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Gather info on CI/CD caching

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- New cache page
- Move manual cache clearing to the new page
- Add all directives of cache in the yaml reference
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3
doc/ci/README.md
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@ -65,7 +65,8 @@ learn how to leverage its potential even more.
environments and use them for different purposes like testing, building and
deploying
- [Job artifacts](../user/project/pipelines/job_artifacts.md)
- [Git submodules](git_submodules.md): How to run your CI jobs when Git
- [Caching dependencies](caching/index.md)
- [Git submodules](git_submodules.md) - How to run your CI jobs when Git
submodules are involved
- [Use SSH keys in your build environment](ssh_keys/README.md)
- [Trigger pipelines through the GitLab API](triggers/README.md)

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@ -0,0 +1,516 @@
# 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 your 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 by default,
starting from GitLab 9.0.
Make sure you read the [`cache` reference](../yaml/README.md#cache) to learn
how it is defined in `.gitlab-ci.yml`.
## 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#using-tags) and use the tag on jobs
that share their cache.
- [Use sticky Runners](../runners/README.md#locking-a-specific-runner-from-being-enabled-for-other-projects)
that will be only available to a particular project.
- [Use a `key`](../yaml/README.md#cache-key) that fits your workflow (e.g.,
different caches on each branch). For that, you can take advantage of the
[CI/CD predefined variables](../variables/README.md#predefined-variables-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 will be 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: ${CI_COMMIT_REF_SLUG}
```
While this feels like it might be safe from accidentally overwriting the cache,
it means merge requests get slow first pipelines, which might be a bad
developer experience. The next time a new commit is pushed to the branch, the
cache will be re-used.
To enable per-job and per-branch caching:
```yaml
cache:
key: "$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-rull-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 will use 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: {}
```
For more fine tuning, read also about the
[`cache: policy`](../yaml/README.md#cache-policy).
## 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
(Nodejs packages, PHP packages, rubygems, python libraries, etc.),
so they don't have to be re-fetched from the public internet.
NOTE: **Note:**
For more examples, check the [GitLab CI Yml](https://gitlab.com/gitlab-org/gitlab-ci-yml)
project.
### Caching Nodejs dependencies
Assuming your project is using [npm](https://www.npmjs.com/) or
[Yarn](https://yarnpkg.com/en/) to install the Nodejs dependencies, the
following example defines `cache` globally so that all jobs inherit it.
Nodejs modules are installed in `node_modules/` and are cached per-branch:
```yaml
#
# https://gitlab.com/gitlab-org/gitlab-ci-yml/blob/master/Nodejs.gitlab-ci.yml
#
image: node:latest
# Cache modules in between jobs
cache:
key: ${CI_COMMIT_REF_SLUG}
paths:
- node_modules/
before_script:
- npm install
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-ci-yml/blob/master/PHP.gitlab-ci.yml
#
image: php:7.2
# Cache libraries in between jobs
cache:
key: ${CI_COMMIT_REF_SLUG}
paths:
- vendor/
before_script:
# Install and run Composer
- curl --show-error --silent https://getcomposer.org/installer | php
- php composer.phar install
test:
script:
- vendor/bin/phpunit --configuration phpunit.xml --coverage-text --colors=never
```
### Caching Python dependencies
Assuming your project is using [pip](https://pip.pypa.io/en/stable/) to install
the python dependencies, the following example defines `cache` globally so that
all jobs inherit it. Python libraries are installed in a virtualenv under `venv/`,
pip's cache is defined under `.cache/pip/` and both are cached per-branch:
```yaml
#
# https://gitlab.com/gitlab-org/gitlab-ci-yml/blob/master/Python.gitlab-ci.yml
#
image: python:latest
# Change pip's cache directory to be inside the project directory since we can
# only cache local items.
variables:
PIP_CACHE_DIR: "$CI_PROJECT_DIR/.cache"
# Pip'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/
- 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-ci-yml/blob/master/Ruby.gitlab-ci.yml
#
image: ruby:2.5
# Cache gems in between builds
cache:
key: ${CI_COMMIT_REF_SLUG}
paths:
- vendor/ruby
before_script:
- ruby -v # Print out ruby version for debugging
- gem install bundler --no-ri --no-rdoc # Bundler is not installed with the image
- bundle install -j $(nproc) --path vendor # Install dependencies into ./vendor/ruby
rspec:
script:
- rspec spec
```
## 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/<user>/<project>/<cache-key>/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/<volume-id>/_data/<user>/<project>/<cache-key>/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
```
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'll not have the issue where
`job B` might execute on a Runner different from `job A`, thus guaranteeing the
cache between stages. That will only work 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, will overwrite
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 will not be 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.
---
Let's assume you have only one Runner assigned to your project, so the cache
will be 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 will use the cache of `job B` which is different
and thus will be ineffective
To fix that, use different `keys` for each job.
