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https://issues.apache.org/jira/browse/SPARK-13587?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16217115#comment-16217115
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Nicholas Chammas commented on SPARK-13587:
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To follow-up on my [earlier
comment|https://issues.apache.org/jira/browse/SPARK-13587?focusedCommentId=15740419&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15740419],
I created a [completely self-contained sample
repo|https://github.com/massmutual/sample-pyspark-application] demonstrating a
technique for bundling PySpark app dependencies in an isolated way. It's the
technique that Ben, I, and several others discussed here in this JIRA issue.
https://github.com/massmutual/sample-pyspark-application
The approach has advantages (like letting you ship a completely isolated Python
environment, so you don't even need Python installed on the workers) and
disadvantages (requires YARN; increases job startup time). Hope some of you
find the sample repo useful until Spark adds more "first-class" support for
Python dependency isolation.
> Support virtualenv in PySpark
> -----------------------------
>
> Key: SPARK-13587
> URL: https://issues.apache.org/jira/browse/SPARK-13587
> Project: Spark
> Issue Type: New Feature
> Components: PySpark
> Reporter: Jeff Zhang
>
> Currently, it's not easy for user to add third party python packages in
> pyspark.
> * One way is to using --py-files (suitable for simple dependency, but not
> suitable for complicated dependency, especially with transitive dependency)
> * Another way is install packages manually on each node (time wasting, and
> not easy to switch to different environment)
> Python has now 2 different virtualenv implementation. One is native
> virtualenv another is through conda. This jira is trying to migrate these 2
> tools to distributed environment
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