Am running Spark job on a standalone cluster and I noticed after sometime the GC starts taking long and the red scary color begins to show.
Here is the resources available:
Cores in use: 80 Total, 76 Used
Memory in use: 312.8 GB Total, 292.0 GB Used
Job details:
spark-submit --class com.mavencode.spark.MonthlyReports
--master spark://192.168.12.14:7077
--deploy-mode cluster --supervise
--executor-memory 16G --executor-cores 4
--num-executors 18 --driver-cores 8
--driver-memory 20G montly-reports-assembly-1.0.jar
How do I fix the GC time taking so long?
I had the same problem and could resolve it by using Parallel GC instead of G1GC. You may add the following options to the executors additional Java options in the submit request
-XX:+UseParallelGC -XX:+UseParallelOldGC
Related
A simple Spark streaming app without any heavy in memory computation is consuming 17GB of Memory as soon after the STATE gets changed to RUNNING.
Cluster setup:
1x master (2 vCPU, 13.0 GB memory)
2x workers (2 vCPU, 13.0 GB memory)
YARN resource manager displays: Mem Total - 18GB, vCore Total - 4
Spark streaming app source code can be found here and as you can see it doesn't do much:
https://github.com/apache/spark/blob/v2.4.3/examples/src/main/java/org/apache/spark/examples/streaming/JavaCustomReceiver.java
Spark submit command (through SSH not GCLOUD SDK):
spark-submit --master yarn \
--deploy-mode cluster \
--num-executors 1 \
--driver-cores 1 \
--executor-memory 1g \
--driver-memory 512m \
--executor-cores 1 \
--class JavaCustomReceiver my_project.jar
Why would such a simple app allocate that much of memory?
I'm using GCP Dataproc default configuration, is there any YARN config that should be amended?
How many tasks does your application require? Note, Dataproc by default has dynamic allocation turned on which will request more executors from YARN as necessary.
When I submit this command, my job failed with error "Container is running beyond physical memory limits".
spark-submit --master yarn --deploy-mode cluster --executor-memory 5G --total-executor-cores 30 --num-executors 15 --conf spark.yarn.executor.memoryOverhead=1000
But adding the parameter: --driver-memory to 5GB (or upper), the job ends without error.
spark-submit --master yarn --deploy-mode cluster --executor-memory 5G --total executor-cores 30 --num-executors 15 --driver-memory 5G --conf spark.yarn.executor.memoryOverhead=1000
Cluster info: 6 nodes with 120GB of Memory. YARN Container Memory Minimum: 1GB
The question is: what is the difference in using or not this parameter?
If increasing the driver memory is helping you to successfully complete the job then it means that driver is having lots of data coming into it from executors. Typically, the driver program is responsible for collecting results back from each executor after the tasks are executed. So, in your case it seems that increasing the driver memory helped to store more results back into the driver memory.
If you read the some points on executor memory, driver memory and the way Driver interacts with executors then you will get better clarity on the situation you are in.
Hope it helps to some extent.
i run spark-shell with this command：
./bin/spark-shell --master yarn --num-executors 16
--executor-memory 14G --executor-cores 8
i have four nodes，every node has 16G memory and 4cores
after i changed num-executors,the spark webUi tell me it worked,
BUT only one executor on node which named "slave" is running
we could see TRANSFER1 and TRANSFER2 is empty
how can i solve this, when i submit job the situation has not changed
should i change worker-instances or num-executors and so on
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 am trying to read a big hbase table in spark (~100GB in size).
Spark Version : 1.6
Spark submit parameters:
spark-submit --master yarn-client --num-executors 10 --executor-memory 4G
--executor-cores 4
--conf spark.yarn.executor.memoryOverhead=2048
Error: ExecutorLostFailure Reason: Container killed by YARN for
exceeding limits.
4.5GB of 3GB physical memory used limits. Consider boosting spark.yarn.executor.memoryOverhead.
I have tried setting spark.yarn.executor.memoryOverhead to 100000. Still getting similar error.
I don't understand why spark doesn't spill to disk if the memory is insufficient OR is YARN causing the problem here.
Please share your code how you try to read in.
And also your cluster architecture
Container killed by YARN for exceeding limits. 4.5GB of 3GB physical memory used limits
Try
spark-submit
--master yarn-client
--num-executors 4
--executor-memory 100G
--executor-cores 4
--conf spark.yarn.executor.memoryOverhead=20480
If you have 128 gRam
The situation is clear, you run out of ram, try to rewrite your code in a disk friendly way.