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:

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
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?

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.

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?

Connecting to AWS AppSync using Amplify for Flutter for our Football Match Center

In the last weeks – or already months – I’ve been working together with Christian, also an AWS Community Builder, on our project named “Football Match Center”. Christian has already been writing a lot about our project on LinkedIn:

Today, I want to put the attention on our chosen framework for the UI and the way that we are connecting from the UI to the backend. Our backend in this project is a GraphQL API endpoint hosted on AWS AppSync.

Building our UI in Flutter

Since last year Amplify Flutter includes support for Web and Desktop. As we are looking to reach users both on mobile as also on the desktop, choosing a cross-platform development tool like Flutter seemed to be an obvious choice. Christian and I are a small team, and we want to focus on building a simple UI quickly without the need to implement for multiple platforms and Flutter allows exactly that.

Flutter provides easily extendable widgets that can be used on all major platforms.

Connecting to our GraphQL backend

Our project is not based on an Amplify backend, but on AWS infrastructure written in AWS CDK. This made it rather difficult to use the Amplify Flutter SDK as most of the documentations and blog posts expect you to connect the Amplify SDK with an Amplify backend (which can then include a GraphQL API).

But that’s not only what made it difficult – I also had very little experience with Amplify or the Amplify SDK when starting to work on the connection.

Using the Flutter SDK for Amplify we will be connecting to our Cognito instance for Authentication and to our existing GraphQL endpoint. In this post I am going to look at the GraphQL connection and not on the integration of Cognito as an authentication endpoint.

Setting up Amplify SDK for Flutter can be done through the amplify cli if you are starting a new project.

This will then also create the required amplifyconfiguration.dart and some example code through amplify init.

You can then set up the Amplify SDK for Flutter from within your main widget using this code:

import 'package:amplify_flutter/amplify_flutter.dart';
import 'package:amplify_api/amplify_api.dart';
import 'amplifyconfiguration.dart';
import 'models/ModelProvider.dart';

….

 Future<void> _configureAmplify() async {
    final api = AmplifyAPI(modelProvider: ModelProvider.instance);
    await Amplify.addPlugin(api);
    await Amplify.configure(amplifyconfig);
    try {
      await Amplify.configure(amplifyconfig);
    } on AmplifyAlreadyConfiguredException {
      safePrint(
          'Tried to reconfigure Amplify; this can occur when your app restarts on Android.');
    }
  }

While this looks easy when reading the documentation (and a lot of very good blog posts), this was rather difficult for me as I was not able to use the amplify init command. Finding out the structure of the “amplifyconfiguration.dart” and the implementation for the “ModelProvider” were my main challenges.

Lately, the related documentation has been updated and it is now easier to work with existing resources.

The Amplify Configuration file

The Amplify Configuration (amplifyconfiguration.dart) configures all of the required Amplify Plugins. In our implementation we started with the GraphQL backend:

const amplifyconfig = """{
"UserAgent": "aws-amplify-cli/2.0",
    "Version": "1.0",
    "api": {
        "plugins": {
            "awsAPIPlugin": {
                "matchcenter": {
                    "endpointType": "GraphQL",
                    "endpoint": "https://xxxx.appsync-api.eu-central-1.amazonaws.com/graphql",
                    "region": "eu-central-1",
                    "authorizationType": "API_KEY",
                    "apiKey": "xx0-3425ddregsGDE42dfw"
                }
            }
        }
    }
}""";

This tells the Amplify SDK to talk to a specific API endpoint when the “Amplify.API” is invoked. As far as I understand this Github issue, right now only one API can be queried from a specific Amplify instance.

When using the apiKey to do the authentication with the API, we will need to regularly update the Flutter application as the default API expires after 7 days.

This documentation was not available when we started to work on the project and I have the suspicion that Salih made this happen 🙂 (if not, still THANKS for the help you gave me! 🙂)

The ModelProvider

The ModelProvider should be a generated file, which you can generate from an existing GraphQL API. If you are using a schema that is not managed by Amplify, you will need to use “amplify codegen” based on an existing schema file.

