I created an EMR 4.0 instance in AWS with all available applications, including Spark. I did it manually, through AWS Console. I started the cluster and SSHed to the master node when it was up. There I ran pyspark. I am getting the following error when pyspark tries to create SparkContext:
2015-09-03 19:36:04,195 ERROR Thread-3 spark.SparkContext
(Logging.scala:logError(96)) - -ec2-user, access=WRITE,
inode="/user":hdfs:hadoop:drwxr-xr-x at
org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.checkFsPermission(FSPermissionChecker.java:271)
I haven't added any custom applications, nor bootstrapping and expected everything to work without errors. Not sure what's going on. Any suggestions will be greatly appreciated.
Login as the user "hadoop" (http://docs.aws.amazon.com/ElasticMapReduce/latest/ManagementGuide/emr-connect-master-node-ssh.html). It has all the proper environment and related settings for working as expected. The error you are receiving is due to logging in as "ec2-user".
I've been working with Spark on EMR this week, and found a few weird things relating to user permissions and relative paths.
It seems that running Spark from a directory which you don't 'own', as a user, is problematic. In some situations Spark (or some of the underlying Java pieces) want to create files or folders, and they think that pwd - the current directory - is the best place to do that.
Try going to the home directory
cd ~
then running pyspark.
Related
I'm currently trying to add a process in EMR 6.1.0 that will use Spark to store aggregated data in mysql.
However, when I actually run Spark, I get the following error.
Exception in thread "main" java.lang.RuntimeException: Failed to load class of driverClassName com.mysql.jdbc.
This error did not occur in EMR 6.0.0.
In the process of updating from EMR 6.0.0 to 6.1.0, I changed the Spark version from 2.4.4 to 3.0.0.
The code itself has not changed significantly, and we know that it is not a network problem.
I've spent a lot of time looking through the AWS documentation and can't seem to find any hints.
Can anyone help me?
Place the MySQL connector jar under $SPARK_HOME/jars folder or pass the the MySQL connector jar path in spark-shell/spark-submit command using --jars flag.
Spark 3.x depends on HikariCP.
https://github.com/apache/spark/blob/v3.0.0/dev/deps/spark-deps-hadoop-3.2-hive-2.3#L1
Preloaded HikariCP can't load your application classes due to ClassLoader.
https://github.com/brettwooldridge/HikariCP/blob/HikariCP-2.5.1/src/main/java/com/zaxxer/hikari/HikariConfig.java#L318
this.getClass().getClassLoader().loadClass(driverClassName)
You should add shade settings if use sbt-assemlby plugin.
assembly / assemblyShadeRules := {
Seq("com.zaxxer.hikari").map { packageName =>
ShadeRule.rename(s"${packageName}.**" -> s"my_app_shade_package.${packageName}.#1").inAll
}
}
I have created a basic EMR cluster in AWS, and I'm trying to use the Jupyter Notebooks provided through the AWS Console. Launching the notebooks seems to work fine, and I'm also able to run basic python code in notebooks started with the pyspark kernel. Two variables are set up in the notebook: spark is a SparkSession instance, and sc is a SparkContext instance. Displaying sc yields <SparkContext master=yarn appName=livy-session-0> (the output can of course vary slightly depending on the session).
The problem arises once I perform operations that actually hit the spark machinery. For example:
sc.parallelize(list(range(10))).map(lambda x: x**2).collect()
I am no spark expert, but I believe this code should distribute the integers from 0 to 9 across the cluster, square them, and return the results in a list. Instead, I get a lengthy stack trace, mostly from the JVM, but also some python components. I believe the central part of the stack trace is the following:
py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 4.0 failed 4 times, most recent failure: Lost task 0.3 in stage 4.0 (TID 116, ip-XXXXXXXXXXXXX.eu-west-1.compute.internal, executor 17): java.lang.RuntimeException: Failed to run command: /usr/bin/virtualenv -p python3 --system-site-packages virtualenv_application_1586243436143_0002_0
The full stack trace is here.
