I've a AWS EMR cluster executing a spark streaming job. It takes streaming data from Kinesis stream and process it. It works fine for few days but after 12-15 days the cluster terminates automatically. I checked in events tab, it shows
cluster has terminated with errors with a reason of STEP_FAILURE.
Anyone has any idea why step failure can occur when the step successfully ran for few days ?
Go to the EMR console, and check the step option. If it is set as follows:
Action on failure:Terminate cluster
then the cluster will be terminated when the step failed.
Related
ADF Pipeline DF task is Stuck in Progress. It was working seamlessly last couple of months but suddenly Dataflow stuck in progress and Time out after certain time. We are using IR managed Virtual Network. I am using forereach loop to run data flow for multiple entities parallel, it always randomly get stuck on last Entity.
What can I try to resolve this?
Error in Dev Environment
Error Code 4508
Spark cluster not found
Error in Prod Environment:
Error code
5000
Failure type
User configuration issue
Details
[plugins.*** ADF.adf-ir-001 WorkspaceType:<ADF> CCID:<f289c067-7c6c-4b49-b0db-783e842a5675>] [Monitoring] Livy Endpoint=[https://hubservice1.eastus.azuresynapse.net:8001/api/v1.0/publish/815b62a1-7b45-4fe1-86f4-ae4b56014311]. Livy Id=[0] Job failed during run time with state=[dead].
Images:
I tried below steps:
By changing IR configuring as below
Tried DF Retry and retry Interval
Also, tried For each loop one batch at a time instead of 4 batch parallel. None of the above trouble-shooting steps worked. These PL is running last 3-4 months without a single failure, suddenly they started to fail last 3 days consistently. DF flow always stuck in progress randomly for different entity and times out in one point by throwing above errors.
Error Code 4508 Spark cluster not found.
This error can cause because of two reasons.
The debug session is getting closed till the dataflow finish its transformation in this case recommendation is to restart the debug session
the second reason is due to resource problem, or an outage in that particular region.
Error code 5000 Failure type User configuration issue Details [plugins.*** ADF.adf-ir-001 WorkspaceType: CCID:] [Monitoring] Livy Endpoint=[https://hubservice1.eastus.azuresynapse.net:8001/api/v1.0/publish/815b62a1-7b45-4fe1-86f4-ae4b56014311]. Livy Id=[0] Job failed during run time with state=[dead].
A temporary error is one that says "Livy job state dead caused by unknown error." At the backend of the dataflow, a spark cluster is used, and this error is generated by the spark cluster. to get the more information about error go to StdOut of sparkpool execution.
The backend cluster may be experiencing a network problem, a resource problem, or an outage.
If error persist my suggestion is to raise Microsoft support ticket here
When I run (in debug mode) a Spark notebook in Azure Synapse Analytics, it doesn't seem to shutdown as expected.
In the last cell I call: mssparkutils.notebook.exit("exiting notebook")
But then when I fire off another notebook (again in debug mode, same pool), I get this error:
AVAILABLE_COMPUTE_CAPACITY_EXCEEDED: Livy session has failed. Session state: Error. Error code: AVAILABLE_COMPUTE_CAPACITY_EXCEEDED. Your job requested 12 vcores. However, the pool only has 0 vcores available out of quota of 12 vcores. Try ending the running job(s) in the pool, reducing the numbers of vcores requested, increasing the pool maximum size or using another pool. Source: User.
So I go to Monitor => Apache Spark applications and I see my the first notebook I ran still in a "Running" status and I can manually stop it.
How do I automatically stop the Notebook / Apache Spark application? I thought that was the notebook.exit() call but apparently not...
In debug mode, the cluster's vcores are supplied to the notebook for the entire duration of the debug (that is one hour of inactivity or until you manually terminate it)
Thus, you have two options:
Work on one notebook at a time, closing the debug before starting another
OR
Configure the session to reduce the number of executors so that the spark cluster can provision all three debug modes at the same time (might need to increase the size of the cluster)
I'm trying to write a component that will start up an EMR cluster, run a Spark pipeline on that cluster, and then shut that cluster down once the pipeline completes.
I've gotten as far as creating the cluster and setting permissions to allow my main cluster's worker machines to start EMR clusters. However, I'm struggling with debugging the created cluster and waiting until the pipeline has concluded. Here is the code I have now. Note I'm using Spark Scala, but this is very close to standard Java code:
val runSparkJob = new StepConfig()
.withName("Run Pipeline")
.withActionOnFailure(ActionOnFailure.TERMINATE_CLUSTER)
.withHadoopJarStep(
new HadoopJarStepConfig()
.withJar("/path/to/jar")
.withArgs(
"spark-submit",
"etc..."
