docs: user guide + runnable examples for distributing expressions (#1547) * docs: user guide page + runnable examples for distributing expressions Wraps up the Expr-pickle work with the user-facing material: * docs/source/user-guide/io/distributing_work.rst — new user guide page covering the multiprocessing, Ray, and datafusion-distributed patterns. Includes the Security section that is the canonical home for the cloudpickle / pickle.loads threat model. * docs/source/user-guide/io/index.rst — toctree entry. * examples/multiprocessing_pickle_expr.py — runnable example: a Pool.map of a closure-capturing UDF across processes, with worker context registration in the initializer. * examples/ray_pickle_expr.py — Ray actor analogue. * examples/datafusion-ffi-example/python/tests/_test_pickle_strict_ffi.py — exercises the strict-mode refusal end to end against an FFI capsule scalar UDF (kept under the FFI example crate because the test needs that crate's compiled artifacts). * examples/README.md — index entries for the new files. Also tightens three docstrings that previously duplicated the security warning so they point at the canonical Security section instead: * PythonLogicalCodec::with_python_udf_inlining (rustdoc): one-line summary plus a relative pointer to distributing_work.rst and the upstream Python pickle module security warning. * SessionContext.with_python_udf_inlining: one-sentence summary plus :doc: link to the user guide. * datafusion.ipc module docstring: cross-reference to the user guide for the full pattern. The crate-level codec.rs module rustdoc also updates "pure-Python scalar UDFs" to "scalar / aggregate / window UDFs" now that all three are covered. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * docs: document Python-version and import portability caveats for inline UDFs Reviewer feedback on the Expr-pickle PRs (#1544) asked that the cloudpickle portability caveats be discoverable on the user-facing page, not only in docstrings. The distributing_work.rst page is the designated canonical home for the distribution story, so add them here: * New 'Portability requirements for inline Python UDFs' subsection covering the matching-Python-minor-version requirement and the by-value vs by-reference import-capture rule (imported modules must be importable on the worker). * Qualify the 'fully portable' Python-UDF bullet to point at the new requirements. * Cross-reference the new subsection from the closure-capture note. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * docs: restore version-byte and cloudpickle-cache rustdoc wording Two codec.rs docstrings were reworded in PR4 in ways that dropped information: * try_encode_python_scalar_udf: restore the `DFPYUDF` family prefix + version byte description of the payload framing (PR4 had collapsed it to `DFPYUDF1` prefix, dropping the version-byte mention). * cloudpickle cached-handle comment: restore "The encode/decode helpers above" wording. * docs: fix reversed tuple order in multiprocessing example docstring The 'Worker layout' docstring described tasks as `(expr, label)` but the code builds and unpacks them as `(label, expr)`. Correct the doc to match. * Respond to first batch of reviewer comments * docs: relocate and restructure distributing-work guide Move the page from user-guide/io/ to the top level of user-guide/ — distributing work is a runtime/operational concern, not a file-format topic, and the shorter "Distributing work" title fits the sidebar cleanly. Restructure the body to lead with the practical worker-setup pattern instead of the four-slot SessionContext taxonomy. The taxonomy survives at the bottom as a reference subsection; the worker-init example and portability rules now reach the reader before they need it. Also addresses reviewer NIT: wrap the `if __name__ == "__main__":` guidance in a `.. note::` admonition and link to the Python multiprocessing docs. Add a header paragraph to each runnable example pointing to the user-guide page so a reader who jumps straight to the example gets the surrounding context. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This is a Python library that binds to Apache Arrow in-memory query engine DataFusion.
DataFusion's Python bindings can be used as a foundation for building new data systems in Python. Here are some examples:
For tips on tuning parallelism, see Maximizing CPU Usage in the configuration guide.
The following example demonstrates running a SQL query against a Parquet file using DataFusion, storing the results in a Pandas DataFrame, and then plotting a chart.
The Parquet file used in this example can be downloaded from the following page:
from datafusion import SessionContext # Create a DataFusion context ctx = SessionContext() # Register table with context ctx.register_parquet('taxi', 'yellow_tripdata_2021-01.parquet') # Execute SQL df = ctx.sql("select passenger_count, count(*) " "from taxi " "where passenger_count is not null " "group by passenger_count " "order by passenger_count") # convert to Pandas pandas_df = df.to_pandas() # create a chart fig = pandas_df.plot(kind="bar", title="Trip Count by Number of Passengers").get_figure() fig.savefig('chart.png')
This produces the following chart:
You can use SessionContext's register_view method to convert a DataFrame into a view and register it with the context.
from datafusion import SessionContext, col, literal # Create a DataFusion context ctx = SessionContext() # Create sample data data = {"a": [1, 2, 3, 4, 5], "b": [10, 20, 30, 40, 50]} # Create a DataFrame from the dictionary df = ctx.from_pydict(data, "my_table") # Filter the DataFrame (for example, keep rows where a > 2) df_filtered = df.filter(col("a") > literal(2)) # Register the dataframe as a view with the context ctx.register_view("view1", df_filtered) # Now run a SQL query against the registered view df_view = ctx.sql("SELECT * FROM view1") # Collect the results results = df_view.collect() # Convert results to a list of dictionaries for display result_dicts = [batch.to_pydict() for batch in results] print(result_dicts)
This will output:
[{'a': [3, 4, 5], 'b': [30, 40, 50]}]
It is possible to configure runtime (memory and disk settings) and configuration settings when creating a context.
