Hi and thank you for your help,
I know that there are a lot of topic with this issue, I read a lot of them, try a lot of solution but nothing happens, my spark-submit job is still ONLY use one core on my 32 available core.
With my spark-submit command, I launch a Pyspark script. This Pyspark script does a spark.sql command over a lot of parquet files (around 6000 files around 6M each, for a total of 600 millions database tuple).
I use an AWS instance with 32 cpu and 128 Go and a 2To EBS DD on which are stored my parquet files (it's not an hdfs file system)
I doesn't launch spark as a master, just using it in standalone solution on my single EC2 instance.
Everything works fine but the process takes 2h using only one core on my 32 cores so I expect to reduce the process time by using all available cores !
I launch my pyspark script like that :
spark-submit --driver-memory 96G --executor-cores 24 ./my_pyspark.py input.txt output.txt
I tried to add master parameters with local like this :
spark-submit --master local[24] --driver-memory 96G ./my_pyspark.py input.txt output.txt
I tried to start my spark as a server and give the url to the master parameter :
spark-class org.apache.spark.deploy.master.Master
spark-submit --master spark://10.0.1.20:7077 --driver-memory 96G --executor-cores 24 ./my_pyspark.py input.txt output.txt
But none of this solution works. Will looking at the process with htop I see that ONLY one core is used. What did I miss ???
Thanx
You spark submit command is wrong.
You should'nt allocate 96G for the driver and you should specifie the number of executor and the number of core for each executor.
For exemple, you can try :
spark-submit --driver-memory 8G --num-executors 15 --executors-memory 7 --executor-cores 2 ./my_pyspark.py input.txt output.txt
And you should probably use yarn as a ressource manager. --master yarn
Also, define master("local") in the sparkContext, override your spark-submit command, you should remove it from your code.
Related
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 ?
I'm using the quickstart cloudera VM (CDH 5.10.1) with Pyspark (1.6.0) and Yarn (MR2 Included) to aggregate numerical data per hour. I've got 1 CPU with 4 cores and 32 Go of RAM.
I've got a file named aggregate.py but until today I never submitted the job with spark-submit, I used pyspark interactive shell and copy/paste the code to test it.
When starting pyspark interactive shell I used :
pyspark --master yarn-client
I followed the treatment in the web UI accessible at quickstart.cloudera:8088/cluster and could see that Yarn created 3 executors and 1 driver with one core each (Not a good configuration but the main purpose is to make a proof of concept, until we move to a real cluster)
When submitting the same code with spark-submit :
spark-submit --verbose
--master yarn
--deploy-mode client \
--num-executors 2 \
--driver-memory 3G \
--executor-memory 6G \
--executor-cores 2 \
aggregate.py
I only have the driver, which also executes the tasks. Note that spark.dynamicAllocation.enabled is set to true in the environment tab, and spark.dynamicAllocation.minExecutors is set to 2.
I tried using spark-submit aggregate.py only, I still got only the driver as executor. I can't manage to have more than 1 executor with spark-submit, yet it works in spark interactive shell !
My Yarn configuration is as follow :
yarn.nodemanager.resource.memory-mb = 17 GiB
yarn.nodemanager.resource.cpu-vcores = 4
yarn.scheduler.minimum-allocation-mb = 3 GiB
yarn.scheduler.maximum-allocation-mb = 16 GiB
yarn.scheduler.minimum-allocation-vcores = 1
yarn.scheduler.maximum-allocation-vcores = 2
If someone can explain me what I'm doing wrong it would be a great help !
You have to set the driver memory and executor memory in to spark-defaults.conf.
