Contributing to Zarr#

Zarr is a community maintained project. We welcome contributions in the form of bug reports, bug fixes, documentation, enhancement proposals and more. This page provides information on how best to contribute.

Asking for help#

If you have a question about how to use Zarr, please post your question on StackOverflow using the “zarr” tag. If you don’t get a response within a day or two, feel free to raise a GitHub issue including a link to your StackOverflow question. We will try to respond to questions as quickly as possible, but please bear in mind that there may be periods where we have limited time to answer questions due to other commitments.

Bug reports#

If you find a bug, please raise a GitHub issue. Please include the following items in a bug report:

  1. A minimal, self-contained snippet of Python code reproducing the problem. You can format the code nicely using markdown, e.g.:

    import zarr
    g =
    # etc.
  2. An explanation of why the current behaviour is wrong/not desired, and what you expect instead.

  3. Information about the version of Zarr, along with versions of dependencies and the Python interpreter, and installation information. The version of Zarr can be obtained from the zarr.__version__ property. Please also state how Zarr was installed, e.g., “installed via pip into a virtual environment”, or “installed using conda”. Information about other packages installed can be obtained by executing pip freeze (if using pip to install packages) or conda env export (if using conda to install packages) from the operating system command prompt. The version of the Python interpreter can be obtained by running a Python interactive session, e.g.:

    $ python
    Python 3.6.1 (default, Mar 22 2017, 06:17:05)
    [GCC 6.3.0 20170321] on linux

Enhancement proposals#

If you have an idea about a new feature or some other improvement to Zarr, please raise a GitHub issue first to discuss.

We very much welcome ideas and suggestions for how to improve Zarr, but please bear in mind that we are likely to be conservative in accepting proposals for new features. The reasons for this are that we would like to keep the Zarr code base lean and focused on a core set of functionalities, and available time for development, review and maintenance of new features is limited. But if you have a great idea, please don’t let that stop you from posting it on GitHub, just please don’t be offended if we respond cautiously.

Contributing code and/or documentation#

Forking the repository#

The Zarr source code is hosted on GitHub at the following location:

You will need your own fork to work on the code. Go to the link above and hit the “Fork” button. Then clone your fork to your local machine:

$ git clone
$ cd zarr-python
$ git remote add upstream

Creating a development environment#

To work with the Zarr source code, it is recommended to set up a Python virtual environment and install all Zarr dependencies using the same versions as are used by the core developers and continuous integration services. Assuming you have a Python 3 interpreter already installed, and you have cloned the Zarr source code and your current working directory is the root of the repository, you can do something like the following:

$ mkdir -p ~/pyenv/zarr-dev
$ python -m venv ~/pyenv/zarr-dev
$ source ~/pyenv/zarr-dev/bin/activate
$ pip install -r requirements_dev_minimal.txt -r requirements_dev_numpy.txt
$ pip install -e .[docs]

To verify that your development environment is working, you can run the unit tests:

$ python -m pytest -v zarr

Creating a branch#

Before you do any new work or submit a pull request, please open an issue on GitHub to report the bug or propose the feature you’d like to add.

It’s best to synchronize your fork with the upstream repository, then create a new, separate branch for each piece of work you want to do. E.g.:

git checkout main
git fetch upstream
git rebase upstream/main
git push
git checkout -b shiny-new-feature
git push -u origin shiny-new-feature

This changes your working directory to the ‘shiny-new-feature’ branch. Keep any changes in this branch specific to one bug or feature so it is clear what the branch brings to Zarr.

To update this branch with latest code from Zarr, you can retrieve the changes from the main branch and perform a rebase:

git fetch upstream
git rebase upstream/main

This will replay your commits on top of the latest Zarr git main. If this leads to merge conflicts, these need to be resolved before submitting a pull request. Alternatively, you can merge the changes in from upstream/main instead of rebasing, which can be simpler:

git fetch upstream
git merge upstream/main

Again, any conflicts need to be resolved before submitting a pull request.

