Architecture | Coding With Taz

Modern distributed systems generate a staggering amount of telemetry. Logs, metrics, and traces flow from dozens or hundreds of independently deployed services. Teams want deep visibility without drowning in operational overhead. They want consistency without slowing down delivery. And they want observability that scales with the system, not against it.

This is where Jaeger v2, OpenTelemetry, and GitOps converge into a clean, modern, future‑proof model.

This series walks through a complete, working setup that combines:

Jaeger v2, built on the OpenTelemetry Collector
OpenTelemetry auto‑instrumentation, with a focus on .NET
ArgoCD, managing everything declaratively through GitOps
A multi‑environment architecture, with dev/staging/prod deployed through ApplicationSets

Before we dive into YAML, pipelines, and instrumentation, it’s worth understanding why these technologies fit together so naturally and why they represent the future of platform‑level observability.

All manifests, ApplicationSets, and configuration used in this series are available in the companion GitHub repository

🧭 The Shift to Jaeger v2: Collector‑First Observability

Jaeger v1 was built around a bespoke architecture: agents, collectors, query services, and storage backends. It worked well for its time, but it wasn’t aligned with the industry’s move toward OpenTelemetry as the standard for telemetry data.

Jaeger v2 changes that.

What’s new in Jaeger v2

Built on the OpenTelemetry Collector
Accepts OTLP as the ingestion protocol
Consolidates components into a simpler deployment
Integrates Jaeger’s query and UI directly into the Collector
Aligns with the OpenTelemetry ecosystem instead of maintaining parallel infrastructure

In practice, Jaeger v2 is no longer a standalone tracing pipeline.
It is a distribution of the OpenTelemetry Collector, with Jaeger’s query and UI components integrated into the same deployment.

This reduces operational complexity and brings Jaeger into the same ecosystem as metrics, logs, and traces, all flowing through the same Collector pipeline.

🌐 OpenTelemetry: The Universal Instrumentation Layer

OpenTelemetry has become the de facto standard for collecting telemetry across languages and platforms. Instead of maintaining language‑specific SDKs, exporters, and agents, teams can rely on a unified model:

One protocol (OTLP)
One collector pipeline
One set of instrumentation libraries
One ecosystem of processors, exporters, and extensions

For application teams, this means:

Less vendor lock‑in
Less custom instrumentation
More consistency across services

For platform teams, it means:

A single collector pipeline to operate
A single place to apply sampling, filtering, and routing
A consistent deployment model across environments

And with the OpenTelemetry Operator, you can enable auto‑instrumentation, especially for languages like .NET, without touching application code. The Operator injects the right environment variables, startup hooks, and exporters automatically.

🚀 Why GitOps (ArgoCD) Completes the Picture

Observability components are critical infrastructure. They need to be:

Versioned
Auditable
Reproducible
Consistent across environments

GitOps provides exactly that.

With ArgoCD:

The Collector configuration lives in Git
The Instrumentation settings live in Git
The Jaeger UI and supporting components live in Git
The applications live in Git
The environment‑specific overrides live in Git

ArgoCD continuously ensures that the cluster matches what’s declared in the repository. If someone changes a Collector config manually, ArgoCD corrects it. If a deployment drifts, ArgoCD heals it. If you want to roll out a new sampling policy, you commit a change and let ArgoCD sync it.

Git becomes the single source of truth for your entire observability stack.

🏗️ How These Pieces Fit Together

Here’s the high‑level architecture this series will build:

OpenTelemetry Collector (Jaeger v2)
- Receives OTLP traffic
- Processes and exports traces
- Hosts the Jaeger v2 query and UI components
Applications
- Auto‑instrumented using OpenTelemetry agents
- Emit traces to the Collector via OTLP
ArgoCD
- Watches the Git repository
- Applies Collector, Instrumentation, and app manifests
- Uses ApplicationSets to generate per‑environment deployments
- Enforces ordering with sync waves
- Ensures everything stays in sync

This architecture is intentionally simple. It’s designed to be:

Easy to deploy
Easy to understand
Easy to extend into production patterns
Easy to scale across environments and clusters

🎯 What You’ll Learn in This Series

Over the next four parts, we’ll walk through:

Part 2 – Deploying Jaeger v2 with the OpenTelemetry Collector

A working Collector configuration, including receivers, processors, exporters, and the Jaeger UI.

