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Apache DataFusion Ray

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# DataFusion for Ray

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> ⚠️ **WARNING:** This project is no longer maintained.

## Overview

DataFusion for Ray is a distributed execution framework that enables DataFusion DataFrame and SQL queries to run on a
Ray cluster. This integration allows users to leverage Ray's dynamic scheduling capabilities while executing
queries in a distributed fashion.

## Execution Modes

DataFusion for Ray supports two execution modes:

### Streaming Execution

This mode mimics the default execution strategy of DataFusion. Each operator in the query plan starts executing
as soon as its inputs are available, leading to a more pipelined execution model.

### Batch Execution

_Note: Batch Execution is not implemented yet. Tracking issue: <https://github.com/apache/datafusion-ray/issues/69>_

In this mode, execution follows a staged model similar to Apache Spark. Each query stage runs to completion, producing
intermediate shuffle files that are persisted and used as input for the next stage.

## Getting Started

See the [contributor guide] for instructions on building DataFusion for Ray.

Once installed, you can run queries using DataFusion's familiar API while leveraging the distributed execution
capabilities of Ray.

```python
# from example in ./examples/http_csv.py
import ray
from datafusion_ray import DFRayContext, df_ray_runtime_env

ray.init(runtime_env=df_ray_runtime_env)

ctx = DFRayContext()
ctx.register_csv(
    "aggregate_test_100",
    "https://github.com/apache/arrow-testing/raw/master/data/csv/aggregate_test_100.csv",
)

df = ctx.sql("SELECT c1,c2,c3 FROM aggregate_test_100 LIMIT 5")

df.show()
```

## Contributing

Contributions are welcome! Please open an issue or submit a pull request if you would like to contribute. See the
[contributor guide] for more information.

## License

DataFusion for Ray is licensed under Apache 2.0.

[contributor guide]: docs/contributing.md

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