Running the test suite#

Zarr includes a suite of unit tests, as well as doctests included in function and class docstrings and in the tutorial and storage spec. The simplest way to run the unit tests is to activate your development environment (see creating a development environment above) and invoke:

$ python -m pytest -v zarr

Some tests require optional dependencies to be installed, otherwise the tests will be skipped. To install all optional dependencies, run:

$ pip install -r requirements_dev_optional.txt

To also run the doctests within docstrings (requires optional dependencies to be installed), run:

$ python -m pytest -v --doctest-plus zarr

To run the doctests within the tutorial and storage spec (requires optional dependencies to be installed), run:

$ python -m doctest -o NORMALIZE_WHITESPACE -o ELLIPSIS docs/tutorial.rst docs/spec/v2.rst

Note that some tests also require storage services to be running locally. To run the Azure Blob Service storage tests, run an Azure storage emulator (e.g., azurite) and set the environment variable ZARR_TEST_ABS=1. If you’re using Docker to run azurite, start the service with:

docker run --rm -p 10000:10000 azurite-blob --loose --blobHost

To run the Mongo DB storage tests, run a Mongo server locally and set the environment variable ZARR_TEST_MONGO=1. To run the Redis storage tests, run a Redis server locally on port 6379 and set the environment variable ZARR_TEST_REDIS=1.

All tests are automatically run via GitHub Actions for every pull request and must pass before code can be accepted. Test coverage is also collected automatically via the Codecov service, and total coverage over all builds must be 100% (although individual builds may be lower due to Python 2/3 or other differences).

Code standards - using pre-commit#

All code must conform to the PEP8 standard. Regarding line length, lines up to 100 characters are allowed, although please try to keep under 90 wherever possible.

Zarr uses a set of pre-commit hooks and the pre-commit bot to format, type-check, and prettify the codebase. pre-commit can be installed locally by running:

$ python -m pip install pre-commit

The hooks can be installed locally by running:

$ pre-commit install

This would run the checks every time a commit is created locally. These checks will also run on every commit pushed to an open PR, resulting in some automatic styling fixes by the pre-commit bot. The checks will by default only run on the files modified by a commit, but the checks can be triggered for all the files by running:

$ pre-commit run --all-files

If you would like to skip the failing checks and push the code for further discussion, use the --no-verify option with git commit.

Test coverage#

Zarr maintains 100% test coverage under the latest Python stable release (currently Python 3.8). Both unit tests and docstring doctests are included when computing coverage. Running:

$ python -m pytest -v --cov=zarr --cov-config=pyproject.toml zarr

will automatically run the test suite with coverage and produce a coverage report. This should be 100% before code can be accepted into the main code base.

When submitting a pull request, coverage will also be collected across all supported Python versions via the Codecov service, and will be reported back within the pull request. Codecov coverage must also be 100% before code can be accepted.


Docstrings for user-facing classes and functions should follow the numpydoc standard, including sections for Parameters and Examples. All examples should run and pass as doctests under Python 3.8. To run doctests, activate your development environment, install optional requirements, and run:

$ python -m pytest -v --doctest-plus zarr

Zarr uses Sphinx for documentation, hosted on Documentation is written in the RestructuredText markup language (.rst files) in the docs folder. The documentation consists both of prose and API documentation. All user-facing classes and functions should be included in the API documentation, under the docs/api folder. Any new features or important usage information should be included in the tutorial (docs/tutorial.rst). Any changes should also be included in the release notes (docs/release.rst).

The documentation can be built locally by running:

$ cd docs
$ make clean; make html
$ open _build/html/index.html

The resulting built documentation will be available in the docs/_build/html folder.

Development best practices, policies and procedures#

The following information is mainly for core developers, but may also be of interest to contributors.

Merging pull requests#

Pull requests submitted by an external contributor should be reviewed and approved by at least one core developers before being merged. Ideally, pull requests submitted by a core developer should be reviewed and approved by at least one other core developers before being merged.

Pull requests should not be merged until all CI checks have passed (GitHub Actions Codecov) against code that has had the latest main merged in.

Compatibility and versioning policies#

Because Zarr is a data storage library, there are two types of compatibility to consider: API compatibility and data format compatibility.

API compatibility#

All functions, classes and methods that are included in the API documentation (files under docs/api/*.rst) are considered as part of the Zarr public API, except if they have been documented as an experimental feature, in which case they are part of the experimental API.

Any change to the public API that does not break existing third party code importing Zarr, or cause third party code to behave in a different way, is a backwards-compatible API change. For example, adding a new function, class or method is usually a backwards-compatible change. However, removing a function, class or method; removing an argument to a function or method; adding a required argument to a function or method; or changing the behaviour of a function or method, are examples of backwards-incompatible API changes.

If a release contains no changes to the public API (e.g., contains only bug fixes or other maintenance work), then the micro version number should be incremented (e.g., 2.2.0 -> 2.2.1). If a release contains public API changes, but all changes are backwards-compatible, then the minor version number should be incremented (e.g., 2.2.1 -> 2.3.0). If a release contains any backwards-incompatible public API changes, the major version number should be incremented (e.g., 2.3.0 -> 3.0.0).

Backwards-incompatible changes to the experimental API can be included in a minor release, although this should be minimised if possible. I.e., it would be preferable to save up backwards-incompatible changes to the experimental API to be included in a major release, and to stabilise those features at the same time (i.e., move from experimental to public API), rather than frequently tinkering with the experimental API in minor releases.

Data format compatibility#

The data format used by Zarr is defined by a specification document, which should be platform-independent and contain sufficient detail to construct an interoperable software library to read and/or write Zarr data using any programming language. The latest version of the specification document is available from the Specifications page.

Here, data format compatibility means that all software libraries that implement a particular version of the Zarr storage specification are interoperable, in the sense that data written by any one library can be read by all others. It is obviously desirable to maintain data format compatibility wherever possible. However, if a change is needed to the storage specification, and that change would break data format compatibility in any way, then the storage specification version number should be incremented (e.g., 2 -> 3).

The versioning of the Zarr software library is related to the versioning of the storage specification as follows. A particular version of the Zarr library will implement a particular version of the storage specification. For example, Zarr version 2.2.0 implements the Zarr storage specification version 2. If a release of the Zarr library implements a different version of the storage specification, then the major version number of the Zarr library should be incremented. E.g., if Zarr version 2.2.0 implements the storage spec version 2, and the next release of the Zarr library implements storage spec version 3, then the next library release should have version number 3.0.0. Note however that the major version number of the Zarr library may not always correspond to the spec version number. For example, Zarr versions 2.x, 3.x, and 4.x might all implement the same version of the storage spec and thus maintain data format compatibility, although they will not maintain API compatibility. The version number of the storage specification that is currently implemented is stored under the zarr.meta.ZARR_FORMAT variable.

Note that the Zarr test suite includes a data fixture and tests to try and ensure that data format compatibility is not accidentally broken. See the test_format_compatibility() function in the zarr.tests.test_storage module for details.

When to make a release#

Ideally, any bug fixes that don’t change the public API should be released as soon as possible. It is fine for a micro release to contain only a single bug fix.

When to make a minor release is at the discretion of the core developers. There are no hard-and-fast rules, e.g., it is fine to make a minor release to make a single new feature available; equally, it is fine to make a minor release that includes a number of changes.

Major releases obviously need to be given careful consideration, and should be done as infrequently as possible, as they will break existing code and/or affect data compatibility in some way.

Release procedure#


Most of the release process is now handled by github workflow which should automatically push a release to PyPI if a tag is pushed.

Before releasing, make sure that all pull requests which will be included in the release have been properly documented in docs/release.rst.

To make a new release, go to zarr-developers/zarr-python and click “Draft a new release”. Choose a version number prefixed with a v (e.g. v0.0.0). For pre-releases, include the appropriate suffix (e.g. v0.0.0a1 or v0.0.0rc2).

Set the description of the release to:

See release notes

replacing the correct version numbers. For pre-release versions, the URL should omit the pre-release suffix, e.g. “a1” or “rc1”.

Click on “Generate release notes” to auto-file the description.

After creating the release, the documentation will be built on Full releases will be available under /stable while pre-releases will be available under /latest.

Also review and merge the conda-forge/zarr-feedstock pull request that will be automatically generated.