By default spark uses 2 executors with one core each, have you allocated more executors using the command line args as - --num-executors 25 --executor-cores x ???
What do you mean by the difference between the nodes is huge ? Regards, Padma Ch On Tue, Mar 15, 2016 at 6:57 PM, bkapukaranov [via Apache Spark User List] < ml-node+s1001560n2650...@n3.nabble.com> wrote: > Hi, > > I'm running a Spark 1.6.0 on YARN on a Hadoop 2.6.0 cluster. > I observe a very strange issue. > I run a simple job that reads about 1TB of json logs from a remote HDFS > cluster and converts them to parquet, then saves them to the local HDFS of > the Hadoop cluster. > > I run it with 25 executors with sufficient resources. However the strange > thing is that the job only uses 2 executors to do most of the read work. > > For example when I go to the Executors' tab in the Spark UI and look at > the "Input" column, the difference between the nodes is huge, sometimes 20G > vs 120G. > > 1. What is the cause for this behaviour? > 2. Any ideas how to achieve a more balanced performance? > > Thanks, > Borislav > > ------------------------------ > If you reply to this email, your message will be added to the discussion > below: > > http://apache-spark-user-list.1001560.n3.nabble.com/Spark-work-distribution-among-execs-tp26502.html > To start a new topic under Apache Spark User List, email > ml-node+s1001560n1...@n3.nabble.com > To unsubscribe from Apache Spark User List, click here > <http://apache-spark-user-list.1001560.n3.nabble.com/template/NamlServlet.jtp?macro=unsubscribe_by_code&node=1&code=bGVhcm5pbmdzLmNoaXR0dXJpQGdtYWlsLmNvbXwxfC03NzExMjUwMg==> > . > NAML > <http://apache-spark-user-list.1001560.n3.nabble.com/template/NamlServlet.jtp?macro=macro_viewer&id=instant_html%21nabble%3Aemail.naml&base=nabble.naml.namespaces.BasicNamespace-nabble.view.web.template.NabbleNamespace-nabble.view.web.template.NodeNamespace&breadcrumbs=notify_subscribers%21nabble%3Aemail.naml-instant_emails%21nabble%3Aemail.naml-send_instant_email%21nabble%3Aemail.naml> > -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Spark-work-distribution-among-execs-tp26502p26503.html Sent from the Apache Spark User List mailing list archive at Nabble.com.
