I am new to spark. When I try to run spark-submit in client mode with 3 executors , I expect 3 java processes (since there are 3 executors ) to show up when I execute ps -ef
$SPARK_HOME/bin/spark-submit --num-executors 3 --class AverageCalculation --master local[1] /home/customer/SimpleETL/target/SimpleETL-0.1.jar hdfs://node1:9000/home/customer/SimpleETL/standard_input.csv
But, I dont see 3 java processes. My undrstanding is that each executor process is a java process. Please advise. Thanks.
Because you use local mode (--master local[1]) executor settings are not applicable. In this case, spark starts only a single JVM to emulate all components, and allocates number of threads specified in local definition (1) as executor threads.
In other modes, exectuors are separate JVM instances.
Each executors are a java process. Each executors comprises a jvm.
jps
Number of java process is same as the number of executors. If the executors are distributed across the worker nodes. Need to check the process the corresponding worker nodes. We can get the information about executors and where it has been launched from spark history server web UI.
In Spark, there are master nodes and worker nodes. Executors run on worker nodes in their own java processes.
In your spark-submit you can add --deploy-mode cluster and see that executors are running on worker nodes in their own JVM instances.
You can check this answer for detailed workflow of Apache Spark.
/home/spark/spark-2.2.1-bin-hadoop2.7/bin/spark-submit --class org.apache.spark.examples.SparkPi \
--num-executors 1000 \
--master yarn --deploy-mode cluster --driver-memory 4g --executor-memory 2g --executor-cores 1 \
--queue default /home/spark/spark-2.2.1-bin-hadoop2.7/examples/jars/spark-examples_2.11-2.2.1.jar
~
I executed the command above. and checked ps -ef | grep java. But I dont see a lot of java processes . Any easy way to identify the executors ?
Related
I am running Spark on YARN in client mode, so I expect that YARN will allocate containers only for the executors. Yet, from what I am seeing, it seems like a container is also allocated for the driver, and I don't get as many executors as I was expecting.
I am running spark submit on the master node. Parameters are as follows:
sudo spark-submit --class ... \
--conf spark.master=yarn \
--conf spark.submit.deployMode=client \
--conf spark.yarn.am.cores=2 \
--conf spark.yarn.am.memory=8G \
--conf spark.executor.instances=5 \
--conf spark.executor.cores=3 \
--conf spark.executor.memory=10G \
--conf spark.dynamicAllocation.enabled=false \
While running this application, Spark UI's Executors page shows 1 driver and 4 executors (5 entries in total). I would expect 5, not 4 executors.
At the same time, YARN UI's Nodes tab shows that on the node that isn't actually used (at least according to Spark UI's Executors page...) there's a container allocated, using 9GB of memory. The rest of the nodes have containers running on them, 11GB of memory each.
Because in my Spark Submit the driver has 2GB less memory than executors, I think that the 9GB container allocated by YARN is for the driver.
Why is this extra container allocated? How can i prevent this?
Spark UI:
YARN UI:
Update after answer by Igor Dvorzhak
I was falsely assuming that the AM will run on the master node, and that it will contain the driver app (so setting spark.yarn.am.* settings will relate to the driver process).
So I've made the following changes:
set the spark.yarn.am.* settings to defaults (512m of memory, 1 core)
set the driver memory through spark.driver.memory to 8g
did not try to set driver cores at all, since it is only valid for cluster mode
Because AM on default settings takes up 512m + 384m of overhead, its container fits into the spare 1GB of free memory on a worker node.
Spark gets the 5 executors it requested, and the driver memory is appropriate to the 8g setting. All works as expected now.
Spark UI:
YARN UI:
Extra container is allocated for YARN application master:
In client mode, the driver runs in the client process, and the application master is only used for requesting resources from YARN.
Even though in client mode driver runs in the client process, YARN application master is still running on YARN and requires container allocation.
There are no way to prevent container allocation for YARN application master.
For reference, similar question asked time ago: Resource Allocation with Spark and Yarn.
You can specify the driver memory and number of executors in spark submit as below.
spark-submit --jars..... --master yarn --deploy-mode cluster --driver-memory 2g --driver-cores 4 --num-executors 5 --executor-memory 10G --executor-cores 3
Hope it helps you.
