261 lines
13 KiB
Markdown
261 lines
13 KiB
Markdown
---
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stage: Verify
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group: Pipeline Execution
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info: To determine the technical writer assigned to the Stage/Group associated with this page, see https://about.gitlab.com/handbook/product/ux/technical-writing/#assignments
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type: reference
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---
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# Pipeline efficiency **(FREE)**
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[CI/CD Pipelines](index.md) are the fundamental building blocks for [GitLab CI/CD](../index.md).
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Making pipelines more efficient helps you save developer time, which:
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- Speeds up your DevOps processes
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- Reduces costs
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- Shortens the development feedback loop
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It's common that new teams or projects start with slow and inefficient pipelines,
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and improve their configuration over time through trial and error. A better process is
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to use pipeline features that improve efficiency right away, and get a faster software
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development lifecycle earlier.
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First ensure you are familiar with [GitLab CI/CD fundamentals](../introduction/index.md)
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and understand the [quick start guide](../quick_start/index.md).
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## Identify bottlenecks and common failures
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The easiest indicators to check for inefficient pipelines are the runtimes of the jobs,
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stages, and the total runtime of the pipeline itself. The total pipeline duration is
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heavily influenced by the:
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- [Size of the repository](../large_repositories/index.md)
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- Total number of stages and jobs.
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- Dependencies between jobs.
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- The ["critical path"](#directed-acyclic-graphs-dag-visualization), which represents
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the minimum and maximum pipeline duration.
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Additional points to pay attention relate to [GitLab Runners](../runners/index.md):
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- Availability of the runners and the resources they are provisioned with.
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- Build dependencies and their installation time.
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- [Container image size](#docker-images).
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- Network latency and slow connections.
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Pipelines frequently failing unnecessarily also causes slowdowns in the development
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lifecycle. You should look for problematic patterns with failed jobs:
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- Flaky unit tests which fail randomly, or produce unreliable test results.
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- Test coverage drops and code quality correlated to that behavior.
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- Failures that can be safely ignored, but that halt the pipeline instead.
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- Tests that fail at the end of a long pipeline, but could be in an earlier stage,
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causing delayed feedback.
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## Pipeline analysis
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Analyze the performance of your pipeline to find ways to improve efficiency. Analysis
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can help identify possible blockers in the CI/CD infrastructure. This includes analyzing:
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- Job workloads.
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- Bottlenecks in the execution times.
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- The overall pipeline architecture.
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It's important to understand and document the pipeline workflows, and discuss possible
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actions and changes. Refactoring pipelines may need careful interaction between teams
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in the DevSecOps lifecycle.
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Pipeline analysis can help identify issues with cost efficiency. For example, [runners](../runners/index.md)
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hosted with a paid cloud service may be provisioned with:
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- More resources than needed for CI/CD pipelines, wasting money.
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- Not enough resources, causing slow runtimes and wasting time.
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### Pipeline Insights
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The [Pipeline success and duration charts](index.md#pipeline-success-and-duration-charts)
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give information about pipeline runtime and failed job counts.
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Tests like [unit tests](../testing/unit_test_reports.md), integration tests, end-to-end tests,
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[code quality](../testing/code_quality.md) tests, and others
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ensure that problems are automatically found by the CI/CD pipeline. There could be many
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pipeline stages involved causing long runtimes.
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You can improve runtimes by running jobs that test different things in parallel, in
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the same stage, reducing overall runtime. The downside is that you need more runners
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running simultaneously to support the parallel jobs.
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The [testing levels for GitLab](../../development/testing_guide/testing_levels.md)
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provide an example of a complex testing strategy with many components involved.
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### Directed Acyclic Graphs (DAG) visualization
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The [Directed Acyclic Graph](../directed_acyclic_graph/index.md) (DAG) visualization can help analyze the critical path in
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the pipeline and understand possible blockers.
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### Pipeline Monitoring
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Global pipeline health is a key indicator to monitor along with job and pipeline duration.
