DataFusion in Python
This is a Python library that binds to Apache Arrow in-memory query engine DataFusion.
Like pyspark, it allows you to build a plan through SQL or a DataFrame API against in-memory data, parquet or CSV files, run it in a multi-threaded environment, and obtain the result back in Python.
It also allows you to use UDFs and UDAFs for complex operations.
The major advantage of this library over other execution engines is that this library achieves zero-copy between Python and its execution engine: there is no cost in using UDFs, UDAFs, and collecting the results to Python apart from having to lock the GIL when running those operations.
Its query engine, DataFusion, is written in Rust, which makes strong assumptions about thread safety and lack of memory leaks.
Technically, zero-copy is achieved via the c data interface.
How to use it
Simple usage:
import datafusion
import pyarrow
# an alias
f = datafusion.functions
# create a context
ctx = datafusion.ExecutionContext()
# create a RecordBatch and a new DataFrame from it
batch = pyarrow.RecordBatch.from_arrays(
[pyarrow.array([1, 2, 3]), pyarrow.array([4, 5, 6])],
names=["a", "b"],
)
df = ctx.create_dataframe([[batch]])
# create a new statement
df = df.select(
f.col("a") + f.col("b"),
f.col("a") - f.col("b"),
)
# execute and collect the first (and only) batch
result = df.collect()[0]
assert result.column(0) == pyarrow.array([5, 7, 9])
assert result.column(1) == pyarrow.array([-3, -3, -3])
UDFs
def is_null(array: pyarrow.Array) -> pyarrow.Array:
return array.is_null()
udf = f.udf(is_null, [pyarrow.int64()], pyarrow.bool_())
df = df.select(udf(f.col("a")))
UDAF
import pyarrow
import pyarrow.compute
class Accumulator:
"""
Interface of a user-defined accumulation.
"""
def __init__(self):
self._sum = pyarrow.scalar(0.0)
def to_scalars(self) -> [pyarrow.Scalar]:
return [self._sum]
def update(self, values: pyarrow.Array) -> None:
# not nice since pyarrow scalars can't be summed yet. This breaks on `None`
self._sum = pyarrow.scalar(self._sum.as_py() + pyarrow.compute.sum(values).as_py())
def merge(self, states: pyarrow.Array) -> None:
# not nice since pyarrow scalars can't be summed yet. This breaks on `None`
self._sum = pyarrow.scalar(self._sum.as_py() + pyarrow.compute.sum(states).as_py())
def evaluate(self) -> pyarrow.Scalar:
return self._sum
df = ...
udaf = f.udaf(Accumulator, pyarrow.float64(), pyarrow.float64(), [pyarrow.float64()])
df = df.aggregate(
[],
[udaf(f.col("a"))]
)
How to install
pip install datafusion
How to develop
This assumes that you have rust and cargo installed. We use the workflow recommended by pyo3 and maturin.
Bootstrap:
# fetch this repo
git clone git@github.com:jorgecarleitao/datafusion-python.git
cd datafusion-python
# prepare development environment (used to build wheel / install in development)
python -m venv venv
venv/bin/pip install maturin==0.8.2 toml==0.10.1
Whenever rust code changes (your changes or via git pull):
venv/bin/maturin develop
venv/bin/python -m unittest discover tests
Release files for datafusion 0.2.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| datafusion-0.2.0.tar.gz | 14.7 kB | Details |
Built distributions (wheels)
Total release size: 41.6 MB
Release files / datafusion-0.2.0.tar.gz
| Download URL | datafusion-0.2.0.tar.gz |
|---|---|
| Size | 14.7 kB |
| Tags | Source |
|
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|
Release files / datafusion-0.2.0-cp39-cp39-manylinux2010_x86_64.whl
| Download URL | datafusion-0.2.0-cp39-cp39-manylinux2010_x86_64.whl |
|---|---|
| Size | 5.1 MB |
| Tags | CPython 3.9 Linux glibc 2.12+ x86-64 |
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|
Release files / datafusion-0.2.0-cp38-none-win_amd64.whl
| Download URL | datafusion-0.2.0-cp38-none-win_amd64.whl |
|---|---|
| Size | 3.5 MB |
| Tags | CPython 3.8 Windows x86-64 |
|
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|
Release files / datafusion-0.2.0-cp38-cp38-manylinux2010_x86_64.whl
| Download URL | datafusion-0.2.0-cp38-cp38-manylinux2010_x86_64.whl |
|---|---|
| Size | 5.1 MB |
| Tags | CPython 3.8 Linux glibc 2.12+ x86-64 |
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|
Release files / datafusion-0.2.0-cp38-cp38-macosx_10_7_x86_64.whl
| Download URL | datafusion-0.2.0-cp38-cp38-macosx_10_7_x86_64.whl |
|---|---|
| Size | 3.6 MB |
| Tags | CPython 3.8 macOS 10.7+ x86-64 |
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Release files / datafusion-0.2.0-cp37-none-win_amd64.whl
| Download URL | datafusion-0.2.0-cp37-none-win_amd64.whl |
|---|---|
| Size | 3.5 MB |
| Tags | CPython 3.7 Windows x86-64 |
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Release files / datafusion-0.2.0-cp37-cp37m-manylinux2010_x86_64.whl
| Download URL | datafusion-0.2.0-cp37-cp37m-manylinux2010_x86_64.whl |
|---|---|
| Size | 5.1 MB |
| Tags | CPython 3.7 CPython 3.7 pymalloc Linux glibc 2.12+ x86-64 |
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Release files / datafusion-0.2.0-cp37-cp37m-macosx_10_7_x86_64.whl
| Download URL | datafusion-0.2.0-cp37-cp37m-macosx_10_7_x86_64.whl |
|---|---|
| Size | 3.6 MB |
| Tags | CPython 3.7 CPython 3.7 pymalloc macOS 10.7+ x86-64 |
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Release files / datafusion-0.2.0-cp36-none-win_amd64.whl
| Download URL | datafusion-0.2.0-cp36-none-win_amd64.whl |
|---|---|
| Size | 3.5 MB |
| Tags | CPython 3.6 Windows x86-64 |
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Release files / datafusion-0.2.0-cp36-cp36m-manylinux2010_x86_64.whl
| Download URL | datafusion-0.2.0-cp36-cp36m-manylinux2010_x86_64.whl |
|---|---|
| Size | 5.1 MB |
| Tags | CPython 3.6 CPython 3.6 pymalloc Linux glibc 2.12+ x86-64 |
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Release files / datafusion-0.2.0-cp36-cp36m-macosx_10_7_x86_64.whl
| Download URL | datafusion-0.2.0-cp36-cp36m-macosx_10_7_x86_64.whl |
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| Size | 3.6 MB |
| Tags | CPython 3.6 CPython 3.6 pymalloc macOS 10.7+ x86-64 |
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