The command expects a schema.graphql to be available in the “root” folder of the Amplify Flutter project. If you execute “amplify codegen models”, required Dart files will be generated in the “lib/models” directory.

The result should be a file similar to this one:

import 'package:amplify_core/amplify_core.dart';
import 'Match.dart';
import 'PaginatedMatches.dart';
import 'PaginatedTeams.dart';
import 'Team.dart';
export 'Match.dart';
export 'PaginatedMatches.dart';
export 'PaginatedTeams.dart';
export 'Team.dart';

class ModelProvider implements ModelProviderInterface {
  @override
  String version = "4ba35f5f4a47ee16223f0e1f4adace8d";
  @override
  List<ModelSchema> modelSchemas = [Match.schema, PaginatedMatches.schema, PaginatedTeams.schema, Team.schema];
  static final ModelProvider _instance = ModelProvider();
  @override
  List<ModelSchema> customTypeSchemas = [];
  static ModelProvider get instance => _instance;
  ModelType getModelTypeByModelName(String modelName) {
    switch(modelName) {
      case "Match":
        return Match.classType;
      case "PaginatedMatches":
        return PaginatedMatches.classType;
      case "PaginatedTeams":
        return PaginatedTeams.classType;
      case "Team":
        return Team.classType;
      default:
        throw Exception("Failed to find model in model provider for model name: " + modelName);
    }
  }
}

Querying our GraphQL API

Now that we have been able to connect to our GraphQL AWS AppSync endpoint, we can start querying data.

Luckily, the preparations we made and the Amplify for Flutter SDK provides convenience methods that returned typed data structures that we can directly interact or work with.

You only need to write the GraphQL query that you are interested in and you can directly read data from the endpoint. In my example below I’m creating a Flutter Widget out of the returned elements and then I am adding them to a list of Widgets that I can display in a Column Widget:

Future<List<TeamWidget>> _getMatchesByCountry(String country) async {
    List<TeamWidget> teamsWidgetList = [];
    try {
      String graphQLDocument = '''query ListTeams {
        getTeamsByCountry(country: "${country}") {
            nextToken
            teams {
              PK
              PrimaryColor
              SK
              SecondaryColor
              TeamName
            }
          }
        }''';
      var operation = Amplify.API
          .query(request: GraphQLRequest<String>(document: graphQLDocument));
      var response = await operation.response;
      var data = response.data;
      if (data != null) {
        Map<String, dynamic> userMap = jsonDecode(data);
        List<dynamic> matches = userMap["getTeamsByCountry"]["teams"];
        matches.forEach((element) {
          if (element != null) {
            if (element["id"] == null) {
              element["id"] = "rnd-id";
            }
            var match = Team.fromJson(element);
            teamsWidgetList.add(TeamWidget(match));
          }
        });
      }
    } on ApiException catch (e) {
      print('Query failed: $e');
    }
    return teamsWidgetList;
  }

It is of course also possible to create, update or delete data.

Just today, we have merged a feature that adds a “subscription” to our AppSync endpoint – as as next step we plan to integrate this within the Amplify Flutter Application which will then allow us to implement notifications to the end users. Unfortunately, the Amplify SDK for Flutter does not yet support in-app messaging as it does for Javascript.

What YOU learned – and what I learned

Through this blog post you have learned how to connect an Flutter application with Amplify using the Flutter SDK for Amplify. You have also got to know our project, the “Football Match Center” – and you’ve seen some code to make your start easier when talking to a GraphQL (AppSync) backend.

I have learned to work with the Amplify for Flutter SDK and also how code generators can help you to speed up your implementation. I’ve also gained experiences in accessing data from AppSync and on working with the returned data in Flutter.