Part 3 – Auto‑instrumenting .NET with OpenTelemetry

How to enable tracing in a .NET application without modifying code, using the OpenTelemetry .NET auto‑instrumentation agent.

Part 4 – Managing Everything with ArgoCD (GitOps)

How to structure your repo, define ArgoCD Applications, and sync the entire observability stack declaratively.

Part 5 – Troubleshooting, Scaling, and Production Hardening

Sampling strategies, storage backends, multi‑cluster patterns, and common pitfalls.

🧩 Why This Matters

Observability is no longer optional. It’s foundational. But the tooling landscape has been fragmented for years. Jaeger v2, OpenTelemetry, and GitOps represent a convergence toward:

Standardisation
Operational simplicity
Developer autonomy
Platform consistency

This series is designed to give you a practical, reproducible path to adopting that model, starting with the simplest working setup and building toward production‑ready patterns.

You can find the full configuration for this part — including the Collector manifests and Argo CD setup — in the GitHub repository

In a previous post I looked at Architecture Diagrams as Code with Structurizr DSL with one workspace and using puppeteer to generate images via an Azure Pipeline.

Since writing that post I’ve been using multiple workspaces and become more familiar with the structurizr-cli as well as using the docker images for structurizr-cli and structurizr-lite.

So, in this post I am going to look at:

Using source control to store your diagrams in DSL format
Editing diagrams locally
Automation of pushing changes to one or more workspaces using Azure Pipelines or GitHub Actions
Optionally generating SVG files for the workspaces that have changed as pipeline artifacts

Setup

Structurizr Workspaces

In Structurizr create the workspaces that you are going to use

The main reason to do this first is so that the ID, API Keys and API Secrets are created and can be copied from each of the workspace settings to add to the pipeline variables, the ID can be used for naming the folders if you choose to

GitHub/Azure DevOps

Now in GitHub or Azure DevOps create a new repository that you are going to use to put your diagrams in. Once that has been created, clone the repository and then create a folder for each workspace you have in Structurizr either using the Workspace Id (from the workspace settings) or with a name that has no spaces, starts with a letter and only contains alphanumeric characters ([a-z], [A-Z], [0-9]) or underscores (_).

Note: The folder name is used by the Pipeline to find the correct secrets to publish the workspace to Structurizr

Edit Diagrams

To get started create a workspace.dsl file in the folder you want to create a diagram in.

Note: You will do this for each workspace

To edit the diagrams locally you can use any text editor but I recommend using Visual Studio Code along with the extension by Ciaran Treanor for code highlighting and use Structurizr Lite to view the diagram. Simon Brown has a great post on Getting started with Structurizr Lite and don’t forget to install Docker if you haven’t already

In your editor create the diagram e.g.

Tip: The DSL reference is very handy when creating diagrams

Using Structurizr lite the diagram can be viewed without having to upload to the cloud. Run the following docker command replacing PATH with the path to the diagram you want to show

docker run -it --rm -p 8080:8080 -v PATH:/usr/local/structurizr structurizr/lite

In your browser navigate to localhost:8080 and you should see the diagram e.g. the above diagram looks like this

Tip: As you make changes to the diagram you can refresh the browser to see the changes

Note: Structurizr Lite only shows 1 workspace, if you have more and want to see those at the same time, run the Docker command as before but change the Port from 8080 to something else e.g. 8089 and change PATH to another diagram

docker run -it --rm -p 8089:8080 -v PATH:/usr/local/structurizr structurizr/lite

Once you are happy with the diagram changes they can be committed and pushed into the repository to share with others.