I am using Spark 2.1. This question is for use cases where some of Spark slave servers run other apps as well. Is there a way to tell the Spark Master server to to use only certain # of CPU cores or memory of a slave server ?
Thanks.
To limit the number of cores used by a spark job, you need to add the --total-executor-cores option into your spark-submit command. To limit the amount of memory used by each executor, use the --executor-memory option. For example:
spark-submit --total-executor-cores 10 \
--executor-memory 8g \
--class com.example.SparkJob \
SparkJob.jar
This also works with spark-shell
spark-shell --total-executor-cores 10 \
--executor-memory 8g
I have setup a Spark standalone cluster, where I can submit jobs with spark-submit:
spark-submit \
--class blah.blah.MyClass \
--master spark://myaddress:6066 \
--executor-memory 8G \
--deploy-mode cluster \
--total-executor-cores 12 \
/path/to/jar/myjar.jar
Problem is when I send multiple jobs at the same time, say over 20 in one go, the first few finished successfully. All the others are now stuck waiting for resources. I noticed all the available memory has gone to the drivers, so in the drivers section they are all running but in the running application section they all are in WAITING state.
How can I tell spark stand alone to first allocate memory to the WAITING executors instead of the SUBMITTED drivers?
thank you
Below is an extract of my spark-defaults.conf
spark.master spark://address:7077
spark.eventLog.enabled true
spark.eventLog.dir /path/tmp/sparkEventLog
spark.driver.memory 5g
spark.local.dir /path/tmp
spark.ui.port xxx
we have installed a spark-jobserver that is launched on a spark cluster using server_start.sh, however no matter how we go about it we cannot make it launch on multiple workers. We can manage to get it to run with several cores and more memory, but not over several nodes.
The commands we have tried are as follows:
./server_start.sh --master spark://IP:PORT --deploy-mode cluster --total-executor cores 6
./server_start.sh --master spark://IP:PORT --deploy-mode cluster --total-executor cores 4 --executor-cores 2
./server_start.sh --master spark://IP:PORT --deploy-mode cluster --conf spark.driver.cores=4 --conf spark.driver.memory=7g
./server_start.sh --master spark://IP:PORT --deploy-mode cluster --conf spark.driver.cores=6 --conf spark.driver.memory=7g
The first two commands launched and showed one worker using one core and 1GB, while the third shows one worker using 4 cores and 7g. The fourth command shows 6 cores to be used, but state SUBMITTED.
We have verified that it does work to launch and application on multiple workers by launching the spark shell with the following command, which shows up as running driver with 2 workers and a total of 6 cores.
./spark-shell --master spark://IP:PORT --total-executor cores 6
Would appreciate any help.
Spark jobserver is actually spark driver not an application as such. I don't you can distribute the load among workers.
I have one Spark job which runs fine locally with less data but when I schedule it on YARN to execute I keep on getting the following error and slowly all executors get removed from UI and my job fails
15/07/30 10:18:13 ERROR cluster.YarnScheduler: Lost executor 8 on myhost1.com: remote Rpc client disassociated
15/07/30 10:18:13 ERROR cluster.YarnScheduler: Lost executor 6 on myhost2.com: remote Rpc client disassociated
I use the following command to schedule Spark job in yarn-client mode
./spark-submit --class com.xyz.MySpark --conf "spark.executor.extraJavaOptions=-XX:MaxPermSize=512M" --driver-java-options -XX:MaxPermSize=512m --driver-memory 3g --master yarn-client --executor-memory 2G --executor-cores 8 --num-executors 12 /home/myuser/myspark-1.0.jar
What is the problem here? I am new to Spark.
I had a very similar problem. I had many executors being lost no matter how much memory we allocated to them.
The solution if you're using yarn was to set --conf spark.yarn.executor.memoryOverhead=600, alternatively if your cluster uses mesos you can try --conf spark.mesos.executor.memoryOverhead=600 instead.
In spark 2.3.1+ the configuration option is now --conf spark.yarn.executor.memoryOverhead=600
It seems like we were not leaving sufficient memory for YARN itself and containers were being killed because of it. After setting that we've had different out of memory errors, but not the same lost executor problem.
You can follow this AWS post to calculate memory overhead (and other spark configs to tune): best-practices-for-successfully-managing-memory-for-apache-spark-applications-on-amazon-emr
When I had the same issue, deleting logs and free up more hdfs space worked.