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https://issues.apache.org/jira/browse/SPARK-6511?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14493592#comment-14493592
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Kannan Rajah commented on SPARK-6511:
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[~pwendell] Just wanted to let you know that we also have a way to add hive and
hbase jars to the classpath. This is useful when a setup has multiple versions
of hive and hbase installed, but a Spark version will only work with specific
version. We have some utility scripts to generate the right classpath entries
based on a supported version of hive, hbase. If you think this will be useful
in Apache distribution, I can create a JIRA and share the code. At a high
level, there are 3 files:
- compatibility.version: File that holds supported versions for each ecosystem
component.
hive_versions=0.13,0.12
hbase_versions=0.98
- compatible_version.sh: Returns the compatible version for a component by
looking up compatibilty.version file. The first version that is available on
the node is used.
- generate_classpath.sh: Uses the above 2 files to generate the classpath. This
script is used in spark-env.sh to generate classpath based on hive and hbase.
> Publish "hadoop provided" build with instructions for different distros
> -----------------------------------------------------------------------
>
> Key: SPARK-6511
> URL: https://issues.apache.org/jira/browse/SPARK-6511
> Project: Spark
> Issue Type: Improvement
> Components: Build
> Reporter: Patrick Wendell
>
> Currently we publish a series of binaries with different Hadoop client jars.
> This mostly works, but some users have reported compatibility issues with
> different distributions.
> One improvement moving forward might be to publish a binary build that simply
> asks you to set HADOOP_HOME to pick up the Hadoop client location. That way
> it would work across multiple distributions, even if they have subtle
> incompatibilities with upstream Hadoop.
> I think a first step for this would be to produce such a build for the
> community and see how well it works. One potential issue is that our fancy
> excludes and dependency re-writing won't work with the simpler "append
> Hadoop's classpath to Spark". Also, how we deal with the Hive dependency is
> unclear, i.e. should we continue to bundle Spark's Hive (which has some fixes
> for dependency conflicts) or do we allow for linking against vanilla Hive at
> runtime.
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