I have a pyspark code stored both on the master node of an AWS EMR cluster and in an s3 bucket that fetches over 140M rows from a MySQL database and stores the sum of a column back in the log files on s3.
When I spark-submit the pyspark code on the master node, the job gets completed successfully and the output is stored in the log files on the S3 bucket.
However, when I spark-submit the pyspark code on the S3 bucket using these- (using the below commands on the terminal after SSH-ing to the master node)
spark-submit --master yarn --deploy-mode cluster --py-files s3://bucket_name/my_script.py
This returns a Error: Missing application resource. error.
spark_submit s3://bucket_name/my_script.py
This shows :
20/07/02 11:26:23 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Exception in thread "main" java.lang.RuntimeException: java.lang.ClassNotFoundException: Class com.amazon.ws.emr.hadoop.fs.EmrFileSystem not found
at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2369)
at org.apache.hadoop.fs.FileSystem.getFileSystemClass(FileSystem.java:2840)
at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2857)
at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:99)
at org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:2896)
at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:2878)
at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:392)
at org.apache.spark.util.Utils$.getHadoopFileSystem(Utils.scala:1911)
at org.apache.spark.util.Utils$.doFetchFile(Utils.scala:766)
at org.apache.spark.deploy.DependencyUtils$.downloadFile(DependencyUtils.scala:137)
at org.apache.spark.deploy.SparkSubmit$$anonfun$prepareSubmitEnvironment$7.apply(SparkSubmit.scala:356)
at org.apache.spark.deploy.SparkSubmit$$anonfun$prepareSubmitEnvironment$7.apply(SparkSubmit.scala:356)
at scala.Option.map(Option.scala:146)
at org.apache.spark.deploy.SparkSubmit.prepareSubmitEnvironment(SparkSubmit.scala:355)
at org.apache.spark.deploy.SparkSubmit.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:782)
at org.apache.spark.deploy.SparkSubmit.doRunMain$1(SparkSubmit.scala:161)
at org.apache.spark.deploy.SparkSubmit.submit(SparkSubmit.scala:184)
at org.apache.spark.deploy.SparkSubmit.doSubmit(SparkSubmit.scala:86)
at org.apache.spark.deploy.SparkSubmit$$anon$2.doSubmit(SparkSubmit.scala:928)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:937)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.lang.ClassNotFoundException: Class com.amazon.ws.emr.hadoop.fs.EmrFileSystem not found
at org.apache.hadoop.conf.Configuration.getClassByName(Configuration.java:2273)
at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2367)
... 20 more
I read about having to add a Spark Step on the AWS EMR cluster to submit a pyspark code stored on the S3.
Am I correct in saying that I would need to create a step in order to submit my pyspark job stored on the S3?
In the 'Add Step' window that pops up on the AWS Console, in the 'Application location' field, it says that I'll have to type in the location to the JAR file. What JAR file are they referring to? Does my pyspark script have to be packaged into a JAR file and how do I do that or do I mention the path to my pyspark script?
In the 'Add Step' window that pops up on the AWS Console, in the Spark-submit options, how do I know what to write for the --class parameter? Can I leave this field empty? If no, why not?
I have gone through the AWS EMR documentation. I have so many questions because I dived nose-down into the problem and only researched when an error popped up.
Your spark submit should be this.
spark-submit --master yarn --deploy-mode cluster s3://bucket_name/my_script.py
--py-files is used if you want to pass the python dependency modules, not the application code.
When you are adding step in EMR to run spark job, jar location is your python file path. i.e. s3://bucket_name/my_script.py
No its not mandatory to use STEP to submit spark job.
You can also use spark-submit
To submit a pyspark script using STEP please refer aws doc and stackoverflow
For problem 1:
By default spark will use python2.
You need to add 2 config
Go to $SPARK_HOME/conf/spark-env.sh and add
export PYSPARK_PYTHON=/usr/bin/python3
export PYSPARK_DRIVER_PYTHON=/usr/bin/python3
Note: If you have any custom bundle add that using --py-files
For problem 2:
A hadoop-assembly jar exists on /usr/share/aws/emr/emrfs/lib/. That contains com.amazon.ws.emr.hadoop.fs.EmrFileSystem.
You need to add this to your classpath.
A better option to me is to create a symbolic link of hadoop-assembly jar to HADOOP_HOME (/usr/lib/hadoop) in your bootstrap action.
Related
I want to connect to a document db which has TLS enabled .I could do that from a lambda function with the rds-combined-ca-bundle.pem copied with lambda code .I could not do the same with databricks as all the node of cluster should have this file when spark try to connect it always time out.I tried to create the init scripts by following below link
https://learn.microsoft.com/en-us/azure/databricks/kb/python/import-custom-ca-cert
However it does not help either .Let me know if any one has any clue on this kind of use case .
Note:I can connect to TLS disabled document-db from same databricks instance .
If you are experiencing connection time out errors when using an init script to import the rds-combined-ca-bundle.pem file on your Spark cluster, try the following steps:
Make sure that the rds-combined-ca-bundle.pem file is available on the driver node of your Spark cluster. The init script will only be executed on the driver node. You will encounter connection time out errors otherwise.
Use the --conf option when starting the spark-shell or spark-submit command to specify the location of the rds-combined-ca-bundle.pem file on the driver node. To specify the location of the rds-combined-ca-bundle.pem file, run:
spark-shell --conf spark.mongodb.ssl.caFile=path/to/rds-combined-ca-bundle.pem
Check the Spark cluster logs whether the init script is being executed correctly or if its encountering any errors.
