Hi as recommended I am caching my Spark job dataframe as dataframe.persist(StorageLevels.MEMORY_AND_DISK_SER) but what I see in Spark job UI is this persist stage runs for so long showing 10 GB of shuffle read and 5 GB of shuffle write it takes to long to finish and because of that sometimes my Spark job throws timeout or throws OOM and hence executors gets killed by YARN. I am using Spark 1.4.1. I am using all sort of optimizations like Tungsten, Kryo I have given storage.memoryFraction as 0.2 and storage.shuffle as 0.2 also. My data is huge around 1 TB I am using default 200 partitions for spark.sql.shuffle.partitions. Please help me I am clueless please guide.
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