Monitoring and adjusting Pipeline concurrency settings in Azure DevOps

Monitoring and adjusting Pipeline concurrency settings in Azure DevOps

Optimizing pipeline concurrency in Azure DevOps is a critical aspect of improving both performance and cost. By efficiently managing the use of parallel jobs, stages, agent resources, and job dependencies, you can maximize throughput while minimizing unnecessary resource consumption.

Here are key strategies and best practices to optimize pipeline concurrency for performance and cost.

1. Utilizing Parallel Jobs and Stages

Parallel jobs and parallel stages are fundamental to speeding up pipeline execution, which improves performance by allowing multiple tasks to run concurrently rather than sequentially. However, efficient use of these resources can also help minimize costs.

1. Parallel Jobs

Parallel jobs enable multiple pipeline runs (jobs) to be executed at the same time rather than waiting for each job to finish before starting the next. This can drastically reduce pipeline duration.

1. Free vs Paid Parallel Jobs:

Azure DevOps offers a limited number of parallel jobs for free (one free parallel job for public repositories and one for private repositories). Additional parallel jobs require a paid license. Optimizing the use of parallel jobs can help reduce unnecessary costs.

2. How to Use:

In Azure DevOps, you can define multiple jobs in a pipeline and run them in parallel by defining them under the jobs keyword in your YAML file:

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jobs:
2
  - job: Build
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    steps:
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      - task: SomeBuildTask
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  - job: Test
6
    steps:
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      - task: SomeTestTask
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  - job: Deploy
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    steps:
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      - task: SomeDeployTask

3. Best Practice:

Prioritize the most resource-intensive jobs (e.g., test or build) for parallel execution to achieve maximum time savings. For example, splitting build jobs for different components or services and running them concurrently can significantly reduce pipeline execution time.

2. Parallel Stages

Stages in Azure Pipelines represent major pipeline phases like build, test, and deploy. You can configure stages to run in parallel to optimize pipeline time.

1. How to Use:

Use the dependsOn condition to run stages in parallel when possible.

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stages:
2
  - stage: Build
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    jobs:
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      - job: Build1
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      - job: Build2
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  - stage: Test
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    jobs:
8
      - job: Test1
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      - job: Test2
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  - stage: Deploy
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    dependsOn: []
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    jobs:
13
      - job: Deploy1

2. Best Practice:

Identify stages that can be run concurrently (e.g., build and test) and adjust their dependencies accordingly. However, keep in mind that running too many jobs in parallel could lead to resource contention or hit service limits, so a balanced approach is necessary.

2. Leveraging Job Dependencies and Conditions

Job dependencies and conditions help control when and how jobs execute, allowing you to optimize concurrency based on specific conditions.

1. Job Dependencies

Job dependencies allow you to control the order of execution. You can specify that certain jobs depend on the completion of others before they start. This ensures that jobs only run when necessary, reducing wasted resources.

1. Using dependsOn:

You can use the dependsOn keyword to make jobs dependent on other jobs' results:

xxxxxxxxxx
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jobs:
2
  - job: Build
3
    steps:
4
      - task: BuildTask
5
  - job: Test
6
    dependsOn: Build
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    steps:
8
      - task: TestTask

2. Best Practice:

Optimize dependencies by allowing jobs to run in parallel where possible, but ensure that critical dependencies are respected. For example, don't run tests before builds, but you can parallelize the build jobs themselves.

2. Conditional Execution with condition

You can use conditions to decide whether a job should run based on the outcome of previous jobs or pipeline parameters.

1. Examples:

Run a job only if the previous job was successful:

xxxxxxxxxx
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jobs:
2
  - job: Build
3
    steps:
4
      - task: BuildTask
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  - job: Deploy
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    condition: succeeded('Build') 
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    steps:
8
      - task: DeployTask

Run a job only when a certain condition is met (e.g., specific branch or tag):

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jobs:
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  - job: DeployToProd
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    condition: and(succeeded(), eq(variables['Build.SourceBranch'], 'refs/heads/main'))
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    steps:
5
      - task: DeployTask

2. Best Practice:

Use conditional logic to optimize pipeline execution, such as skipping non-critical tasks on feature branches or triggering deployments only when certain criteria are met (e.g., successful tests, specific branch).

