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apache-sparkcontainershadoop-yarnhortonworks-data-platformexecutor

Spark on YARN resource manager: Relation between YARN Containers and Spark Executors

I'm new to Spark on YARN and don't understand the relation between the YARN Containers and the Spark Executors. I tried out the following configuration, based on the results of the yarn-utils.py script, that can be used to find optimal cluster configuration.

The Hadoop cluster (HDP 2.4) I'm working on:

  • 1 Master Node:
    • CPU: 2 CPUs with 6 cores each = 12 cores
    • RAM: 64 GB
    • SSD: 2 x 512 GB
  • 5 Slave Nodes:
    • CPU: 2 CPUs with 6 cores each = 12 cores
    • RAM: 64 GB
    • HDD: 4 x 3 TB = 12 TB
  • HBase is installed (this is one of the parameters for the script below)

So I ran python yarn-utils.py -c 12 -m 64 -d 4 -k True (c=cores, m=memory, d=hdds, k=hbase-installed) and got the following result:

 Using cores=12 memory=64GB disks=4 hbase=True
 Profile: cores=12 memory=49152MB reserved=16GB usableMem=48GB disks=4
 Num Container=8
 Container Ram=6144MB
 Used Ram=48GB
 Unused Ram=16GB
 yarn.scheduler.minimum-allocation-mb=6144
 yarn.scheduler.maximum-allocation-mb=49152
 yarn.nodemanager.resource.memory-mb=49152
 mapreduce.map.memory.mb=6144
 mapreduce.map.java.opts=-Xmx4915m
 mapreduce.reduce.memory.mb=6144
 mapreduce.reduce.java.opts=-Xmx4915m
 yarn.app.mapreduce.am.resource.mb=6144
 yarn.app.mapreduce.am.command-opts=-Xmx4915m
 mapreduce.task.io.sort.mb=2457

These settings I made via the Ambari interface and restarted the cluster. The values also match roughly what I calculated manually before.

I have now problems

  • to find the optimal settings for my spark-submit script
    • parameters --num-executors, --executor-cores & --executor-memory.
  • to get the relation between the YARN container and the Spark executors
  • to understand the hardware information in my Spark History UI (less memory shown as I set (when calculated to overall memory by multiplying with worker node amount))
  • to understand the concept of the vcores in YARN, here I couldn't find any useful examples yet

However, I found this post What is a container in YARN? , but this didn't really help as it doesn't describe the relation to the executors.

Can someone help to solve one or more of the questions?

Solution

  • I will report my insights here step by step:

    • First important thing is this fact (Source: this Cloudera documentation):

      When running Spark on YARN, each Spark executor runs as a YARN container. [...]

    • This means the number of containers will always be the same as the executors created by a Spark application e.g. via --num-executors parameter in spark-submit.

    • Set by the yarn.scheduler.minimum-allocation-mb every container always allocates at least this amount of memory. This means if parameter --executor-memory is set to e.g. only 1g but yarn.scheduler.minimum-allocation-mb is e.g. 6g, the container is much bigger than needed by the Spark application.

    • The other way round, if the parameter --executor-memory is set to somthing higher than the yarn.scheduler.minimum-allocation-mb value, e.g. 12g, the Container will allocate more memory dynamically, but only if the requested amount of memory is smaller or equal to yarn.scheduler.maximum-allocation-mb value.

    • The value of yarn.nodemanager.resource.memory-mb determines, how much memory can be allocated in sum by all containers of one host!

    => So setting yarn.scheduler.minimum-allocation-mb allows you to run smaller containers e.g. for smaller executors (else it would be waste of memory).

    => Setting yarn.scheduler.maximum-allocation-mb to the maximum value (e.g. equal to yarn.nodemanager.resource.memory-mb) allows you to define bigger executors (more memory is allocated if needed, e.g. by --executor-memory parameter).