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datafusion-python

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A Python library to run analytics workloads with the performance of Rust, the flexibility of Python and O(1) cost in moving data between the two. Uses Apache Arrow in-memory format and respective query engine DataFusion.

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## DataFusion in Python

This code has been donated to [Datafusion Apache Arrow](https://github.com/apache/arrow-datafusion) and development will continue there. Thank you for your understanding!

This is a Python library that binds to [Apache Arrow](https://arrow.apache.org/) in-memory query engine [DataFusion](https://github.com/apache/arrow/tree/master/rust/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
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

```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(f.col("a")))
```

### UDAF

```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(f.col("a"))]
)
```

## How to install

```bash
pip install datafusion
```

## 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: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):

```bash
venv/bin/maturin develop
venv/bin/python -m unittest discover tests
```

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