)
)
// Create a cluster and run the Spark job on it
val clusterName = "REDACTED Cluster"
val createClusterRequest =
new RunJobFlowRequest()
.withName(clusterName)
.withReleaseLabel(Configs.EMR_RELEASE_LABEL)
.withSteps(enableDebugging, runSparkJob)
.withApplications(new Application().withName("Spark"))
.withLogUri(Configs.LOG_URI_PREFIX)
.withServiceRole(Configs.SERVICE_ROLE)
.withJobFlowRole(Configs.JOB_FLOW_ROLE)
.withInstances(
new JobFlowInstancesConfig()
.withEc2SubnetId(Configs.SUBNET)
.withInstanceCount(Configs.INSTANCE_COUNT)
.withKeepJobFlowAliveWhenNoSteps(false)
.withMasterInstanceType(Configs.MASTER_INSTANCE_TYPE)
.withSlaveInstanceType(Configs.SLAVE_INSTANCE_TYPE)
)
val newCluster = emr.runJobFlow(createClusterRequest)
I have two concrete questions:
The call to emr.runJobFlow returns immediately upon submitting the result. Is there any way that I can make it block until the cluster is shut down or otherwise wait until the workflow has concluded?
My cluster is actually not coming up and when I go to the AWS Console -> EMR -> Events view I see a failure:
Amazon EMR Cluster j-XXX (REDACTED...) has terminated with errors at 2019-06-13 19:50 UTC with a reason of VALIDATION_ERROR.
Is there any way I can get my hands on this error programmatically in my Java/Scala application?
Yes, it is very possible to wait until an EMR cluster is terminated.
There is are waiters that will block execution until the cluster (i.e. job flow) gets to a certain state.
val newCluster = emr.runJobFlow(createClusterRequest);
val describeRequest = new DescribeClusterRequest()
.withClusterId(newCluster.getClusterId())
// Wait until terminated
emr.waiters().clusterTerminated().run(new WaiterParameters(describeRequest))
Also, if you want to get the status of the cluster (i.e. job flow), you can call the describeCluster function of the EMR client. Check out the linked documentation as you can get state and status information about the cluster to determine if it's successful or erred.
val result = emr.describeCluster(describeRequest)
Note: Not the best Java-er so the above is my best guess and how it would work based on the documentation but I have not tested the above.
I am using JupyterHub on AWS EMR cluster. I am using EMR version 5.16
I submitted a spark application using a pyspark3 notebook.
My application is trying to write 1TB data to s3.
I am using autoscaling feature of the EMR to scale us the task node.
Hardware configurations:
1.Master node:32 GB RAM with 16 cores
2.Core node:32 GB RAM with 16 cores
3.Task node:16 GB with 8 cores each. (Task nodes scales up 15)
I have observed that Spark application gets killed after running for 50 to 60 minutes.
I tried debugging:
1. My cluster still had scope for scaling up. So it is not an issue with a shortage of resources.
2. Livy session also gets killed.
3. In the job log, I saw error message RECVD TERM SIGNAL "Shutdown hook
received"
Please note:
1. I have kept :spark.dynamicAllocation.enabled=true"
2. I am using the yarn fair scheduler with user impersonation in Jupiter hub
Can you please help me in understanding the problem and solution for it?
I think that I faced the same problem and I found the solution thanks to this answer.
The issue comes from the Livy configuration parameter livy.server.session.timeout, which sets the timeout for a session by default to 1 hour.
You should set it by adding the following line into the configurations of the EMR cluster.
[{'classification': 'livy-conf','Properties': {'livy.server.session.timeout':'5h'}}]
This solved the issue for me.
I changed my initialization script after creating a cluster with 2 worker nodes for spark. Then I changed the script a bit and tried to update the cluster with 2 more worker nodes. The script failed because I simply forgot to apt-get update before apt-get install, so dataproc reports error and the cluster's status changed to ERROR. When I try to reduce the size back to 2 nodes again, it doesn't work anymore with the following message
ERROR: (gcloud.dataproc.clusters.update) Cluster 'cluster-1' must be running before it can be updated, current cluster state is 'ERROR'.
The two worker nodes are still added, but they don't seem to be detected by a running spark application at first because no more executors are added. I manually reset the two instances on the Google Compute Engine page, and then 4 executors are added. So it seems everything is working fine again except that the cluster's status is still ERROR, and I cannot increase or decrease the number of worker nodes anymore.
How can I update the cluster status back to normal (RUNNING)?
In your case ERROR indicates that workflow to re-configure the cluster has failed, and Dataproc is not sure of its health. At this point Dataproc cannot guarantee that another reconfigure attempt will succeed so further updates are disallowed. You can however submit jobs.
Your best bet is to delete it and start over.