Caching in GitLab CI/CD (FREE)
A cache is one or more files that a job downloads and saves. Subsequent jobs that use the same cache don't have to download the files again, so they execute more quickly.
To learn how to define the cache in your
How cache is different from artifacts
Use cache for dependencies, like packages you download from the internet. Cache is stored where GitLab Runner is installed and uploaded to S3 if distributed cache is enabled.
Use artifacts to pass intermediate build results between stages. Artifacts are generated by a job, stored in GitLab, and can be downloaded.
Both artifacts and caches define their paths relative to the project directory, and can't link to files outside it.
- Define cache per job by using the
cache:keyword. Otherwise it is disabled.
- Subsequent pipelines can use the cache.
- Subsequent jobs in the same pipeline can use the cache, if the dependencies are identical.
- Different projects cannot share the cache.
- Define artifacts per job.
- Subsequent jobs in later stages of the same pipeline can use artifacts.
- Different projects cannot share artifacts.
Good caching practices
To ensure maximum availability of the cache, do one or more of the following:
- Tag your runners and use the tag on jobs that share the cache.
- Use runners that are only available to a particular project.
keythat fits your workflow. For example, you can configure a different cache for each branch.
For runners to work with caches efficiently, you must do one of the following:
- Use a single runner for all your jobs.
- Use multiple runners that have distributed caching, where the cache is stored in S3 buckets. Shared runners on GitLab.com behave this way. These runners can be in autoscale mode, but they don't have to be.
- Use multiple runners with the same architecture and have these runners share a common network-mounted directory to store the cache. This directory should use NFS or something similar. These runners must be in autoscale mode.
Use multiple caches
You can have a maximum of four caches:
test-job: stage: build cache: - key: files: - Gemfile.lock paths: - vendor/ruby - key: files: - yarn.lock paths: - .yarn-cache/ script: - bundle install --path=vendor - yarn install --cache-folder .yarn-cache - echo Run tests...
If multiple caches are combined with a fallback cache key, the fallback cache is fetched every time a cache is not found.
Use a fallback cache key
Introduced in GitLab Runner 13.4.
If a cache with this tag is not found, you can use
specify a cache to use when none exists.
In the following example, if the
$CI_COMMIT_REF_SLUG is not found, the job uses the key defined
variables: CACHE_FALLBACK_KEY: fallback-key job1: script: - echo cache: key: "$CI_COMMIT_REF_SLUG" paths: - binaries/
Disable cache for specific jobs
If you define the cache globally, each job uses the same definition. You can override this behavior for each job.
To disable it completely for a job, use an empty hash:
job: cache: 
Inherit global configuration, but override specific settings per job
You can override cache settings without overwriting the global cache by using
anchors. For example, if you want to override the
policy for one job:
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 information, see
Common use cases for caches
Usually you use caches to avoid downloading content, like dependencies or libraries, each time you run a job. Node.js packages, PHP packages, Ruby gems, Python libraries, and others can be cached.
For examples, see the GitLab CI/CD templates.
Share caches between jobs in the same branch
To have jobs in each branch use the same cache, define a cache with the
cache: key: $CI_COMMIT_REF_SLUG
This configuration prevents you from accidentally overwriting the cache. However, the first pipeline for a merge request is slow. The next time a commit is pushed to the branch, the cache is re-used and jobs run faster.
To enable per-job and per-branch caching:
cache: key: "$CI_JOB_NAME-$CI_COMMIT_REF_SLUG"
To enable per-stage and per-branch caching:
cache: key: "$CI_JOB_STAGE-$CI_COMMIT_REF_SLUG"
Share caches across jobs in different branches
To share a cache across all branches and all jobs, use the same key for everything:
cache: key: one-key-to-rule-them-all
To share a cache between branches, but have a unique cache for each job:
cache: key: $CI_JOB_NAME
Cache Node.js dependencies
If your project uses npm to install 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). However, you
can't cache things outside of the project directory.
