Spark cluster is not dynamically allocating resources to jobs

The cluster is HDInsight 4.0 and has 250 GB RAM and 75 VCores.

I am running only one job and the cluster is always allocating 66 GB, 7 VCores and 7 Containers to the job even though we have 250 GB and 75 VCores available for use. This is not particular to one job. I have ran 3 different jobs and all have this issue. when I run 3 jobs in parallel , the cluster is still allocating 66 GB RAM to each job. Looks like there is some static setting configured.

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The following is the queue setup

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I am using a tool called Talend(ETL tool similar to informatica) where I have a GUI to create a job . The tool is eclipse based and below is the code generated for spark configuration. This is then given to LIVY for submission in the cluster.

sparkConfiguration.set("spark.hadoop.talendStudioTimeZone", java.util.TimeZone.getDefault().getID());
    sparkConfiguration.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer");
    sparkConfiguration.set("spark.kryo.registrator", TalendKryoRegistrator.class.getName());
    sparkConfiguration.set("spark.yarn.submit.waitAppCompletion", "true");

    tuningConf.put("spark.yarn.maxAppAttempts", "1");

    tuningConf.put("spark.scheduler.mode", "FIFO");

This is the screen shot of spark defaults

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  • The behavior is expected as 6 execuors * 10 GB per executor memory = 60G.

    If want to use allocate more resources, try to increase exeucotr number such as 24