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From "danny mor (JIRA)" <j...@apache.org>
Subject [jira] [Created] (SPARK-22046) Streaming State cannot be scalable
Date Mon, 18 Sep 2017 07:04:01 GMT
danny mor created SPARK-22046:
---------------------------------

             Summary: Streaming State cannot be scalable
                 Key: SPARK-22046
                 URL: https://issues.apache.org/jira/browse/SPARK-22046
             Project: Spark
          Issue Type: Bug
          Components: Structured Streaming
    Affects Versions: 2.2.0
         Environment: OS: amazon linux, 
Streaming Source: kafka 0.10
vm: aws ec2
cluster resources: 16Gb per worker, single executor per worker, 8 cores per executor
storage: hdfs
            Reporter: danny mor
            Priority: Minor


State cannot be distributed on the cluster.
When the {color:#59afe1}StateStoreRDD{color}'s {color:#59afe1}getPrefferedLocation {color}is
called it 
creates a {color:#59afe1} StateStoreId(checkpointLocation, operatorId, partition.index){color},
send it to the {color:#59afe1}StateStoreCoordinator {color},which holds a hashmap of {color:#59afe1}StateStoreId
{color}to {color:#59afe1}ExecutorCacheTaskLocation{color}, and returns the executorId if it
is cached.
the operatorId is generated once every batch in the {color:#59afe1}IncrementalExecution {color}instance
but it is almost always 0 since {color:#59afe1}IncrementalExecution {color}is instantiated
each batch
the partition index is limited to the configured value "spark.sql.shuffle.partitions" (in
my case the default 200)
so this limits cache to 200 entries which has no regard to the key itself .
When introducing new Executors to the cluster and new keys to streaming data, it does not
effect the distribution of state because the {color:#59afe1}StateStoreId {color}does not regard
those variables.





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