--- stage: Verify group: Continuous Integration info: To determine the technical writer assigned to the Stage/Group associated with this page, see https://about.gitlab.com/handbook/engineering/ux/technical-writing/#designated-technical-writers type: index, concepts, howto --- # Cache dependencies in GitLab CI/CD GitLab CI/CD provides a caching mechanism that can be used to save time when your jobs are running. Caching is about speeding the time a job is executed by reusing the same content of a previous job. It can be particularly useful when you are developing software that depends on other libraries which are fetched via the internet during build time. If caching is enabled, it's shared between pipelines and jobs at the project level by default, starting from GitLab 9.0. Caches are not shared across projects. Make sure you read the [`cache` reference](../yaml/README.md#cache) to learn how it is defined in `.gitlab-ci.yml`. ## Cache vs artifacts Be careful if you use cache and artifacts to store the same path in your jobs as **caches are restored before artifacts** and the content could be overwritten. Don't use caching for passing artifacts between stages, as it is designed to store runtime dependencies needed to compile the project: - `cache`: **For storing project dependencies** Caches are used to speed up runs of a given job in **subsequent pipelines**, by storing downloaded dependencies so that they don't have to be fetched from the internet again (like npm packages, Go vendor packages, etc.) While the cache could be configured to pass intermediate build results between stages, this should be done with artifacts instead. - `artifacts`: **Use for stage results that are passed between stages.** Artifacts are files generated by a job which are stored and uploaded, and can then be fetched and used by jobs in later stages of the **same pipeline**. In other words, [you can't create an artifact in job-A in stage-1, and then use this artifact in job-B in stage-1](https://gitlab.com/gitlab-org/gitlab/-/issues/25837). This data is be available in different pipelines, but is available to be downloaded from the UI. The name `artifacts` sounds like it's only useful outside of the job, like for downloading a final image, but artifacts are also available in later stages within a pipeline. So if you build your application by downloading all the required modules, you might want to declare them as artifacts so that subsequent stages can use them. There are some optimizations like declaring an [expiry time](../yaml/README.md#artifactsexpire_in) so you don't keep artifacts around too long, or using [dependencies](../yaml/README.md#dependencies) to control which jobs fetch the artifacts. Caches: - Are disabled if not defined globally or per job (using `cache:`). - Are available for all jobs in your `.gitlab-ci.yml` if enabled globally. - Can be used in subsequent pipelines by the same job in which the cache was created (if not defined globally). - Are stored where GitLab Runner is installed **and** uploaded to S3 if [distributed cache is enabled](https://docs.gitlab.com/runner/configuration/autoscale.html#distributed-runners-caching). - If defined per job, are used: - By the same job in a subsequent pipeline. - By subsequent jobs in the same pipeline, if they have identical dependencies. Artifacts: - Are disabled if not defined per job (using `artifacts:`). - Can only be enabled per job, not globally. - Are created during a pipeline and can be used by the subsequent jobs of that currently active pipeline. - Are always uploaded to GitLab (known as coordinator). - Can have an expiration value for controlling disk usage (30 days by default). Both artifacts and caches define their paths relative to the project directory, and can't link to files outside it. ## Good caching practices We have the cache from the perspective of the developers (who consume a cache within the job) and the cache from the perspective of the runner. Depending on which type of runner you are using, cache can act differently. From the perspective of the developer, to ensure maximum availability of the cache, when declaring `cache` in your jobs, use one or a mix of the following: - [Tag your runners](../runners/README.md#use-tags-to-limit-the-number-of-jobs-using-the-runner) and use the tag on jobs that share their cache. - [Use sticky runners](../runners/README.md#prevent-a-specific-runner-from-being-enabled-for-other-projects) that are only available to a particular project. - [Use a `key`](../yaml/README.md#cachekey) that fits your workflow (for example, different caches on each branch). For that, you can take advantage of the [CI/CD predefined variables](../variables/README.md#predefined-environment-variables). TIP: **Tip:** Using the same runner for your pipeline, is the most simple and efficient way to cache files