Caching in GitLab CI/CD (FREE ALL)

A cache is one or more files 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 .gitlab-ci.yml file, see the cache reference.

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.

Cache

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.
By default, protected and non-protected branches do not share the cache. However, you can change this behavior.

Artifacts

Define artifacts per job.
Subsequent jobs in later stages of the same pipeline can use artifacts.
Different projects cannot share artifacts.
Artifacts expire after 30 days by default. You can define a custom expiration time.
The latest artifacts do not expire if keep latest artifacts is enabled.
Use dependencies to control which jobs fetch the 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.
Use a key that 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

Introduced in GitLab 13.10.

Feature flag removed, in GitLab 13.12.

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 config set --local path 'vendor/ruby'
    - bundle install
    - yarn install --cache-folder .yarn-cache
    - echo Run tests...

If multiple caches are combined with a fallback cache key, the global fallback cache is fetched every time a cache is not found.

Use a fallback cache key

Per-cache fallback keys

Introduced in GitLab 16.0

Each cache entry supports up to five fallback keys with the fallback_keys keyword. When a job does not find a cache key, the job attempts to retrieve a fallback cache instead. Fallback keys are searched in order until a cache is found. If no cache is found, the job runs without using a cache. For example:

test-job:
  stage: build
  cache:
    - key: cache-$CI_COMMIT_REF_SLUG
      fallback_keys:
        - cache-$CI_DEFAULT_BRANCH
        - cache-default
      paths:
        - vendor/ruby
  script:
    - bundle config set --local path 'vendor/ruby'
    - bundle install
    - echo Run tests...

In this example:

The job looks for the cache-$CI_COMMIT_REF_SLUG cache.
If cache-$CI_COMMIT_REF_SLUG is not found, the job looks for cache-$CI_DEFAULT_BRANCH as a fallback option.
If cache-$CI_DEFAULT_BRANCH is also not found, the job looks for cache-default as a second fallback option.
If none are found, the job downloads all the Ruby dependencies without using a cache, but creates a new cache for cache-$CI_COMMIT_REF_SLUG when the job completes.

Fallback keys follow the same processing logic as cache:key:

If you clear caches manually, per-cache fallback keys are appended with an index like other cache keys.
If the Use separate caches for protected branches setting is enabled, per-cache fallback keys are appended with -protected or -non_protected.

Global fallback key

Introduced in GitLab Runner 13.4.

You can use the $CI_COMMIT_REF_SLUG predefined variable to specify your cache:key. For example, if your $CI_COMMIT_REF_SLUG is test, you can set a job to download cache that's tagged with test.

If a cache with this tag is not found, you can use CACHE_FALLBACK_KEY to 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 by the CACHE_FALLBACK_KEY variable:

variables:
  CACHE_FALLBACK_KEY: fallback-key

job1:
  script:
    - echo
  cache:
    key: "$CI_COMMIT_REF_SLUG"
    paths:
      - binaries/

The order of caches extraction is:

Retrieval attempt for cache:key
Retrieval attempts for each entry in order in fallback_keys
Retrieval attempt for the global fallback key in CACHE_FALLBACK_KEY

The cache extraction process stops after the first successful cache is retrieved.

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 list:

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:

default:
  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 cache: policy.

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 key: $CI_COMMIT_REF_SLUG:

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

Use a variable to control a job's cache policy

Introduced in GitLab 16.1.

To reduce duplication of jobs where the only difference is the pull policy, you can use a CI/CD variable.

For example:

conditional-policy:
  rules:
    - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH
      variables:
        POLICY: pull-push
    - if: $CI_COMMIT_BRANCH != $CI_DEFAULT_BRANCH
      variables:
        POLICY: pull
  stage: build
  cache:
    key: gems
    policy: $POLICY
    paths:
      - vendor/bundle
  script:
    - echo "This job pulls and pushes the cache depending on the branch"
    - echo "Downloading dependencies..."

In this example, the job's cache policy is:

pull-push for changes to the default branch.
pull for changes to other branches.

Cache Node.js dependencies

If your project uses npm to install Node.js dependencies, the following example defines a default cache 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:

default:
  image: node:latest
  cache:  # Cache modules in between jobs
    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

Compute the cache key from the lock file

You can use cache:key:files to compute the cache key from a lock file like package-lock.json or yarn.lock, and reuse it in many jobs.

default:
  cache:  # Cache modules using lock file
    key:
      files:
        - package-lock.json
    paths:
      - .npm/

If you're using Yarn, you can use yarn-offline-mirror to cache the zipped node_modules tarballs. The cache generates more quickly, because fewer files have to be compressed:

job:
  script:
    - echo 'yarn-offline-mirror ".yarn-cache/"' >> .yarnrc
    - echo 'yarn-offline-mirror-pruning true' >> .yarnrc
    - yarn install --frozen-lockfile --no-progress
  cache:
    key:
      files:
        - yarn.lock
    paths:
      - .yarn-cache/

