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[CELEBORN-1789][DOC] Document on Java Columnar Shuffle
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### What changes were proposed in this pull request?
Introduction to Celeborn's Java Columnar Shuffle

### Why are the changes needed?
Introduction to Celeborn's Java Columnar Shuffle

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
CI

Closes #3010 from kerwin-zk/CELEBORN-1789.

Authored-by: xiyu.zk <xiyu.zk@alibaba-inc.com>
Signed-off-by: mingji <fengmingxiao.fmx@alibaba-inc.com>
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kerwin-zk authored and FMX committed Dec 24, 2024
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56 changes: 56 additions & 0 deletions docs/developers/java-columnar-shuffle.md
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---
license: |
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
https://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.
---

# Introduction to Celeborn's Java Columnar Shuffle

## Overview

Celeborn presents a Java Columnar Shuffle designed to enhance performance and efficiency in SparkSQL and DataFrame operations. This innovative approach leverages a columnar format for shuffle operations, achieving a higher compression rate than traditional Row-based Shuffle methods. This improvement leads to significant savings in disk space usage during shuffle operations.

## Benefits

- **High Compression Rate**: By organizing data into a columnar format, this feature significantly increases the compression ratio, reducing the disk space required for Shuffle data.
However, enabling this optimization incurs overhead for row-to-column and column-to-row transformations. If disk space is a higher priority, it is recommended to enable this feature.
If performance is a higher priority, it is advisable to weigh the trade-offs.

## Configuration

To leverage Celeborn's Java Columnar Shuffle, you need to apply a patch and configure certain settings in Spark 3.x. Follow the steps below for implementation:

### Step 1: Apply this patch to obtain the schema information for shuffle

1. Obtain the `https://github.com/apache/celeborn/tree/main/assets/spark-patch/Celeborn_Columnar_Shuffle_spark3.patch` file that contains the modifications needed for enabling Columnar Shuffle in Spark 3.x.
2. Navigate to your Spark source directory.
3. Apply the patch.

### Step 2: Configure Celeborn Settings

To enable Columnar Shuffle, adjust the following configurations in your Spark application:
Open the Spark configuration file or set these parameters in your Spark application.
Add the following configuration settings:

```
spark.celeborn.columnarShuffle.enabled true
spark.celeborn.columnarShuffle.encoding.dictionary.enabled true
```

If you require further performance optimization, consider enabling code generation with:

```
spark.celeborn.columnarShuffle.codegen.enabled true
```
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- Overview: developers/client.md
- LifecycleManager: developers/lifecyclemanager.md
- ShuffleClient: developers/shuffleclient.md
- JavaColumnarShuffle: developers/java-columnar-shuffle.md
- Configuration: developers/configuration.md
- Fault Tolerant: developers/faulttolerant.md
- Worker Exclusion: developers/workerexclusion.md
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