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[CI/CD analytics](index.md#pipeline-success-and-duration-charts) give a visual
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representation of pipeline health.
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Instance administrators have access to additional [performance metrics and self-monitoring](../../administration/monitoring/index.md).
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You can fetch specific pipeline health metrics from the [API](../../api/index.md).
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External monitoring tools can poll the API and verify pipeline health or collect
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metrics for long term SLA analytics.
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For example, the [GitLab CI Pipelines Exporter](https://github.com/mvisonneau/gitlab-ci-pipelines-exporter)
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for Prometheus fetches metrics from the API and pipeline events. It can check branches in projects automatically
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and get the pipeline status and duration. In combination with a Grafana dashboard,
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this helps build an actionable view for your operations team. Metric graphs can also
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be embedded into incidents making problem resolving easier. Additionally, it can also export metrics about jobs and environments.
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If you use the GitLab CI Pipelines Exporter, you should start with the [example configuration](https://github.com/mvisonneau/gitlab-ci-pipelines-exporter/blob/main/docs/configuration_syntax.md).
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Alternatively, you can use a monitoring tool that can execute scripts, like
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[`check_gitlab`](https://gitlab.com/6uellerBpanda/check_gitlab) for example.
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#### Runner monitoring
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You can also [monitor CI runners](https://docs.gitlab.com/runner/monitoring/) on
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their host systems, or in clusters like Kubernetes. This includes checking:
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- Disk and disk IO
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- CPU usage
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- Memory
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- Runner process resources
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The [Prometheus Node Exporter](https://prometheus.io/docs/guides/node-exporter/)
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can monitor runners on Linux hosts, and [`kube-state-metrics`](https://github.com/kubernetes/kube-state-metrics)
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runs in a Kubernetes cluster.
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You can also test [GitLab Runner auto-scaling](https://docs.gitlab.com/runner/configuration/autoscale.html)
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with cloud providers, and define offline times to reduce costs.
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#### Dashboards and incident management
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Use your existing monitoring tools and dashboards to integrate CI/CD pipeline monitoring,
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or build them from scratch. Ensure that the runtime data is actionable and useful
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in teams, and operations/SREs are able to identify problems early enough.
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[Incident management](../../operations/incident_management/index.md) can help here too,
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with embedded metric charts and all valuable details to analyze the problem.
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### Storage usage
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Review the storage use of the following to help analyze costs and efficiency:
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- [Job artifacts](job_artifacts.md) and their [`expire_in`](../yaml/index.md#artifactsexpire_in)
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configuration. If kept for too long, storage usage grows and could slow pipelines down.
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- [Container registry](../../user/packages/container_registry/index.md) usage.
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- [Package registry](../../user/packages/package_registry/index.md) usage.
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## Pipeline configuration
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Make careful choices when configuring pipelines to speed up pipelines and reduce
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resource usage. This includes making use of GitLab CI/CD's built-in features that
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make pipelines run faster and more efficiently.
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### Reduce how often jobs run
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Try to find which jobs don't need to run in all situations, and use pipeline configuration
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to stop them from running:
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- Use the [`interruptible`](../yaml/index.md#interruptible) keyword to stop old pipelines
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when they are superseded by a newer pipeline.
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- Use [`rules`](../yaml/index.md#rules) to skip tests that aren't needed. For example,
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skip backend tests when only the frontend code is changed.
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- Run non-essential [scheduled pipelines](schedules.md) less frequently.
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### Fail fast
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Ensure that errors are detected early in the CI/CD pipeline. A job that takes a very long
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time to complete keeps a pipeline from returning a failed status until the job completes.
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Design pipelines so that jobs that can [fail fast](../testing/fail_fast_testing.md)
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run earlier. For example, add an early stage and move the syntax, style linting,
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Git commit message verification, and similar jobs in there.
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Decide if it's important for long jobs to run early, before fast feedback from
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faster jobs. The initial failures may make it clear that the rest of the pipeline
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shouldn't run, saving pipeline resources.