I am running Sqoop 1.4.7 on AWS EMR 5.21.1 and am trying to import data from a database. I have successfully been able to do this manually where I create an EMR instance with Sqoop installed via the EMR Console.
Here are the preliminary steps that I performed in order to run sqoop on EMR
Download the JDBC Driver
Move the JDBC driver to the /usr/lib/sqoop/lib directory
I was able to successfully run a sqoop import when I was sshd into an EMR cluster with these commands:
wget -O mssql-jdbc.jar https://repo1.maven.org/maven2/com/microsoft/sqlserver/mssql-jdbc/8.4.0.jre8/mssql-jdbc-8.4.0.jre8.jar
sudo mv mssql-jdbc.jar /usr/lib/sqoop/lib/
When I try to run these commands from an EMR bootstrap script however I get the error:
usr/lib/sqoop/lib/ No such file or directory
After doing some investigation I realized this is because "Bootstrap actions execute before core services, such as Hadoop or Spark, are installed", as found here
So the /usr/lib/sqoop/lib directory doesnt exist when I run my bootstrap steps.
Here are some solutions which work but they feel like work-arounds
Create the /usr/lib/sqoop/lib directory in my bootstrap script and then place the jar in it
Add the jar to this directory as an EMR step. (Turns out this this is the correct approach, look at below accepted answer)
What is the correct way of installing this JDBC driver on EMR?
The 2nd option is the correct way to do it. The documentation explains running bash scripts as an EMR step.
You can also use the jar command-runner.jar and the arguments to be
bash -c "wget -O mssql-jdbc.jar https://repo1.maven.org/maven2/com/microsoft/sqlserver/mssql-jdbc/8.4.0.jre8/mssql-jdbc-8.4.0.jre8.jar;sudo mv mssql-jdbc.jar /usr/lib/sqoop/lib/"
I have a pyspark script to read an xml file(present in S3). I need to add this as a step in aws. I have used the following command
aws emr add-steps — cluster-id <cluster_id> — steps Type=spark,Name=POI,Args=[ — deploy-mode,cluster, — master,yarn, — conf,spark.yarn.submit.waitAppCompletion=true,<s3 location of pyspark script>],ActionOnFailure=CONTINUE
I have downloaded the spark-xml jar to the master node during bootstrap and its present under
/home/hadoop
location. Also in the python script I have included
conf = SparkConf().setAppName('Project').set("spark.jars", "/home/hadoop/spark-xml_2.11-0.4.1.jar").set("spark.driver.extraClassPath", "/home/hadoop/spark-xml_2.11-0.4.1.jar")
But still its showing
py4j.protocol.Py4JJavaError: An error occurred while calling o56.load.
: java.lang.ClassNotFoundException: Failed to find data source: com.databricks.spark.xml. Please find packages at http://spark.apache.org/third-party-projects.html
You have set master as yarn and deploy-mode as cluster. That means your spark driver will be in one of CORE nodes.
Anyway, EMR by default is configured to create Application master on one of the CORE node and application master will have the driver in it.
Please refer this article for more info.
So you have to put your jar in all CORE nodes (Not in MASTER) and refer the file file:///home/hadoop/spark-xml_2.11-0.4.1.jar in this manner.
Or there is better way to put it in HDFS (Lets say under hdfs:///user/hadoop) and refer that hdfs:///user/hadoop/spark-xml_2.11-0.4.1.jar
I'm trying to run my code of machine learning from images using tensorflow in Google CloudML. However, it seems the submitted job can't access to my files in my cloud shell or in GCS. Even though it is working fine in my local machine, I get the following error once I submit my job using the command gcloud from the cloud shell:
ERROR 2017-12-19 13:52:28 +0100 service IOError: [Errno 2] No such file or directory: '/home/user/pores-project-googleML/trainer/train.txt'
This folder can be found for sure in cloud shell, and I can check it when I type:
ls /home/user/pores-project-googleML/trainer/train.txt
I tried putting my file train.txt in GCS and access to it from my code (by specifying the path gs://my_bucket/my_path), but once the job submitted, I got a 'No such file or directory' error with the corresponding path.
To check where the job I submitted using gcloud is running, I added print(os.getcwd()) in the beginning of my python code trainer/task.py, which printed as a result in the logs: /user_dir. I couldn't find this path using the cloud shell, not even in GCS. So my question is how can I know in which machine my job is running? If it's in a certain container somewhere, how can I access from it to my files using the cloud shell and in GCS?
Before I do all of this, I succesfully completed the 'Image Classification using Flowers Dataset' tutorial.
The command I used to submit my job is:
gcloud ml-engine jobs submit training $JOB_NAME --job-dir $JOB_DIR --packages trainer-0.1.tar.gz --module-name $MAIN_TRAINER_MODULE --region us-central1
where:
TRAINER_PACKAGE_PATH=/home/use/pores-project-googleML/trainer
MAIN_TRAINER_MODULE="trainer.task"
JOB_DIR="gs://pores/AlexNet_CloudML/job_dir/"
JOB_NAME="census$(date +"%Y%m%d_%H%M%S")"
Regular Python IO library is not able to access files on GCS. Instead, you need to use GCS python client or gstuil cli to access GCS files.
Note that TensorFlow itself has native support of GCS (i.e., it can read GCS files directly).
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.