Unfortunately, I have also found out that using the Flutter SDK for Amplify I can right now not implement the planned in-app notifications that Christian and I wanted to build for our Football Match Center to notify users about upcoming or recently completed games.

We will need to find a workaround to that and not rely on the Flutter SDK for amplify – rather implement notifications using the flutter_local_notifications plugin or by using the Firebase possibility for notification.

Looking forward to hear your feedback if you have any ideas on how to make this happen!

In the next post about this project I will look at how we have set up our CI/CD pipeline in Amazon CodeCatalyst for this project!

How CodeCatalyst compares to other AWS Services related to Development and CI/CD processes

At re:Invent 2022 AWS announced Amazon CodeCatalyst and as you might have read on my blog or seen on my YouTube Channel I have been playing around with the service a lot.
A few days ago, Brian asked me a few interesting questions, one of them being:

What’s the diff between CodeCatalyst and AppComposer?
Brian Tarbox, AWS Hero

Lately we had a Community Builders session with the Amazon CodeCatalyst team and similar questions came up in regards to comparing CodeCatalyst with other, already existing services.

And to be honest, the amount of AWS services that are related to building, managing or deploying software projects on AWS has grown a lot in the last years and it gets difficult to keep an overview of how these services play together and which tool has which functionality.

In this post we are aiming to compare and place CodeCatalyst in relation to other (new or already existing) AWS Services. We are also going to look at missing functionalities that are currently available in other services but not in CodeCatalyst.

Please be aware that these are all our personal opinions and based on our own understanding – some of it being assumptions.

This post was Co-Authored with AWS Community Hero Brian Tarbox – Thanks for your support!

AWS Services that we are going to compare CodeCatalyst with:

Amplify
Application Composer
App Runner
Beanstalk
CodePipeline / CodeCommit / CodeBuild/ CodeStar / CodeArtifact
Proton

Amplify

Amplify was released at re:Invent 2018 and has since then been improved gradually.

Amplify is a complete solution that lets frontend web and mobile developers easily build, ship, and host full-stack applications on AWS, with the flexibility to leverage the breadth of AWS services as use cases evolve.

With that AWS positions Amplify as a service that is able to reduce the heavy lifting on web or mobile developers that want to get started on AWS. AWS has extended Amplify into being a service that offers nearly all building blocks required as part of your SDLC process. It does not offer source code repositories, but CI/CD capabilities. You are able to configure the CI/CD pipeline and also provide your own build images.
With the release of Amplify Studio in 2021 AWS extended the capabilities to include a “No-Code/Low-Code” capability that allows rapid-prototyping for web and mobile applications. The target audience for Amplify are Front-End and Mobile developers with no to minimal experience on AWS.

Application Composer

This is a new AWS service announced at re:Invent 2022 mainly focused on “rapid prototyping” helping you to quickly “paint” serverless applications – build our your architecture out with visualizations, Application Composer will create the required “starting code” (Cloudformation, but also Lambda code) in the background.
As output you get a project in code that you can then commit to a Git repository or deploy out to AWS. Application composer enables Serverless developers to quickly prototype serverless applications and convert them into code that can then be used as a starting point for your project.
Application composer does not provide Source Code management or CI/CD capabilities.

The service, which reached GA on March 8th of 2023, points at developers starting new serverless projects that quickly want to get both an architecture diagram as well as a starting point for further developments.

App Runner

This is a AWS service announced in 2021 and it can be used to build, deploy and run web applications based on containerized workloads. It allows you to stay focused on your application with the service taking responsibility to provision and host your application. It also takes care of creating a container from your source code. You can connect App Runner either to your source code management system or to a container registry.

Beanstalk

This is one of the “ancient” AWS services – it was announced in 2011 and has since then been around. In the community I have more than once heard that “Beanstalk is dead” and not being actively developed anymore, but still – it works and can be used to provision your web applications. At the same time, you will still be able to access the infrastructure that is required to host your service. The “message” is similar to App Runner – it helps developers to focus on writing business code and ignore the deployment strategy. Beanstalk supports Java, .NET, PHP, Node.js, Python, Ruby, Go and Docker web applications. In order to use Beanstalk, you will need to upload a source bundle – it is not possible to connect beanstalk automatically to a Git repository, but you can update the source bundle automatically using APIs.