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### Directed Acyclic Graphs (DAG)
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In a basic configuration, jobs always wait for all other jobs in earlier stages to complete
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before running. This is the simplest configuration, but it's also the slowest in most
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cases. [Directed Acyclic Graphs](../directed_acyclic_graph/index.md) and
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[parent/child pipelines](downstream_pipelines.md#parent-child-pipelines) are more flexible and can
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be more efficient, but can also make pipelines harder to understand and analyze.
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### Caching
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Another optimization method is to [cache](../caching/index.md) dependencies. If your
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dependencies change rarely, like [NodeJS `/node_modules`](../caching/index.md#cache-nodejs-dependencies),
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caching can make pipeline execution much faster.
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You can use [`cache:when`](../yaml/index.md#cachewhen) to cache downloaded dependencies
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even when a job fails.
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### Docker Images
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Downloading and initializing Docker images can be a large part of the overall runtime
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of jobs.
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If a Docker image is slowing down job execution, analyze the base image size and network
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connection to the registry. If GitLab is running in the cloud, look for a cloud container
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registry offered by the vendor. In addition to that, you can make use of the
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[GitLab container registry](../../user/packages/container_registry/index.md) which can be accessed
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by the GitLab instance faster than other registries.
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#### Optimize Docker images
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Build optimized Docker images because large Docker images use up a lot of space and
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take a long time to download with slower connection speeds. If possible, avoid using
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one large image for all jobs. Use multiple smaller images, each for a specific task,
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that download and run faster.
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Try to use custom Docker images with the software pre-installed. It's usually much
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faster to download a larger pre-configured image than to use a common image and install
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software on it each time. Docker's [Best practices for writing Dockerfiles](https://docs.docker.com/develop/develop-images/dockerfile_best-practices/)
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has more information about building efficient Docker images.
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Methods to reduce Docker image size:
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- Use a small base image, for example `debian-slim`.
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- Do not install convenience tools such as vim or curl if they aren't strictly needed.
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- Create a dedicated development image.
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- Disable man pages and docs installed by packages to save space.
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- Reduce the `RUN` layers and combine software installation steps.
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- Use [multi-stage builds](https://blog.alexellis.io/mutli-stage-docker-builds/)
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to merge multiple Dockerfiles that use the builder pattern into one Dockerfile, which can reduce image size.
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- If using `apt`, add `--no-install-recommends` to avoid unnecessary packages.
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- Clean up caches and files that are no longer needed at the end. For example
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`rm -rf /var/lib/apt/lists/*` for Debian and Ubuntu, or `yum clean all` for RHEL and CentOS.
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- Use tools like [dive](https://github.com/wagoodman/dive) or [DockerSlim](https://github.com/docker-slim/docker-slim)
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to analyze and shrink images.
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To simplify Docker image management, you can create a dedicated group for managing
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[Docker images](../docker/index.md) and test, build and publish them with CI/CD pipelines.
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## Test, document, and learn
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Improving pipelines is an iterative process. Make small changes, monitor the effect,
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then iterate again. Many small improvements can add up to a large increase in pipeline
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efficiency.
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It can help to document the pipeline design and architecture. You can do this with
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[Mermaid charts in Markdown](../../user/markdown.md#mermaid) directly in the GitLab
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repository.
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Document CI/CD pipeline problems and incidents in issues, including research done
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and solutions found. This helps onboarding new team members, and also helps
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identify recurring problems with CI pipeline efficiency.
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### Learn More
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- [CI Monitoring Webcast Slides](https://docs.google.com/presentation/d/1ONwIIzRB7GWX-WOSziIIv8fz1ngqv77HO1yVfRooOHM/edit?usp=sharing)
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- [GitLab.com Monitoring Handbook](https://about.gitlab.com/handbook/engineering/monitoring/)
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- [Buildings dashboards for operational visibility](https://aws.amazon.com/builders-library/building-dashboards-for-operational-visibility/)
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