| commit | 5c90187207e218393297ff3cbe4f08b69049d224 | [log] [tgz] |
|---|---|---|
| author | Andy Grove <andygrove73@gmail.com> | Mon Apr 10 17:37:22 2023 -0600 |
| committer | GitHub <noreply@github.com> | Mon Apr 10 17:37:22 2023 -0600 |
| tree | f5d65222598bc47ac155e3909eebcb8d548136b8 | |
| parent | bb54b10a2532915fc24b49664fa7db0c5dd14b5b [diff] |
Prepare for 22.0.0 release (#320) * Prepare for 22.0.0 release * fix tests * lint
This is a Python library that binds to Apache Arrow in-memory query engine DataFusion.
DataFusion's Python bindings can be used as an end-user tool as well as providing a foundation for building new systems.
Here is a comparison with similar projects that may help understand when DataFusion might be suitable and unsuitable for your needs:
DuckDB is an open source, in-process analytic database. Like DataFusion, it supports very fast execution, both from its custom file format and directly from Parquet files. Unlike DataFusion, it is written in C/C++ and it is primarily used directly by users as a serverless database and query system rather than as a library for building such database systems.
Polars is one of the fastest DataFrame libraries at the time of writing. Like DataFusion, it is also written in Rust and uses the Apache Arrow memory model, but unlike DataFusion it does not provide full SQL support, nor as many extension points.
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:
See examples for more information.
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 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 Conda or Pip. Both approaches should offer the same experience. Multiple approaches are only offered to appease developer preference. Bootstrapping for both Conda and Pip are as follows.
Bootstrap (Conda):
# fetch this repo git clone git@github.com:apache/arrow-datafusion-python.git # create the conda environment for dev conda env create -f ./conda/environments/datafusion-dev.yaml -n datafusion-dev # activate the conda environment conda activate datafusion-dev
Bootstrap (Pip):
# fetch this repo git clone git@github.com:apache/arrow-datafusion-python.git # 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 (for Python 3.8+) python -m pip install -r requirements-310.txt
The tests rely on test data in git submodules.
git submodule init
git submodule update
Whenever rust code changes (your changes or via git pull):
# make sure you activate the venv using "source venv/bin/activate" first maturin develop python -m pytest
arrow-datafusion-python takes advantage of (pre-commit)[https://pre-commit.com/] to assist developers in 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 keep PRs clean and concise.
Our pre-commit hooks can be installed by running pre-commit install which will install the configurations in your ARROW_DATAFUSION_PYTHON_ROOT/.github directory and run each time you perform a commit failing to perform the commit if an offending lint is found giving you the opportunity to make changes locally before pushing.
The pre-commit hooks can also be ran ad-hoc without installing them by simply running pre-commit run --all-files
To change test dependencies, change the requirements.in and run
# install pip-tools (this can be done only once), also consider running in venv python -m pip install pip-tools python -m piptools compile --generate-hashes -o requirements-310.txt
To update dependencies, run with -U
python -m piptools compile -U --generate-hashes -o requirements-310.txt
More details here