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DataFusion Substrait blog post #322

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131 changes: 131 additions & 0 deletions _posts/2023_02_27_datafusion_substrait.md
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---
layout: post
title: "DataFusion Now Supports Substrait"
date: "2023-02-27 00:00:00"
author: "pmc"
categories: [arrow]
---

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## Introduction

The Apache Arrow PMC is pleased to announce that the DataFusion project has accepted the donation of the
datafusion-substrait crate, which was developed by the DataFusion community under the
[datafusion-contrib](https://github.com/datafusion-contrib/) GitHub organization.

Substrait provides a standardized representation of query plans and expressions. In many ways, the project's goal
is similar to that of the Arrow project. Arrow standardizes the memory representation of columnar data. Substrait
standardizes the representation of operations on data, such as filter and query plans.

Now that DataFusion can directly run Substrait query plans, there are several exciting new integration possibilities:

- Pass serialized query plan across language boundaries, such as passing from Python to Rust or Rust to C++. For
, a Python based SQL frontend could pass a Substrait plan to DataFusion which is written in Rust.
- Mixing and matching query engine front-ends and back-ends based on their specific strengths. For example, using
DataFusion for query planning, and Velox for execution, or Calcite for query planning and DataFusion for execution.
- Easier integration for other DataFusion based projects. For example, the related Ballista project, which already
provides “distributed DataFusion” execution plans, serializes query plans using a protobuf format that predates
the Substrait project. By adopting Substrait, Ballista can provide distributed scheduling for query engines
other than DataFusion.

## Logical Plan Support

DataFusion currently supports serialization and deserialization of the following logical operators and expressions with Substrait.

### Operators / Relations

| DataFusion | SQL | DataFusion Supported Subtypes |
| ------------- |-------------------------| --------------------------------------- |
| Projection | SELECT | |
| TableScan | FROM | |
| Filter | WHERE | |
| Aggregate | GROUP BY | |
| Sort | ORDER BY | |
| Join | JOIN | LEFT, RIGHT, FULL, LEFT ANTI, LEFT SEMI |
| Limit | LIMIT | |
| Distinct | DISTINCT | |
| SubqueryAlias | \<subquery> AS \<alias> | |

### Expressions

| DataFusion | SQL | DataFusion Supported Subtypes |
| ----------------- |-----------------------------| ------------------------------------------------------------------------------------------------------------- |
| AggregateFunction | | |
| Alias | \<expr> AS \<alias> | |
| Column | \<identifier> | |
| BinaryExpr | \<expr> | |
| Between | BETWEEN \<expr> AND \<expr> | |
| Case | CASE ... WHEN ... END | |
| Literal | | Int8, Int16, Int32, Int64, Boolean, Float32, Float64, Decimal128, Utf8, LargeUtf8, Binary, LargeBinary, Date32 |
| Literal Null | | Int8 | Int16 | Int32 | Int64 | Decimal128 |

## Physical Plan Support

There is also preliminary work on supporting serialization of physical plans. The tracking issue for this is
[#5173](https://github.com/apache/arrow-datafusion/issues/5173).

## Python Bindings

Substrait support is also available from DataFusion’s [Python bindings](https://github.com/apache/arrow-datafusion-python/).

Source code for this example is available [here](https://github.com/apache/arrow-datafusion-python/blob/main/examples/substrait.py).

```python
from datafusion import SessionContext
from datafusion import substrait as ss

# Create a DataFusion context
ctx = SessionContext()

# Register table with context
ctx.register_parquet('aggregate_test_data', './testing/data/csv/aggregate_test_100.csv')

substrait_plan = ss.substrait.serde.serialize_to_plan("SELECT * FROM aggregate_test_data", ctx)
# type(substrait_plan) -> <class 'datafusion.substrait.plan'>

# Alternative serialization approaches
# type(substrait_bytes) -> <class 'list'>, at this point the bytes can be distributed to file, network, etc safely
# where they could subsequently be deserialized on the receiving end.
substrait_bytes = ss.substrait.serde.serialize_bytes("SELECT * FROM aggregate_test_data", ctx)

# Imagine here bytes would be read from network, file, etc ... for example brevity this is omitted and variable is simply reused
# type(substrait_plan) -> <class 'datafusion.substrait.plan'>
substrait_plan = ss.substrait.serde.deserialize_bytes(substrait_bytes)

# type(df_logical_plan) -> <class 'substrait.LogicalPlan'>
df_logical_plan = ss.substrait.consumer.from_substrait_plan(ctx, substrait_plan)

# Back to Substrait Plan just for demonstration purposes
# type(substrait_plan) -> <class 'datafusion.substrait.plan'>
substrait_plan = ss.substrait.producer.to_substrait_plan(df_logical_plan)
```

## Availability

Substrait support is available in DataFusion 18.0.0 and version 0.8.0 of the Python bindings.

## Get Involved

The Substrait support is at an early stage of development, and we would welcome more contributors to expand the
functionality and to help with compatibility testing with other data infrastructure that supports Substrait.

If you are interested in getting involved, an excellent place to start is to read our communication and
contributor guides.