Skip to content
forked from NVIDIA/sparkucx

A high-performance, scalable and efficient ShuffleManager plugin for Apache Spark, utilizing UCX communication layer

License

Notifications You must be signed in to change notification settings

brianplus/sparkucx

 
 

Repository files navigation

UCX for Apache Spark Plugin

UCX for Apache Spark is a high performance ShuffleManager plugin for Apache Spark, that uses RDMA and other high performance transports that are supported by UCX, to perform Shuffle data transfers in Spark jobs.

Runtime requirements

Installation

Obtain UCX for Apache Spark

Please use the "Releases" page to download SparkUCX jar file for your spark version (e.g. ucx-spark-1.1-for-spark-2.4.0-jar-with-dependencies.jar). Put ucx-spark jar file in $SPARK_UCX_HOME on all the nodes in your cluster.
If you would like to build the project yourself, please refer to the "Build" section below.

Ucx binaries must be in Spark classpath on every Spark Worker. It can be obtained by installing the latest version from Ucx release page

Configuration

Provide Spark the location of the SparkUCX plugin jars and ucx shared binaries by using the extraClassPath option.

spark.driver.extraClassPath     $SPARK_UCX_HOME/spark-ucx-1.0-for-spark-2.4.0-jar-with-dependencies.jar
spark.executor.extraClassPath   $SPARK_UCX_HOME/spark-ucx-1.0-for-spark-2.4.0-jar-with-dependencies.jar:$UCX_PREFIX/lib

To enable the UCX for Apache Spark Shuffle Manager plugin, add the following configuration property to spark (e.g. in $SPARK_HOME/conf/spark-defaults.conf):

spark.shuffle.manager   org.apache.spark.shuffle.UcxShuffleManager
spark.executorEnv.UCX_ERROR_SIGNALS "" 

Build

Building the SparkUCX plugin requires Apache Maven and Java 8+ JDK

Build instructions:

% git clone https://github.com/nvidia/sparkucx
% cd sparkucx
% mvn -DskipTests clean package -Pspark-3.0

About

A high-performance, scalable and efficient ShuffleManager plugin for Apache Spark, utilizing UCX communication layer

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Scala 92.7%
  • Shell 7.3%