3. Right-Sizing Pipeline Agents

Choosing the right size and type of pipeline agents (both hosted and self-hosted) is crucial for optimizing performance and cost.

1. Use Self-Hosted Agents

Self-hosted agents can offer faster execution times and lower costs, especially if you already have infrastructure in place. Azure provides free private agents that can be reused for multiple pipelines, so you don’t incur additional costs for each job run.

Advantages:

1. Faster execution as the agent is already set up with the necessary tools.

2. No additional cost for hosted agents (after initial setup).

3. Greater control over the agent's environment.

How to Use:

Set up a self-hosted agent by installing the Azure Pipelines Agent on your machine or VM, then register it with your Azure DevOps organization.

2. Choose the Right Agent Pool

You can set up multiple agent pools in Azure DevOps, enabling you to use the best agents for specific tasks.

1. How to Use:

In your YAML pipeline, specify the agent pool for each job:

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pool:
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  name: 'MyAgentPool'

2. Best Practice:

If you're using self-hosted agents, make sure they’re appropriately sized for the tasks they will perform. For example, if a job requires significant CPU and memory resources (like building large Docker images), ensure your self-hosted agent is provisioned with sufficient resources.

3. Optimize the Resource Allocation

Make sure the agents are configured optimally for your build or test workloads.

1. Use lightweight jobs: For tasks that don’t require heavy resource usage (like linting or simple unit tests), use smaller agents (e.g., agents with lower CPU and memory).

2. Larger jobs: For resource-heavy jobs like large-scale tests, builds, or image builds, ensure your agents have the required CPU, memory, and storage capacity.

4. Monitoring and Adjusting Concurrency Settings

Effective monitoring of your pipeline’s concurrency settings can help identify performance bottlenecks and optimize resource usage.

1. Use Azure Pipelines Reports

Azure DevOps provides built-in reporting tools that let you monitor the concurrency and performance of your pipelines.

1. Pipeline Run Analytics: Use pipeline run summaries to analyze run times, success rates, and durations for different pipeline jobs.

2. Agent Utilization: Check how much time agents spend on each task, and adjust your resources accordingly.

2. Azure Monitor and Application Insights

Azure Monitor and Application Insights can be used to track pipeline metrics like job duration, failure rates, and resource utilization.

You can integrate Application Insights directly into your pipeline to monitor telemetry data such as build times and failures.

3. Adjust Concurrency Based on Pipeline Feedback

1. Analyze Resource Bottlenecks: If your pipeline is consistently slow, check the agent usage, job durations, and any potential build or test step bottlenecks.

2. Adjust Parallel Jobs: Based on feedback from your pipeline performance, increase or decrease the number of parallel jobs and stages, ensuring you strike a balance between resource usage and build time.

3. Optimize Triggers: Configure pipelines to run only when necessary (e.g., after a successful merge or pull request approval). Use scheduled triggers to optimize timing and avoid unnecessary runs.

Summary

Optimizing pipeline concurrency in Azure DevOps for performance and cost requires a mix of best practices and tools.

Here’s a summary of the key strategies:

1. Use parallel jobs and stages: Split jobs into smaller tasks and run them in parallel to reduce pipeline time.

2. Leverage job dependencies and conditions: Control job execution order and conditionally run tasks to minimize resource usage.

3. Right-size pipeline agents: Choose self-hosted agents for better performance and cost savings, and ensure your agents are appropriately sized for the tasks they will handle.

4. Monitor and adjust concurrency settings: Use built-in Azure DevOps reports and integrate Azure Monitor and Application Insights to track pipeline performance and resource usage.

By applying these strategies, you can optimize your Azure DevOps pipelines to be faster, more efficient, and cost-effective, while maintaining the flexibility needed for different workloads and deployment scenarios.