I am able to do a spark-submit to my cloudera cluster. the job dies after a few minutes with exceptions complaining it can not find various classes. These are classes that are in the spark dependency path. I keep adding the jars one at a time using command line args --jars, the yarn log keeps dumping out the next jar it can't find.
What setting allows the spark/yarn job to find all the dependent jars?
I already set the "spark.home" attribute to the correct path - /opt/cloudera/parcels/CDH/lib/spark
I found it!
remove
.set("spark.driver.host", "driver computer ip address")
from your driver code.
Related
I am trying to run the spark-submit command on my Hadoop cluster Here is a summary of my Hadoop Cluster:
The cluster is built using 5 VirtualBox VM's connected on an internal network
There is 1 namenode and 4 datanodes created.
All the VM's were built from the Bitnami Hadoop Stack VirtualBox image
I am trying to run one of the spark examples using the following spark-submit command
spark-submit --class org.apache.spark.examples.SparkPi $SPARK_HOME/examples/jars/spark-examples_2.12-3.0.3.jar 10
I get the following error:
[2022-07-25 13:32:39.253]Container exited with a non-zero exit code 1. Error file: prelaunch.err.
Last 4096 bytes of prelaunch.err :
Last 4096 bytes of stderr :
Error: Could not find or load main class org.apache.spark.deploy.yarn.ExecutorLauncher
I get the same error when trying to run a script with PySpark.
I have tried/verified the following:
environment variables: HADOOP_HOME, SPARK_HOME and HADOOP_CONF_DIR have been set in my .bashrc file
SPARK_DIST_CLASSPATH and HADOOP_CONF_DIR have been defined in spark-env.sh
Added spark.master yarn, spark.yarn.stagingDir hdfs://hadoop-namenode:8020/user/bitnami/sparkStaging and spark.yarn.jars hdfs://hadoop-namenode:8020/user/bitnami/spark/jars/ in spark-defaults.conf
I have uploaded the jars into hdfs (i.e. hadoop fs -put $SPARK_HOME/jars/* hdfs://hadoop-namenode:8020/user/bitnami/spark/jars/ )
The logs accessible via the web interface (i.e. http://hadoop-namenode:8042 ) do not provide any further details about the error.
This section of the Spark documentation seems relevant to the error since the YARN libraries should be included, by default, but only if you've installed the appropriate Spark version
For with-hadoop Spark distribution, since it contains a built-in Hadoop runtime already, by default, when a job is submitted to Hadoop Yarn cluster, to prevent jar conflict, it will not populate Yarn’s classpath into Spark. To override this behavior, you can set spark.yarn.populateHadoopClasspath=true. For no-hadoop Spark distribution, Spark will populate Yarn’s classpath by default in order to get Hadoop runtime. For with-hadoop Spark distribution, if your application depends on certain library that is only available in the cluster, you can try to populate the Yarn classpath by setting the property mentioned above. If you run into jar conflict issue by doing so, you will need to turn it off and include this library in your application jar.
https://spark.apache.org/docs/latest/running-on-yarn.html#preparations
Otherwise, yarn.application.classpath in yarn-site.xml refers to local filesystem paths in each of ResourceManager servers where JARs are available for all YARN applications (spark.yarn.jars or extra packages should get layered onto this)
Another problem could be file permissions. You probably shouldn't put Spark jars into an HDFS user folder if they're meant to be used by all users. Typically, I'd put it under hdfs:///apps/spark/<version>, then give that 744 HDFS permissions
In the Spark / YARN UI, it should show the complete classpath of the application for further debugging
I figured out why I was getting this error. It turns out that I made an error while specifying spark.yarn.jars in spark-defaults.conf
The value of this property must be
hdfs://hadoop-namenode:8020/user/bitnami/spark/jars/*
instead of
hdfs://hadoop-namenode:8020/user/bitnami/spark/jars/
i.e. Basically, we need to specify the jar files as the value to this property and not the folder containing the jar files.
While analyzing the yarn launch_container.sh logs for a spark job, I got confused by some part of log.
I will point out those asks step by step here
When you will submit a spark job with spark-submit having --pyfiles and --files on cluster mode on YARN:
The config files passed in --files , executable python files passed in --pyfiles are getting uploaded into .sparkStaging directory created under user hadoop home directory.