runtime = ( RuntimeEnvBuilder() .with_disk_manager_os() .with_fair_spill_pool(10000000) ) config = ( SessionConfig() .with_create_default_catalog_and_schema(True) .with_default_catalog_and_schema("foo", "bar") .with_target_partitions(8) .with_information_schema(True) .with_repartition_joins(False) .with_repartition_aggregations(False) .with_repartition_windows(False) .with_parquet_pruning(False) .set("datafusion.execution.parquet.pushdown_filters", "true") ) ctx = SessionContext(config, runtime)
Refer to the API documentation for more information.
Printing the context will show the current configuration settings.
print(ctx)
For information about how to extend DataFusion Python, please see the extensions page of the online documentation.
See examples for more information.
uv add datafusion
pip install datafusion # or python -m pip install datafusion
conda install -c conda-forge datafusion
You can verify the installation by running:
>>> import datafusion >>> datafusion.__version__ '0.6.0'
This project ships a SKILL.md that teaches AI coding assistants how to write idiomatic DataFusion Python. It follows the Agent Skills open standard.
Preferred: npx skills add apache/datafusion-python — installs the skill in Claude Code, Cursor, Windsurf, Cline, Codex, Copilot, Gemini CLI, and other supported agents.
Manual: paste this line into your project's AGENTS.md / CLAUDE.md:
For DataFusion Python code, see https://github.com/apache/datafusion-python/blob/main/skills/datafusion_python/SKILL.md
This assumes that you have rust and cargo installed. We use the workflow recommended by pyo3 and maturin. The Maturin tools used in this workflow can be installed either via uv or pip. Both approaches should offer the same experience. It is recommended to use uv since it has significant performance improvements over pip.
Currently for protobuf support either protobuf or cmake must be installed.
Bootstrap (uv):
By default uv will attempt to build the datafusion python package. For our development we prefer to build manually. This means that when creating your virtual environment using uv sync you need to pass in the additional --no-install-package datafusion and for uv run commands the additional parameter --no-project
# fetch this repo git clone git@github.com:apache/datafusion-python.git # cd to the repo root cd datafusion-python/ # create the virtual environment uv sync --dev --no-install-package datafusion # activate the environment source .venv/bin/activate
Bootstrap (pip):
# fetch this repo git clone git@github.com:apache/datafusion-python.git # cd to the repo root cd datafusion-python/ # prepare development environment (used to build wheel / install in development) python3 -m venv .venv # activate the venv source .venv/bin/activate # update pip itself if necessary python -m pip install -U pip # install dependencies python -m pip install -r pyproject.toml
The tests rely on test data in git submodules.
git submodule update --init
Whenever rust code changes (your changes or via git pull):
# make sure you activate the venv using "source venv/bin/activate" first maturin develop --uv python -m pytest
Alternatively if you are using uv you can do the following without needing to activate the virtual environment:
uv run --no-project maturin develop --uv uv run --no-project pytest
To run the FFI tests within the examples folder, after you have built datafusion-python with the previous commands:
cd examples/datafusion-ffi-example uv run --no-project maturin develop --uv uv run --no-project pytest python/tests/_test_*py
datafusion-python takes advantage of pre-commit to assist developers with code linting to help reduce the number of commits that ultimately fail in CI due to linter errors. Using the pre-commit hooks is optional for the developer but certainly helpful for keeping PRs clean and concise.
Our pre-commit hooks can be installed by running pre-commit install, which will install the configurations in your DATAFUSION_PYTHON_ROOT/.github directory and run each time you perform a commit, failing to complete the commit if an offending lint is found allowing you to make changes locally before pushing.
The pre-commit hooks can also be run adhoc without installing them by simply running pre-commit run --all-files.
NOTE: the current pre-commit hooks require docker, and cmake. See note on protobuf above.
There are scripts in ci/scripts for running Rust and Python linters.
./ci/scripts/python_lint.sh ./ci/scripts/rust_clippy.sh ./ci/scripts/rust_fmt.sh ./ci/scripts/rust_toml_fmt.sh
This project includes an AI agent skill for auditing which features from the upstream Apache DataFusion Rust library are not yet exposed in these Python bindings. This is useful when adding missing functions, auditing API coverage, or ensuring parity with upstream.
The skill accepts an optional area argument:
scalar functions aggregate functions window functions dataframe session context ffi types all
If no argument is provided, it defaults to checking all areas. The skill will fetch the upstream DataFusion documentation, compare it against the functions and methods exposed in this project, and produce a coverage report listing what is currently exposed and what is missing.
The skill definition lives in .ai/skills/check-upstream/SKILL.md and follows the Agent Skills open standard. It can be used by any AI coding agent that supports skill discovery, or followed manually.
To change test dependencies, change the pyproject.toml and run
uv sync --dev --no-install-package datafusion