CodePipeline / CodeCommit / CodeBuild / CodeStar / CodeArtifact

We treat these services in one group as they belong together from a strategic point of view. They have been around for a few years and the teams that built these are now involved in CodeCatalyst. CodeCatalyst partly uses them “under the hood”. CodeCommit is a managed git hosting, CodeBuild is a managed “build” system, CodeStar is a “project management” tool. CodePipeline allows combining multiple CodeBuild steps to form a pipeline. CDK Pipelines integrate with CodePipeline today. With CodeArtifact users are able to store artifacts and software packages.

All of these services are tied to a specific AWS Account and live within the AWS Console. This has forced organizations and AWS customers to create “toolchain accounts” that centrally host these services. These tools might be considered as building blocks rather than a full solution.

CodeCatalyst

As we are comparing the other services with CodeCatalyst, we also need to define what CodeCatalyst is: a new AWS service announced at re:Invent 2022 that will cover the full lifecycle of product development on AWS, starting from the source up to the deployment part. It is an “All-in-one” solution to help you build software on AWS efficiently. You can manage your planning and issue tracking in it as well as your source code and your CI/CD workflows. I have a few introduction videos recorded available on YouTube.
CodeCatalyst lives “off” the AWS Console and this means that you do not need to be logged in to use it – and it can access multiple AWS accounts by an integrated authorization process.

Proton

This is a AWS service announced in 2020 – and AWS describes Proton as a service to allow central teams to build and provide central infrastructure components that can easily be shared with users while at the same time maintaining the integrity of the deployed infrastructure. With that, the tool is focused on infrastructure provisioning (=deployment) pipelines. Proton allows the central “platform team” to provide templates to be used by application teams – with only minor changes or configurations to it.

Which problem(s) does CodeCatalyst address?

CodeCatalyst addresses the need of developers or of development teams that need to cover all parts of the product life cycle or parts of it with a tool natively built on AWS. It can be used for issue management and planning as well as source code management. It has natively built CI/CD capabilities with workflows for Continuous Integration and Deployment. CodeCatalyst offers an opinionated solution for addressing software development best practices on AWS. It also allows online-editing of source code with the Dev Environments and supports the management with reports on resources and workflows managed as part of CodeCatalyst.With Blue Prints it allows developers to quickly start a new project and reduces the time to get a new project started. It can be seen as an opinionated approach to development.

So, how does CodeCatalyst relate to the other services?

Out of the six services we looked at, a few can at first glance not compete or be compared with CodeCatalyst as they target a different audience or address different problems as CodeCatalyst:

Proton – does not help with building or deploying code, it is targeted towards “composing” an application from various pieces. As such, it might be part of a solution but not the whole solution
Application Composer – while this service can be used to do a rapid-prototyping for serverless architectures, it does not allow source code management or deployment of the built architecture. I hope that we will see Application Composer as a new option for starting off a new project in CodeCatalyst going forward
Beanstalk – is not a “developer focused” tool as it comes with pre-build environments and CI/CD pipelines and expects you to manage the source code externally

Based on this, the services we want to look at in more details are:

Amplify
App Runner
CodePipeline / CodeCommit / CodeBuild / CodeStar / CodeArtifact

CodeCatalyst vs. Amplify

While Amplify allows to build CI/CD pipelines and manage deployments for both Front-End and Back-End components of an application, the pipelines and deployments are limited to the services supported by Amplify and the capabilities of the automatically generated CI/CD pipeline. There is not much flexibility to adjust the pipelines. In addition to that, Amplify does not allow you to store your source code or to manage your software project. It has no build-in issue management or tracking system.

