There are some additional risks to running simultaneous jobs. Right now, Hadoop does a very bad job dealing with out-of-space conditions. If you run two jobs, where the total amount of temporary space (for map outputs) between both jobs is greater than the amount of space available on the cluster, then they will both fail. If you run them serially, they should both succeed.
In the very least, it's probably wise to take into account more than just scheduling priority in any scheduler. (Expected) temporary space demands, bandwidth limits, and size of jobs should be some of the criteria available to the scheduler. On 5/19/06, Andrzej Bialecki <[EMAIL PROTECTED]> wrote:
Andrzej Bialecki wrote: > Hi all, > > I'm running Hadoop on a relatively small cluster (5 nodes) with > growing datasets. > > I noticed that if I start a job that is configured to run more map > tasks than is the cluster capacity (mapred.tasktracker.tasks.maximum * > number of nodes, 20 in this case), of course only that many map tasks > will run, and when they are finished the next map tasks from that job > will be scheduled. > > However, when I try to start another job in parallel, only its reduce > tasks will be scheduled (uselessly spin-waiting for map output, and > only reducing the number of available tasks in the cluster...), and no > map tasks from this job will be scheduled - until the first job > completes. This feels wrong - not only I'm not making progress on the > second job, but I'm also taking the slots away from the first job! > > I'm somewhat miffed about this - I'd think that jobtracker should > split the available resources evenly between these two jobs, i.e. it > should schedule some map tasks from the first job and some from the > second one. This is not what is happening, though ... > > Is this a configuration error, a bug, or a feature? :) > It seems it's a feature - I found the code in JobTracker.pollForNewTask(), and I'm not too happy about it. Let's consider the following example: if I'm running a Nutch fetcher, the main limitation is the available bandwidth to fetch pages, and not the capacity of the cluster. I'd love to be able to execute other jobs in parallel, so that I don't have to wait until fetcher completes. I could sacrifice some of the task slots on tasktrackers for that other job, because the fetcher job wouldn't suffer from this anyway (at least not too much). So, I'd like to change this code to pick up a random job from the list jobsByArrival, and take job.obtainNewMapTask from that randomly selected job. Would that work? Additionally, if no map tasks from that job have been allocated I'd like to skip adding reduce tasks from that job, later in lines 721-750. Perhaps we should extend JobInProgress to include a priority, and implement something a la Unix scheduler. -- Best regards, Andrzej Bialecki <>< ___. ___ ___ ___ _ _ __________________________________ [__ || __|__/|__||\/| Information Retrieval, Semantic Web ___|||__|| \| || | Embedded Unix, System Integration http://www.sigram.com Contact: info at sigram dot com
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