docs/hadoop-provided.md

---
layout: global
displayTitle: Using Spark's "Hadoop Free" Build
title: Using Spark's "Hadoop Free" Build
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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 %}