in one stage or pipeline, and pass this cache to subsequent stages or pipelines in a guaranteed manner. From the perspective of the runner, in order for cache to work effectively, one of the following must be true: - Use a single runner for all your jobs. - Use multiple runners (in autoscale mode or not) that use [distributed caching](https://docs.gitlab.com/runner/configuration/autoscale.html#distributed-runners-caching), where the cache is stored in S3 buckets (like shared runners on GitLab.com). - Use multiple runners (not in autoscale mode) of the same architecture that share a common network-mounted directory (using NFS or something similar) where the cache is stored. TIP: **Tip:** Read about the [availability of the cache](#availability-of-the-cache) to learn more about the internals and get a better idea how cache works. ### Sharing caches across the same branch Define a cache with the `key: ${CI_COMMIT_REF_SLUG}` so that jobs of each branch always use the same cache: ```yaml cache: key: ${CI_COMMIT_REF_SLUG} ``` While this feels like it might be safe from accidentally overwriting the cache, it means merge requests get slow first pipelines, which might be a bad developer experience. The next time a new commit is pushed to the branch, the cache is re-used. To enable per-job and per-branch caching: ```yaml cache: key: "$CI_JOB_NAME-$CI_COMMIT_REF_SLUG" ``` To enable per-branch and per-stage caching: ```yaml cache: key: "$CI_JOB_STAGE-$CI_COMMIT_REF_SLUG" ``` ### Sharing caches across different branches If the files you are caching need to be shared across all branches and all jobs, you can use the same key for all of them: ```yaml cache: key: one-key-to-rule-them-all ``` To share the same cache between branches, but separate them by job: ```yaml cache: key: ${CI_JOB_NAME} ``` ### Disabling cache on specific jobs If you have defined the cache globally, it means that each job uses the same definition. You can override this behavior per-job, and if you want to disable it completely, use an empty hash: ```yaml job: cache: {} ``` ### Inherit global config, but override specific settings per job You can override cache settings without overwriting the global cache by using [anchors](../yaml/README.md#anchors). For example, if you want to override the `policy` for one job: ```yaml cache: &global_cache key: ${CI_COMMIT_REF_SLUG} paths: - node_modules/ - public/ - vendor/ policy: pull-push job: cache: # inherit all global cache settings <<: *global_cache # override the policy policy: pull ``` For more fine tuning, read also about the [`cache: policy`](../yaml/README.md#cachepolicy). ## Common use cases The most common use case of cache is to preserve contents between subsequent runs of jobs for things like dependencies and commonly used libraries (Node.js packages, PHP packages, rubygems, Python libraries, etc.), so they don't have to be re-fetched from the public internet. For more examples, check out our [GitLab CI/CD templates](https://gitlab.com/gitlab-org/gitlab/tree/master/lib/gitlab/ci/templates). ### Caching Node.js dependencies Assuming your project is using [npm](https://www.npmjs.com/) to install the Node.js dependencies, the following example defines `cache` globally so that all jobs inherit it. By default, npm stores cache data in the home folder `~/.npm` but since [you can't cache things outside of the project directory](../yaml/README.md#cachepaths), we tell npm to use `./.npm` instead, and it is cached per-branch: ```yaml # # https://gitlab.com/gitlab-org/gitlab/tree/master/lib/gitlab/ci/templates/Nodejs.gitlab-ci.yml # image: node:latest # Cache modules in between jobs cache: key: ${CI_COMMIT_REF_SLUG} paths: - .npm/ before_script: - npm ci --cache .npm --prefer-offline test_async: script: - node ./specs/start.js ./specs/async.spec.js ``` ### Caching PHP dependencies Assuming your project is using [Composer](https://getcomposer.org/) to install the PHP dependencies, the following example defines `cache` globally so that all jobs inherit it. PHP libraries modules are installed in `vendor/` and are cached per-branch: ```yaml # # https://gitlab.com/gitlab-org/gitlab/tree/master/lib/gitlab/ci/templates/PHP.gitlab-ci.yml # image: php:7.2 # Cache libraries in between jobs cache: key: ${CI_COMMIT_REF_SLUG} paths: - vendor/ before_script: # Install and run Composer - curl --show-error --silent https://getcomposer.org/installer | php - php composer.phar install test: script: - vendor/bin/phpunit --configuration phpunit.xml --coverage-text --colors=never ``` ### Caching Python dependencies Assuming your project is using [pip](https://pip.pypa.io/en/stable/) to install the Python dependencies, the following example defines `cache` globally so that all jobs inherit it. Python libraries are installed in a virtual environment under `venv/`, pip's cache is defined under `.cache/pip/` and both are cached per-branch: ```yaml # # https://gitlab.com/gitlab-org/gitlab/tree/master/lib/gitlab/ci/templates/Python.gitlab-ci.yml # image: python:latest # Change pip's cache directory to be inside the project directory since we can # only cache local items. variables: PIP_CACHE_DIR: "$CI_PROJECT_DIR/.cache/pip" # Pip's cache doesn't store the python packages # https://pip.pypa.io/en/stable/reference/pip_install/#caching # # If you want to also cache the installed packages, you have to install # them in a virtualenv and cache it as well. cache: paths: - .cache/pip - venv/ before_script: - python -V # Print out python version for debugging - pip install virtualenv - virtualenv venv - source venv/bin/activate test: script: - python setup.py test - pip install flake8 - flake8 . ``` ### Caching Ruby dependencies Assuming your project is using [Bundler](https://bundler.io) to install the gem dependencies, the following example defines `cache` globally so that all jobs inherit it. Gems are installed in `vendor/ruby/` and are cached per-branch: ```yaml # # https://gitlab.com/gitlab-org/gitlab/tree/master/lib/gitlab/ci/templates/Ruby.gitlab-ci.yml # image: ruby:2.6 # Cache gems in between builds cache: key: ${CI_COMMIT_REF_SLUG} paths: - vendor/ruby before_script: - ruby -v # Print out ruby version for debugging - bundle install -j $(nproc) --path vendor/ruby # Install dependencies into ./vendor/ruby rspec: script: - rspec spec ``` ### Caching Go dependencies Assuming your project is using [Go Modules](https://github.com/golang/go/wiki/Modules) to install Go dependencies, the following example defines `cache` in a `go-cache` template, that any job can extend. Go modules are installed in `${GOPATH}/pkg/mod/` and are cached for all of the `go` projects: ```yaml .go-cache: variables: GOPATH: $CI_PROJECT_DIR/.go before_script: - mkdir -p .go cache: paths: - .go/pkg/mod/ test: image: golang:1.13 extends: .go-cache script: - go test ./... -v -short ``` ## Availability of the cache Caching is an optimization, but isn't guaranteed to always work, so you need to be prepared to regenerate any cached files in each job that needs them. Assuming you have properly [defined `cache` in `.gitlab-ci.yml`](../yaml/README.md#cache) according to your workflow, the availability of the cache ultimately depends on how the runner has been configured (the executor type and whether different runners are used for passing the cache between jobs). ### Where the caches are stored Since the runner is the one responsible for storing the cache, it's essential to know **where** it's stored. All the cache paths defined under a job in `.gitlab-ci.yml` are archived in a single `cache.zip` file and stored in the runner's configured cache location. By default, they are stored locally in the machine where the runner is installed and depends on the type of the executor. | GitLab Runner executor | Default path of the cache | | ---------------------- | ------------------------- | | [Shell](https://docs.gitlab.com/runner/executors/shell.html) | Locally, stored under the `gitlab-runner` user's home directory: `/home/gitlab-runner/cache////cache.zip`. | | [Docker](https://docs.gitlab.com/runner/executors/docker.html) | Locally, stored under [Docker volumes](https://docs.gitlab.com/runner/executors/docker.html#the-builds-and-cache-storage): `/var/lib/docker/volumes//_data////cache.zip`. | | [Docker machine](https://docs.gitlab.com/runner/executors/docker_machine.html) (autoscale runners) | Behaves the same as the Docker executor. | ### How archiving and extracting works In the most simple scenario, consider that you use only one machine where the runner is installed, and all jobs of your project run on the same host. Let's see the following example of two jobs that belong to two consecutive stages: ```yaml stages: - build - test before_script: - echo "Hello" job A: stage: build script: - mkdir vendor/ - echo "build" > vendor/hello.txt cache: key: build-cache paths: - vendor/ after_script: - echo "World" job B: stage: test script: - cat vendor/hello.txt cache: key: build-cache paths: - vendor/ ``` Here's what happens behind the scenes: 1. Pipeline starts. 1. `job A` runs. 1. `before_script` is executed. 1. `script` is executed. 1. `after_script` is executed. 1. `cache` runs and the `vendor/` directory is zipped into `cache.zip`. This file is then saved in the directory based on the [runner's setting](#where-the-caches-are-stored) and the `cache: key`. 1. `job B` runs. 1. The cache is extracted (if found). 1. `before_script` is executed. 1. `script` is executed. 