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Spark saveAsTextFile with SnappyCodec on YARN getting : "native snappy library not available: this version of libhadoop was built without snappy support."
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Apache Spark
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Apache YARN
Created on ‎06-08-2016 08:59 AM - edited ‎08-19-2019 03:00 AM
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Hello,
I have a spark job that is run by spark-submit and at the end it saves some output using SparkContext saveAsTextFile with SnappyCodec. To test I am using sandbox 2.3.4
This is running fine using master = local[x], after following the suggestion in this thread. However Once I changed master = yarn-client I am getting the same "native snappy library not available: this version of libhadoop was built without snappy support." on YARN.
However I thought I have done all the necessary setup - any suggestions welcome!
When I check the Spark History server I can see the following in the environment:
Spark properties:
system properties:
Furthermore in Ambari -> Yarn -> config -> Advanced yarn-env -> yarn-env template -> LD_LIBRARY_PATH:
Anything else I could do to make snappy available as a compression codec on YARN?
Thank you.
Created ‎06-08-2016 09:08 AM
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can you check whether snappy lib is installed on the cluster nodes using command 'hadoop checknative'?
Created ‎06-08-2016 09:08 AM
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can you check whether snappy lib is installed on the cluster nodes using command 'hadoop checknative'?
Created on ‎06-08-2016 09:10 AM - edited ‎08-19-2019 03:00 AM
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yes it is - as I said I was able to save in local mode.
Created ‎06-08-2016 09:21 AM
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can you add the following parameters in spark conf and see if it works
spark.executor.extraClassPath /usr/hdp/current/hadoop-client/lib/snappy-java-*.jar spark.executor.extraLibraryPath /usr/hdp/current/hadoop-client/native spark.executor.extraJavaOptions -Djava.library.path=/usr/hdp/current/hadoop-client/lib/native/lib
Created on ‎06-08-2016 01:09 PM - edited ‎08-19-2019 03:00 AM
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Thanks @Rajkumar Singh I have tried the mapred.child.java.opts and a few of the mapreduce settings suggested by @Jitendra Yadav but at the end just adding this spark.executor.extraLibraryPath would do the job.
Created ‎12-07-2018 12:24 PM
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spark.executor.extraLibraryPath did the job. Thanks! @David Tam and @Jitendra Yadav
