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This is a fairly large PR to clean up and update the docs for 1.0. The major changes are: * A unified programming guide for all languages replaces language-specific ones and shows language-specific info in tabs * New programming guide sections on key-value pairs, unit testing, input formats beyond text, migrating from 0.9, and passing functions to Spark * Spark-submit guide moved to a separate page and expanded slightly * Various cleanups of the menu system, security docs, and others * Updated look of title bar to differentiate the docs from previous Spark versions You can find the updated docs at http://people.apache.org/~matei/1.0-docs/_site/ and in particular http://people.apache.org/~matei/1.0-docs/_site/programming-guide.html. Author: Matei Zaharia <matei@databricks.com> Closes #896 from mateiz/1.0-docs and squashes the following commits: 03e6853 [Matei Zaharia] Some tweaks to configuration and YARN docs 0779508 [Matei Zaharia] tweak ef671d4 [Matei Zaharia] Keep frames in JavaDoc links, and other small tweaks 1bf4112 [Matei Zaharia] Review comments 4414f88 [Matei Zaharia] tweaks d04e979 [Matei Zaharia] Fix some old links to Java guide a34ed33 [Matei Zaharia] tweak 541bb3b [Matei Zaharia] miscellaneous changes fcefdec [Matei Zaharia] Moved submitting apps to separate doc 61d72b4 [Matei Zaharia] stuff 181f217 [Matei Zaharia] migration guide, remove old language guides e11a0da [Matei Zaharia] Add more API functions 6a030a9 [Matei Zaharia] tweaks 8db0ae3 [Matei Zaharia] Added key-value pairs section 318d2c9 [Matei Zaharia] tweaks 1c81477 [Matei Zaharia] New section on basics and function syntax e38f559 [Matei Zaharia] Actually added programming guide to Git a33d6fe [Matei Zaharia] First pass at updating programming guide to support all languages, plus other tweaks throughout 3b6a876 [Matei Zaharia] More CSS tweaks 01ec8bf [Matei Zaharia] More CSS tweaks e6d252e [Matei Zaharia] Change color of doc title bar to differentiate from 0.9.0
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| global | Third-Party Hadoop Distributions |
Spark can run against all versions of Cloudera's Distribution Including Apache Hadoop (CDH) and the Hortonworks Data Platform (HDP). There are a few things to keep in mind when using Spark with these distributions:
Compile-time Hadoop Version
When compiling Spark, you'll need to specify the Hadoop version by defining the hadoop.version
property. For certain versions, you will need to specify additional profiles. For more detail,
see the guide on building with maven:
mvn -Dhadoop.version=1.0.4 -DskipTests clean package
mvn -Phadoop-2.2 -Dhadoop.version=2.2.0 -DskipTests clean package
The table below lists the corresponding hadoop.version code for each CDH/HDP release. Note that
some Hadoop releases are binary compatible across client versions. This means the pre-built Spark
distribution may "just work" without you needing to compile. That said, we recommend compiling with
the exact Hadoop version you are running to avoid any compatibility errors.
CDH Releases
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HDP Releases
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In SBT, the equivalent can be achieved by setting the SPARK_HADOOP_VERSION flag:
SPARK_HADOOP_VERSION=1.0.4 sbt/sbt assembly
Linking Applications to the Hadoop Version
In addition to compiling Spark itself against the right version, you need to add a Maven dependency on that
version of hadoop-client to any Spark applications you run, so they can also talk to the HDFS version
on the cluster. If you are using CDH, you also need to add the Cloudera Maven repository.
This looks as follows in SBT:
{% highlight scala %} libraryDependencies += "org.apache.hadoop" % "hadoop-client" % ""
// If using CDH, also add Cloudera repo resolvers += "Cloudera Repository" at "https://repository.cloudera.com/artifactory/cloudera-repos/" {% endhighlight %}
Or in Maven:
{% highlight xml %} ... org.apache.hadoop hadoop-client [version]
... Cloudera repository https://repository.cloudera.com/artifactory/cloudera-repos/{% endhighlight %}
Where to Run Spark
As described in the Hardware Provisioning guide, Spark can run in a variety of deployment modes:
- Using dedicated set of Spark nodes in your cluster. These nodes should be co-located with your Hadoop installation.
- Running on the same nodes as an existing Hadoop installation, with a fixed amount memory and cores dedicated to Spark on each node.
- Run Spark alongside Hadoop using a cluster resource manager, such as YARN or Mesos.
These options are identical for those using CDH and HDP.
Inheriting Cluster Configuration
If you plan to read and write from HDFS using Spark, there are two Hadoop configuration files that should be included on Spark's classpath:
hdfs-site.xml, which provides default behaviors for the HDFS client.core-site.xml, which sets the default filesystem name.
The location of these configuration files varies across CDH and HDP versions, but
a common location is inside of /etc/hadoop/conf. Some tools, such as Cloudera Manager, create
configurations on-the-fly, but offer a mechanisms to download copies of them.
To make these files visible to Spark, set HADOOP_CONF_DIR in $SPARK_HOME/spark-env.sh
to a location containing the configuration files.