Amazon Codecatalyst reaches “GA” status and becomes available for general use

The new service announced by Amazon in Las Vegas at re:Invent 2022 which is an integrated DevOps service to empower development teams to develop and deliver software faster finally reaches the “general availability” status. As I have previously outlined, this achievement is very important for Amazon and the CodeCatalyst team. Congratulations to the team for reaching this goal, which I can imagine is not an easy step for this product. The tool touches a lot of very sensitive parts of a software project and I can imagine the security standards being really high. 

A hugh achievement – thank you everyone in the team for investing into CodeCatalyst and for listening as closely to the customer feedback as you are!

What changes did get implemented for GA?

As part of the GA release we see a lot of minor improvements in the User Interface and color changes. In the last weeks, we have seen a few “bigger” changes – like the possibility to use Dev Environments for Github based projects. We also got “graviton based” execution environments for CI/CD workflows which, according to AWS, should reduce our costs.

It is still hard to track down all of the changes in CodeCatalyst, as there is – to my knowledge – no public or semi-public roadmap. This is one of the things that I’d love to see, as for an integrated service that is at the core of the Developer Experience for teams, any minor change can either improve or destroy the “usage experience”. If you as a team invest into adoption a new tool like CodeCatalyst, they will need to know how changes in workflow, features or user interface can influence their day-to-day activities. Let’s see, maybe the team can share “something” like a “changelog” with us (or even an RFC process like Amplify or AppSync)?

Reached “GA” – so who can start using it now?

As of today CodeCatalyst is only availble in US regions and this means that it can be adopted mainly by US enterprise customers. CodeCatalyst already gives you the possibility to set up different Spaces for your account and within a space you can manage multiple projects. So in theory, CodeCatalyst is “ready to be used” by everyone. 

Practically speaking, it is easier to adapt the service for new projects than for existing projects , as there is no real “import” functionality. Yes, you can integrate existing Github projects, but that only integrates the source code. Unfortunately that does not make all of the “cool” things available right from the start of integrating the source: existing workflows (CI/CD pipelines) are lost and need to be re-build, issues/tickets are not imported into CodeCatalyst (tho they can be made available through the JIRA integration). 

I have been regularly using CodeCatalyst (both for imported and “new” projects) – and I really think that the tool already works very well. 

The “killer feature” that I see for new projects are the “blue prints” which essentially get you started within minutes, e.g. to deploy a SPA application, or to have a “true” CI/CD pipeline for a full stack application following the DPRA

Right now I would recommend using CodeCatalyst for any new project that you start to start building out your workflows and best practices.

So what do I still need to recommend CodeCatalyst for existing projects?

There are a few things that I have already been writing about:

  • “Import” of existing CI/CD workflows (e.g. Github actions, CDK Pipelines or CodePipelines)
  • Fully import projects
    • existing issues from Github or JIRA
    • Git-based projects including the history
  • Tighter security settings and permissions
    • Fine granular roles to allow or forbid access to specific parts of a project
    • Options to allow or forbid execution of workflows (or to deployments)
  • Additional workflow options
    • Manual approvals are very high on my wish list
    • Integration of other AWS services natively

A question for the readers: What do YOU think that you need to adopt CodeCatalyst?

A big question for the CodeCatalyst team – HOW MANY AWS TEAMS ARE USING CODECATALYST FOR PRODUCTION DEPLOYMENTS TODAY?

Where do I see the potential for CodeCatalyst?

CodeCatalyst is a big bet by AWS. There is a big potential that can really improve the life of development teams and these are the main things that I believe that can out-grow other existing solutions:

  • Integration of AWS Services / deployments metrics
    • the true integration with AWS APIs
    • Integration into “post-deployment” verifications (e.g. auto roll-back after failed CloudWatch metrics)
  • “At-hand” developer support to improve efficiency
    • with CodeWhisperer (who recently reached GA) AWS already aims to support developers during the development phase, but with CodeCatalyst AWS can take this to the next level:
    • AI support during Pull Request Reviews (or automated approvals for PRs – e.g. by including CodeGuru, etc., automated merges, etc.)
    • AI support during workflow executions (when to approve, when to deloy, when to promote, etc.)
    • With improvement proposals for workflows if the “AI model” recognizes patterns (in issue workflows or CI/CD workflows)
  • With automated improvements for existing projects based on blue prints
    • Best practices change – and so blue prints change – and if the CodeCatalyst team can automatically apply them for existing projects, customers will benefit from it

And last but not least:

I trully believe that every software project should start with a CI/CD pipeline – and with the Blue Prints including the CI/CD workflow that follows DPRA and other AWS best practices, we can trully make this possible: Empower developers to deliver their software projects in minutes right after starting their project.