Cache PHP dependencies

If your project uses Composer to install PHP dependencies, the following example defines a default cache so that all jobs inherit it. PHP libraries modules are installed in vendor/ and are cached per-branch:

default:
  image: php:7.2
  cache:  # Cache libraries in between jobs
    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 a default cache so that all jobs inherit it. pip's cache is defined under .cache/pip/ and is cached per-branch:

default:
  image: python:latest
  cache:                      # Pip's cache doesn't store the python packages
    paths:                    # https://pip.pypa.io/en/stable/topics/caching/
      - .cache/pip
  before_script:
    - python -V               # Print out python version for debugging
    - pip install virtualenv
    - virtualenv venv
    - source venv/bin/activate

variables:  # Change pip's cache directory to be inside the project directory since we can only cache local items.
  PIP_CACHE_DIR: "$CI_PROJECT_DIR/.cache/pip"

test:
  script:
    - python setup.py test
    - pip install ruff
    - ruff --format=gitlab .

Cache Ruby dependencies

If your project uses Bundler to install gem dependencies, the following example defines a default cache so that all jobs inherit it. Gems are installed in vendor/ruby/ and are cached per-branch:

default:
  image: ruby:2.6
  cache:                                            # Cache gems in between builds
    key: $CI_COMMIT_REF_SLUG
    paths:
      - vendor/ruby
  before_script:
    - ruby -v                                       # Print out ruby version for debugging
    - bundle config set --local path 'vendor/ruby'  # The location to install the specified gems to
    - bundle install -j $(nproc)                    # Install dependencies into ./vendor/ruby

rspec:
  script:
    - rspec spec

If you have jobs that need different gems, use the prefix 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:

default:
  cache:
    key:
      files:
        - Gemfile.lock
      prefix: $CI_JOB_NAME
    paths:
      - vendor/ruby

test_job:
  stage: test
  before_script:
    - bundle config set --local path 'vendor/ruby'
    - bundle install --without production
  script:
    - bundle exec rspec

deploy_job:
  stage: production
  before_script:
    - bundle config set --local path 'vendor/ruby'   # The location to install the specified gems to
    - bundle install --without test
  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 ${GOPATH}/pkg/mod/ and are cached for all of the go projects:

.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 .gitlab-ci.yml, 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 cache.zip file. 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 `gitlab-runner` user's home directory: `/home/gitlab-runner/cache/<user>/<project>/<cache-key>/cache.zip`.
Docker	Locally, under Docker volumes: `/var/lib/docker/volumes/<volume-id>/_data/<user>/<project>/<cache-key>/cache.zip`.
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.

Cache key names

Introduced in GitLab 15.0.

A suffix is added to the cache key, with the exception of the global fallback cache key.

As an example, assuming that cache.key is set to $CI_COMMIT_REF_SLUG, and that we have two branches main and feature, then the following table represents the resulting cache keys:

Branch name	Cache key
`main`	`main-protected`
`feature`	`feature-non_protected`

Use the same cache for all branches

Introduced in GitLab 15.0.

If you do not want to use cache key names, you can have all branches (protected and unprotected) use the same cache.

The cache separation with cache key names is a security feature and should only be disabled in an environment where all users with Developer role are highly trusted.

To use the same cache for all branches:

On the left sidebar, at the top, select Search GitLab ({search}) to find your project.
Select Settings > CI/CD.
Expand General pipelines.
Clear the Use separate caches for protected branches checkbox.
Select Save changes.

How archiving and extracting works

This example shows two jobs in two consecutive stages:

stages:
  - build
  - test

default:
  cache:
    key: build-cache
    paths:
      - vendor/
  before_script:
    - echo "Hello"

job A:
  stage: build
  script:
    - mkdir vendor/
    - echo "build" > vendor/hello.txt
  after_script:
    - echo "World"

job B:
  stage: test
  script:
    - cat vendor/hello.txt

If one machine has one runner installed, then all jobs for your project run on the same host:

Pipeline starts.
job A runs.
The cache is extracted (if found).
before_script is executed.
script is executed.
after_script is executed.
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 and the cache: key.
job B runs.
The cache is extracted (if found).
before_script is executed.
script is executed.
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 build stage to 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 A overwrites things in the archive of job B.

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 `cache:key`

Change the value for cache: key in your .gitlab-ci.yml file. The next time the pipeline runs, the cache is stored in a different location.

Clear the cache manually

You can clear the cache in the GitLab UI:

On the left sidebar, at the top, select Search GitLab ({search}) to find your project.
On the left sidebar, select Build > Pipelines.
In the upper-right corner, select Clear runner caches.

On the next commit, your CI/CD jobs use a new cache.

NOTE: 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.

Troubleshooting

Cache mismatch

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 `key` for jobs where they cache different paths.	Use different cache keys so that the cache archive is stored to a different location and doesn't overwrite wrong caches.
You have not enabled the distributed runner caching on your runners.	Set `Shared = false` and re-provision your runners.

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/

job A runs.
public/ is cached as cache.zip.
job B runs.
The previous cache, if any, is unzipped.
vendor/ is cached as cache.zip and overwrites the previous one.
The next time job A runs it uses the cache of job B which 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 is different):

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.