With Amplify Studio and the corresponding tutorials you get the possibility to quickly get started on specific use cases. This is not as flexible as the CodeCatalyst Blue Prints but gets you started pretty quickly. Amplify Studio is awesome as a “low-code”, getting you started tool – it allows you to quickly build full-stack applications through a User Interface and for that use case it is definitely better than CodeCatalyst. At the Berlin Summit in 2022 I attended a Live Demo of Rene Brandle and was amazed by the functionalities.

Amplify Studio lives “outside” the AWS Console in the same way as CodeCatalyst and it also requires an AWS account to be connected for deployments. Each Amplify project can be connected to one AWS account. This is more flexible in CodeCatalyst.

Still, Amplify misses a lot of things that are required for an end-to-end “DevOps” tool to manage all processes and requirements of an agile software development project.

CodeCatalyst vs. CodePipeline / CodeCommit / CodeBuild / CodeStar / CodeArtifact

Comparing CodeCatalyst to the Code* services (CodePipeline / CodeCommit / CodeBuild / CodeStar / CodeArtifact) feels a bit like comparing a Tesla Model 3 with Karl Benz’ Patent-Motorwagen 🙂

The Code* services feel complex to use, although they provide similar functionality than CodeCatalyst if you combine them together. They are “building blocks” that you as a developer can use to build “your own version” of an integrated Developer Toolchain.

In addition to that they live in a specific AWS account, as mentioned above, which makes the handling of access complicated and requires you to have an IAM user that is allowed to access them.

The user interface and possible integrations are minimal and feel “developer unfriendly”.
CodeCommit has the CodeGuru Reviewer integration which is currently not available in CodeCatalyst.

CodeBuild (and with that CodePipeline) is very slow in bringing up new, fresh “build instances” – so starting a new pipeline execution can take minutes which is bad for developer productivity. This is something that CodeCatalyst is addressing with the “lambda” execution environment.

Summary, takeaways and our wishes

As per the messaging, blog posts and announcements from AWS around CodeCatalyst, we believe that the service today aims to offer an opinionated tool for development teams that want to practice “You build it, you run it” – in line with the DevOps mentality. It also means that AWS shows the courage to not only give builders a tool at hand but also “influences” what they build with Blue Prints that include best practices. The vision for CodeCatalyst however could be even more than that: a tool, powered by KI capabilities that empowers builders to efficiently develop and build high quality software by reducing the manual work and efforts through automation.

However, CodeCatalyst is not yet there and it’s going to take some time and effort from the team to reach this.

Wishes for Developer Tooling in General

This post has shown that AWS offers a lot of different possibilities to handle software projects on AWS. We made clear that all of the available tools serve a different purpose and target a different audience. While Amplify focusses on Web or Mobile developer and Application Composer targets Serverless developers, Code Catalyst takes a more generalist approach.

Overall, the “Developer Tools” landscape on AWS needs:

More and better guidance on WHEN to use WHICH service
Better “HOW TOS” instead of hard-to-read documentation or specification

Wishes for CodeCatalyst

Compiling a wish-list for CodeCatalyst can be a big effort as there are still a lot of features that we would like to see. We’ll touch on a few ones here:

General
- Single Sign On without Builder ID – Okta/Ping/etc.
- Other regions support
- Allow “Open Source” projects
Issues / Tracking
- Epics
- Roadmap / Timeline
- Integration with Workflows & Automation
Source
- Import projects from Git providers
- Automations on Pull Request
- CodeGuru
- Security Review
- Best Practice Review
- Support of pre-commit hooks when editing online
- Verifications, linting, etc. automated
Workflows
- More triggers (e.g. by PR, by schedule, by API)
- Conditional Steps
- Manual approvals
- App Store / Play Store deploy actions
- Projen Action
- Better integration with AWS services
- Import existing CodePipelines
  - Pipeline as Code – CDKPipelines like option to create workflows from code

What wishes do YOU have for Code Catalyst? What is your “most hated” or “most loved” feature today?

