README.md - datafusion-python - Git at Google

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 # DataFusion in Python

 [![Python test](https://github.com/apache/arrow-datafusion-python/actions/workflows/test.yaml/badge.svg)](https://github.com/apache/arrow-datafusion-python/actions/workflows/test.yaml)
 [![Python Release Build](https://github.com/apache/arrow-datafusion-python/actions/workflows/build.yml/badge.svg)](https://github.com/apache/arrow-datafusion-python/actions/workflows/build.yml)

 This is a Python library that binds to [Apache Arrow](https://arrow.apache.org/) in-memory query engine [DataFusion](https://github.com/apache/arrow-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](https://www.rust-lang.org/), which makes strong assumptions about thread safety and lack of memory leaks.

 Technically, zero-copy is achieved via the [c data interface](https://arrow.apache.org/docs/format/CDataInterface.html).

 ## How to use it

 Simple usage:

 ```python
 import datafusion
 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])
 ```

 ### UDFs

 ```python
 import pyarrow
 from datafusion import udf

 def is_null(array: pyarrow.Array) -> pyarrow.Array:
     return array.is_null()

 is_null_arr = udf(is_null, [pyarrow.int64()], pyarrow.bool_(), 'stable')

 # 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]])

 df = df.select(is_null_arr(col("a")))

 result = df.collect()[0]

 assert result.column(0) == pyarrow.array([False] * 3)
 ```

 ### UDAF

 ```python
 import pyarrow
 import pyarrow.compute
 import datafusion
 from datafusion import udaf, Accumulator
 from datafusion import col


 class MyAccumulator(Accumulator):
     """
     Interface of a user-defined accumulation.
     """
     def __init__(self):
         self._sum = pyarrow.scalar(0.0)

     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 state(self) -> pyarrow.Array:
         return pyarrow.array([self._sum.as_py()])

     def evaluate(self) -> pyarrow.Scalar:
         return self._sum

 # 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]])

 my_udaf = udaf(MyAccumulator, pyarrow.float64(), pyarrow.float64(), [pyarrow.float64()], 'stable')

 df = df.aggregate(
     [],
     [my_udaf(col("a"))]
 )

 result = df.collect()[0]

 assert result.column(0) == pyarrow.array([6.0])
 ```

 ## How to install (from pip)

 ```bash
 pip install datafusion
 # or
 python -m pip install datafusion
 ```

 You can verify the installation by running:

 ```python
 >>> import datafusion
 >>> datafusion.__version__
 '0.6.0'
 ```

 ## How to develop

 This assumes that you have rust and cargo installed. We use the workflow recommended by [pyo3](https://github.com/PyO3/pyo3) and [maturin](https://github.com/PyO3/maturin).

 Bootstrap:

 ```bash
 # 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.

 ```bash
 git submodule init
 git submodule update
 ```

 Whenever rust code changes (your changes or via `git pull`):

 ```bash
 # 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

 ```bash
 # 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`

 ```bash
 python -m piptools compile -U --generate-hashes -o requirements-310.txt
 ```

 More details [here](https://github.com/jazzband/pip-tools)
	<!---
	Licensed to the Apache Software Foundation (ASF) under one
	or more contributor license agreements. See the NOTICE file
	distributed with this work for additional information
	regarding copyright ownership. The ASF licenses this file
	to you under the Apache License, Version 2.0 (the
	"License"); you may not use this file except in compliance
	with the License. You may obtain a copy of the License at

	http://www.apache.org/licenses/LICENSE-2.0

	Unless required by applicable law or agreed to in writing,
	software distributed under the License is distributed on an
	"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
	KIND, either express or implied. See the License for the
	specific language governing permissions and limitations
	under the License.
	-->

	# DataFusion in Python

	[![Python test](https://github.com/apache/arrow-datafusion-python/actions/workflows/test.yaml/badge.svg)](https://github.com/apache/arrow-datafusion-python/actions/workflows/test.yaml)
	[![Python Release Build](https://github.com/apache/arrow-datafusion-python/actions/workflows/build.yml/badge.svg)](https://github.com/apache/arrow-datafusion-python/actions/workflows/build.yml)

	This is a Python library that binds to [Apache Arrow](https://arrow.apache.org/) in-memory query engine [DataFusion](https://github.com/apache/arrow-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](https://www.rust-lang.org/), which makes strong assumptions about thread safety and lack of memory leaks.

	Technically, zero-copy is achieved via the [c data interface](https://arrow.apache.org/docs/format/CDataInterface.html).

	## How to use it

	Simple usage:

	```python
	import datafusion
	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])
	```

	### UDFs

	```python
	import pyarrow
	from datafusion import udf

	def is_null(array: pyarrow.Array) -> pyarrow.Array:
	return array.is_null()

	is_null_arr = udf(is_null, [pyarrow.int64()], pyarrow.bool_(), 'stable')

	# 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]])

	df = df.select(is_null_arr(col("a")))

	result = df.collect()[0]

	assert result.column(0) == pyarrow.array([False] * 3)
	```

	### UDAF

	```python
	import pyarrow
	import pyarrow.compute
	import datafusion
	from datafusion import udaf, Accumulator
	from datafusion import col


	class MyAccumulator(Accumulator):
	"""
	Interface of a user-defined accumulation.
	"""
	def __init__(self):
	self._sum = pyarrow.scalar(0.0)

	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 state(self) -> pyarrow.Array:
	return pyarrow.array([self._sum.as_py()])

	def evaluate(self) -> pyarrow.Scalar:
	return self._sum

	# 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]])

	my_udaf = udaf(MyAccumulator, pyarrow.float64(), pyarrow.float64(), [pyarrow.float64()], 'stable')

	df = df.aggregate(
	[],
	[my_udaf(col("a"))]
	)

	result = df.collect()[0]

	assert result.column(0) == pyarrow.array([6.0])
	```

	## How to install (from pip)

	```bash
	pip install datafusion
	# or
	python -m pip install datafusion
	```

	You can verify the installation by running:

	```python
	>>> import datafusion
	>>> datafusion.__version__
	'0.6.0'
	```

	## How to develop

	This assumes that you have rust and cargo installed. We use the workflow recommended by [pyo3](https://github.com/PyO3/pyo3) and [maturin](https://github.com/PyO3/maturin).

	Bootstrap:

	```bash
	# 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.

	```bash
	git submodule init
	git submodule update
	```

	Whenever rust code changes (your changes or via `git pull`):

	```bash
	# 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

	```bash
	# 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`

	```bash
	python -m piptools compile -U --generate-hashes -o requirements-310.txt
	```

	More details [here](https://github.com/jazzband/pip-tools)