Thanks a lot @ Dongjin, @Ryan I am using Spark 1.6. I agree with your assesment Ryan. Further investigation seemed to suggest that our cluster was probably at 100% capacity at that point of time. Though tasks were failing on that slave, still it was accepting the task, and task retries exhausted much faster than the other slaves freeing up to accept those tasks.
Regards Sumit Chawla On Mon, Apr 24, 2017 at 9:48 AM, Ryan Blue <rb...@netflix.com> wrote: > Looking at the code a bit more, it appears that blacklisting is disabled > by default. To enable it, set spark.blacklist.enabled=true. > > The updates in 2.1.0 appear to provide much more fine-grained settings for > this, like the number of tasks that can fail before an executor is > blacklisted for a stage. In that version, you probably want to set > spark.blacklist.task.maxTaskAttemptsPerExecutor. See the settings docs > <http://spark.apache.org/docs/latest/configuration.html> and search for > “blacklist” to see all the options. > > rb > > > On Mon, Apr 24, 2017 at 9:41 AM, Ryan Blue <rb...@netflix.com> wrote: > >> Chawla, >> >> We hit this issue, too. I worked around it by setting >> spark.scheduler.executorTaskBlacklistTime=5000. The problem for us was >> that the scheduler was using locality to select the executor, even though >> it had already failed there. The executor task blacklist time controls how >> long the scheduler will avoid using an executor for a failed task, which >> will cause it to avoid rescheduling on the executor. The default was 0, so >> the executor was put back into consideration immediately. >> >> In 2.1.0 that setting has changed to spark.blacklist.timeout. I’m not >> sure if that does exactly the same thing. The default for that setting is >> 1h instead of 0. It’s better to have a non-zero default to avoid what >> you’re seeing. >> >> rb >> >> >> On Fri, Apr 21, 2017 at 1:32 PM, Chawla,Sumit <sumitkcha...@gmail.com> >> wrote: >> >>> I am seeing a strange issue. I had a bad behaving slave that failed the >>> entire job. I have set spark.task.maxFailures to 8 for my job. Seems >>> like all task retries happen on the same slave in case of failure. My >>> expectation was that task will be retried on different slave in case of >>> failure, and chance of all 8 retries to happen on same slave is very less. >>> >>> >>> Regards >>> Sumit Chawla >>> >>> >> >> >> -- >> Ryan Blue >> Software Engineer >> Netflix >> > > > > -- > Ryan Blue > Software Engineer > Netflix >