A second look at Amazon CodeCatalyst – CI/CD natively on AWS to empower developers to deliver faster and reduce heavy lifting for small to medium software engineering and DevOps teams

A few weeks ago, on december 1st 2022, Werner Vogels announced Amazon CodeCatalyst. I’ve previously shared my initial thoughts and findings in a blog post. In this post, I’m going to share a few more findings and insights into using Amazon CodeCatalyst and will also see if any of my wishes from the wishlist for CI/CD on AWS have been resolved with CodeCatalyst.

What I have been playing around with…

One of my personal projects that I am working on together with a few friends is pegasus-galaxy.net and the CI/CD pipeline that I had built with CDK Pipelines (that I also presented at re:Invent 2022) was the first one to try to move over.
In context, we’re talking about a Flutter application for Web running behind CloudFront, deployed using CDK.

I decided to try CodeCatalyst out and go “all in” – and that means moving the code from Bitbucket into CodeCatalyst as well as setting up the other users in CodeCatalyst and moving the workflows (=CI/CD pipelines) over to CodeCatalyst.

In this article I am going to go through each of the sections in CodeCatalyst and will share my experiences, thoughts and findings.

Where I have ideas on how to improve the day-to-day work with the tool, will try to share that.

Before going into details, lets start with the most important thing:

Amazon CodeCatalyst works very well and reliable and the current version of the service is a great foundation for moving all of your CI/CD and development practices to AWS.

The CodeCatalyst team has been very supportive on re:Post, so if you have a question, feel free to ask it there!

CodeCatalyst Overview – Spaces and Projects

Spaces are the “Top-Level” option to organize your CodeCatalyst account. You will need to associate an AWS Account for billing used AWS resources. Each AWS (billing) account can be associated only with one CodeCatalyst Space.

While this seems like a limitation as you will need to create a different billing account for a 2nd space, I can right now not see an impact for my day to day work. For anything that I run on the same AWS account, I would assume that using a project within the same space should be good.

You can manage Projects, Members and AWS Account connections on the space page. In the “extensions”, CodeCatalyst currently allows a connection only to the JIRA Cloud. I would expect that additional 3rd party extensions will be supported in the GA version of CodeCatalyst.

Projects Overview and options

A project is a “unit of work” in your product or software that you are building.
Within projects, you can manage issues, manage your code repositories, execute workflows (CI/CD) and review report results.

Projects are associated to a Space – and you can create as much projects in a Space as you want. You can add team members to a project, that are not able to access all projects in the space. Unfortunately I have not yet found an option to “hide” projects from Team members that are added on the Space itself.

Managing issues / tickets

CodeCatalyst currently provides two options to manage your issues or tasks:
1) Link to JIRA Cloud Project
2) Internal issue management

If you use the option to link to a JIRA cloud project, the “issues” link is replaced by a link to your JIRA Cloud project.

Internal issue management

The internal issue management system currently offers everything that is required for a simple Kanban workflow. You can create issues, add them to a backlog or a Kanban board, assign them to project members and track their current status.
I personally think that the current functionalities are good enough for small teams and simple projects – I’m actually already working with it in a small project and will add additional feedback as soon as I gain more experience.

Code

Within the “Source” part of a project, you can manage source repositories or connections to source repositories in Github. I expect that other providers will be added going forward (e.g. Gitlab, CodeCommit, Bitbucket, …).
You can also manage pull requests and approvals – I was only able to test this using internal source repositories, not using a linked repository.

The last option – the Dev Environments – is the most exciting functionality – it gives you the possibility to host development environments (similar to Gitpod) on AWS using Cloud9 but also, and this is really cool, using Visual Studio or JetBrains IDEs.
When using that option, the IDE on your local PC is only the “presentation layer”, the source code is stored and run on an AWS instance and the IDE uses remote connectivity to talk to the Dev Environment in the background.