Do you see the potential in CodeCatalyst? If you do not see any potential in the tool – why not?

Views: 1677

Pipeline strategies for a mono-repo – experiences with our Football Match Center projects in CodeCatalyst

Both Christian and I have been writing about our “Football Match Center” project – and as part of this project we obviously also needed a CI/CD (Continuous Integration and Continuous Deployment) pipeline. Our aim was to be able to integrate changes that we do regularly and see commits to the main branch being directly and automatically deployed to our environments.

I will first try to define some pre-requisites and then talk about learnings and experiences. 

What is a mono-repo

A mono-repo is an abbreviation of a “mono repository” which I understand as being a single repository, where different microservices or components are stored and saved in the same git repository. This can be various different services, infrastructure or user interface components or backend services.

A mono-repo has special requirements when building the CI/CD pipeline.

Expectations for our CI/CD pipeline

For our CI/CD pipeline we wanted to be able push changes to production quickly and be able to iterate fast. We wanted to achieve 100% automation for everything required for our project. As we have been writing, we completely develop this project using Amazon CodeCatalyst and thus the pipeline also should be build using the Workflows in CodeCatalyst.

Going forward we want to ensure that the pipeline also includes all CI/CD best practices as well as security scans and automated integration or end to end tests.

How to structure your pipelines

In this article we will purely focus on the CI/CD pipeline for your “main” or “trunk” branch – the production branch that will be used to deploy your software or product to the production environment.

We will not consider pipelines that should be executed on feature branches or on pull request creation.

The “one-pipeline-to-rule-them-all” approach

In this approach all services are deployed within the same pipeline. This means that there is only a single pipeline for the “main” branch. All services that are independed rom each other can deployed in parallel, services that have a dependency need to be deployed one after another. Dependencies or information from one to another service can be pushed through the pipeline using environment variables.

This can lead to longer deployment/execution timelines but ensures that “one commit” to this “main” branch is always deployed completely after a commit. If tests are included in the pipeline, they will need to cover all aspects of the application.

The “context-specific” or “component-specific” approach

Different components or contextes get a different pipeline – which means that e.g. the backend-services are deployed in one pipeline and the frontend-services in a different pipeline. 

In this approach, you automate the deployments for components and need to ensure that, if there are dependencies between the components, the pipeline verifies the dependencies. If one component requires information from another one you need to pass these dependencies using other options.

This can lead to faster iteration cycles for specific components but increases the complexity of the pipeline dependencies. You can also do not directly see if a specific commit has been deployed for all components or not.

The “one-pipeline-for-each-service” approach

This is the most decoupled option for building a CI/CD pipeline. Each service (lambda function, backend, microservice) gets its own pipeline. For each service, you can implement service specific steps as part of the pipeline. 

One of the main requirement for this is that the services are fully decoupled, otherwise managing dependencies can get very difficult. However, this allows a very fast iteration and development cycle for each microservice as the pipeline execution for each service is usually very fast.

The pipeline needs to verify the dependencies for each service as it executes the deployment.

Football Match Center – our experiences with building our CI/CD pipeline in Amazon CodeCatalyst

For our project we decided to start with a “mono-repo” – in our case today, we have a CDK application (written in Typescript) that describes the required infrastructure and includes Lambda functions (where required) and a user interface which is written in Flutter.

From a deployment perspective, the CDK application needs to be deployed on AWS and the Flutter application then needs to be deployed on a S3 bucket to serve as a Single Page Application (SPA) behind Cloudfront. Obviously this deployment/upload has the pre-requisite of the S3 bucket to be already available.

How we started

We started, very classic, with the “one-pipeline-to-rule-them-all” approach. We had one single pipeline that was used to deploy all services that are part of the infrastructure.

This pipeline started with “cdk synth” using the “CDK deploy” action in CodeCatalyst and then had other steps that depended on the first one – to executing the “flutter build” and later the “UI deploy” (using the S3 deploy action).

In this first version, the CDK deploy step had variables/output with the name of the S3 bucket and the CloudFront distribution ID passing it it to the next step where the output of “flutter build” was then uploaded and the CloudFront distribution invalidation request was triggered.

In this approach a commit to the “main” branch always triggered the same pipeline and this pipeline deployed the complete application.

We also used only natively available CodeCatalyst actions for deployment – “cdk deploy” and “build”. For the Flutter action we used a Github Action for flutter.

