I want to get the cluster link (or the cluster ID to manually compose the link) inside a running Spark job.
This will be used to print the link in an alerting message, making it easier for engineers to reach the logs.
Is it possible to achieve that in a Spark job running in Databricks?
When Databricks cluster starts, there is a number of Spark configuration properties added. Most of them are having name starting with spark.databricks. - you can find all of the in the Environment tab of the Spark UI.
Cluster ID is available as spark.databricks.clusterUsageTags.clusterId property and you can get it as:
spark.conf.get("spark.databricks.clusterUsageTags.clusterId")
You can get workspace host name via dbutils.notebook.getContext().apiUrl.get call (for Scala), or dbutils.notebook.entry_point.getDbutils().notebook().getContext().apiUrl().get() (for Python)
Related
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
I have an Apache Spark service instance on IBM cloud(light plan). After I submit a Spark job I want to see its progress, it would be perfect to see it the Spark way - get the Spark progress UI with number of partitions and everything. I would also like to get a connection to the history server.
I saw that I can run ./spark-submit.sh ... --status <app id> but I would like to get something more informative.
I saw the comment
You can track the current execution of your running application and see the details of previously run jobs on the Spark job history UI by clicking Job History on the Analytics for Apache Spark service console.
here, but fail to understand where exactly I get this console/history thing.
As a side note, is there any detailed technical documentation of this service, e.g. number of concurrent jobs which can run, technology stack etc..?
As per spark Documentation:
Every SparkContext launches a web UI, by default on port 4040, that displays useful information about the application. This includes:
A list of scheduler stages and tasks
A summary of RDD sizes and memory usage
Environmental information.
Information about the running executors
You can access this interface by simply opening http://{driver-node}:4040 in a web browser. If multiple SparkContexts are running on the same host, they will bind to successive ports beginning with 4040 (4041, 4042, etc).
Bottom Line : http://{driver-node}:4040 (replace driver-node with the node where spark job invoked) and you should be good to go
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.
I have a production environment that consists of several (persistent and ad-hoc) EMR Spark clusters.
I would like to use one instance of spark-jobserver to manage the job JARs for this environment in general, and be able to specify the intended master right when I POST /jobs, and not permanently in the config file (using master = "local[4]" configuration key).
Obviously I would prefer to have spark-jobserver running on a standalone machine, and not on any of the masters.
Is this somehow possible?
You can write a SparkMasterProvider
https://github.com/spark-jobserver/spark-jobserver/blob/master/job-server/src/spark.jobserver/util/SparkMasterProvider.scala
A complex example is here https://github.com/spark-jobserver/jobserver-cassandra/blob/master/src/main/scala/spark.jobserver/masterLocators/dse/DseSparkMasterProvider.scala
I think all you have to do is write one that will return the config input as spark master, that way you can pass it as part of job config.
Is there a way to submit Spark jobs to Google Cloud Dataproc from within the Scala code?
val Config = new SparkConf()
.setMaster("...")
What should the master URI look like?
What key-value pairs should be set to authenticate with an API key or keypair?
In this case, I'd strongly recommend an alternative approach. This type of connectivity has not been tested or recommended for a few reasons:
It requires opening firewall ports to connect to the cluster
Unless you use a tunnel, your data may be exposed
Authentication is not enabled by default
Is SSHing into the master node (the node which is named cluster-name-m) a non-starter? It is pretty easy to SSH into the master node to directly use Spark.