I'm trying to setup a standalone Spark 2.0 server to process an analytics function in parallel. To do this I want to run 8 workers, with a single core per each worker. However, the Spark Master/Worker UI doesn't seem to be reflecting my configuration.
I'm using :
Standalone Spark 2.0
8 Cores 24gig RAM
windows server 2008
pyspark
spark-env.sh file is configured as follows:
SPARK_WORKER_INSTANCES = 8
SPARK_WORKER_CORES = 1
SPARK_WORKER_MEMORY = 2g
spark-defaults.conf is configured as follows:
spark.cores.max = 8
I start the master:
spark-class org.apache.spark.deploy.master.Master
I start the workers by running this command 8 times within a batch file:
spark-class org.apache.spark.deploy.worker.Worker spark://10.0.0.10:7077
The problem is that the UI shows up as follows:
As you can see each worker has 8 cores instead of the 1 core I have assigned it via the SPARK_WORKER_CORES setting. Also the memory is reflective of the entire machine memory not the 2g assigned to each worker. How can I configure Spark to run with 1 core/2g per each worker in standalone mode?
I fixed this to adding the cores and memory arguments to the worker itself.
start spark-class org.apache.spark.deploy.worker.Worker --cores 1 --memory 2g spark://10.0.0.10:7077
Related
I'm a beginner trying to learn about the behavior of applications and drivers by going through some examples. I'm starting off with:
Running a standalone cluster manager
Running a single master calling ./sbin/start-master.sh
Running a single worker calling ./sbin/start-slave.sh spark://localhost:7077
Launching a test application in client mode by calling:
./bin/spark-submit \
--master spark://localhost:7077 \
./examples/src/main/python/pi.py
According to the Docs:
The process running the main() function of the application and creating the SparkContext
My takeaway from this is there should be at least one driver program that runs when an application runs. However, I'm not seeing this in the web UI for the master:
Alive Workers: 1
Cores in use: 4 Total, 0 Used
Memory in use: 15.0 GB Total, 0.0 B Used
Applications: 0 Running, 1 Completed
Drivers: 0 Running, 0 Completed
Status: ALIVE
Shouldn't I expect to see 1 driver running or completed? I've included some config details below.
./conf/spark-defaults.conf:
spark.master=spark://localhost:7077
spark.eventLog.enabled=true
spark.eventLog.dir=./tmp/spark-events/
spark.history.fs.logDirectory=.tmp/spark-events/
spark.driver.memory=5g
If you are running an interactive shell, e.g. pyspark (CLI or via an IPython notebook), by default you are running in client mode.
Client mode does not triggers Driver program but cluster mode does.
NOTE : AFAIK you cannot run pyspark or any other interactive shell in cluster mode.
So try running the application in cluster mode using --deploy-mode cluster
./bin/spark-submit \
--master spark://localhost:7077 \
--deploy-mode cluster
./examples/src/main/python/pi.py
We have all 6 machine, hdfs and yarn service on all node, 1 master and 6 slaves.
And we install Spark on 3 machine, 1 master, 3 workers ( 1 node master + worker) .
We know when --master spark://[host]:[port], the job will run only 3 node use standalone mode.
And when use spark-submit --master yarn submit a jar, it's would use all 6 server cpu and memory or just use 3 spark worker node machine ?
And if can run all 6 node, How left 3 server can know it's the Spark job?
Spark: 2.3.1
Hadoop: 2.7.3
In yarn mode, spark-submit send resource allocation resource to yarn and the containers will be launched on different node managers based on resource availability.
Google dataproc one node cluster, VCores Total = 8. I've tried from user spark:
/usr/lib/spark/sbin/start-thriftserver.sh --num-executors 2 --executor-cores 4
tried to change /usr/lib/spark/conf/spark-defaults.conf
tried to execute
export SPARK_WORKER_INSTANCES=6
export SPARK_WORKER_CORES=8
before start-thriftserver.sh
No success. In yarn UI I can see that thrift app use only 2 cores and 6 cores available.
UPDATE1:
environment tab at spark ui:
spark.submit.deployMode client
spark.master yarn
spark.dynamicAllocation.minExecutors 6
spark.dynamicAllocation.maxExecutors 10000
spark.executor.cores 4
spark.executor.instances 1
It depends on what yarn mode is that app in.
Can be yarn client - 1 core for Application Master(the app will be running on the machine where you ran command start-thriftserver.sh).
In case of yarn cluster - Driver will be inside AM container, so you can tweak cores with spark.driver.cores. Other cores will be used by executors (1 executor = 1 core by default)
Beware that --num-executors 2 --executor-cores 4 wouldn't work as you have 8 cores max and +1 will be needed for AM container (total of 9)
You can check cores usage from Spark UI - http://sparkhistoryserverip:18080/history/application_1534847473069_0001/executors/
Options below are only for Spark standalone mode:
export SPARK_WORKER_INSTANCES=6
export SPARK_WORKER_CORES=8
Please review all configs here - Spark Configuration (latest)
In your case you can edit spark-defaults.conf and add:
spark.executor.cores 3
spark.executor.instances 2
Or use local[8] mode as you have only one node anyway.
If you want YARN shows you proper number of cores allocated to executors change value in capacity-scheduler.xml for:
yarn.scheduler.capacity.resource-calculator
from:
org.apache.hadoop.yarn.util.resource.DefaultResourceCalculator
to:
org.apache.hadoop.yarn.util.resource.DominantResourceCalculator
Otherwise it doesn't matter how many cores you ask for your executors, YARN will show you only one core per container.
Actually this config changes resource allocation behavior. More details: https://hortonworks.com/blog/managing-cpu-resources-in-your-hadoop-yarn-clusters/
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 EMR cluster with following configuration:
Number of cores, RAM(GB), yarn.nodemanager.resource.memory-mb(MB)
Master: 4 15 11532
core(slave1): 16 30 23040
core(slave2): 16 30 23040
core(slave3): 16 30 23040
core(slave4): 16 30 23040
I am starting a spark application with one job that gets divided into 2 stages using --master yarn-client with following configurations:
--num-executors 12 --executor-cores 5 --executor-memory 7G ---->(1)
--num-executors 12 --executor-cores 5 --executor-memory 6G ---->(2)
I have not modified any other parameter so spark.storage.* and spark.shuffle.* fractions are default.
calculations that I performed to find above configuration (master node is not performing any computation i.e verified using Ganglia except serving as a driver) are:
1. allocated 15 cores to yarn per node and started 3 executors/node
which implies 4(# of slave nodes)*3 = 12 executors.
2. 15 cores/3 executors = 5 cores per executor
3. 23040*(1-0.07) ~ 21G. Dividing this among three executors i.e
21/3=7G
In the (1) configuration, it is not launching 12 executors whereas in the (2) case it is able to do so. Though the memory is available per executor to do so, why it is not able to launch 12 executors in the (1) case?
What is your memory utilization like? Have you checked yarn-site.xml on the node managers hosts to see if all that memory and cpu is being exposed via node manager configuration?
You can do yarn node -list for a list of nodes and then yarn node -status (I beieve) to see a listing of what this node exposes to yarn as far as resources.
consult yarn log -applicationId to see a detailed log of your application interaction including captures of output.
Finally look at yarn logs on the resource manager host to see if there are any issues there