Pipelines

Now we have the diagrams in source control and can track the changes, we still want to push those changes to Structurizr to share with others who perhaps want to review the diagrams in Structurizr or see them in another application e.g. Atlassian Confluence

We can automate this process by creating a pipeline to publish diagrams to Structurizr when changes are made

Our pipeline has some requirements:

Only run on the main branch
Do not run on a Pull Request
Only publish the diagrams that have been changed
Optionally output an SVG of the changed diagram as artifacts
Not include any Workspace Keys and Secrets in the source controlled pipeline files

Note: Secrets need to be in the format of WORKSPACE_<Type>_<Folder Name> e.g.

WORKSPACE_ID_MYPROJECT
WORKSPACE_KEY_MYPROJECT
WORKSPACE_SECRET_MYPROJECT

Azure Pipelines

If you are using Azure Pipelines then read on or you can skip to the GitHub actions section.

So, let’s create the YAML file for the pipeline. In the root folder of your repository create a azure-pipelines.yml file and open that in an editor and add the following YAML

trigger:
  - main

pr: none
 
pool:
    vmImage: ubuntu-latest

parameters:
  - name: folderAsId
    type: boolean
    default: false
  - name: createImages
    type: boolean
    default: false

variables:
  downloadFolder: 'downloads'
  
steps:

The first step is to get the changes since the last push, this helps with the requirement of pushing only diagrams that have changed. Unlike GitHub actions there is not a pre-defined variable for this so this PowerShell script uses the Azure DevOps Rest API to obtain the git commit id before the changes and sets a variable to store the id for later use

- pwsh: |
    $devops_event_before = $env:BUILD_BUILDSOURCEVERSION
    $uri = "$env:SYSTEM_TEAMFOUNDATIONSERVERURI$env:SYSTEM_TEAMPROJECT/_apis/build/builds/$($env:BUILD_BUILDID)/changes?api-version=6.1"    
    $changes = Invoke-RestMethod -Method Get -Headers @{ Authorization = "Bearer $env:SYSTEM_ACCESSTOKEN" } -Uri $uri -Verbose    
    if ($changes.count -gt 0) {
      $firstCommit = $changes.value[$changes.count-1].id
      # Go back to the commit before the first change
      $devops_event_before = git rev-parse $firstCommit^           
    }
    Write-Host $devops_event_before 
    Write-Host "##vso[task.setvariable variable=DEVOPS_EVENT_BEFORE]$devops_event_before"
  displayName: 'Get Start Commit Id'
  env:
    SYSTEM_ACCESSTOKEN: $(System.AccessToken)

Next step is to optionally install Graphviz which is used to create the SVG files

- bash: |
    sudo apt-get install graphviz
  displayName: 'Install dependencies'
  condition: and(succeeded(), eq(${{ parameters.createImages }}, 'true'))

Now we can call a PowerShell script that will perform the publish action passing in the Workspace secrets as environment variables.

Note: Azure Pipelines automatically creates an Environment Variable for pipeline variables if they are not set a secrets (e.g. WORKSPACE_ID_MYPROJECT is not a secret). Once they are a secret they need to be explicitly added in the env property to be used inside the script as Environment Variables.

- task: PowerShell@2
  displayName: 'Publish Workspace'
  inputs:
    targetType: 'filePath'
    filePath: ./publish.ps1
    arguments: -StartCommitId $(DEVOPS_EVENT_BEFORE) -CommitId $(Build.SourceVersion) -DownloadFolder $(downloadFolder) -FolderAsId $${{ parameters.folderAsId }} -CreateImages $${{ parameters.createImages }}
  env: 
    WORKSPACE_KEY_MYPROJECT: $(WORKSPACE_KEY_MYPROJECT)
    WORKSPACE_SECRET_MYPROJECT: $(WORKSPACE_SECRET_MYPROJECT)
    WORKSPACE_KEY_OTHERPROJECT: $(WORKSPACE_KEY_OTHERPROJECT)
    WORKSPACE_SECRET_OTHERPROJECT: $(WORKSPACE_SECRET_OTHERPROJECT)

