I am using spark 2.4.3 v.
I have a spark sql streaming job to consume data from my kafka topic.
my spark job running on a aws emr cluster.
my job has following configuration
--driver-memory 4g \
--driver-cores 2 \
--num-executors 20 \
--executor-cores 2 \
--executor-memory 768m \
Even though my job started and running fine , when checked the spark-UI , Jobs & Stages pages showing empty screen. What is wrong here and how to fix it ?
Sometimes it show when I reduce --executor-memory 512m \
What is wrong here and how to fix it ?
Related
I am trying to submit a spark scala job with below configuration:
spark-submit --class abcd --queue new --master yarn --executor-cores 1 --executor-memory 4g --driver-memory 2g --num-executors 1
The allocated space for the queue is 700GB and it is taking entire 700GB and running.
Is there a way to restrict to 100GB only?
Thanks in advance.
I am running a spark job with 3 files each of 100MB size, for some reason my spark UI shows all dataset concentrated into 2 executors.This is making the job run for 19 hrs and still running.
Below is my spark configuration . spark 2.3 is the version used.
spark2-submit --class org.mySparkDriver \
--master yarn-cluster \
--deploy-mode cluster \
--driver-memory 8g \
--num-executors 100 \
--conf spark.default.parallelism=40 \
--conf spark.yarn.executor.memoryOverhead=6000mb \
--conf spark.dynamicAllocation.executorIdleTimeout=6000s \
--conf spark.executor.cores=3 \
--conf spark.executor.memory=8G \
I tried repartitioning inside the code which works , as this makes the file go into 20 partitions (i used rdd.repartition(20)). But why should I repartition , i believe specifying spark.default.parallelism=40 in the script should let spark divide the input file to 40 executors and process the file in 40 executors.
Can anyone help.
Thanks,
Neethu
I am assuming you're running your jobs in YARN if yes, you can check following properties.
yarn.scheduler.maximum-allocation-mb
yarn.nodemanager.resource.memory-mb
yarn.scheduler.maximum-allocation-vcores
yarn.nodemanager.resource.cpu-vcores
In YARN these properties would affect number of containers that can be instantiated in a NodeManager based on spark.executor.cores, spark.executor.memory property values (along with executor memory overhead)
For example, if a cluster with 10 nodes (RAM : 16 GB, cores : 6) and set with following yarn properties
yarn.scheduler.maximum-allocation-mb=10GB
yarn.nodemanager.resource.memory-mb=10GB
yarn.scheduler.maximum-allocation-vcores=4
yarn.nodemanager.resource.cpu-vcores=4
Then with spark properties spark.executor.cores=2, spark.executor.memory=4GB you can expect 2 Executors/Node so total you'll get 19 executors + 1 container for Driver
If the spark properties are spark.executor.cores=3, spark.executor.memory=8GB then you will get 9 Executor (only 1 Executor/Node) + 1 container for Driver
you can refer to link for more details
Hope this helps
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 using spark-summit command for executing Spark jobs with parameters such as:
spark-submit --master yarn-cluster --driver-cores 2 \
--driver-memory 2G --num-executors 10 \
--executor-cores 5 --executor-memory 2G \
--class com.spark.sql.jdbc.SparkDFtoOracle2 \
Spark-hive-sql-Dataframe-0.0.1-SNAPSHOT-jar-with-dependencies.jar
Now i want to execute the same program using Spark's Dynamic Resource allocation. Could you please help with the usage of Dynamic Resource Allocation in executing Spark programs.
In Spark dynamic allocation spark.dynamicAllocation.enabled needs to be set to true because it's false by default.
This requires spark.shuffle.service.enabled to be set to true, as spark application is running on YARN. Check this link to start the shuffle service on each NodeManager in YARN.
The following configurations are also relevant:
spark.dynamicAllocation.minExecutors,
spark.dynamicAllocation.maxExecutors, and
spark.dynamicAllocation.initialExecutors
These options can be configured to Spark application in 3 ways
1. From Spark submit with --conf <prop_name>=<prop_value>
spark-submit --master yarn-cluster \
--driver-cores 2 \
--driver-memory 2G \
--num-executors 10 \
--executor-cores 5 \
--executor-memory 2G \
--conf spark.dynamicAllocation.minExecutors=5 \
--conf spark.dynamicAllocation.maxExecutors=30 \
--conf spark.dynamicAllocation.initialExecutors=10 \ # same as --num-executors 10
--class com.spark.sql.jdbc.SparkDFtoOracle2 \
Spark-hive-sql-Dataframe-0.0.1-SNAPSHOT-jar-with-dependencies.jar
2. Inside Spark program with SparkConf
Set the properties in SparkConf then create SparkSession or SparkContext with it
val conf: SparkConf = new SparkConf()
conf.set("spark.dynamicAllocation.minExecutors", "5");
conf.set("spark.dynamicAllocation.maxExecutors", "30");
conf.set("spark.dynamicAllocation.initialExecutors", "10");
.....
3. spark-defaults.conf usually located in $SPARK_HOME/conf/
Place the same configurations in spark-defaults.conf to apply for all spark applications if no configuration is passed from command-line as well as code.
Spark - Dynamic Allocation Confs
I just did a small demo with Spark's dynamic resource allocation. The code is on my Github. Specifically, the demo is in this release.
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