feat: import user-defined physical optimizer rules over FFI (#1557) * feat: user-defined OptimizerRule and AnalyzerRule from Python Expose `SessionContext.add_optimizer_rule` and `SessionContext.add_analyzer_rule` symmetric with the existing `remove_optimizer_rule`. Each accepts a Python subclass of the new `datafusion.optimizer.OptimizerRule` / `AnalyzerRule` ABCs. Implementation: * New `crates/core/src/optimizer_rules.rs` wraps user Python instances in `PyOptimizerRuleAdapter` / `PyAnalyzerRuleAdapter`, which implement the upstream `OptimizerRule` / `AnalyzerRule` traits. * `OptimizerRule.rewrite(plan)` returns `None` for "no change" or a new `LogicalPlan`. The adapter maps that to `Transformed::no` / `Transformed::yes` so the upstream optimizer's fixed-point loop terminates correctly. * `AnalyzerRule.analyze(plan)` must always return a `LogicalPlan`; returning `None` surfaces a `DataFusionError::Execution` naming the offending rule. * The upstream `&dyn OptimizerConfig` / `&ConfigOptions` arguments are not surfaced to Python in this MVP; rules that need configuration should capture it at construction time (for example by holding a `SessionContext` reference) or be implemented in Rust. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * feat: import FFI physical optimizer rules; drop Python logical rules Replace the Python-defined OptimizerRule/AnalyzerRule approach with FFI-imported physical optimizer rules. The Python logical-rule approach could observe plans but not transform them: there are no Python constructors for LogicalPlan node variants, so a rule could only return None or the input plan unchanged. The audience for custom rules also overlaps strongly with people who can write Rust. DataFusion exposes no FFI bridge for the logical OptimizerRule/AnalyzerRule traits, but it does export FFI_PhysicalOptimizerRule for the physical PhysicalOptimizerRule trait. This commit imports those instead. Changes: * Remove crates/core/src/optimizer_rules.rs, python/datafusion/optimizer.py, python/tests/test_optimizer.py, and the SessionContext.add_optimizer_rule / add_analyzer_rule methods. remove_optimizer_rule is unchanged (pre-existing). * New crates/core/src/physical_optimizer.rs reads a __datafusion_physical_optimizer_rule__ capsule and converts it via Arc<dyn PhysicalOptimizerRule>::from(&FFI_PhysicalOptimizerRule). * SessionContext gains a physical_optimizer_rules constructor argument. Upstream offers no API to add physical rules to a live context, so they are appended to the builder at construction time only. * The datafusion-ffi-example crate gains MyPhysicalOptimizerRule, a counter-backed rule used by _test_physical_optimizer_rule.py to prove the rule fires over FFI during physical planning. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * refactor: type physical_optimizer_rules with an Exportable Protocol Replace the `list[Any]` hint on the SessionContext `physical_optimizer_rules` argument with a `PhysicalOptimizerRuleExportable` Protocol, matching the existing `TableProviderExportable` / `*Exportable` pattern used for other FFI-capsule objects. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * docs: reference PhysicalOptimizerRuleExportable in SessionContext docstring Point the `physical_optimizer_rules` argument docs at the new `PhysicalOptimizerRuleExportable` Protocol instead of describing the duck type inline. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * docs: move FFI capsule detail to PhysicalOptimizerRuleExportable The PyCapsule / FFI_PhysicalOptimizerRule mechanics describe the Protocol, not the SessionContext constructor. Move that detail onto PhysicalOptimizerRuleExportable and leave the constructor argument docs focused on behavior. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * docs: drop redundant comment in SessionContext constructor Remove the explanatory comment about FFI bridge availability; the same information already lives on PhysicalOptimizerRuleExportable. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * docs: drop module-level doc comment from physical_optimizer Sibling FFI-import modules (udf, udaf, catalog, table) carry no module-level docs, and the rst-style markup did not match Rust conventions. The function doc comment already states intent. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * refactor: import physical optimizer rule via from_pycapsule! macro Replace the hand-written crates/core/src/physical_optimizer.rs with a `from_pycapsule!` invocation in the util crate, matching `physical_codec_from_pycapsule` and the other FFI capsule importers. The macro already handles the hasattr/getattr/cast/validate/pointer_checked sequence and the infallible `Arc::from(&FFI)` conversion, so the dedicated module is no longer needed. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * docs: trim PhysicalOptimizerRuleExportable docstring Drop the sentence about logical-rule FFI availability; it is background, not type-hint information, and keeps the Protocol docstring in line with the other *Exportable hints. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * Minor refactor * refactor: register physical optimizer rules via live add method Drop the `physical_optimizer_rules` constructor argument on `SessionContext` and replace it with `add_physical_optimizer_rule`, matching the existing `register_*` shape on the same class. The new method rebuilds the session state via `SessionStateBuilder::new_from_existing` so previously registered tables, UDFs, and catalogs are preserved. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * test: drop redundant FFI physical optimizer rule export test Coverage subsumed by test_ffi_physical_optimizer_rule_runs_during_planning, which exercises the same capsule export via add_physical_optimizer_rule. Co-Authored-By: Claude Opus 4.7 (1M context) <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