It's located at
$SPARK_HOME/conf/spark-defaults.conf
and if there is a file like
spark-defaults.conf.template
then you have to rename the file as
spark-defaults.conf
and then set the number of executors, executor-memory ,number of executor-cores. you get the example from the template file or check this link
https://spark.apache.org/docs/latest/configuration.html.
or
When we used pyspark It's used default executor-memory but here in spark-submit you set executor-memory = 6G. I think you have to reduce the memory or remove this field so it can used default memory.
just a guess, as you said earlier "Yarn created 3 executors and 1 driver with one core each", so you have 4-cores in total.
Now as per your spark-submit statement,
cores = num-executors 2 * executor-cores 2 + for_driver 1 = 5
#but in total you have 4 cores. So it is unable to give you executors(as after driver only 3 cores left)
#Check if this is the issue.
I'm totally new at this, so I don't understand really well how it's doing.
I need to run spark on my machine (login with ssh) and set up memory 60g, and 6 cores for execution.
This is what I've tried.
spark-submit --master yarn --deploy-mode cluster --executor-memory 60g --executor-cores 6
And this is what I got:
SPARK_MAJOR_VERSION is set to 2, using Spark2
Exception in thread "main" java.lang.IllegalArgumentException: Missing application resource.
at org.apache.spark.launcher.CommandBuilderUtils.checkArgument(CommandBuilderUtils.java:253)
at org.apache.spark.launcher.SparkSubmitCommandBuilder.buildSparkSubmitArgs(SparkSubmitCommandBuilder.java:160)
at org.apache.spark.launcher.SparkSubmitCommandBuilder.buildSparkSubmitCommand(SparkSubmitCommandBuilder.java:276)
at org.apache.spark.launcher.SparkSubmitCommandBuilder.buildCommand(SparkSubmitCommandBuilder.java:151)
at org.apache.spark.launcher.Main.main(Main.java:87)
So, I guess there is some things to add to this code line for running and I have no idea what.
Here:
spark-submit --master yarn --deploy-mode cluster --executor-memory 60g --executor-cores 6
you don't specify which the entry point and your application!
Check the spark-submit documentation, which states:
Some of the commonly used options are:
--class: The entry point for your application (e.g. org.apache.spark.examples.SparkPi)
--master: The master URL for the cluster (e.g. spark://23.195.26.187:7077)
--deploy-mode: Whether to deploy your driver on the worker nodes (cluster) or locally as an external client (client) (default: client)
†
--conf: Arbitrary Spark configuration property in key=value format. For values that contain spaces wrap “key=value” in quotes (as shown).
application-jar: Path to a bundled jar including your application and
all dependencies. The URL must be globally visible inside of your
cluster, for instance, an hdfs:// path or a file:// path that is
present on all nodes.
application-arguments: Arguments passed to the main method of your
main class, if any
For Python applications, simply pass a .py file in the place of <application-jar> instead of a JAR, and add Python .zip, .egg or .py files to the search path with --py-files.
Here is an example that takes some JARs and a python file (I didn't include your additional parameters for simplicity):
./spark-submit --jars myjar1.jar,myjar2.jar --py-files path/to/my/main.py arg1 arg2
I hope I can entry to spark shell (with that much memory and cores) and type code in there
Then you need pyspark, not spark-submit! What is the difference between spark-submit and pyspark?
So what you really want to do is this:
pyspark --master yarn --deploy-mode cluster --executor-memory 60g --executor-cores 6
If I understand your question correctly, you total number of cores=6 and total memory is 60GB. The parameeters
--executor-memory
--executor-cores
are actually for each executor inside spark. probably you should try
--executor-memory 8G
--executor-cores 1
this will create about 6 executors of 8Gb each (total 6*8 = 48GB). The rest 12 GB for operating system processing and metadata.
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 Spark installed, but when I launch, there is always only one executor allocated to the application (and that is the driver one). I’ve tried everything, but haven’t been able to find out why this is happening.
Here’s the command I used to launch, to give you an idea of all the parameters:
dcos spark run --submit-args='--class <class-name> --executor-memory 6g --total-executor-cores 32 --driver-memory 6g <jar-file-source> <application-command-line-params>