---
In another case, let's assume you have more than one Runners assigned to your
project, but the distributed cache is not enabled. We want the second time the
pipeline is run, `job A` and `job B` to re-use their cache (which in this case
will be 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 the cached files to "get cleaned" before each stage if the
jobs run on different Runners in the subsequent pipelines.
## Clearing the cache
GitLab 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 will be stored in a different location.
### Clearing the cache manually
> [Introduced](https://gitlab.com/gitlab-org/gitlab-ce/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. This will have an impact on all caches of your project as
name of the cache directory will be renamed by appending an integer to it
(`-1`, `-2`, etc.):
1. Navigate to your project's **CI/CD > Pipelines** page.
1. Click on the **Clear Runner caches** to clean up the cache.
1. On the next push, your CI/CD job will use 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. After a push, a
new key is generated and the old cache is not valid anymore.
## Cache vs artifacts
NOTE: **Note:**
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 would be overwritten.
Don't mix the caching with passing artifacts between stages. Caching is not
designed to pass artifacts between stages. Cache is for runtime dependencies
needed to compile the project:
- `cache` - **Use for temporary storage for project dependencies.** Not useful
for keeping intermediate build results, like `jar` or `apk` files.
Cache was designed to be used to speed up invocations of subsequent runs of a
given job, by keeping things like dependencies (e.g., npm packages, Go vendor
packages, etc.) so they don't have to be re-fetched from the public internet.
While the cache can be abused to pass intermediate build results between stages,
there may be cases where artifacts are a better fit.
- `artifacts` - **Use for stage results that will be passed between stages.**
Artifacts were designed to upload some compiled/generated bits of the build,
and they can be fetched by any number of concurrent Runners. They are
guaranteed to be available and are there to pass data between jobs. They are
also exposed to be downloaded from the UI.
It's sometimes confusing because the name artifact sounds like something that
is only useful outside of the job, like for downloading a final image. But
artifacts are also available in between 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 each subsequent stage can depend on them
being there. There are some optimizations like declaring an
[expiry time](../yaml/README.md#artifacts-expire_in) so you don't keep artifacts
around too long, and using [dependencies](../yaml/README.md#dependencies) to
control exactly where artifacts are passed around.
So, to sum up:
- Caches are disabled if not defined globally or per job (using `cache:`)
- Caches are available for all jobs in your `.gitlab-ci.yml` if enabled globally
- Caches can be used by subsequent pipelines of that very same job (a script in
a stage) in which the cache was created (if not defined globally).
- Caches are stored where the Runner is installed **and** uploaded to S3 if
[distributed cache is enabled](https://docs.gitlab.com/runner/configuration/autoscale.html#distributed-runners-caching)
- Caches defined per job are only used either a) for the next pipeline of that job,
or b) if that same cache is also defined in a subsequent job of the same pipeline
- Artifacts are disabled if not defined per job (using `artifacts:`)
- Artifacts can only be enabled per job, not globally
- Artifacts are created during a pipeline and can be used by the subsequent
jobs of that currently active pipeline
- Artifacts are always uploaded to GitLab (known as coordinator)
- Artifacts can have an expiration value for controlling disk usage (30 days by default)

26
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@ -146,24 +146,7 @@ To protect/unprotect Runners:
## Manually clearing the Runners cache
> [Introduced](https://gitlab.com/gitlab-org/gitlab-ce/issues/41249) in GitLab 10.4.
GitLab 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, you can easily do it via GitLab's UI:
1. Navigate to your project's **CI/CD > Pipelines** page.
1. Click on the **Clear Runner caches** to clean up the cache.
1. On the next push, your CI/CD job will use a new cache.
That way, you don't have to change the [cache key](../yaml/README.md#cache-key)
in your `.gitlab-ci.yml`.
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. After a push, a
new key is generated and the old cache is not valid anymore.
Read [clearing the cache](../caching/index.md#clearing-the-cache).
## How shared Runners pick jobs
@ -227,15 +210,16 @@ that it may encounter on the projects it's shared over. This would be
problematic for large amounts of projects, if it wasn't for tags.
By tagging a Runner for the types of jobs it can handle, you can make sure
shared Runners will only run the jobs they are equipped to run.
shared Runners will [only run the jobs they are equipped to run](../yaml/README.md#tags).
For instance, at GitLab we have Runners tagged with "rails" if they contain
the appropriate dependencies to run Rails test suites.
### Preventing Runners with tags from picking jobs without tags
You can configure a Runner to prevent it from picking jobs with tags when
the Runner does not have tags assigned. This setting can be enabled the first
You can configure a Runner to prevent it from picking
[jobs with tags](../yaml/README.md#tags) when the Runner does not have tags
assigned. This setting can be enabled the first
time you [register a Runner][register] and can be changed afterwards under
each Runner's settings.

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@ -674,6 +674,10 @@ as Review Apps. You can see a simple example using Review Apps at
by default.
- From GitLab 9.2, caches are restored before [artifacts](#artifacts).
TIP: **Learn more:**
Read how caching works and find out some good practices in the
[caching dependencies documentation](../caching/index.md).