Along with these files pyspark.zip and py4j-version_number.zip from $SPARK_HOME/python/lib is also getting copied
into .sparkStaging directory created under user hadoop home directory
After this launch_container.sh is getting triggered by yarn and this will export all env variables required.
If we have exported anything explicitly such as PYSPARK_PYTHON in .bash_profile or at the time of building the spark-submit job in a shell script or in spark_env.sh , the default value will be replaced by the value which we
are providing
This PYSPARK_PYTHON is a path in my edge node.
Then how a container launched in another node will be able to use this python version ?
The default python version in data nodes of my cluster is 2.7.5.
So without setting this pyspark_python , containers are using 2.7.5.
But when I will set pyspark_python to 3.5.x , they are using what I have given.
It is defining PWD='/data/complete-path'
Where this PWD directory resides ?
This directory is getting cleaned up after job completion.
I have even tried to run the job in one session of putty
and kept the /data folder opened in another session of putty to see
if any directories are getting created on run time. but couldn't find any?
It is also setting the PYTHONPATH to $PWD/pyspark.zip:$PWD/py4j-version.zip
When ever I am doing a python specific operation
in spark code , its using PYSPARK_PYTHON. So for what purpose this PYTHONPATH is being used?
3.After this yarn is creating softlinks using ln -sf for all the files in step 1
soft links are created for for pyspark.zip , py4j-<version>.zip,
all python files mentioned in step 1.
Now these links are again pointing to '/data/different_directories'
directory (which I am not sure where they are present).
I know soft links can be used for accessing remote nodes ,
but here why the soft links are created ?
Last but not the least , whether this launch_container.sh will run for each container launch ?
Then how a container launched in another node will be able to use this python version ?
First of all, when we submit a Spark application, there are several ways to set the configurations for the Spark application.
Such as:
Setting spark-defaults.conf
Setting environment variables
Setting spark-submit options (spark-submit —help and —conf)
Setting a custom properties file (—properties-file)
Setting values in code (exposed in both SparkConf and SparkContext APIs)
Setting Hadoop configurations (HADOOP_CONF_DIR and spark.hadoop.*)
In my environment, the Hadoop configurations are placed in /etc/spark/conf/yarn-conf/, and the spark-defaults.conf and spark-env.sh is in /etc/spark/conf/.
As the order of precedence for configurations, this is the order that Spark will use:
Properties set on SparkConf or SparkContext in code
Arguments passed to spark-submit, spark-shell, or pyspark at run time
Properties set in /etc/spark/conf/spark-defaults.conf, a specified properties file
Environment variables exported or set in scripts
So broadly speaking:
For properties that apply to all jobs, use spark-defaults.conf,
for properties that are constant and specific to a single or a few applications use SparkConf or --properties-file,
for properties that change between runs use command line arguments.
Now, regarding the question:
In Cluster mode of Spark, the Spark driver is running in container in YARN, the Spark executors are running in container in YARN.
In Client mode of Spark, the Spark driver is running outside of the Hadoop cluster(out of YARN), and the executors are always in YARN.
So for your question, it is mostly relative with YARN.
When an application is submitted to YARN, first there will be an ApplicationMaster container, which nigotiates with NodeManager, and is responsible to control the application containers(in your case, they are Spark executors).
NodeManager will then create a local temporary directory for each of the Spark executors, to prepare to launch the containers(that's why the launch_container.sh has such a name).
We can find the location of the local temporary directory is set by NodeManager's ${yarn.nodemanager.local-dirs} defined in yarn-site.xml.
And we can set yarn.nodemanager.delete.debug-delay-sec to 10 minutes and review the launch_container.sh script.
In my environment, the ${yarn.nodemanager.local-dirs} is /yarn/nm, so in this directory, I can find the tempory directories of Spark executor containers, they looks like:
/yarn/nm/nm-local-dir/container_1603853670569_0001_01_000001.
And in this directory, I can find the launch_container.sh for this specific container and other stuffs for running this container.
Where this PWD directory resides ?
I think this is a special Environment Variable in Linux OS, so better not to modify it unless you know how it works percisely in your application.
As per above, if you export this PWD environment at the runtime, I think it is passed to Spark as same as any other Environment Variables.
I'm not sure how the PYSPARK_PYTHON Environment Variable is used in Spark's launch scripts chain, but here you can find the instruction in the official documentation, showing how to set Python binary executable while you are using spark-submit:
spark-submit --conf spark.pyspark.python=/<PATH>/<TO>/<FILE>
As for the last question, yes, YARN will create a temp dir for each of the containers, and the launch_container.sh is included in the dir.
