Have you tried setting spark.executor.instances=0 to a positive non-zero
value? Also, since its a streaming application set executor cores > 1.
On Wed, Aug 23, 2017 at 3:38 AM, Karthik Palaniappan <karthik...@hotmail.com
> wrote:
> I ran the HdfsWordCount example using this command:
>
> spark-submit run-example \
> --conf spark.streaming.dynamicAllocation.enabled=true \
> --conf spark.executor.instances=0 \
> --conf spark.dynamicAllocation.enabled=false \
> --conf spark.master=yarn \
> --conf spark.submit.deployMode=client \
> org.apache.spark.examples.streaming.HdfsWordCount /foo
>
> I tried it on both Spark 2.1.1 (through HDP 2.6) and Spark 2.2.0 (through
> Google Dataproc 1.2), and I get the same message repeatedly that Spark
> cannot allocate any executors.
>
> 17/08/22 19:34:57 INFO org.spark_project.jetty.util.log: Logging
> initialized @1694ms
> 17/08/22 19:34:57 INFO org.spark_project.jetty.server.Server:
> jetty-9.3.z-SNAPSHOT
> 17/08/22 19:34:57 INFO org.spark_project.jetty.server.Server: Started
> @1756ms
> 17/08/22 19:34:57 INFO org.spark_project.jetty.server.AbstractConnector:
> Started ServerConnector@578782d6{HTTP/1.1,[http/1.1]}{0.0.0.0:4040}
> 17/08/22 19:34:58 INFO
> com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystemBase:
> GHFS version: 1.6.1-hadoop2
> 17/08/22 19:34:58 INFO org.apache.hadoop.yarn.client.RMProxy: Connecting
> to ResourceManager at hadoop-m/10.240.1.92:8032
> 17/08/22 19:35:00 INFO org.apache.hadoop.yarn.client.api.impl.YarnClientImpl:
> Submitted application application_1503036971561_0022
> 17/08/22 19:35:04 WARN org.apache.spark.streaming.StreamingContext:
> Dynamic Allocation is enabled for this application. Enabling Dynamic
> allocation for Spark Streaming applications can cause data loss if Write
> Ahead Log is not enabled for non-replayable sources like Flume. See the
> programming guide for details on how to enable the Write Ahead Log.
> 17/08/22 19:35:21 WARN org.apache.spark.scheduler.cluster.YarnScheduler:
> Initial job has not accepted any resources; check your cluster UI to ensure
> that workers are registered and have sufficient resources
> 17/08/22 19:35:36 WARN org.apache.spark.scheduler.cluster.YarnScheduler:
> Initial job has not accepted any resources; check your cluster UI to ensure
> that workers are registered and have sufficient resources
> 17/08/22 19:35:51 WARN org.apache.spark.scheduler.cluster.YarnScheduler:
> Initial job has not accepted any resources; check your cluster UI to ensure
> that workers are registered and have sufficient resources
>
> I confirmed that the YARN cluster has enough memory for dozens of
> executors, and verified that the application allocates executors when using
> Core's spark.dynamicAllocation.enabled=true, and leaving spark.streaming.
> dynamicAllocation.enabled=false.
>
> Is streaming dynamic allocation actually supported? Sean Owen suggested it
> might have been experimental: https://issues.apache.org/jira/browse/SPARK-
> 21792.
>
--
Cheers!