`cache` is used to specify a list of files and directories which should be
cached between jobs. You can only use paths that are within the project
workspace.
@ -681,18 +685,93 @@ workspace.
If `cache` is defined outside the scope of jobs, it means it is set
globally and all jobs will use that definition.
Cache all files in `binaries` and `.config`:
### `cache:paths`
Use the `paths` directive to choose which files or directories will be cached.
Wildcards can be used as well.
Cache all files in `binaries` that end in `.apk` and the `.config` file:
```yaml
rspec:
script: test
cache:
paths:
- binaries/
- binaries/*.apk
- .config
```
Cache all Git untracked files:
Locally defined cache overrides globally defined options. The following `rspec`
job will cache only `binaries/`:
```yaml
cache:
paths:
- my/files
rspec:
script: test
cache:
paths:
- binaries/
```
### `cache:key`
> Introduced in GitLab Runner v1.0.0.
Since the cache is shared between jobs, if you're using different
paths for different jobs, you should also set a different `cache:key`
otherwise cache content can be overwritten.
The `key` directive allows you to define the affinity of caching between jobs,
allowing to have a single cache for all jobs, cache per-job, cache per-branch
or any other way that fits your workflow. This way, you can fine tune caching,
allowing you to cache data between different jobs or even different branches.
The `cache:key` variable can use any of the
[predefined variables](../variables/README.md), and the default key, if not set,
is `$CI_JOB_NAME-$CI_COMMIT_REF_NAME` which translates as "per-job and
per-branch". It is the default across the project, therefore everything is
shared between pipelines and jobs running on the same branch by default.
NOTE: **Note:**
The `cache:key` variable cannot contain the `/` character, or the equivalent
URI-encoded `%2F`; a value made only of dots (`.`, `%2E`) is also forbidden.
For example, to enable per-branch caching:
```yaml
cache:
key: "$CI_COMMIT_REF_SLUG"
paths:
- binaries/
```
If you use **Windows Batch** to run your shell scripts you need to replace
`$` with `%`:
```yaml
cache:
key: "%CI_JOB_STAGE%-%CI_COMMIT_REF_SLUG%"
paths:
- binaries/
```
If you use **Windows PowerShell** to run your shell scripts you need to replace
`$` with `$env:`:
```yaml
cache:
key: "$env:CI_JOB_STAGE-$env:CI_COMMIT_REF_SLUG"
paths:
- binaries/
```
### `cache:untracked`
Set `untracked: true` to cache all files that are untracked in your Git
repository:
```yaml
rspec:
@ -712,103 +791,6 @@ rspec:
- binaries/
```
Locally defined cache overrides globally defined options. The following `rspec`
job will cache only `binaries/`:
```yaml
cache:
paths:
- my/files
rspec:
script: test
cache:
key: rspec
paths:
- binaries/
```
Note that since cache is shared between jobs, if you're using different
paths for different jobs, you should also set a different **cache:key**
otherwise cache content can be overwritten.
NOTE: **Note:**
The cache is provided on a best-effort basis, so don't expect that the cache
will be always present.
### `cache:key`
> Introduced in GitLab Runner v1.0.0.
The `key` directive allows you to define the affinity of caching
between jobs, allowing to have a single cache for all jobs,
cache per-job, cache per-branch or any other way that fits your needs.
This way, you can fine tune caching, allowing you to cache data between
different jobs or even different branches.
The `cache:key` variable can use any of the
[predefined variables](../variables/README.md), and the default key, if not set,
is set as `$CI_JOB_NAME-$CI_COMMIT_REF_NAME` which translates as "per-job and
per-branch". It is the default across the project, therefore everything is
shared between pipelines and jobs running on the same branch by default.
NOTE: **Note:**
The `cache:key` variable cannot contain the `/` character, or the equivalent
URI-encoded `%2F`; a value made only of dots (`.`, `%2E`) is also forbidden.
**Example configurations**
To enable per-job caching:
```yaml
cache:
key: "$CI_JOB_NAME"
untracked: true
```
To enable per-branch caching:
```yaml
cache:
key: "$CI_COMMIT_REF_SLUG"
untracked: true
```
To enable per-job and per-branch caching:
```yaml
cache:
key: "$CI_JOB_NAME-$CI_COMMIT_REF_SLUG"
untracked: true
```
To enable per-branch and per-stage caching:
```yaml
cache:
key: "$CI_JOB_STAGE-$CI_COMMIT_REF_SLUG"
untracked: true
```
If you use **Windows Batch** to run your shell scripts you need to replace
`$` with `%`:
```yaml
cache:
key: "%CI_JOB_STAGE%-%CI_COMMIT_REF_SLUG%"
untracked: true
```
If you use **Windows PowerShell** to run your shell scripts you need to replace
`$` with `$env:`:
```yaml
cache:
key: "$env:CI_JOB_STAGE-$env:CI_COMMIT_REF_SLUG"
untracked: true
```
### `cache:policy`
> Introduced in GitLab 9.4.