I can only submit 10 jobs to the PBS system at the same time.
If I have independent 99 scripts, I would like to have a script to finish all these 99 scripts by one click.
jobs1.sh
jobs2.sh
jobs3.sh
.
.
jobs99.sh
The purpose is to submit another 10 jobs after finishing previous 10 jobs.
Currently, I'm using sleep to separate every 10 jobs by estimating how much time they need. I know it's not a nice way.....
You need to check out the PBS section about dependencies. There are good ways of queuing jobs to run after the previous ones have finished. For example-
qsub -W depend=afterok:Job_ID job11.sh
Job_ID can be the JOB_ID for job1.sh.
So job11 runs only after job1 has finished. You can elaborate on this idea and set up a loop.
Related
I have around 20 cron jobs that run at various times over 3 computers, some of them run at the same time, some at even hours, some at odd hours. Some take 5 minute to complete, other up to 20 hours. The jobs i'm running are bash scripts.
I would like some of these jobs to run in a seamless fashion on any node of a set of computers, but always one instance at the time (for now i handle this "one at the time maximum" using /usr/bin/flock).
One constraint i have is that i want certain jobs to run only on the same node due to hardware and network limitations.
But i still want to have only one cron file to edit. For now i have to edit as many cron files that i have nodes, which is unpractical.
Is there a conventional way to achieve this ?
I am using NodeJS,MongoDB and node-cron npm module to schedule jobs. For 10K of jobs it is taking less time and less memory. But when i am scheduling 100k jobs it is taking more than 10 minutes to schedule jobs and taking nearly 1.5GB of RAM and some times out of memory. Is there any best way achieve this like using activemq or rabbitmq?
One strategy is that you only schedule the next job to run. When it runs, you query the database and find the next job and schedule it.
If you add a new job, you check if it wants to run sooner than the now current next job and, if so, you schedule it and deschedule the previous next job (it will get rescheduled later after this new job runs).
If you remove a job, you check if it is the current next job. If it is, you deschedule it and find the next job in the database and schedule it.
If your database is configured for efficiently querying by job run time, this can be very efficient, uses hardly any memory and scales to an infinitely large number of jobs.
I have a batch job that has 10 steps in STEP5. I have written an internal JCL and I want after Internal reader step are completed successfully my next step in the parent job which is STEP06 to execute. Could you please give any resolution to this problem.
For what you have described, there are 2 approaches:
Break your process into 3 jobs - steps 1-5 as one job, the second
job consisting of the JCL submitted in sep 5 and the third job
consisting of step 6 (or steps 6-10 - you do not make it clear if
the main JCL has 6 steps and the 'inner' JCL 4 steps, making the 10
steps you mention, or if the main JCL has 10 steps).
The execution of the 3 jobs will need to be serialised somehow.
Simply have the 'inner' JCL as a series of steps in the 'outer' JCL
so that you only have once job with steps that run in order.
The typical approach to this sort of issue would be to use scheduler to handle the 3 part process as 3 jobs the middle one perhaps not submitted by the scheduler but monitored/tracked by it.
With a good scheduler setup, there is a good chance that even if the jobs were run on different machines or even different types of machines that they could be tracked.
To have a single job delayed halfway through is possible but would require some sort of program to run in a loop (waiting so as not to use excessive cpu) checking for an event (a dataset being created or deleted, the job could itself could be checked or numerous other ways).
Another way could be to have part 1 submit a job to do part 2 and that job to then submit another as part 3.
Yet another way, perhaps frowned upon depedning upon it's importance, would be to have three jobs the first part submitted to run, the third part submitted but on hold. The first submits the second which releases the third.
Of course there is also the possibility that one job could do everthing as a single job.
Dear UNIX/PBS experts:
I am user of a UNIX HPC system (CentOS Linux 7 (Core),Linux 3.10.0-693.5.2.el7.x86_64) and I do not have any root privileges.
Various jobs have been submitted at an HPC system and almost all resources are being used.
Jobs from other users may run for weeks while my submitted job would finish in less than a day.
My goal is to run my job exactly after the first resources will be freed instead of waiting for
all other users to have their jobs finished.
My submitted job has a number qid 66005.pbs.
However the last job running at this moment has number 55004.pbs.
By checking the status of job: qstat 55005,
I obtain: qstat: Unknown Job Id 55005.pbs
Thus my question is whether it is possible to change the name of job 66005.pbs to 55005.pbs, and if this action will allow my job to run?
If yes, how can this be achieved?
If not, are there any other solutions/alternatives for making sure that my jobs run before those ones of other users in queue?
Thank you very much for your help and any suggestion.
The good thing about the computer system is that it is not human. It will be unfair to run your job (which clearly was submitted after other users) before other users and because of that "No" it is not possible to change your job-id.
You can work with your admin to move the job to a higher priority queue instead.
I am running a dummy spark job that does the exactly same set of operations in every iteration. The following figure shows 30 iterations, where each job corresponds to one iteration. It can be seen the duration is always around 70 ms except for job 0, 4, 16, and 28. The behavior of job 0 is expected as it is when the data is first loaded.
But when I click on job 16 to enter its detailed view, the duration is only 64 ms, which is similar to the other jobs, the screen shot of this duration is as follows:
I am wondering where does Spark spend the (2000 - 64) ms on job 16?
Gotcha! That's exactly the very same question I asked myself few days ago. I'm glad to share the findings with you (hoping that when I'm lucking understanding others chime in and fill the gaps).
The difference between what you can see in Jobs and Stages pages is the time required to schedule the stage for execution.
In Spark, a single job can have one or many stages with one or many tasks. That creates an execution plan.
By default, a Spark application runs in FIFO scheduling mode which is to execute one Spark job at a time regardless of how many cores are in use (you can check it in the web UI's Jobs page).
Quoting Scheduling Within an Application:
By default, Spark’s scheduler runs jobs in FIFO fashion. Each job is divided into "stages" (e.g. map and reduce phases), and the first job gets priority on all available resources while its stages have tasks to launch, then the second job gets priority, etc. If the jobs at the head of the queue don’t need to use the whole cluster, later jobs can start to run right away, but if the jobs at the head of the queue are large, then later jobs may be delayed significantly.
You should then see how many tasks a single job will execute and divide it by the number of cores the Spark application have assigned (you can check it in the web UI's Executors page).
That will give you the estimate on how many "cycles" you may need to wait before all tasks (and hence the jobs) complete.
NB: That's where dynamic allocation comes to the stage as you may sometimes want more cores later and start with a very few upfront. That's what the conclusion I offered to my client when we noticed a similar behaviour.
I can see that all the jobs in your example have 1 stage with 1 task (which make them very simple and highly unrealistic in production environment). That tells me that your machine could have got busier at different intervals and so the time Spark took to schedule a Spark job was longer but once scheduled the corresponding stage finished as the other stages from other jobs. I'd say it's a beauty of profiling that it may sometimes (often?) get very unpredictable and hard to reason about.
Just to shed more light on the internals of how web UI works. web UI uses a bunch of Spark listeners that collect current status of the running Spark application. There is at least one Spark listener per page in web UI. They intercept different execution times depending on their role.
Read about org.apache.spark.scheduler.SparkListener interface and review different callback to learn about the variety of events they can intercept.