Once we create an AMI that we’d like to deploy, performing a rolling update on existing instances is fairly easy. Usage of Auto scaling groups and CloudFormation makes it even easier - since AWS Auto scaling groups support the rolling updates out of the box.

Or, if your applications requires blue-green deployments, using CloudFormation is almost essential.

Welcome to the second part of my series on deployment pipelines. If you missed the intro, check out the video where I describe a typical pipeline here. The other parts of this series are part 1: test phase and part 3: deployment onto an environment.

After the test phase of the pipeline, once the quality of the code has been checked, we must build a deployable artefact for this version (commit) of the code. In case of AWS, this would be an AMI (Amazon Machine Image), which can then be deployed as a new instance in our environment.

If you’ve ever wrote AWS CloudFormation template, you probably know that it can be a daunting task. Luckily, it can be much easier, if you use Python’s library “Troposphere”.

Troposphere lets you create Python objects in place of CloudFormation elements, does some basic validation of your input and generates the JSON template for CloudFormation for you. It is much easier and cleaner to use that writing JSON templates manually.