Note: You may have noticed there is an additional $ on the parameters and think this is a typo but it’s actually a little hack, parameters of type boolean are really strings and so when passing to PowerShell you get an error that says it cannot convert a System.String to a System.Boolean adding a $ results in $true or $false and then is correctly read in by PowerShell

And the last step is to optionally upload the SVGs as artifacts if they were requested to be created

- publish: $(downloadFolder)
  displayName: Publish Diagrams
  artifact: 'architecture'
  condition: and(succeeded(), eq(${{ parameters.createImages }}, 'true'))

With the pipeline configured the next thing to do is add the secrets for each workspace using pipeline variables

You can also use variable groups but you need to update the variables section to load in a group e.g. if I had a group called structurizr_workspaces

variables:
  - name: downloadFolder
    value: 'downloads'
  - group: structurizr_workspaces

The final pipeline then is:

trigger:
  - main

pr: none
 
pool:
    vmImage: ubuntu-latest
parameters:
  - name: folderAsId
    type: boolean
    default: false
  - name: createImages
    type: boolean
    default: false

variables:
  downloadFolder: 'downloads'
  
steps:
- pwsh: |
    $devops_event_before = $env:BUILD_BUILDSOURCEVERSION
    $uri = "$env:SYSTEM_TEAMFOUNDATIONSERVERURI$env:SYSTEM_TEAMPROJECT/_apis/build/builds/$($env:BUILD_BUILDID)/changes?api-version=6.1"    
    $changes = Invoke-RestMethod -Method Get -Headers @{ Authorization = "Bearer $env:SYSTEM_ACCESSTOKEN" } -Uri $uri -Verbose    
    if ($changes.count -gt 0) {
      $firstCommit = $changes.value[$changes.count-1].id
      # Go back to the commit before the first change
      $devops_event_before = git rev-parse $firstCommit^           
    }
    Write-Host $devops_event_before 
    Write-Host "##vso[task.setvariable variable=DEVOPS_EVENT_BEFORE]$devops_event_before"
  displayName: 'Get Start Commit Id'
  env:
    SYSTEM_ACCESSTOKEN: $(System.AccessToken)
- bash: |
    sudo apt-get install graphviz
  displayName: 'Install dependencies'
  condition: and(succeeded(), eq(${{ parameters.createImages }}, 'true'))
- task: PowerShell@2
  displayName: 'Publish Workspace'
  inputs:
    targetType: 'filePath'
    filePath: ./publish.ps1
    arguments: -StartCommitId $(DEVOPS_EVENT_BEFORE) -CommitId $(Build.SourceVersion) -DownloadFolder $(downloadFolder) -FolderAsId $${{ parameters.folderAsId }} -CreateImages $${{ parameters.createImages }}
  env: 
    WORKSPACE_KEY_MYPROJECT: $(WORKSPACE_KEY_MYPROJECT)
    WORKSPACE_SECRET_MYPROJECT: $(WORKSPACE_SECRET_MYPROJECT)
    WORKSPACE_KEY_OTHERPROJECT: $(WORKSPACE_KEY_OTHERPROJECT)
    WORKSPACE_SECRET_OTHERPROJECT: $(WORKSPACE_SECRET_OTHERPROJECT)
- publish: $(downloadFolder)
  displayName: Publish Diagrams
  artifact: 'architecture'
  condition: and(succeeded(), eq(${{ parameters.createImages }}, 'true'))

GitHub Actions

If you prefer to use GitHub actions instead of Azure Pipeline then let’s create the YAML file for the pipeline. Create a folder in your repository called .github\workflows and then create a ci.yml file in that folder and then open the ci.yml in an editor and add the following YAML

name: Structurizr Workspace Pipeline
on: 
  push: 
    branches: [ main ]
env:
  CREATE_IMAGES: ${{ false }}
  FOLDER_AS_ID: ${{ false }}

jobs:
  structurizr_workspace_pipeline:
    runs-on: ubuntu-latest
    steps:

Unlike Azure Pipelines the code is not automatically checked out so the first step is to checkout the code.

