Apologies in advance as I am new to spark. I have created a spark cluster in standalone mode with 4 workers, and after successfully being able to configure worker properties, I wanted to know how to configure the master properties.
I am writing an application and connecting it to the cluster using SparkSession.builder (I do not want to submit it using spark-submit.)
I know that that the workers can be configured in the conf/spark-env.sh file and has parameters which can be set such as 'SPARK_WORKER_MEMORY' and 'SPARK_WORKER_CORES'
My question is: How do I configure the properties for the master? Because there is no 'SPARK_MASTER_CORES' or 'SPARK_MASTER_MEMORY' in this file.
I thought about setting this in the spark-defaults.conf file, however it seems that this is only used for spark-submit.
I thought about setting it in the application using SparkConf().set("spark.driver.cores", "XX") however this only specifies the number of cores for this application to use.
Any help would be greatly appreciated.
Thanks.
Three ways of setting the configurations of Spark Master node (Driver) and spark worker nodes. I will show examples of setting the memory of the master node. Other settings can be found here
1- Programatically through SpackConf class.
Example:
new SparkConf().set("spark.driver.memory","8g")
2- Using Spark-Submit: make sure not to set the same configuraiton in your code (Programatically like 1) and while doing spark submit. if you already configured settings programatically, every job configuration mentioned in spark-submit that overlap with (1) will be ignored.
example :
spark-submit --driver-memory 8g
3- through the Spark-defaults.conf:
In case none of the above is set this settings will be the defaults.
example :
spark.driver.memory 8g
Related
I just created a Google Cloud cluster (1 master and 6 workers) and by default Spark is configured.
I have a pure python code that uses NLTK to build the dependency tree for each line from a text file. When I run this code on the master spark-submit run.py I get the same execution time when I run it using my machine.
How to make sure that the master is using the workers in order to reduce the execution time ?
You can check the spark UI. If its running on top of yarn, please open the yarn UI and click on your application id which will open the spark UI. Check under the executors tab it will have the node ip address also.
could you please share your spark submit config.
Your command 'spark-submit run.py' doesn't seem to send your job to YARN. To do such thing, you need to add the --master parameter. For example, a valid command to execute a job in YARN is:
./bin/spark-submit --master yarn python/pi.py 1000
If you execute your job from the master, this execution will be straightforward. Anyway, check this link for another parameter that spark-submit accept.
For a Dataproc cluster (Hadoop Google cluster) you have two options to check the job history including the ones that are running:
By command line from the master: yarn application -list, this option sometimes needs additional configuration. If you have troubles, this link will be useful.
By UI. Dataproc enables you to access the Spark Web UI, it improves monitoring tasks. Check this link to learn how to access the Spark UI and other Dataproc UIs. In summary, you have to create a tunnel and configure your browser to use socks proxy.
Hope the information above help you.
My application connects to a spark cluster (just starting a master and some slaves, not used yarn), now it throws StackOverwflowErr for ObjectInputStream, so i should increase -Xss of driver and executor.
The documents said you can't change driver properties in your application code (using sparkConf).
changing spark-defaults.conf not worked (i guesss it only works with spark-submit)
So how should i change driver related configs?
I just used:
conf.set("spark.executor.extraJavaOptions","-Xss4m") in my java code and now spark executor run with xss=4m. I was biased with this part of spark documentation:
Note: In client mode, this config must not be set through the
SparkConf directly in your application, because the driver JVM has
already started at that point. Instead, please set this through the
--driver-java-options command line option or in your default properties file.
I am currently running Azure HD insight Spark cluster and by default if an application fails, it retries 5 times. I would like to change this and set it to 2. Do you know how to do this?
You can pass this in as a configuration point to spark-submit.
spark-submit ... --conf spark.yarn.maxAppAttempts=2
You can also set an override at the cluster level in Ambari. You can access Custom spark-defaults via spark->configs and set overrides via the ambari UI.
I am running a spark streaming job on cluster mode , i have created a pool with memory of 200GB(CDH). I wanted to run my spark streaming job on that pool, i tried setting
sc.setLocalProperty("spark.scheduler.pool", "pool")
in code but its not working and i also tried the
spark.scheduler.pool seems not working in spark streaming, whenever i run the job it goes in the default pool. What would be the possible issue? Is there any configuration i can add while submitting the job?
In yarn we can add the
--conf spark.yarn.queue="que_name"
to the spark-submit command . Then it will use that particular queue and its resources only.