A bit of digging in the AWS portal led me to log output from the nodes. stdout from one of the nodes includes the following:
The path python3 (from --python=python3) does not exist
I tried running the /usr/bin/virtualenv command on the master node manually (after logging in through), and that worked fine, but the error is of course still present after I did that.
While this error occurs most of the time, I was able to get this working in one session, where I could run several operations against the spark cluster as I was expecting.
Technical information on the cluster setup:
emr-6.0.0
Applications installed are "Ganglia 3.7.2, Spark 2.4.4, Zeppelin 0.9.0, Livy 0.6.0, JupyterHub 1.0.0, Hive 3.1.2". Hadoop is also included.
3 nodes (one of them as master), all r5a.2xlarge.
Any ideas what I'm doing wrong? Note that I am completely new to EMR and Spark.
Edit: Added the stdout log and information about running the virtualenv command manually on the master node through ssh.
I have switched to using emr-5.29.0, which seems to resolve the problem. Perhaps this is an issue with emr-6.0.0? In any case, I have a functional workaround.
The issue for me was that the virtualenv was being made on the executors with a python path that didn't exist. Pointing the executors to the right one did the job for me:
"spark.pyspark.python": "/usr/bin/python3.7"
Here is how I reconfiged the spark app at the beginning of the notebook:
{"conf":{"spark.pyspark.python": "/usr/bin/python3.7",
"spark.pyspark.virtualenv.enabled": "true",
"spark.pyspark.virtualenv.type": "native",
"spark.pyspark.virtualenv.bin.path":"/usr/bin/virtualenv"}
}
I am attempting to run a spark application on aws emr in client mode. I have setup a bootstrap action to import needed files and the jar from s3, and I have a step to run a single spark job.
However when the step executes, the jar I have imported isn't found. Here is the stderr output:
19/12/01 13:42:05 WARN DependencyUtils: Local jar /mnt/var/lib/hadoop/steps/s-2HLX7KPZCA07B/~/myApplicationDirectory does not exist, skipping.
I am able to successfully import the jar and other needed files for the application from my s3 bucket to the master instance, I simply import them to home/ec2-user/myApplicationDirectory/myJar.jar via a bootstrap action.
However I don't understand why the step is looking for the jar at mnt/var/lib/hadoop/...etc.
here are the relevant parts of the cli configuration:
--steps '[{"Args":["spark-submit",
"--deploy-mode","client",
"--num-executors","1",
“--driver-java-options","-Xss4M",
"--conf","spark.driver.maxResultSize=20g",
"--class”,”myApplicationClass”,
“~/myApplicationDirectory”,
“myJar.jar",
…
application specific arguments and paths to folders here
…],
”Type":"CUSTOM_JAR",
thanks for any help,
It looks like it doesn't understand the ~ as referring to the home directory. Try changing "~/myApplicationDirectory" to "/home/ec2-user/myApplicationDirectory".
A little warning: in the sample in your question, straight quotation marks " are mixed with "smart" ones “. Make sure the "smart" quotation marks don't end up in your configuration file, or you will get very confusing error messages.
I am facing issues with s3-dist-cp command in emr-5.0.0 version. In my application, I need to push some files from hdfs to S3. I am using s3-dist-cp command to achieve this. It was working fine in emr-4.2.0. But its not working in emr-5.0.0. If I run the command manually it works fine. But it fails in my application. I didn't make any change in my application to run it on emr-5.
Do I need to make any change if I need to use emr-5? Has there been any change in way we use s3-dist-cp command in emr-5?
I am using following command:
s3-dist-cp --src /user/hive/warehouse/abc.text --dest s3n://bucket/abc.text
s3-dist-cp is only available on the master node(s3-dist-cp.jar).
The following is the location of the application.
/usr/share/aws/emr/s3-dist-cp/
The s3-dist-cp.jar is not available in the slave nodes.
You can login into slave machine and verify it.
