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layout: global displayTitle: Using Spark‘s “Hadoop Free” Build title: Using Spark’s “Hadoop Free” Build 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

 http://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.

Spark uses Hadoop client libraries for HDFS and YARN. Starting in version Spark 1.4, the project packages “Hadoop free” builds that lets you more easily connect a single Spark binary to any Hadoop version. To use these builds, you need to modify SPARK_DIST_CLASSPATH to include Hadoop's package jars. The most convenient place to do this is by adding an entry in conf/spark-env.sh.

This page describes how to connect Spark to Hadoop for different types of distributions.

Apache Hadoop

For Apache distributions, you can use Hadoop's ‘classpath’ command. For instance:

{% highlight bash %}

in conf/spark-env.sh

If ‘hadoop’ binary is on your PATH

export SPARK_DIST_CLASSPATH=$(hadoop classpath)

With explicit path to ‘hadoop’ binary

export SPARK_DIST_CLASSPATH=$(/path/to/hadoop/bin/hadoop classpath)

Passing a Hadoop configuration directory

export SPARK_DIST_CLASSPATH=$(hadoop --config /path/to/configs classpath)

{% endhighlight %}

Hadoop Free Build Setup for Spark on Kubernetes

To run the Hadoop free build of Spark on Kubernetes, the executor image must have the appropriate version of Hadoop binaries and the correct SPARK_DIST_CLASSPATH value set. See the example below for the relevant changes needed in the executor Dockerfile:

{% highlight bash %}

Set environment variables in the executor dockerfile

ENV SPARK_HOME=“/opt/spark”
ENV HADOOP_HOME=“/opt/hadoop”
ENV PATH=“$SPARK_HOME/bin:$HADOOP_HOME/bin:$PATH”
...

#Copy your target hadoop binaries to the executor hadoop home

COPY /opt/hadoop3 $HADOOP_HOME
...

#Copy and use the Spark provided entrypoint.sh. It sets your SPARK_DIST_CLASSPATH using the hadoop binary in $HADOOP_HOME and starts the executor. If you choose to customize the value of SPARK_DIST_CLASSPATH here, the value will be retained in entrypoint.sh

ENTRYPOINT [ “/opt/entrypoint.sh” ] ...
{% endhighlight %}