I ran into this same issue with Spark 2.4. In my case, the problem was resolved by removing the default "spark.scheduler.pool" option in my Spark config.
I traced the issue to a bug in Spark - https://issues.apache.org/jira/browse/SPARK-26988. The problem is that if you set the config property "spark.scheduler.pool" in the base configuration, you can't then override it using setLocalProperty. Removing it from the base configuration made it work correctly. See the bug description for more detail.
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
When I provide the path to application jar which is in my local machine, would spark-submit automatically upload it to my Cluster?
I'm using
bin/spark-submit
--class com.my.application.XApp
--master yarn-cluster --executor-memory 100m
--num-executors 50 /Users/nish1013/proj1/target/x-service-1.0.0-201512141101-assembly.jar
1000
and getting error
Diagnostics: java.io.FileNotFoundException: File file:/Users/nish1013/proj1/target/x-service-1.0.0-201512141101- does not exist
In Documentation ,http://spark.apache.org/docs/latest/submitting-applications.html#launching-applications-with-spark-submit
Advanced Dependency Management When using spark-submit, the
application jar along with any jars included with the --jars option
will be automatically transferred to the cluster.
But seems like it does not !
I see you are quoting the spark-submit page from Spark Docs but I would spend a lot more time on the Running Spark on YARN page. Bottom-line, look at:
There are two deploy modes that can be used to launch Spark
applications on YARN. In yarn-cluster mode, the Spark driver runs
inside an application master process which is managed by YARN on the
cluster, and the client can go away after initiating the application.
In yarn-client mode, the driver runs in the client process, and the
application master is only used for requesting resources from YARN.
Further you note, "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"
So I agree with you that you are right to run --master yarn-cluster instead of --master yarn-client
(and one comment notes what might just be a syntax error where you dropped "assembly.jar" but I think this will apply as well...)
Some of the basic assumptions about non-YARN implementations change a lot when YARN is introduced, mostly related to Classpaths and the need to push jars to the workers.
From an email on the Apache Spark User list:
YARN cluster mode. Spark submit does upload your jars to the cluster.
In particular, it puts the jars in HDFS so your driver can just read
from there. As in other deployments, the executors pull the jars from
the driver.
So finally, from the Apache Spark YARN doc:
Ensure that HADOOP_CONF_DIR or YARN_CONF_DIR points to the directory
which contains the (client side) configuration files for the Hadoop
cluster. These configs are used to write to HDFS and connect to the
YARN ResourceManager.
NOTE: I only see you adding a single JAR, if there's a need to add other JARs there's a special note about doing that with YARN:
In yarn-cluster mode, the driver runs on a different machine than the
client, so SparkContext.addJar won’t work out of the box with files
that are local to the client. To make files on the client available to
SparkContext.addJar, include them with the --jars option in the launch
command.
That page in the link has some examples.
And of course you downloaded or built the YARN-specific version of Spark.
Background, in a standalone cluster deployment using spark-submit and the option --deploy-mode cluster, yes you do need to make sure every worker node has access to all the dependencies, Spark will not push them to the cluster. This is because in "standalone cluster" mode with Spark as the job manager, you don't know which node the driver will run on! But that doesn't apply to your case.
But if I could, depending on the size of the jars you are uploading, I would still explicitly put the jars on each node, or "globally available" via HDFS, for another reason from the docs:
From Advanced Dependency Management, it seems to present the best of both worlds, but also a great reason for manually pushing your jars out to all nodes:
local: - a URI starting with local:/ is expected to exist as a local
file on each worker node. This means that no network IO will be
incurred, and works well for large files/JARs that are pushed to each
worker, or shared via NFS, GlusterFS, etc.
But I assume that local:/... would change to hdfs:/ ... not sure on that one.
Yes and no. It depends on what you mean. Spark deploys the .jar to the nodes in the cluster. However, it won't upload your .jar file from your local machine to the cluster.
You can find more info in the Submitting Applications page. As you can see, in the arguments you pass to spark-submit, there is one that needs to be globally visible: the application-jar.
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.
As far as I understand, what you want is to use yarn-client, not yarn-cluster. This will run the driver in the client (e.g., the machine which you are trying to call spark-submit on, for example your laptop), without the need of copying the .jar file on the cluster. More about this here.
Try adding --jars option before your /path/to/jar/file
spark-submit --jars /tmp/test.jar