Experiences and pipeline adjustments

With this approach we had the problem that the Flutter build step took ~8 minutes and blocked a new iteration of changes in the CDK application or the lambda function. Thus, this slowed down our development cycle.

In addition to that we found out that there was no possibility to influence the CDK version with the CDK deploy action – but we wanted to be able to use the version defined in our Projen project – to be able to deploy to development environments from our local with the same version as from the CI/CD pipeline.

Both of these findings and experiences brought us to implement some changes to the pipeline:

  • We separated the UI build from the CDK build
  • We moved away from using “cdk deploy” and replaced it with a “build” step – to be able to trigger “projen” as part of the pipeline

So now we have two pipelines:

  1. CDK deployment
    • Triggered on changes to the “cdk-app/*” folder
      • Executing CDK synth, build and deploy steps – but not using the “cdk deploy” action but a normal build step instead
      • We adjusted the CDK app to include Cloudformation exports that exports the S3 bucket name and the Cloudfront distribution ID
  2. Ui deployment
    • Triggered on changes to the “ui/*” folder
    • Reads the values for the S3 bucket and the CloudFront distribution ID from the CloudFormation exports using the AWS cli
    • Executing the Flutter build steps and the S3 deploy action

These changes reduced in faster iterations for the development cycle of the CDK app and allowed decoupling the backend from the UI part. We were also able to fix the CDK version to the version we have selected in Projen.

In our project we have chosen the “context-specific” approach for the pipeline.

My recommendations for building CI/CD pipelines for a mono-repo

Our CI/CD pipeline is not perfect yet and we’re yet to add some important things to our pipeline.

From the experiences we have made I am still not convinced that our “context-spefic” approach is the right path.

As of writing this post in early April 2023 I’m inclined to move towards a model where we combine the “context specific” and the “one-pipeline-to-rule-them-all” approach: context-specific for “lower”, non production environments and then a single pipeline that does the promotion to our production environment.

Today we do not yet have a production environment, so we did not answer that question yet 🙂

How do you solve this challenge around building CI/CD pipelines for mono-repos?

Views: 445

What can we expect for the General Availability (GA) of Amazon CodeCatalyst?

At re:Invent 2022, as usualy, different new AWS Services or functionalities have been announced in Preview. Now, at the beginning of April 2023, a few of them have already reached the “General Availability” (GA) status – Application Composer (in early march), Latice (in late march). My favourite new service, Amazon CodeCatalyst, has not yet reached this goal – but I have a feeling that now is the right time to think about what and when we can expect this status.

You wonder what CodeCatalyst is? Watch this video on my YouTube channel or read my two initial posts about it.

Why is reaching the “GA” milestone so important?

Before starting with my assumptions on what we can expect for GA, lets clarify why reaching this milestone is so important. Being “in Preview” can mean a lot of different things. In a lot of organizations this usually translate to “limited availability”, a service not being available in all regions or not being reliable or scalable. For other organizations, it means that specific aspects of the product can be immature or not reliable. It can also mean that bigger API change are yet to be implemented or missing security guardrails. 

In general, this can be seen as a “beta” offering which is not appropiate to use for productive workloads.

Because of these reasons and maybe others, a lot of organizations (especially US based) do not allow using or adapting services that are in “Preview”.

For all of my experiences, tests, videos and projects I was so far able to only be on the free tier. And I assume that this will also be the truth for most of my readers: You can get a long way using the Free Tier that Amazon CodeCatalyst offers today.

So thats another big reason for AWS to push this service out of “Preview”: It gives organizations that are forbidden to use the service in “Preview” the possibility to start using and adopting the service – and with that Amazon can start earning money with the service which unil now might be difficult.

And as we know, AWS tries to “work backwards” from client requirements and the early usage of CodeCatalyst will drive further investments into the service.

What to expect for GA of CodeCatalyst?

Simple: Nothing big – most probably only regional rollout.

I do personally not expect any major new features for the service as the team has been constantely releasing new features and functionalities to the service on a regular cadance. There was simply not more time to work on bigger features while preparing the “General Availability” (GA).

What the CodeCatalyst already has delivered until today…

Let’s look at what has been added to CodeCatalyst since its official release in december 2022:

  • Additional Reporting auto-discovery
  • Change Tracking – the possibility to see which changes have been deployed to a certain environment
  • Additional Workflow native actions and improvements, E.g.
    • a problem with the CDK action to be able to define the “workpath” of a CDK app
    • Additional native actions
  • Linked issues to Pull Requests – you are now able to link issues to a pull request
  • UX improvements
    • Log files wider accessible in UI – at the beginning you where not able to make the log view larger, now this is possible
    • Page title adjustments
  • New Blue Prints (like the “Textract” one)
  • Development environments for Github based projects

This is not a complete list, but the things that I personally noticed and that I liked to see.

