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====================
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:
.. code-block:: python
import datafusion
from datafusion import functions as f
from datafusion import col
import pyarrow
# create a context
ctx = datafusion.SessionContext()
# 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(
col("a") + col("b"),
col("a") - 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])
We can also execute a query against data stored in CSV
.. code-block:: bash
echo "a,b\n1,4\n2,5\n3,6" > example.csv
.. code-block:: python
import datafusion
from datafusion import functions as f
from datafusion import col
import pyarrow
# create a context
ctx = datafusion.SessionContext()
# register a CSV
ctx.register_csv('example', 'example.csv')
# create a new statement
df = ctx.table('example').select(
col("a") + col("b"),
col("a") - 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])
And how to execute a query against a CSV using SQL:
.. code-block:: python
import datafusion
from datafusion import functions as f
from datafusion import col
import pyarrow
# create a context
ctx = datafusion.SessionContext()
# register a CSV
ctx.register_csv('example', 'example.csv')
# create a new statement via SQL
df = ctx.sql("SELECT a+b, a-b FROM example")
# 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
----
.. code-block:: python
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(col("a")))
UDAF
----
.. code-block:: python
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(col("a"))]
)
How to install (from pip)
=========================
.. code-block:: shell
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:
.. code-block:: shell
# fetch this repo
git clone git@github.com:apache/arrow-datafusion.git
cd arrow-datafusion/python
# prepare development environment (used to build wheel / install in development)
python3 -m venv venv
# activate the venv
source venv/bin/activate
pip install -r requirements.txt
Whenever rust code changes (your changes or via `git pull`):
.. code-block:: shell
# make sure you activate the venv using "source venv/bin/activate" first
maturin develop
python -m pytest
How to update dependencies
==========================
To change test dependencies, change the `requirements.in` and run
.. code-block:: shell
# install pip-tools (this can be done only once), also consider running in venv
pip install pip-tools
# change requirements.in and then run
pip-compile --generate-hashes
To update dependencies, run
.. code-block:: shell
pip-compile update
More details about pip-tools `here `_
API reference
=============
.. toctree::
:maxdepth: 2
api