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[CELEBORN-1789][DOC] Document on Java Columnar Shuffle
### 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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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. | ||
--- | ||
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# Introduction to Celeborn's Java Columnar Shuffle | ||
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## Overview | ||
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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. | ||
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## Benefits | ||
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- **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. | ||
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## Configuration | ||
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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: | ||
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### Step 1: Apply this patch to obtain the schema information for shuffle | ||
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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. | ||
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### Step 2: Configure Celeborn Settings | ||
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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: | ||
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``` | ||
spark.celeborn.columnarShuffle.enabled true | ||
spark.celeborn.columnarShuffle.encoding.dictionary.enabled true | ||
``` | ||
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If you require further performance optimization, consider enabling code generation with: | ||
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``` | ||
spark.celeborn.columnarShuffle.codegen.enabled true | ||
``` |
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