Hi Umesh, How much memory does each node have? I'd at least budget 2GB per executor..
Thanks Vinoth On Fri, Mar 8, 2019 at 11:20 PM Umesh Kacha <[email protected]> wrote: > Nishit thanks ok this I did not know it is physically possible if I have 4 > node managers can we spawn 74 executors from them? How is it possible? How > much memory should we give to those 72 executors since we have only 4 > nodes?? Please guide. I am sorry I may be wrong but I want to clear out > things. > > On Sat, Mar 9, 2019 at 3:08 AM nishith agarwal <[email protected]> > wrote: > > > Umesh, > > > > Yes, I understand what you are trying to convey. > > > > Since you have YARN, you can just use the following spark configurations > : > > *--num-executors 72 --num-cores 1* > > > > An executor is just a JVM started by Spark on a YARN node/container. The > > above config should let you use all the cores. > > > > Thanks, > > Nishith > > > > On Fri, Mar 8, 2019 at 1:18 PM Umesh Kacha <[email protected]> > wrote: > > > > > Hi Nishit thanks I get that 1 executor + 18 cores = 18 executors + 1 > core > > > but what if I don't have those many executors?? I use yarn and I have 4 > > > nodes so 18 into 4 equals 72 cores now let's say we have 72 parquet > files > > > so as per you I can use 4 executor with one core each processing 4 > > parquet > > > files at a time and wasting unnecessarily parallel cores?? You getting > me > > > what I am trying to explain. > > > > > > On Sat, Mar 9, 2019, 2:33 AM nishith agarwal <[email protected]> > > wrote: > > > > > > > Umesh, > > > > > > > > What kind of resource scheduler are you using ? Is it Spark's > > standalone > > > > service ? If yes, you can start 18 executors by changing the > > > > spark-default.conf and restarting your spark cluster (see configs > here > > > > < > > > > > > > > > > https://spark.apache.org/docs/latest/spark-standalone.html#cluster-launch-scripts > > > > >) > > > > and information on how to do it here > > > > < > > > > > > > > > > https://spark.apache.org/docs/latest/spark-standalone.html#executors-scheduling > > > > >. > > > > Find details on how to do it for other resources schedulers on Spark > > > > Deployment tab in the documentation. > > > > > > > > Now, 1 executor + 18 cores = 18 executors + 1 core. Hence, you can > get > > > the > > > > same parallelism either way. > > > > Unit of parallelism in Spark = Task = 1 core > > > > > > > > Thousands of parquet files will be spread over multiples tasks with > 18 > > of > > > > them running in parallel in your case since you have 18 cores at your > > > > disposal. > > > > (PS : The OS might also do some pipelining and context switching for > a > > > > single core but that's not very relevant here) > > > > > > > > Hope this helps. > > > > > > > > Thanks, > > > > Nishith > > > > > > > > > > > > > > > > On Fri, Mar 8, 2019 at 12:23 PM Umesh Kacha <[email protected]> > > > wrote: > > > > > > > > > Ok that seems like moving away from distributed to single > processing > > I > > > > have > > > > > 18 cores per executor now if I dont use all the cores what's the > > point > > > of > > > > > having distributed systems. Also I am just curious how will spark > > unit > > > of > > > > > parallelism work here if we have just one core per executor if I > have > > > > > thousands of parquet files it means few executors each with one > core > > so > > > > at > > > > > a time few parquet files will be loaded in spark partitions/tasks. > > > Please > > > > > correct me if I am wrong. Thanks. > > > > > > > > > > On Sat, Mar 9, 2019 at 1:46 AM nishith agarwal < > [email protected]> > > > > > wrote: > > > > > > > > > > > Umesh, > > > > > > > > > > > > This issue still persists. Could you please use num-cores = 1 ? > You > > > can > > > > > > scale out using num-executors. > > > > > > > > > > > > -Nishith > > > > > > > > > > > > On Fri, Mar 8, 2019 at 12:06 PM Umesh Kacha < > [email protected] > > > > > > > > wrote: > > > > > > > > > > > > > I think issue is this https://github.com/uber/hudi/issues/227 > I > > > get > > > > > the > > > > > > > same error and I tried to use multiple executor cores 4 and I > am > > > > using > > > > > > > Spark 2.2.0. Is this issue fixed? > > > > > > > > > > > > > > > > > > > > > > > > > > > > On Fri, Mar 8, 2019 at 6:58 PM Vinoth Chandar < > [email protected] > > > > > > > > wrote: > > > > > > > > > > > > > > > Could you please share the entire stack trace? > > > > > > > > > > > > > > > > On Fri, Mar 8, 2019 at 1:56 AM Umesh Kacha < > > > [email protected]> > > > > > > > wrote: > > > > > > > > > > > > > > > > > Hi I am using Spark Shell to save spark dataframe as Hoodie > > > > dataset > > > > > > > using > > > > > > > > > bulk insert option inside Hoodie spark datasource. It seems > > to > > > be > > > > > > > working > > > > > > > > > and trying to save but in the end it fails giving the > > following > > > > > > > exception > > > > > > > > > > > > > > > > > > Failed to initialize HoodieStorageWriter for path > > > > > > > > > /tmp/hoodie-test/2019/blabla.parquet > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >
