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<h1 id="contributing">Contributing<a class="headerlink" href="#contributing" title="Permanent link">&para;</a></h1>
<p>We welcome contributions to Apache Iceberg! To learn more about contributing to Apache Iceberg, please refer to the <a href="https://iceberg.apache.org/contribute/">official Iceberg contribution guidelines</a>. These guidelines are intended as helpful suggestions to make the contribution process as seamless as possible, and are not strict rules.</p>
<p>If you would like to discuss your proposed change before contributing, we encourage you to visit our <a href="https://iceberg.apache.org/community/">Community</a> page. There, you will find various ways to connect with the community, including Slack and our mailing lists. Alternatively, you can open a <a href="https://github.com/apache/iceberg-python/issues">new issue</a> directly in the GitHub repository.</p>
<p>For first-time contributors, feel free to check out our <a href="https://github.com/apache/iceberg-python/issues/?q=is%3Aissue%20state%3Aopen%20label%3A%22good%20first%20issue%22">good first issues</a> for an easy way to get started.</p>
<h2 id="contributing-to-pyiceberg">Contributing to PyIceberg<a class="headerlink" href="#contributing-to-pyiceberg" title="Permanent link">&para;</a></h2>
<p>The PyIceberg Project is hosted on GitHub at <a href="https://github.com/apache/iceberg-python">https://github.com/apache/iceberg-python</a>.</p>
<p>For development, <a href="https://docs.astral.sh/uv/">uv</a> is used for dependency management and packaging. uv is a Python package installer and resolver, written in Rust, that serves as a drop-in replacement for pip, and virtualenv.</p>
<h3 id="getting-started">Getting Started<a class="headerlink" href="#getting-started" title="Permanent link">&para;</a></h3>
<p>Install uv and set up the development environment:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>make<span class="w"> </span>install
</code></pre></div>
<p>This will install uv if needed, create a virtual environment in <code>.venv</code>, and install all dependencies.</p>
<p>If you only want to just install uv:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>make<span class="w"> </span>install-uv
</code></pre></div>
<h3 id="python-version-selection">Python Version Selection<a class="headerlink" href="#python-version-selection" title="Permanent link">&para;</a></h3>
<p>You can specify which Python version to use when creating your virtual environment:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a><span class="nv">PYTHON</span><span class="o">=</span><span class="m">3</span>.12<span class="w"> </span>make<span class="w"> </span>install<span class="w"> </span><span class="c1"># Create environment with Python 3.12</span>
<a id="__codelineno-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a>make<span class="w"> </span><span class="nb">test</span><span class="w"> </span><span class="c1"># Run tests against Python 3.12</span>
</code></pre></div>
<blockquote>
<p><strong>Tip:</strong> <code>uv python list</code> shows available interpreters. <code>uv python install 3.12</code> can install one if needed.</p>
</blockquote>
<h3 id="ide-setup">IDE Setup<a class="headerlink" href="#ide-setup" title="Permanent link">&para;</a></h3>
<p>After running <code>make install</code>, configure your IDE to use the Python interpreter at <code>.venv/bin/python</code>.</p>
<p><strong>To set up IDEA with uv:</strong></p>
<ul>
<li>Open up the Python project in IntelliJ</li>
<li>Make sure that you're on latest main</li>
<li>Go to File -&gt; Project Structure (⌘;)</li>
<li>Go to Platform Settings -&gt; SDKs</li>
<li>Add Python SDK -&gt; Virtualenv Environment -&gt; Existing environment</li>
<li>Point to <code>.venv/bin/python</code></li>
</ul>
<p><strong>VS Code:</strong></p>
<ul>
<li>Press Cmd/Ctrl+Shift+P -&gt; "Python: Select Interpreter"</li>
<li>Choose <code>.venv/bin/python</code></li>
</ul>
<h3 id="advanced-uv-usage">Advanced uv Usage<a class="headerlink" href="#advanced-uv-usage" title="Permanent link">&para;</a></h3>
<p>For full control over your environment, you can use uv commands directly. See the <a href="https://docs.astral.sh/uv/">uv documentation</a> to learn more about:</p>
<ul>
<li>Managing dependencies with <code>uv add</code> and <code>uv remove</code></li>
<li>Python version management with <code>uv python</code></li>
<li>Running commands with <code>uv run</code></li>
<li>Lock file management with <code>uv.lock</code></li>
</ul>
<h3 id="lock-file-management">Lock File Management<a class="headerlink" href="#lock-file-management" title="Permanent link">&para;</a></h3>
<p><code>uv.lock</code> is a cross-platform lockfile that contains exact information about the project's dependencies.
See the <a href="https://docs.astral.sh/uv/guides/projects/#uvlock">uv.lock documentation</a> for more details.</p>
<p>When modifying dependencies in <code>pyproject.toml</code>, regenerate the lock file:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a>make<span class="w"> </span>uv-lock
</code></pre></div>
<p>Separately, to verify that the lock file is up to date without modifying it:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-4-1" name="__codelineno-4-1" href="#__codelineno-4-1"></a>make<span class="w"> </span>uv-lock-check
</code></pre></div>
<h2 id="installation-from-source">Installation from source<a class="headerlink" href="#installation-from-source" title="Permanent link">&para;</a></h2>
<p>Clone the repository for local development:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a>git<span class="w"> </span>clone<span class="w"> </span>https://github.com/apache/iceberg-python.git
<a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a><span class="nb">cd</span><span class="w"> </span>iceberg-python
<a id="__codelineno-5-3" name="__codelineno-5-3" href="#__codelineno-5-3"></a>pip3<span class="w"> </span>install<span class="w"> </span>-e<span class="w"> </span><span class="s2">&quot;.[s3fs,hive]&quot;</span>
</code></pre></div>
<p>Install it directly for GitHub (not recommended), but sometimes handy:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-6-1" name="__codelineno-6-1" href="#__codelineno-6-1"></a>pip<span class="w"> </span>install<span class="w"> </span><span class="s2">&quot;git+https://github.com/apache/iceberg-python.git#egg=pyiceberg[pyarrow]&quot;</span>
</code></pre></div>
<h2 id="linting">Linting<a class="headerlink" href="#linting" title="Permanent link">&para;</a></h2>
<p><code>prek</code> is used for autoformatting and linting:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-7-1" name="__codelineno-7-1" href="#__codelineno-7-1"></a>make<span class="w"> </span>lint
</code></pre></div>
<p><code>prek</code> will automatically fix the violations such as import orders, formatting etc. Pylint errors you need to fix yourself.</p>
<p>In addition to manually running <code>make lint</code>, you can install the pre-commit hooks in your local repo with <code>prek install</code>. By doing this, linting is run automatically every time you make a commit.</p>
<p>You can bump the integrations to the latest version using <code>prek auto-update</code>. This will check if there is a newer version of <code>{ruff,mypy,...}</code> and update the yaml.</p>
<h2 id="cleaning">Cleaning<a class="headerlink" href="#cleaning" title="Permanent link">&para;</a></h2>
<p>Removal of old cached files generated during the Cython build process:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-8-1" name="__codelineno-8-1" href="#__codelineno-8-1"></a>make<span class="w"> </span>clean
</code></pre></div>
<p>Helps prevent build failures and unexpected behavior by removing outdated files, ensuring that only up-to-date sources are used &amp; the build environment is always clean.</p>
<h2 id="testing">Testing<a class="headerlink" href="#testing" title="Permanent link">&para;</a></h2>
<p>For Python, <code>pytest</code> is used a testing framework in combination with <code>coverage</code> to enforce 90%+ code coverage.</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-9-1" name="__codelineno-9-1" href="#__codelineno-9-1"></a>make<span class="w"> </span><span class="nb">test</span>
</code></pre></div>
<p>By default, S3 and ADLS tests are ignored because that require minio and azurite to be running.
To run the S3 suite:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-10-1" name="__codelineno-10-1" href="#__codelineno-10-1"></a>make<span class="w"> </span>test-s3
</code></pre></div>
<p>To run the ADLS suite:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-11-1" name="__codelineno-11-1" href="#__codelineno-11-1"></a>make<span class="w"> </span>test-adls
</code></pre></div>
<p>To pass additional arguments to pytest, you can use <code>PYTEST_ARGS</code>.</p>
<h3 id="run-pytest-in-verbose-mode">Run pytest in verbose mode<a class="headerlink" href="#run-pytest-in-verbose-mode" title="Permanent link">&para;</a></h3>
<div class="highlight"><pre><span></span><code><a id="__codelineno-12-1" name="__codelineno-12-1" href="#__codelineno-12-1"></a>make<span class="w"> </span><span class="nb">test</span><span class="w"> </span><span class="nv">PYTEST_ARGS</span><span class="o">=</span><span class="s2">&quot;-v&quot;</span>
</code></pre></div>
<h3 id="run-pytest-with-pdb-enabled">Run pytest with pdb enabled<a class="headerlink" href="#run-pytest-with-pdb-enabled" title="Permanent link">&para;</a></h3>
<div class="highlight"><pre><span></span><code><a id="__codelineno-13-1" name="__codelineno-13-1" href="#__codelineno-13-1"></a>make<span class="w"> </span><span class="nb">test</span><span class="w"> </span><span class="nv">PYTEST_ARGS</span><span class="o">=</span><span class="s2">&quot;--pdb&quot;</span>
</code></pre></div>
<p>To see all available pytest arguments, run <code>make test PYTEST_ARGS="--help"</code>.</p>
<h3 id="integration-tests">Integration tests<a class="headerlink" href="#integration-tests" title="Permanent link">&para;</a></h3>
<p>PyIceberg has integration tests with Apache Spark. Spark will create a new database and provision some tables that PyIceberg can query against.</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-14-1" name="__codelineno-14-1" href="#__codelineno-14-1"></a>make<span class="w"> </span>test-integration
</code></pre></div>
<p>This will restart the containers, to get to a clean state, and then run the PyTest suite. In case something changed in the Dockerfile or the provision script, you can run:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-15-1" name="__codelineno-15-1" href="#__codelineno-15-1"></a>make<span class="w"> </span>test-integration-rebuild
</code></pre></div>
<p>To rebuild the containers from scratch.</p>
<h4 id="running-integration-tests-against-rest-catalogs">Running Integration Tests against REST Catalogs<a class="headerlink" href="#running-integration-tests-against-rest-catalogs" title="Permanent link">&para;</a></h4>
<div class="admonition warning">
<p class="admonition-title">Do not run against production catalogs</p>
<p>The integration tests will delete data throughout the entirety of your catalog. Running these integration tests against production catalogs will result in data loss.</p>
</div>
<p>PyIceberg supports the ability to run our catalog tests against an arbitrary REST Catalog.</p>
<p>In order to run the test catalog, you will need to specify which REST catalog to run against with the <code>PYICEBERG_TEST_CATALOG</code> environment variable</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-16-1" name="__codelineno-16-1" href="#__codelineno-16-1"></a><span class="nb">export</span><span class="w"> </span><span class="nv">PYICEBERG_TEST_CATALOG</span><span class="o">=</span>test_catalog
</code></pre></div>
<p>The catalog in question can be configured either through the ~/.pyiceberg.yaml file or through environment variables.</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-17-1" name="__codelineno-17-1" href="#__codelineno-17-1"></a><span class="nt">catalog</span><span class="p">:</span>
<a id="__codelineno-17-2" name="__codelineno-17-2" href="#__codelineno-17-2"></a><span class="w"> </span><span class="nt">test_catalog</span><span class="p">:</span>
<a id="__codelineno-17-3" name="__codelineno-17-3" href="#__codelineno-17-3"></a><span class="w"> </span><span class="nt">uri</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">http://rest-catalog/ws/</span>
<a id="__codelineno-17-4" name="__codelineno-17-4" href="#__codelineno-17-4"></a><span class="w"> </span><span class="nt">credential</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">t-1234:secret</span>
</code></pre></div>
<div class="highlight"><pre><span></span><code><a id="__codelineno-18-1" name="__codelineno-18-1" href="#__codelineno-18-1"></a><span class="nb">export</span><span class="w"> </span><span class="nv">PYICEBERG_CATALOG__TEST_CATALOG__URI</span><span class="o">=</span>thrift://localhost:9083
<a id="__codelineno-18-2" name="__codelineno-18-2" href="#__codelineno-18-2"></a><span class="nb">export</span><span class="w"> </span><span class="nv">PYICEBERG_CATALOG__TEST_CATALOG__ACCESS_KEY_ID</span><span class="o">=</span>username
<a id="__codelineno-18-3" name="__codelineno-18-3" href="#__codelineno-18-3"></a><span class="nb">export</span><span class="w"> </span><span class="nv">PYICEBERG_CATALOG__TEST_CATALOG__SECRET_ACCESS_KEY</span><span class="o">=</span>password
</code></pre></div>
<h2 id="notebooks-for-experimentation">Notebooks for Experimentation<a class="headerlink" href="#notebooks-for-experimentation" title="Permanent link">&para;</a></h2>
<p>PyIceberg provides Jupyter notebooks for quick experimentation and learning. Two Make commands are available depending on your needs:</p>
<h3 id="pyiceberg-examples-make-notebook">PyIceberg Examples (<code>make notebook</code>)<a class="headerlink" href="#pyiceberg-examples-make-notebook" title="Permanent link">&para;</a></h3>
<p>For basic PyIceberg experimentation without additional infrastructure:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-19-1" name="__codelineno-19-1" href="#__codelineno-19-1"></a>make<span class="w"> </span>notebook
</code></pre></div>
<p>This will install notebook dependencies and launch Jupyter Lab in the <code>notebooks/</code> directory.</p>
<p><strong>PyIceberg Example Notebook</strong> (<code>notebooks/pyiceberg_example.ipynb</code>) is based on the <a href="https://py.iceberg.apache.org/#getting-started-with-pyiceberg">Getting Started with PyIceberg</a> page. It demonstrates basic PyIceberg operations like creating catalogs, schemas, and querying tables without requiring any external services.</p>
<h3 id="spark-integration-examples-make-notebook-infra">Spark Integration Examples (<code>make notebook-infra</code>)<a class="headerlink" href="#spark-integration-examples-make-notebook-infra" title="Permanent link">&para;</a></h3>
<p>For working with PyIceberg alongside Spark, use the infrastructure-enabled notebook environment:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-20-1" name="__codelineno-20-1" href="#__codelineno-20-1"></a>make<span class="w"> </span>notebook-infra
</code></pre></div>
<p>This command spins up the full integration test infrastructure via Docker Compose, including:</p>
<ul>
<li><strong>Spark</strong> (with Spark Connect)</li>
<li><strong>Iceberg REST Catalog</strong> (using the <a href="https://hub.docker.com/r/apache/iceberg-rest-fixture"><code>apache/iceberg-rest-fixture</code></a> image)</li>
<li><strong>Hive Metastore</strong></li>
<li><strong>S3-compatible object storage</strong> (Minio)</li>
</ul>
<p><strong>Spark Example Notebook</strong> (<code>notebooks/spark_integration_example.ipynb</code>) is based on the <a href="https://iceberg.apache.org/docs/nightly/spark-getting-started/">Spark Getting Started</a> guide. This notebook demonstrates how to work with PyIceberg alongside Spark, leveraging the Docker-based testing setup for a complete local development environment.</p>
<p>After running <code>make notebook-infra</code>, open <code>spark_integration_example.ipynb</code> in the Jupyter Lab interface to explore Spark integration capabilities.</p>
<h2 id="code-standards">Code standards<a class="headerlink" href="#code-standards" title="Permanent link">&para;</a></h2>
<p>Below are the formalized conventions that we adhere to in the PyIceberg project. The goal of this is to have a common agreement on how to evolve the codebase, but also using it as guidelines for newcomers to the project.</p>
<h3 id="api-compatibility">API Compatibility<a class="headerlink" href="#api-compatibility" title="Permanent link">&para;</a></h3>
<p>It is important to keep the Python public API compatible across versions. The Python official <a href="https://peps.python.org/pep-0008/">PEP-8</a> defines public methods as: <em>Public attributes should have no leading underscores</em>. This means not removing any methods without any notice, or removing or renaming any existing parameters. Adding new optional parameters is okay.</p>
<p>If you want to remove a method, please add a deprecation notice by annotating the function using <code>@deprecated</code>:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-21-1" name="__codelineno-21-1" href="#__codelineno-21-1"></a><span class="kn">from</span><span class="w"> </span><span class="nn">pyiceberg.utils.deprecated</span><span class="w"> </span><span class="kn">import</span> <span class="n">deprecated</span>
<a id="__codelineno-21-2" name="__codelineno-21-2" href="#__codelineno-21-2"></a>
<a id="__codelineno-21-3" name="__codelineno-21-3" href="#__codelineno-21-3"></a>
<a id="__codelineno-21-4" name="__codelineno-21-4" href="#__codelineno-21-4"></a><span class="nd">@deprecated</span><span class="p">(</span>
<a id="__codelineno-21-5" name="__codelineno-21-5" href="#__codelineno-21-5"></a> <span class="n">deprecated_in</span><span class="o">=</span><span class="s2">&quot;0.1.0&quot;</span><span class="p">,</span>
<a id="__codelineno-21-6" name="__codelineno-21-6" href="#__codelineno-21-6"></a> <span class="n">removed_in</span><span class="o">=</span><span class="s2">&quot;0.2.0&quot;</span><span class="p">,</span>
<a id="__codelineno-21-7" name="__codelineno-21-7" href="#__codelineno-21-7"></a> <span class="n">help_message</span><span class="o">=</span><span class="s2">&quot;Please use load_something_else() instead&quot;</span><span class="p">,</span>
<a id="__codelineno-21-8" name="__codelineno-21-8" href="#__codelineno-21-8"></a><span class="p">)</span>
<a id="__codelineno-21-9" name="__codelineno-21-9" href="#__codelineno-21-9"></a><span class="k">def</span><span class="w"> </span><span class="nf">load_something</span><span class="p">():</span>
<a id="__codelineno-21-10" name="__codelineno-21-10" href="#__codelineno-21-10"></a> <span class="k">pass</span>
</code></pre></div>
<p>Which will warn:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-22-1" name="__codelineno-22-1" href="#__codelineno-22-1"></a>Call to load_something, deprecated in 0.1.0, will be removed in 0.2.0. Please use load_something_else() instead.
</code></pre></div>
<p>If you want to remove a property or notify about a behavior change, please add a deprecation notice by calling the deprecation_message function:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-23-1" name="__codelineno-23-1" href="#__codelineno-23-1"></a><span class="kn">from</span><span class="w"> </span><span class="nn">pyiceberg.utils.deprecated</span><span class="w"> </span><span class="kn">import</span> <span class="n">deprecation_message</span>
<a id="__codelineno-23-2" name="__codelineno-23-2" href="#__codelineno-23-2"></a>
<a id="__codelineno-23-3" name="__codelineno-23-3" href="#__codelineno-23-3"></a><span class="n">deprecation_message</span><span class="p">(</span>
<a id="__codelineno-23-4" name="__codelineno-23-4" href="#__codelineno-23-4"></a> <span class="n">deprecated_in</span><span class="o">=</span><span class="s2">&quot;0.1.0&quot;</span><span class="p">,</span>
<a id="__codelineno-23-5" name="__codelineno-23-5" href="#__codelineno-23-5"></a> <span class="n">removed_in</span><span class="o">=</span><span class="s2">&quot;0.2.0&quot;</span><span class="p">,</span>
<a id="__codelineno-23-6" name="__codelineno-23-6" href="#__codelineno-23-6"></a> <span class="n">help_message</span><span class="o">=</span><span class="s2">&quot;The old_property is deprecated. Please use the something_else property instead.&quot;</span><span class="p">,</span>
<a id="__codelineno-23-7" name="__codelineno-23-7" href="#__codelineno-23-7"></a><span class="p">)</span>
</code></pre></div>
<p>Which will warn:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-24-1" name="__codelineno-24-1" href="#__codelineno-24-1"></a>Deprecated in 0.1.0, will be removed in 0.2.0. The old_property is deprecated. Please use the something_else property instead.
</code></pre></div>
<h3 id="logging">Logging<a class="headerlink" href="#logging" title="Permanent link">&para;</a></h3>
<p>PyIceberg uses Python's standard logging module. You can control the logging level using either:</p>
<p><strong>CLI option:</strong></p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-25-1" name="__codelineno-25-1" href="#__codelineno-25-1"></a>pyiceberg<span class="w"> </span>--log-level<span class="w"> </span>DEBUG<span class="w"> </span>describe<span class="w"> </span>my_table
</code></pre></div>
<p><strong>Environment variable:</strong></p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-26-1" name="__codelineno-26-1" href="#__codelineno-26-1"></a><span class="nb">export</span><span class="w"> </span><span class="nv">PYICEBERG_LOG_LEVEL</span><span class="o">=</span>DEBUG
<a id="__codelineno-26-2" name="__codelineno-26-2" href="#__codelineno-26-2"></a>pyiceberg<span class="w"> </span>describe<span class="w"> </span>my_table
</code></pre></div>
<p>Valid log levels are: <code>DEBUG</code>, <code>INFO</code>, <code>WARNING</code> (default), <code>ERROR</code>, <code>CRITICAL</code>.</p>
<p>Debug logging is particularly useful for troubleshooting issues with FileIO implementations, catalog connections, and other integration points.</p>
<h3 id="type-annotations">Type annotations<a class="headerlink" href="#type-annotations" title="Permanent link">&para;</a></h3>
<p>For the type annotation the types from the <code>Typing</code> package are used.</p>
<h3 id="third-party-libraries">Third party libraries<a class="headerlink" href="#third-party-libraries" title="Permanent link">&para;</a></h3>
<p>PyIceberg naturally integrates into the rich Python ecosystem, however it is important to be hesitant adding third party packages. Adding a lot of packages makes the library heavyweight, and causes incompatibilities with other projects if they use a different version of the library. Also, big libraries such as <code>s3fs</code>, <code>adlfs</code>, <code>pyarrow</code>, <code>thrift</code> should be optional to avoid downloading everything, while not being sure if is actually being used.</p>
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