Note: although the fetch-depth is normally 1 this needs to be 0 to make sure we get all the changes on a push

- name: Checkout
   uses: actions/checkout@v3
   with:
     fetch-depth: 0

Next step is to optionally install Graphviz which is used to create the SVG files

- name: Install dependencies
  if: ${{ env.CREATE_IMAGES == 'true' }}
  run: sudo apt-get install graphviz

Now we need to get our secret variables into Environment Variables, unlike Azure Pipelines secrets can be added to Environment Variables and not have to be explicitly added to scripts in order to be used

- name: Create Secrets as Envs
   run: |
     while IFS="=" read -r key value
     do
       echo "$key=$value" >> $GITHUB_ENV
     done < <(jq -r "to_entries|map(\"\(.key)=\(.value)\")|.[]" <<< "$SECRETS_CONTEXT")
   env:
     SECRETS_CONTEXT: ${{ toJson(secrets) }}

As with Azure Pipelines we can call a PowerShell script that will perform the publish action

Note: the github.event.before pre-defined variable contains the start commit id

- name: Publish Workspace
   run: |
        ./publish.ps1 -StartCommitId ${{ github.event.before }} -CommitId ${{ github.sha }} -DownloadFolder 'downloads' -FolderAsId $${{ env.FOLDER_AS_ID }} -CreateImages $${{ env.CREATE_IMAGES }}
   shell: pwsh

Note: As with the Azure Pipeline the double $ hack to get a string into PowerShell as a boolean value

And the last step is to optionally upload the SVGs as artifacts if they were requested to be created

- name: Publish Diagrams
   if: ${{ env.CREATE_IMAGES == 'true' }}
   uses: actions/upload-artifact@v2
   with:  
     name: architecture
     path: downloads

Now we have the GitHub action defined we now need to add the secrets for each workspace using Secrets in GitHub

The final pipeline then is:

name: Structurizr Workspace Pipeline
on: 
  push: 
    branches: [ main ]
env:
  CREATE_IMAGES: ${{ false }}
  FOLDER_AS_ID: ${{ false }}

jobs:
  structurizr_workspace_pipeline:
    runs-on: ubuntu-latest
    steps:
    - name: Checkout
      uses: actions/checkout@v3
      with:
        fetch-depth: 0
    - name: Install dependencies
      if: ${{ env.CREATE_IMAGES == 'true' }}
      run: sudo apt-get install graphviz
    - name: Create Secrets as Envs
      run: |
        while IFS="=" read -r key value
        do
          echo "$key=$value" >> $GITHUB_ENV
        done < <(jq -r "to_entries|map(\"\(.key)=\(.value)\")|.[]" <<< "$SECRETS_CONTEXT")
      env:
        SECRETS_CONTEXT: ${{ toJson(secrets) }}
    - name: Publish Workspace
      run: |
        ./publish.ps1 -StartCommitId ${{ github.event.before }} -CommitId ${{ github.sha }} -DownloadFolder 'downloads' -FolderAsId $${{ env.FOLDER_AS_ID }} -CreateImages $${{ env.CREATE_IMAGES }}
      shell: pwsh
    - name: Publish Diagrams
      if: ${{ env.CREATE_IMAGES == 'true' }}
      uses: actions/upload-artifact@v2
      with:  
        name: architecture
        path: downloads

The PowerShell

As you may have noticed both pipelines run the same PowerShell script to publish the workspaces, this script detects the changes in each of the folders and pushes the workspace to structurizr using the cli and then optionally exports an svg file of the diagrams.

