I have been using Google Dataproc for a few weeks now and since I started I had a problem with canceling and stopping jobs.
It seems like there must be some server other than those created on cluster setup, that keeps track of and supervises jobs.
I have never had a process that does its job without error actually stop when I hit stop in the dev console. The spinner just keeps spinning and spinning.
Cluster restart or stop does nothing, even if stopped for hours.
Only when the cluster is entirely deleted will the jobs disappear... (But wait there's more!) If you create a new cluster with the same settings, before the previous cluster's jobs have been deleted, the old jobs will start on the new cluster!!!
I have seen jobs that terminate on their own due to OOM errors restart themselves after cluster restart! (with no coding for this sort of fault tolerance on my side)
How can I forcefully stop Dataproc jobs? (gcloud beta dataproc jobs kill does not work)
Does anyone know what is going on with these seemingly related issues?
Is there a special way to shutdown a Spark job to avoid these issues?
Jobs keep running
In some cases, errors have not been successfully reported to the Cloud Dataproc service. Thus, if a job fails, it appears to run forever even though it (has probably) failed on the back end. This should be fixed by a soon-to-be released version of Dataproc in the next 1-2 weeks.
Job starts after restart
This would be unintended and undesirable. We have tried to replicate this issue and cannot. If anyone can replicate this reliably, we'd like to know so we can fix it! This may (is provably) be related to the issue above where the job has failed but appears to be running, even after a cluster restarts.
Best way to shutdown
Ideally, the best way to shutdown a Cloud Dataproc cluster is to terminate the cluster and start a new one. If that will be problematic, you can try a bulk restart of the Compute Engine VMs; it will be much easier to create a new cluster, however.
Related
I have scheduled an ADB notebook to run on a schedule. Will the notebook run if the cluster is down? Right now the cluster is busy so unable to stop and try it out. Will the notebook start the cluster and run or would wait for the cluster to be up?
If you're scheduling the notebook to run on the existing cluster, then cluster will be started if it's stopped. But in reality, it's better to execute the notebook on the new cluster - there will be less chance of breaking things if you change library version or something like. If you need to speedup the job execution you may look onto instance pools.
I have a spark job that periodically hangs, leaving my AWS EMR cluster in a state where an application is RUNNING but really the cluster is stuck. I know that if my job doesn't get stuck, it'll finish in 5 hours or less. If it's still running after that, it's a sign that the job is stuck. Yarn and the Spark UI is still responsive, the it's just that an executor gets stuck on a task.
Background: I'm using an ephemeral EMR cluster that performs only one step before terminating, so it's not a problem to kill it off if I notice this job is hanging.
What's the easiest way to kill the task, job, or cluster in this case? Ideally this would not involve setting up some extra service to monitor the job -- ideally there would be some kind of spark / yarn / emr setting I could use.
Note: I've tried using spark speculation to unblock the stuck spark job, but that doesn't help.
EMR has a Bootstrap Actions feature where you can run scripts that start up when initializing the cluster. I've used this feature along with a startup script that monitors how long the cluster has been online and terminates itself after a certain time.
I use a script based off this one for the bootstrap action. https://github.com/thomhopmans/themarketingtechnologist/blob/master/6_deploy_spark_cluster_on_aws/files/terminate_idle_cluster.sh
Basically make a script that checks /proc/uptime to see how long the EC2 machine has been online and after uptime surpasses your time limit you can send a shutdown command to the cluster.
I have a daily pipeline running on Spark Standalone 2.1. Its deployed in and runs on AWS EC2 and uses S3 for its persistence layer. For the most part, the pipeline runs without a hitch, but occasionally the job hangs on a single worker node during a reduceByKey operation. When I work into the worker, I notice that the CPU (as seen via top) is pegged at 100%. My remedy so far is to reboot the worker node so that Spark re-assigns the task and the job proceeds fine from there.
I would like to be able to mitigate this issue. I gather that I can prevent CPU pegging by switching to use YARN as my cluster manager, but I wonder whether I could configure Spark Standalone to prevent CPU pegging by maybe limiting the number of cores that get assigned to the Spark job ? Any suggestions would be greatly appreciated.
For some reason sometimes the cluster seems to misbehave for I suddenly see surge in number of YARN jobs.We are using HDInsight Linux based Hadoop cluster. We run Azure Data Factory jobs to basically execute some hive script pointing to this cluster. Generally average number of YARN apps at any given time are like 50 running and 40-50 pending. None uses this cluster for ad-hoc query execution. But once in few days we notice something weird. Suddenly number of Yarn apps start increasing, both running as well as pending, but especially pending apps. So this number goes more than 100 for running Yarn apps and as for pending it is more than 400 or sometimes even 500+. We have a script that kills all Yarn apps one by one but it takes long time, and that too is not really a solution. From our experience we found that the only solution, when it happens, is to delete and recreate the cluster. It may be possible that for some time cluster's response time is delayed (Hive component especially) but in that case even if ADF keeps retrying several times if a slice is failing, is it possible that the cluster is storing all the supposedly failed slice execution requests (according to ADF) in a pool and trying to run when it can? That's probably the only explanation why it could be happening. Has anyone faced this issue?
Check if all the running jobs in the default queue are Templeton jobs. If so, then your queue is deadlocked.
Azure Data factory uses WebHCat (Templeton) to submit jobs to HDInsight. WebHCat spins up a parent Templeton job which then submits a child job which is the actual Hive script you are trying to run. The yarn queue can get deadlocked if there are too many parents jobs at one time filling up the cluster capacity that no child job (the actual work) is able to spin up an Application Master, thus no work is actually being done. Note that if you kill the Templeton job this will result in Data Factory marking the time slice as completed even though obviously it was not.
If you are already in a deadlock, you can try adjusting the Maximum AM Resource from the default 33% to something higher and/or scaling up your cluster. The goal is to be able to allow some of the pending child jobs to run and slowly draining the queue.
As a correct long term fix, you need to configure WebHCat so that parent templeton job is submitted to a separate Yarn queue. You can do this by (1) creating a separate yarn queue and (2) set templeton.hadoop.queue.name to the newly created queue.
To create queue you can do this via the Ambari > Yarn Queue Manager.
To update WebHCat config via Ambari go to Hive tab > Advanced > Advanced WebHCat-site, and update the config value there.
More info on WebHCat config:
https://cwiki.apache.org/confluence/display/Hive/WebHCat+Configure
I have a test cluster on 3 machines where 2 are seeds all centos7 and all cassandra 3.4.
Yesterday all was fine they were chating and i had the "brilliant" idea to ....power all those machines off to simulate a power failure.
As a newbie that i am, i simply powered the machines back and i expected probably some kind of supermagic, but here it is my cluster is not up again, each individual refuses to connect.
And yes, my firewalld is disabled.
My question : what damage was made and how can i recover the cluster to the previous running state?
Since you abruptly shutdown your cluster, that simply means, nodes were not able to drain themselves.
Don't worry, it is unlikely any data loss happened because of this, as cassandra maintains commit logs, and will read from it when it is restarted.
First, find your seed node ip from cassandra.yaml
Start your seed node first.
Check the start up logs in cassandra.log and system.log and wait for it to start up completely, it will take sometime.
As it will read from commit log for pending actions, and will replay them.
Once it finishes starting up, start other nodes, and tail their log files.