I am running spark-submit to run on Kubernetes (Spark 2.3). My problem is that the InitContainer does not download my jar file if it's specified as an s3a:// path but does work if I put my jar on an HTTP server and use http://. The spark driver fails, of course, because it can't find my Class (and the jar file in fact is not in the image).
I have tried two approaches:
specifying the s3a path to jar as the argument to spark-submit and
using --jars to specify the jar file's location on s3a, but both fail in the same way.
edit: also, using local:///home/myuser/app.jar does not work with the same symptoms.
On a failed run (dependency on s3a), I logged into the container and found the directory /var/spark-data/spark-jars/ to be empty. The init-container logs don't indicate any type of error.
Questions:
What is the correct way to specify remote dependencies on S3A?
Is S3A not supported yet? Only http(s)?
Any suggestions on how to further debug the InitContainer to determine why the download doesn't happen?
I want to do some ip to location computation on spark, after exploring the net ,find IPLocator https://github.com/miraclesu/IPLocator,
the IP to location need to use a file which contains the mapping information.
After packaging the jar, I can run it through on using local java, the package just runs with the IPLocator.jar and qqwry.dat in the same directory.
But I want to use this jar using spark , I tryed to use --jars IPLocator.jar qqwry.dat when starting spark-shell, but when launching , the functions still can not read get the file .
the file reading code is like
QQWryFile.class.getClassLoader().getResource("qqwry.dat")
I also tried to package qqwry.dat file into the jar, and It did not work.
You need to use --files and then SparkFiles.get inside of your program
Try to use comma delimitor and check if IPLocator.jar and qqwry.dat are distributed to spark staging folder(.sparkStaging/application_xxx).
--jars IPLocator.jar,qqwry.dat
I have been following this tutorial in order to set up Zeppelin on a Spark cluster (version 1.5.2) in HDInsight, on Linux. Everything worked fine, I have managed to successfully connect to the Zeppelin notebook through the SSH tunnel. However, when I try to run any kind of paragraph, the first time I get the following error:
java.io.IOException: No FileSystem for scheme: wasb
After getting this error, if I try to rerun the paragraph, I get another error:
java.net.SocketException: Broken pipe
at java.net.SocketOutputStream.socketWrite0(Native Method)
These errors occur regardless of the code I enter, even if there is no reference to the hdfs. What I'm saying is that I get the "No FileSystem" error even for a trivial scala expression, such as parallelize.
Is there a missing configuration step?
I am download the tar ball that the script that you pointed to as I type. But want I am guessing is that your zeppelin install and spark install are not complete to work with wasb. In order to get spark to work with wasb you need to add some jars to the Class path. To do this you need to add something like this to your spark-defaults.conf (the paths might be different in HDInsights, this is from HDP on IaaS)
spark.driver.extraClassPath /usr/hdp/2.3.0.0-2557/hadoop/lib/azure-storage-2.2.0.jar:/usr/hdp/2.3.0.0-2557/hadoop/lib/microsoft-windowsazure-storage-sdk-0.6.0.jar:/usr/hdp/2.3.0.0-2557/hadoop/hadoop-azure-2.7.1.2.3.0.0-2557.jar
spark.executor.extraClassPath /usr/hdp/2.3.0.0-2557/hadoop/lib/azure-storage-2.2.0.jar:/usr/hdp/2.3.0.0-2557/hadoop/lib/microsoft-windowsazure-storage-sdk-0.6.0.jar:/usr/hdp/2.3.0.0-2557/hadoop/hadoop-azure-2.7.1.2.3.0.0-2557.jar
Once you have spark working with wasb, or next step is make those sames jar in zeppelin class path. A good way to test your setup is make a notebook that prints your env vars and class path.
sys.env.foreach(println(_))
val cl = ClassLoader.getSystemClassLoader
cl.asInstanceOf[java.net.URLClassLoader].getURLs.foreach(println)
Also looking at the install script, it trying to pull the zeppelin jar from wasb, you might want to change that config to somewhere else while you try some of these changes out. (zeppelin.sh)
export SPARK_YARN_JAR=wasb:///apps/zeppelin/zeppelin-spark-0.5.5-SNAPSHOT.jar
I hope this helps, if you are still have problems I have some other ideas, but would start with these first.