So the reason your application failure might be, In new emr you might be using some workflow management tool which deploy the application on slaves and start from there. As s3 s3-dist-cp is not available and it fails.
Work Around
First Option
bundle the jar and use following commands
hadoop jar s3-dist-cp.jar --src location --dest location
Second
Boot Strap the s3-dist-cp.jars on the cluster
You can even run it as java program
First thing, s3n:// is now deprecated, start using s3:// for S3 paths.
Secondly, if you're merely copying a file into S3 from a local file on your cluster, you can use aws s3 cp:
aws s3 cp /user/hive/warehouse/abc.text s3://bucket/abc.text
The syntax that you have used for s3-dist-cp is incorrect. Please try again with the command below.
s3-dist-cp --src hdfs:///user/hive/warehouse/abc.text --dest s3n://bucket/abc.text
Let me know if this solves your proble.
I have a cluster up and running. I am trying to add a step to run my code. The code itself works fine on a single instance. Only thing is I can't get it to work off S3.
aws emr add-steps --cluster-id j-XXXXX --steps Type=spark,Name=SomeSparkApp,Args=[--deploy-mode,cluster,--executor-memory,0.5g,s3://<mybucketname>/mypythonfile.py]
This is exactly what examples show I should do. What am I doing wrong?
Error I get:
Exception in thread "main" java.lang.IllegalArgumentException: Unknown/unsupported param List(--executor-memory, 0.5g, --executor-cores, 2, --primary-py-file, s3://<mybucketname>/mypythonfile.py, --class, org.apache.spark.deploy.PythonRunner)
Usage: org.apache.spark.deploy.yarn.Client [options]
Options:
--jar JAR_PATH Path to your application's JAR file (required in yarn-cluster
mode)
.
.
.
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Command exiting with ret '1'
When I specify as this instead:
aws emr add-steps --cluster-id j-XXXXX --steps Type=spark,Name= SomeSparkApp,Args=[--executor-memory,0.5g,s3://<mybucketname>/mypythonfile.py]
I get this error instead:
Error: Only local python files are supported: Parsed arguments:
master yarn-client
deployMode client
executorMemory 0.5g
executorCores 2
EDIT: IT gets further along when I manually create the python file after SSH'ing into the cluster, and specifying as follows:
aws emr add-steps --cluster-id 'j-XXXXX' --steps Type=spark,Name= SomeSparkApp,Args=[--executor-memory,1g,/home/hadoop/mypythonfile.py]
But, not doing the job.
Any help appreciated. This is really frustrating as a well documented method on AWS's own blog here https://blogs.aws.amazon.com/bigdata/post/Tx578UTQUV7LRP/Submitting-User-Applications-with-spark-submit does not work.
I will ask, just in case, you used your correct buckets and cluster ID-s?
But anyways, I had similar problems, like I could not use --deploy-mode,cluster when reading from S3.
When I used --deploy-mode,client,--master,local[4] in the arguments, then I think it worked. But I think I still needed something different, can't remember exactly, but I resorted to a solution like this:
Firstly, I use a bootstrap action where a shell script runs the command:
aws s3 cp s3://<mybucket>/wordcount.py wordcount.py
and then I add a step to the cluster creation through the SDK in my Go application, but I can recollect this info and give you the CLI command like this:
aws emr add-steps --cluster-id j-XXXXX --steps Type=CUSTOM_JAR,Name="Spark Program",Jar="command-runner.jar",ActionOnFailure=CONTINUE,Args=["spark-submit",--master,local[4],/home/hadoop/wordcount.py,s3://<mybucket>/<inputfile.txt> s3://<mybucket>/<outputFolder>/]
I searched for days and finally discovered this thread which states
PySpark currently only supports local
files. This does not mean it only runs in local mode, however; you can
still run PySpark on any cluster manager (though only in client mode). All
this means is that your python files must be on your local file system.
Until this is supported, the straightforward workaround then is to just
copy the files to your local machine.