Note: The part of the script that looks for the changes in the Git commit is:

git diff-tree --no-commit-id --name-only --diff-filter=cd -r <commit id>

The –diff-filter is used to reduce what files to including in the diff, uppercase filters e.g. AD would only include Add and Delete where as using them in lowercase would exclude them. In this instance any Copied or Delete statuses are excluded.

The full script that is used looks like this:

<#
.SYNOPSIS
    PowerShell script to upload diagram changes to Structurizr Cloud

.DESCRIPTION
    This PowerShell script works out the changes between git commits where the files are of extension .dsl and upload to Structurizr Cloud
    and optionally creates SVG files of the changes

.PARAMETER StartCommitId
    The commit hash of the starting commit to look for changes

.PARAMETER CommitId
    The commit has of the end commit to look for changes

.PARAMETER DownloadFolder
    The folder to use as the download folder

.PARAMETER FolderAsId
    A boolean flag to denote if the Structurizr workspace ID is the folder name

.PARAMETER CreateImages
    A boolean flag to denote if SVG files should be created ready for upload

.EXAMPLE
    Example syntax for running the script or function
    PS C:\> ./publish.ps1 -StartCommitId $startCommitHash -CommitId $commitHash -DownloadFolder 'downloads' -FolderAsId $false CreateImages $false
#>

param (
    [Parameter(Mandatory)]
    [string]$StartCommitId,
    [Parameter(Mandatory)]
    [string]$CommitId,
    [Parameter(Mandatory)]
    [string]$DownloadFolder = 'downloads',
    [bool]$FolderAsId = $false,
    [bool]$CreateImages = $false
)

git diff-tree --no-commit-id --name-only --diff-filter=cd -r "$StartCommitId..$CommitId" | Where-Object { $_.EndsWith('.dsl') } | Foreach-Object {

    $filePath = ($_ | Resolve-Path -Relative) -replace "^./"
    $workspaceFolder = Split-Path -Path $filePath -Parent 
    $workspaceFile = $filePath 
    Write-Host "folder: $workspaceFolder"
    Write-Host "file: $workspaceFile"

    if ( $FolderAsId -eq $true ) {
        $workspaceIdValue = $workspaceFolder
    }
    else {
        $workspaceId = "WORKSPACE_ID_$($workspaceFolder)".ToUpper()
        $workspaceIdValue = (Get-item env:$workspaceId).Value
    }
    
    $workspaceKey = "WORKSPACE_KEY_$($workspaceFolder)".ToUpper()
    $workspaceKeyValue = (Get-item env:$workspaceKey).Value

    $workspaceSecret = "WORKSPACE_SECRET_$($workspaceFolder)".ToUpper()
    $workspaceSecretValue = (Get-item env:$workspaceSecret).Value

    docker run -i --rm -v ${pwd}:/usr/local/structurizr structurizr/cli push -id $workspaceIdValue -key $workspaceKeyValue -secret $workspaceSecretValue -workspace $workspaceFile
    $outputPath = "$DownloadFolder/$workspaceIdValue"
    if ( $CreateImages -eq $true ) {
        docker run --rm -v ${pwd}:/usr/local/structurizr structurizr/cli export -workspace $workspaceFile -format dot -output $outputPath
        sudo chown ${env:USER}:${env:USER} $outputPath

        Write-Host 'Convert exported files to svg'
        Get-ChildItem -Path $outputPath | Foreach-Object {
            $exportPath = ($_ | Resolve-Path -Relative)
            $folder = Split-Path -Path $exportPath -Parent
            $name = Split-Path -Path $exportPath -LeafBase

            Write-Host "Writing file: $folder/$name.svg"
            dot -Tsvg $exportPath > $folder/$name.svg
            rm $exportPath
        }
    }
}

Final Thoughts

I am big fan of using the C4 model and Structurizr and I hope sharing this idea of using a monorepo with multiple diagrams and automatically updating Structurizr via Pipeline has been a useful post.

Happy C4 diagramming 🙂

As always the example files, pipelines and script can be found in GitHub.

Coding With Taz