1. Pipeline finishes. By using a single runner on a single machine, you don't have the issue where `job B` might execute on a runner different from `job A`, thus guaranteeing the cache between stages. That only works if the build goes from stage `build` to `test` in the same runner/machine, otherwise, you [might not have the cache available](#cache-mismatch). During the caching process, there's also a couple of things to consider: - If some other job, with another cache configuration had saved its cache in the same zip file, it is overwritten. If the S3 based shared cache is used, the file is additionally uploaded to S3 to an object based on the cache key. So, two jobs with different paths, but the same cache key, overwrites their cache. - When extracting the cache from `cache.zip`, everything in the zip file is extracted in the job's working directory (usually the repository which is pulled down), and the runner doesn't mind if the archive of `job A` overwrites things in the archive of `job B`. The reason why it works this way is because the cache created for one runner often isn't valid when used by a different one which can run on a **different architecture** (e.g., when the cache includes binary files). And since the different steps might be executed by runners running on different machines, it is a safe default. ### Cache mismatch In the following table, you can see some reasons where you might hit a cache mismatch and a few ideas how to fix it. | Reason of a cache mismatch | How to fix it | | -------------------------- | ------------- | | You use multiple standalone runners (not in autoscale mode) attached to one project without a shared cache | Use only one runner for your project or use multiple runners with distributed cache enabled | | You use runners in autoscale mode without a distributed cache enabled | Configure the autoscale runner to use a distributed cache | | The machine the runner is installed on is low on disk space or, if you've set up distributed cache, the S3 bucket where the cache is stored doesn't have enough space | Make sure you clear some space to allow new caches to be stored. Currently, there's no automatic way to do this. | | You use the same `key` for jobs where they cache different paths. | Use different cache keys to that the cache archive is stored to a different location and doesn't overwrite wrong caches. | Let's explore some examples. #### Examples Let's assume you have only one runner assigned to your project, so the cache is stored in the runner's machine by default. If two jobs, A and B, have the same cache key, but they cache different paths, cache B would overwrite cache A, even if their `paths` don't match: We want `job A` and `job B` to re-use their cache when the pipeline is run for a second time. ```yaml stages: - build - test job A: stage: build script: make build cache: key: same-key paths: - public/ job B: stage: test script: make test cache: key: same-key paths: - vendor/ ``` 1. `job A` runs. 1. `public/` is cached as cache.zip. 1. `job B` runs. 1. The previous cache, if any, is unzipped. 1. `vendor/` is cached as cache.zip and overwrites the previous one. 1. The next time `job A` runs it uses the cache of `job B` which is different and thus isn't effective. To fix that, use different `keys` for each job. In another case, let's assume you have more than one runner assigned to your project, but the distributed cache is not enabled. The second time the pipeline is run, we want `job A` and `job B` to re-use their cache (which in this case is different): ```yaml stages: - build - test job A: stage: build script: build cache: key: keyA paths: - vendor/ job B: stage: test script: test cache: key: keyB paths: - vendor/ ``` In that case, even if the `key` is different (no fear of overwriting), you might experience that the cached files "get cleaned" before each stage if the jobs run on different runners in the subsequent pipelines. ## Clearing the cache Runners use [cache](../yaml/README.md#cache) to speed up the execution of your jobs by reusing existing data. This however, can sometimes lead to an inconsistent behavior. To start with a fresh copy of the cache, there are two ways to do that. ### Clearing the cache by changing `cache:key` All you have to do is set a new `cache: key` in your `.gitlab-ci.yml`. In the next run of the pipeline, the cache is stored in a different location. ### Clearing the cache manually > [Introduced](https://gitlab.com/gitlab-org/gitlab-foss/-/issues/41249) in GitLab 10.4. If you want to avoid editing `.gitlab-ci.yml`, you can easily clear the cache via GitLab's UI: 1. Navigate to your project's **CI/CD > Pipelines** page. 1. Click on the **Clear runner caches** button to clean up the cache. ![Clear runner caches](img/clear_runners_cache.png) 1. On the next push, your CI/CD job uses a new cache. Behind the scenes, this works by increasing a counter in the database, and the value of that counter is used to create the key for the cache by appending an integer to it: `-1`, `-2`, etc. After a push, a new key is generated and the old cache is not valid anymore.