So…when is “the date”?

Hard to guess, but I would expect “soon”. Ideally right before a month starts, which will make the billing cycle easier 🙂 

So I would guess “end of april” which would bring the service right in time for the Berlin Summit (3rd of May).

Next steps for CodeCatalyst

In my last posts I have already been communicating my thoughts and features that I would love to see. But what will AWS implement?

Given reaching the “GA” status opens the way to “enterprise clients” I would expect that one of the first features will be Single-Sign-On functionalities, maybe with an integration to Okta, Ping, Azure Active Directory or other already existing IdPs.

In addition to that I believe that the User Interface needs to get some tweaks to streamline the navigation and workflow – that’s something that I personally experience every day: not knowing when and where to click to get on the rigth place. Also I think that additional service integrations will be added – e.g. StepFunctions or SNS, maybe SQS – see also my post about sending notifications from workflows.

And then there is one last thing which has been getting only limited attention so far: APIs and CLI integrations that can be used – so I would expect a major update there.

I’m really looking forward to see CodeCatalyst reaching GA – I’ve had various conversations with the team in the last months and I know that they have a true vision to make CodeCatalyst successfull as a trully AWS integrated and fully functional DevOps tool.

Are there features you are missing? Please let me know and I will forward them to the team.

Views: 739

How we automated infrastructure creation and Windows workloads through Lambda and StepFunctions

This article starts at the very beginning of my own and personal story to the cloud and to where I am today in my career: Back in 2015, when I barely knew that something big as “AWS” existed.

Of course, being a tech nerd since I started my career, I knew what “AWS” was and that I also had glimpse at guessing its tremendous power and opportunities, but I was not aware of the details and of the possibilities I would see.

How we started

At that time me and my team had built out a lift and shift solution on EC2 instances, where our product was manually deployed on. We aimed to grow our business, but we knew that this would not be possible without automation. Our product had different automation requirements and we did automate these running the required jobs through the Windows task manager.

Now, as we decided to be able to offer our service to other and accitional customers, we needed to find additional possibilities for automation and better operational support.

The first thing that we did was to move away from manually provisioning EC2 instances towards using Cloudformation for bringing up the instances and all of the required infratructure (VPC, sub-nets, load balancers, etc.). This already helped us a lot towards being able to deliver our solution faster. But we still had these Windows tasks which needed to be set up on the different instances. And this is where we looked at additional services in AWS to be able to replace these windows tasks with other possibilities.

Adding Serverless capabilities to the mix

At that time, we looked at using AWS Lambda and Stepfunctions – where were a “brand new thing” at that time – to be able to automate and orchestrate our workflows. With StepFunctions being really new, we recorded a “This is my architecture” video at re:Invent 2018. To be honest, when we started to look at Lambda and StepFunctions, I personally was not very convinced. Coming from a Windows /  Java background, moving orchestration capabilities “outside” the “server” (=EC2 instance) feeld wrong as I had not thought about orchestrated workfloads which could run across multiple infrastructure components before. 

Through StepFunctions, we orchestrated retrieving data, starting a new EC2 instance, automatically installing our software on it and then running the required workfload. AWS Lambda helped us in this case to be able to start EC2 instances programatically. At the same time StepFunctions gave us the possibility to get an overview on the current status of the executions through the AWS Console. The integration with CloudWatch, which was already available at that time, allowed us to implement alarms and enabled monitoring of execution time. 

During the process of testing and implementing this orchestration, we regularly hit new obstacles – e.g. a specific instance type not being available in an availability zone or a different error while reading data from S3. We often thought about giving up our approach, but moved on after seing the benefits of automation and being less dependend on a specific EC2 instance.

Orchestrating workflows using AWS StepFunctions is way easier today than at the time that I was part of this project. In 2017, there were only minimal possibilities of direct integrations with other AWS Services from AWS StepFunctions (like Lambda). Today, AWS StepFunctions offers more than 200 service integrations, “normal” workflows (which are quite expensive) and “express” workflows. 

I have been using StepFunctions in different projects lately, also in my project around building my own online & mobile game using serverless technologies at pegasus-galaxy.net.

How do you orchestrate your serverless workflows?

What are your experiences with AWS Step Functions?

Views: 126