Instead, tell npm to use
./.npm, and cache it per-branch:
# # 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
Cache PHP dependencies
If your project uses Composer to install
PHP dependencies, the following example defines
cache globally so that
all jobs inherit it. PHP libraries modules are installed in
are cached per-branch:
# # 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: $CI_COMMIT_REF_SLUG paths: - vendor/ before_script: # Install and run Composer - curl --show-error --silent "https://getcomposer.org/installer" | php - php composer.phar install test: script: - vendor/bin/phpunit --configuration phpunit.xml --coverage-text --colors=never
Cache Python dependencies
If your project uses pip to install
Python dependencies, the following example defines
cache globally so that
all jobs inherit it. Python libraries are installed in a virtual environment under
pip's cache is defined under
.cache/pip/ and both are cached per-branch:
# # https://gitlab.com/gitlab-org/gitlab/-/tree/master/lib/gitlab/ci/templates/Python.gitlab-ci.yml # image: python:latest # Change pip's cache directory to be inside the project directory since we can # only cache local items. variables: PIP_CACHE_DIR: "$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 .
Cache Ruby dependencies
If your project uses Bundler to install
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:
# # 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
If you have jobs that need different gems, use the
keyword in the global
cache definition. This configuration generates a different
cache for each job.
For example, a testing job might not need the same gems as a job that deploys to production:
cache: key: files: - Gemfile.lock prefix: $CI_JOB_NAME paths: - vendor/ruby test_job: stage: test before_script: - bundle install --without production --path vendor/ruby script: - bundle exec rspec deploy_job: stage: production before_script: - bundle install --without test --path vendor/ruby script: - bundle exec deploy
Cache Go dependencies
If your project uses Go 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
are cached for all of the
.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 it isn't guaranteed to always work. You might need to regenerate cached files in each job that needs them.
After you define a cache in
the availability of the cache depends on:
- The runner's executor type.
- Whether different runners are used to pass the cache between jobs.
Where the caches are stored
All caches defined for a job are archived in a single
The runner configuration defines where the file is stored. By default, the cache
is stored on the machine where GitLab Runner is installed. The location also depends on the type of executor.
|Runner executor||Default path of the cache|
|Shell||Locally, under the
|Docker||Locally, under Docker volumes:
|Docker Machine (autoscale runners)||The same as the Docker executor.|
If you use cache and artifacts to store the same path in your jobs, the cache might be overwritten because caches are restored before artifacts.
How archiving and extracting works
This example shows two jobs in two consecutive stages:
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/
If one machine has one runner installed, then all jobs for your project run on the same host:
- Pipeline starts.
cacheruns and the
vendor/directory is zipped into
cache.zip. This file is then saved in the directory based on the runner's setting and the
- The cache is extracted (if found).
- 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. This setup guarantees the
cache can be reused between stages. It only works if the execution goes from the
test stage in the same runner/machine. Otherwise, the cache might not be available.
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 Aoverwrites things in the archive of
It works this way because the cache created for one runner often isn't valid when used by a different one. A different runner may run on a different architecture (for example, when the cache includes binary files). Also, because the different steps might be executed by runners running on different machines, it is a safe default.
Clearing the cache
Runners use cache to speed up the execution of your jobs by reusing existing data. This can sometimes lead to inconsistent behavior.
There are two ways to start with a fresh copy of the cache.
Clear the cache by changing
Change the value for
cache: key in your
The next time the pipeline runs, the cache is stored in a different location.
Clear the cache manually
Introduced in GitLab 10.4.
You can clear the cache in the GitLab UI:
- On the top bar, select Menu > Projects and find your project.
- On the left sidebar, select CI/CD > Pipelines page.
- In the top right, select Clear runner caches.
On the next commit, your CI/CD jobs use a new cache.
Each time you clear the cache manually, the internal cache name is updated. The name uses the format
cache-<index>, and the index increments by one. The old cache is not deleted. You can manually delete these files from the runner storage.
If you have a cache mismatch, follow these steps to troubleshoot.
|Reason for 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. There's no automatic way to do this.|
|You use the same
||Use different cache keys to that the cache archive is stored to a different location and doesn't overwrite wrong caches.|
Cache mismatch example 1
If you have only one runner assigned to your project, the cache is stored on the runner's machine by default.
If two jobs have the same cache key but a different path, the caches can be overwritten. For example:
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/
public/is cached as cache.zip.
- The previous cache, if any, is unzipped.
vendor/is cached as cache.zip and overwrites the previous one.
- The next time
job Aruns it uses the cache of
job Bwhich is different and thus isn't effective.
To fix this issue, use different
keys for each job.
Cache mismatch example 2
In this example, you have more than one runner assigned to your project, and distributed cache is not enabled.
The second time the pipeline runs, you want
job A and
job B to re-use their cache (which in this case
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/
Even if the
key is different, the cached files might get "cleaned" before each
stage if the jobs run on different runners in subsequent pipelines.