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Continuous Delivery with CodePipeline, CodeBuild and Maven

Saturday, 02 December 2017, 21:47

Neue Arbeit, neue Stadt, neue Blog Post. My adventure in HRS has finished. Time for the next step, but next step starts next Monday, so I have some time to spend with family and update my blog a little. As recently I was mainly involved into managing infrastructure of various projects on AWS and picked CloudFormation as a tool to manage all of the resources, I discovered a lot of quirks, sometimes undocumented behaviors, but mostly just built simple stuff from simple pieces that are working great with a minimum effort. Want to share some of my knowledge here, especially that AWS documentation is usually very fragmented, unclear or missing; which doesn't change the fact, that still - it provides all you need to build the stuff you want, managing most of them for you.

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CloudFormation custom resource providers - with any runtime, any toolchain, any deployment pipeline

Thursday, 27 May 2021, 22:05

Yes, CloudFormation again. It may seems funny how many trouble case-studies and troubleshooting guides I can write for a tool I claim is best IaaC solution for AWS cloud… but I bet this is how it works for most of popular tools - you always have some edge cases and new features that require stabilization, yet if the tool is flexible enough you can overcome the downsides. In CloudFormation you can do so by using macros, modules, custom resources ("old way") or - subject of this post - resource providers. Resource providers allow for extending CloudFormation capabilities to handle custom resources in a standardized approach. You can think of it as a superior replacement for AWS::CloudFormation::CustomResource. Custom resources had a lot of limitations: you had to manage permissions for each instance - in resource providers it's the provider that defines required permissions; resource handler execution time was limited to single Lambda execution time (in fact you could count it times three thanks for handling retries) which was up to 45 minutes in current setup - resource providers allow you to report progress and integrate stabilization flow to extend this time to tens of hours. Resource providers are more integrated with CloudFormation service itself - in fact huge part of natively-supported resources is released open-source by AWS as resource providers. As always there is also some but and here I will try to describe my little experiment lifting the boundaries of resource providers to cover scenarios and needs that AWS claims are not "supported" right now.

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Bootstrapping AWS account with CloudFormation and CodePipeline

Saturday, 09 June 2018, 08:45

Yes, I love CloudFormation when working with AWS. With all of it's limitations and quirks, it's a really simple and extensible tool, that allows you fully manage your resources in an automated Infrastructure-as-a-code approach. If you create a project from scratch you probably want to automate as much as possible - if you decide on CloudFormation you probably want to figure out how to provision as much as possible from your AWS cloud with it? The thing is: you can manage everything - by using just a single command it's possible to bootstrap your account in a fully automated manner.

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Variables namespaces in CodePipeline for multi-stage deployment pipelines

Thursday, 16 July 2020, 22:26

In November 2019 AWS CodePipeline introduced variables namespaces to allow passing outcomes of one pipeline action into another. Previously passing values between stages was very tricky and required involving of additional services (eg. S3 or DynamoDB). Things worked however little different if you were provisioning your pipeline with CloudFormation - since your pipeline definition was wrapped in a template you had a chance to inject deployment-time values into it via templating functions. To make things even more usable, but also complex, if you were defining CloudFormation step in CodePipeline you had an option to use Fn::GetParam function (which you couldn't use anywhere else, it was it's sole purpose). Now, with variables namespaces in CodePipeline and support for this feature in CloudFormation building more dynamic pipelines that manage your AWS resources in infrastructure-as-a-code approach and vice-versa became much simpler and robust!

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Multiple Amazon API Gateway stages

Sunday, 03 December 2017, 23:08

Short time ago I described CI/CD pipeline design, that allows for handling multiple environment deployments replicated from same CloudFormation template. Process is usually quite easy if you have some EC2-based deployment, per-environment ECS cluster or Lambda functions that you can deploy freely however you like. Things get complicated when you integrate API Gateway. The easiest way would be to deploy separate APIs for each of your environments. But it seems so wrong, isn't it? API Gateway has a feature of stages, that seems to be perfect for such cases.

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Deploying Lambda@Edge with pl.wrzasq.lambda

Thursday, 22 November 2018, 13:12

When working with cloud, one of the most efficient approaches you can leverage is serverless architecture. This concept allows you to define your entire application as set of interacting resources, without worrying about underlying infrastructure. Serverless applications can scale virtually to infinity, are cost-effective, lower maintenance costs (forget about patching kernels, installing system packages, defining upgrade paths etc.). Per-se, serverless it is a general pattern, but I will focus on AWS as this is my area of expertise - here, the main computing unit in serverless world is Lambda. It is a FaaS component that allows you to run your piece of code "in the cloud", which means decoupled form any computing hardware. Such code pieces can be triggered by you to perform some computation, but can also act as a handlers for various events across the platform (like SNS message processors, API Gateway authorization handlers and many many more). One of such integration ways is Lambda@Edge, which allows for handling CloudFront request events.

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Monitoring Lambda memory usage - X-Ray aware version

Tuesday, 30 July 2019, 07:17

In serverless architecture on AWS Lambda is a basic computing service. AWS provides some basic metrics related to it, like execution time, invocations count etc. but one important metric is missing - memory consumption. Knowing how much memory your functions use allows you to optimize resources and reduce costs (or increase to avoid failures). It can be very simply generated out of logs (since Lambda logs execution report after each invocation) with CloudWatch metric filter feature. And this approach has been described plenty of times around the web, including ready solutions. So I will not elaborate that much on the concept, as has already been done. But there is one trick - if you use X-Ray this solutions will not work. At least - not out of the box.

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