61e21fe7f4
Here's my crack at Bertrand's suggestion. The Github `README.md` contains build info that's outdated. It should just point to the current online docs, and reflect that Maven is the primary build now. (Incidentally, the stanza at the end about contributions of original work should go in https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark too. It won't hurt to be crystal clear about the agreement to license, given that ICLAs are not required of anyone here.) Author: Sean Owen <sowen@cloudera.com> Closes #2014 from srowen/SPARK-3069 and squashes the following commits: 501507e [Sean Owen] Note that Zinc is for Maven builds too db2bd97 [Sean Owen] sbt -> sbt/sbt and add note about zinc be82027 [Sean Owen] Fix additional occurrences of building-with-maven -> building-spark 91c921f [Sean Owen] Move building-with-maven to building-spark and create a redirect. Update doc links to building-spark.html Add jekyll-redirect-from plugin and make associated config changes (including fixing pygments deprecation). Add example of SBT to README.md 999544e [Sean Owen] Change "Building Spark with Maven" title to "Building Spark"; reinstate tl;dr info about dev/run-tests in README.md; add brief note about building with SBT c18d140 [Sean Owen] Optionally, remove the copy of contributing text from main README.md 8e83934 [Sean Owen] Add CONTRIBUTING.md to trigger notice on new pull request page b1c04a1 [Sean Owen] Refer to current online documentation for building, and remove slightly outdated copy in README.md
121 lines
4.5 KiB
Markdown
121 lines
4.5 KiB
Markdown
---
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layout: global
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title: Third-Party Hadoop Distributions
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---
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Spark can run against all versions of Cloudera's Distribution Including Apache Hadoop (CDH) and
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the Hortonworks Data Platform (HDP). There are a few things to keep in mind when using Spark
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with these distributions:
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# Compile-time Hadoop Version
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When compiling Spark, you'll need to specify the Hadoop version by defining the `hadoop.version`
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property. For certain versions, you will need to specify additional profiles. For more detail,
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see the guide on [building with maven](building-spark.html#specifying-the-hadoop-version):
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mvn -Dhadoop.version=1.0.4 -DskipTests clean package
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mvn -Phadoop-2.2 -Dhadoop.version=2.2.0 -DskipTests clean package
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The table below lists the corresponding `hadoop.version` code for each CDH/HDP release. Note that
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some Hadoop releases are binary compatible across client versions. This means the pre-built Spark
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distribution may "just work" without you needing to compile. That said, we recommend compiling with
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the _exact_ Hadoop version you are running to avoid any compatibility errors.
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<table>
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<tr valign="top">
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<td>
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<h3>CDH Releases</h3>
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<table class="table" style="width:350px; margin-right: 20px;">
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<tr><th>Release</th><th>Version code</th></tr>
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<tr><td>CDH 4.X.X (YARN mode)</td><td>2.0.0-cdh4.X.X</td></tr>
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<tr><td>CDH 4.X.X</td><td>2.0.0-mr1-cdh4.X.X</td></tr>
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<tr><td>CDH 3u6</td><td>0.20.2-cdh3u6</td></tr>
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<tr><td>CDH 3u5</td><td>0.20.2-cdh3u5</td></tr>
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<tr><td>CDH 3u4</td><td>0.20.2-cdh3u4</td></tr>
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</table>
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</td>
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<td>
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<h3>HDP Releases</h3>
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<table class="table" style="width:350px;">
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<tr><th>Release</th><th>Version code</th></tr>
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<tr><td>HDP 1.3</td><td>1.2.0</td></tr>
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<tr><td>HDP 1.2</td><td>1.1.2</td></tr>
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<tr><td>HDP 1.1</td><td>1.0.3</td></tr>
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<tr><td>HDP 1.0</td><td>1.0.3</td></tr>
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<tr><td>HDP 2.0</td><td>2.2.0</td></tr>
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</table>
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</td>
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</tr>
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</table>
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In SBT, the equivalent can be achieved by setting the the `hadoop.version` property:
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sbt/sbt -Dhadoop.version=1.0.4 assembly
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# Linking Applications to the Hadoop Version
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In addition to compiling Spark itself against the right version, you need to add a Maven dependency on that
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version of `hadoop-client` to any Spark applications you run, so they can also talk to the HDFS version
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on the cluster. If you are using CDH, you also need to add the Cloudera Maven repository.
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This looks as follows in SBT:
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{% highlight scala %}
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libraryDependencies += "org.apache.hadoop" % "hadoop-client" % "<version>"
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// If using CDH, also add Cloudera repo
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resolvers += "Cloudera Repository" at "https://repository.cloudera.com/artifactory/cloudera-repos/"
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{% endhighlight %}
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Or in Maven:
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{% highlight xml %}
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<project>
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<dependencies>
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...
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<dependency>
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<groupId>org.apache.hadoop</groupId>
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<artifactId>hadoop-client</artifactId>
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<version>[version]</version>
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</dependency>
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</dependencies>
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<!-- If using CDH, also add Cloudera repo -->
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<repositories>
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...
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<repository>
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<id>Cloudera repository</id>
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<url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>
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</repository>
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</repositories>
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</project>
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{% endhighlight %}
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# Where to Run Spark
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As described in the [Hardware Provisioning](hardware-provisioning.html#storage-systems) guide,
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Spark can run in a variety of deployment modes:
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* Using dedicated set of Spark nodes in your cluster. These nodes should be co-located with your
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Hadoop installation.
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* Running on the same nodes as an existing Hadoop installation, with a fixed amount memory and
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cores dedicated to Spark on each node.
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* Run Spark alongside Hadoop using a cluster resource manager, such as YARN or Mesos.
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These options are identical for those using CDH and HDP.
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# Inheriting Cluster Configuration
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If you plan to read and write from HDFS using Spark, there are two Hadoop configuration files that
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should be included on Spark's classpath:
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* `hdfs-site.xml`, which provides default behaviors for the HDFS client.
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* `core-site.xml`, which sets the default filesystem name.
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The location of these configuration files varies across CDH and HDP versions, but
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a common location is inside of `/etc/hadoop/conf`. Some tools, such as Cloudera Manager, create
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configurations on-the-fly, but offer a mechanisms to download copies of them.
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To make these files visible to Spark, set `HADOOP_CONF_DIR` in `$SPARK_HOME/spark-env.sh`
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to a location containing the configuration files.
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