CI/CD

CodeCatalyst currently uses the same approach as Github Actions to manage your workflows or CI/CD pipelines – you are able to manage your Workflows using YAML files. The syntax is simple and understandable. There is a minimal set of Actions available as part of the preview. You are also able to use existing Github Actions as part of your workflow.

The workflow functionality is very powerful. In my tests I have not yet been able to test all parts of the capabilities. Workflows can be defined for certain directories, for certain triggers or branches. Test reports will be exposed in the “reports” functionality.

CodeCatalyst offers a graphical overview for workflows and alows to edit them in the UI, too. This functionaly works pretty well and helps to quickly get you started building your first workflow in CodeCatalyst.

I’ll need to test the workflows more to be able to give additional insights into how good or bad they are currently running. My simple pipeline that builds a Flutter Application, deploys my Infrastructure as Code using CDK and then publishes the new version of the Flutter app runs without problems.

One of my main concerns so far is the execution time, however the team has been working on a possibility to use Lambda as an execution environment.
This option however does not yet support the execution of Github actions and also has some other limitations.

The other features that are part of the “CI/CD” – Environments, Compute and Secrets – I did not have time to play around with this. If you have any experiences with it, please add your thoughts in a comment to this article!

Reports

The reports today only suport test reports. I have not used the functionality enough to assess this, but I am sure that the CodeCatalyst team is going to add additional reporting options going forward.

Things I like most about CodeCatalyst (Preview) after 6 weeks of usage

Just a short list of things that I already like:
– Integration of Github Actions as workflow actions
– Managing workflows using UI & code

Things I miss in CodeCatalyst (Preview) after 6 weeks of usage

– macOS builds (e.g. for Flutter iOS apps) are still not possible
– granular permissions for workflow and Pull Request triggers
– and….

Let’s talk about Open Source Projects

Right now there is no option to share a project or a repository that is hosted within CodeCatalyst as an Open Source project. This is really a limitation if you want to use CodeCatalyst for Open Source project – or if I would like to share a CodeCatalyst repository with example workflows.
I hope this functionality will be added soon.

Wrap up and next steps for me with CodeCatalyst

I need to admit – writing this post took longer than expected 🙂
I wanted to publish it before christmas and now it seems to be a bit “late” already as I am sure that a lot of you have made your own experiences with CodeCatalyst today – please SHARE your findings with me – links of Blogs that you have written or other content you have created, I am eager to consume it!

My next steps with CodeCatalyst

I am working on migrating my project pegasus-galaxy.net completely to CodeCatalyst and collaborate with my team on it there. With that, I will be able to proof CodeCatalyst in a “real world” application that it is “multi-platform” application – using Flutter for Web, Android and iOS – and a Serverless AWS based backend.
If you’re interested to join this project, please do not hesitate to reach out – skills that we need right now:
AppSync, DynamoDB and development/software engineering (Flutter, Typescript, Java, or Node?)

A few personal stories from re:Invent 2022

As we are now in the post:Invent phase of 2022 and over 10 days have passed since re:Invent 2022 in Las Vegas was concluded, it’s time for a lot of re:Cap Blog posts and events.
I’ve read so many of those “major announcments” articles that I’ve decided to write a different type of re:Cap for myself this year:
Sharing a few stories from my 10+ days in Las Vegas, as they are as equally important as the technical announcements made by AWS.

Indeed, it was a great conference with a lot of learnings and a lot of very interesting sessions. I focused on Chalk Talks, Builder’s sessions and events (like Gameday) as these are not recorded.

Making new friends before re:Invent kicks off

My flight this year got moved from Saturday to Friday, so I had one more day to get over Jet-Lag. On Friday, I spend a good time shopping and besides that met with Oliver vor dinner. On saturday morning (early morning!), I looked at the AWS Community Builders Slack and found out that Traian set up a “spontanous breakfast” for the Jet-lagged folks – and I ended up sitting over two hours with different parties, having fun, chatting and getting to know people. It was exciting to meet Rafael, who had been our Solutions Architect for a while, for the first time in person – without planning it 🙂
It was also great to meet Heitor in person – the person that owns the Lambda Power Tools at AWS. His talk is now on Youtube and I would encourage you to listen to it if you are interested in Open Source.

