apache-sparkazure-hdinsightambari

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.

enter image description here

The following is the queue setup

enter image description here

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

enter image description here

Solution

  • 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