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.
Related
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.
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'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 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>
I'm trying to submit a Spark app from local machine Terminal to my Cluster. I'm using --master yarn-cluster. I need to run the driver program on my Cluster too, not on the machine I do submit the application i.e my local machine
I'm using
bin/spark-submit
--class com.my.application.XApp
--master yarn-cluster --executor-memory 100m
--num-executors 50 hdfs://name.node.server:8020/user/root/x-service-1.0.0-201512141101-assembly.jar
1000
and getting error
Diagnostics: java.io.FileNotFoundException: File
file:/Users/nish1013/Dev/spark-1.4.1-bin-hadoop2.6/lib/spark-assembly-1.4.1-hadoop2.6.0.jar
does not exist
I can see in my service list ,
YARN + MapReduce2 2.7.1.2.3 Apache Hadoop NextGen MapReduce (YARN)
Spark 1.4.1.2.3 Apache Spark is a fast and general engine for
large-scale data processing.
already installed.
My spark-env.sh in local machine
export HADOOP_CONF_DIR=/Users/nish1013/Dev/hadoop-2.7.1/etc/hadoop
Has anyone encountered similar before ?
I think the right command to call is like following:
bin/spark-submit
--class com.my.application.XApp
--master yarn-cluster --executor-memory 100m
--num-executors 50 --conf spark.yarn.jars=hdfs://name.node.server:8020/user/root/x-service-1.0.0-201512141101-assembly.jar
1000
or you can add
spark.yarn.jars hdfs://name.node.server:8020/user/root/x-service-1.0.0-201512141101-assembly.jar
in your spark.default.conf file