The rest of the saturday I spend with Markus, who shared so much Las Vegas knowledge with me that I think my brain is still burning – and I would not dare to claim remembering more than half of what we discussed – but it was a great saturday which ended with meeting Philipp for dinner at “The Cheasecake factory”.

I kicked off the sunday with a lot of excitement about my very first talk at re:Invent – final technical check in the “Speaker Ready Room” for my slide deck! That needed to be early morning, because afterwards I had planned to go out hiking with fellow AWS Community Builders.

Hiking across time zones and cultures

Definately one of my highlights this year: The ever first AWS Community Builders pre:Invent Hiking Trip!

Thanks to everyone that joined – Oliver, Richard, Jenn, Ganesh, Traian, Pubudu, Niklas. It was great to see how we supported each other, had great conversations and all managed to get across different challenges we had to fight!

Thank you Oliver (and kreuzwerker) for the amazing video.
Traian, you’re my hero. Congratulations on finishing off the hike with us with. Thats an achievement noone can take away.

It was fascinating to meet you all for the first time and notice that we get a long well, without ever meeting before. That’s the power of the AWS community!

We got back at 6pm, after a over 5 hours hike, just in time to get our AWS re:Invent badges and to meet other AWS Community Builders from around the globe for a great dinner. Lilly & Jason – thanks for joining us, that really made me happy!

Kicking off re:Invent with a GameDay with a great team an Jeff Barr

I decided to kick off my re:Invent on Monday with a GameDay – which is a fascinating opportunity for gamified learning. On sunday, during our hike, I had aligned with Niklas to form a team together – and the other two team members, JaeJun and Martin, we met in the morning. We had great fun, ended up 4th even tho Jeff Barr distracted us for some time as we won him on our table with a quizz. It was great meeting him in person – and I can tell you: He is a human as we are, even if his Newsblog is legendary 🙂

Meeting people from the AWS Community

I had so much great hours in Las Vegas – thanks for the time spend together, everyone that I’ve met – Stefanie, Oliver, Manuel, Mike, Stefan, Philipp, Markus, Thorsten – and others – from the german community.

Finally met Danielle and Matt in person. Another of my highlights.

The Community Builders Mixer and the User Group Leader Mixer where both great events to get to know each other better and network with great people from everywhere in the world. I met so many people that I had been interacting with in written (Slack, Twitter, LinkedIn) – it was a blast for myself.

Famous Jenga-game with AWS Heros – so much fun!

Speaking at re:Invent 2022 – my DevChat

As I’ve already shared before this year I had the opportunity to speak at re:Invent – COM307 – Using CDK pipelines (in Java) to build a multi-platform Flutter application

Thanks for everyone that made this possible: Ernesto, Shantavia, Lilly, Jason, Maria! It was my biggest honor to share my experiences and my open source initiative. Looking forward to keep sharing knowledge!

Announcing Amazon CodeCatalyst

With the announcement of Amazon CodeCatalyst the conference brought for me a new service that I am eager to use and try out as I am very much interested in CI/CD on AWS.
This was for me definately the most exciting anouncement of re:Invent 2022 and I had a lot of interest to talk to the service team, product managers and others after the service had been announced. I’m looking forward to share more about that as I get to play around with it more.

Flying out

On saturday my trip to re:Invent was over and it ended as my re:Invent trip began: meeting AWS interested persons at the airport (Thanks Maria for the introduction!) and with great conversations with Oliver on the way back to Frankfurt.

Thanks to everyone that I met and talked to at re:Invent 2022 – you really made this conference be a different one for me than it was before.

I’m looking forward to hopefully meet all of you again in 2023!