I wrote code with Scala to run a Cluster in EMR. Also, I have a Spark application written in Scala. I want to run this Spark application on EMR Cluster. But is it possible for me to do this in the first script (that launch EMR Cluster)? I want to do all of them with the SDK, not through the console or CLI. It has to be a kind of automatization, not a single manual job (or minimize manual job).
Basically;
Launch EMR Cluster -> Run Spark Job on EMR -> Terminate after job finished
How do I do it if possible?
Thanks.
HadoopJarStepConfig sparkStepConf = new HadoopJarStepConfig()
.withJar("command-runner.jar")
.withArgs(params);
final StepConfig sparkStep = new StepConfig()
.withName("Spark Step")
.withActionOnFailure("CONTINUE")
.withHadoopJarStep(sparkStepConf);
AddJobFlowStepsRequest request = new AddJobFlowStepsRequest(clusterId)
.withSteps(new ArrayList<StepConfig>(){{add(sparkStep);}});
AddJobFlowStepsResult result = emr.addJobFlowSteps(request);
return result.getStepIds().get(0);
If you are looking just for automation you should read about Pipeline Orchestration-
EMR is the AWS service which allows you to run distributed applications
AWS DataPipeline is an Orchestration tool that allows you to run jobs (activities) on resources (EMR or even EC2)
If you'd just like to run a spark job consistently, I would suggest creating a data pipeline, and configuring your pipeline to have one step, which is to run the Scala spark jar on the master node using a "shellcommandactivity". Another benefit is that the jar you are running can be stored in AWS S3 (object storage service) and you'd just provide the s3 path to your DataPipeline and it will pick up that jar, log onto the EMR service it has brought up (with the configurations you've provided)- clone that jar on the master node, run the jar with the configuration provided in the "shellcommandactivity", and once the the job exits (successfully or with an error) it will kill the EMR cluster so you aren't paying for it and log the output
Please read more into it: https://aws.amazon.com/datapipeline/ & https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/what-is-datapipeline.html
And if you'd like you can trigger this pipeline via the AWS SDK or even set the pipeline to run on a schedule
Related
I've a realtime spark job which runs in EMR cluster and I've another batch job which runs in another EMR cluster and this job is triggered at specific time.
How to run both these jobs in one EMR cluster ?
Any suggestions.
If the steps in both the EMR are not dependent on each other, then you can use the feature called Concurrency in the EMR to solve your use case. This feature simply means that you can run more than 1 step in parallel at a time.
This feature is there from the EMR version 5.28.0. If you are using the older version than this then you can not use this feature.
While launching the EMR from the AWS console, this feature is termed as 'Concurrency' in the UI. you can choose any number between 1 to 256.
If you are launching the EMR from the AWS CLI, then this feature is termed as 'StepConcurrencyLevel'.
You can read more about this at multiple steps now in EMR and AWS CLI details
I'm trying to minimize changes in my code so I'm wondering if there is a way to submit a spark-streaming job from my personal PC/VM as follows:
spark-submit --class path.to.your.Class --master yarn --deploy-mode client \
[options] <app jar> [app options]
without using GCP SDK.
I also have to specify a directory with configuration files HADOOP_CONF_DIR which I was able to download from Ambari.
Is there a way to do the same?
Thank you
Setting up an external machine as a YARN client node is generally difficult to do and not a workflow that will work easily with Dataproc.
In a comment you mention that what you really want to do is
Submit a Spark job to the Dataproc cluster.
Run a local script on each "batchFinish" (StreamingListener.onBatchCompleted?).
The script has dependencies that mean it cannot run inside of the Dataproc master node.
Again, configuring a client node outside of the Dataproc cluster and getting it to work with spark-submit is not going to work directly. However, if you can configure your network such that the Spark driver (running within Dataproc) has access to the service/script you need to run, and then invoke that when desired.
If you run your service on a VM that has access to the network of the Dataproc cluster, then your Spark driver should be able to access the service.
I want to migrate my python scripts from local to run on cloud, specifically on a cluster created on Azure Databricks.
Can pure python script run in parallel (using multiple nodes in a cluster at the same time) without having to be converted into pyspark?
Is it possible to check whether the job is running in parallel?
No
If you have submitted a spark job, you can see logs and it will show you number of executors it is using. That's how you 'll know job is running in parallel
I've wrote a spark program, which needs to be executed on EMR cluster. But there are some dependent files and modules being used by python program. So is there any way around to setup dependent components on a running cluster ?
Can we mount the s3 bucket and mount that one cluster nodes, and can put all the dependent component on s3 ? Is this a good idea, and using Python how we can mount the s3 buckets on EMR ?
(During cluster creation): You can use Amazon EMR bootstrap custom actions which is capable of executing a bash script at the time of creation of the cluster. You can install all the dependent components using this script. Bootstrap action will be performed on all nodes of the cluster.
(On a running cluster): You can use Amazon EMR step option to create a s3-dist-cp command-runner step to copy files from s3.
I am submitting a Spark job to Dataproc using either gcloud or Google Cloud DataProc API. One of the arguments is '--jars' (or its Java API equivalent), where I supply comma separated list of jar files to be provided to the executor and driver classpaths:
gs://google-storage-bucket/lib/x1.jar,gs://google-storage-bucket/lib/x2.jar, etc...
Same JAR files are copied from Google storage bucket to the working directory for each SparkContext on the executor nodes every time I submit a job and it takes about 2 minutes, before the job really starts execution (I can see that on the Google Cloud console - https://console.cloud.google.com/dataproc/jobs/...).
Is it possible to somehow cache these jar files on Spark nodes and use them in the classpath with every job submission? That would save about 50% of the run time.
Thanks,
Victor
Indeed, if you pass in arguments of the form file:///your/path/on/the/cluster/nodes/filesystem then it will be interpreted as referring to files on the cluster nodes themselves.
You can either copy files from GCS into the nodes at cluster creation time using an initiailization action or try to run some kind of Spark job to do it on an existing cluster and/or manually SSH'ing in to stage those jars.