Everything works smoothly if I do the 99%-removal filter in Hive first. So, all the baggage from garbage collection was breaking it.
Is there a way to filter() out 99% of the data without having to garbage collect 99% of the RDD? On Sun, Mar 1, 2015 at 9:56 AM, Arun Luthra <arun.lut...@gmail.com> wrote: > I tried a shorter simper version of the program, with just 1 RDD, > essentially it is: > > sc.textFile(..., N).map().filter().map( blah => (id, > 1L)).reduceByKey().saveAsTextFile(...) > > Here is a typical GC log trace from one of the yarn container logs: > > 54.040: [GC [PSYoungGen: 9176064K->28206K(10704896K)] > 9176064K->28278K(35171840K), 0.0234420 secs] [Times: user=0.15 sys=0.01, > real=0.02 secs] > 77.864: [GC [PSYoungGen: 9204270K->150553K(10704896K)] > 9204342K->150641K(35171840K), 0.0423020 secs] [Times: user=0.30 sys=0.26, > real=0.04 secs] > 79.485: [GC [PSYoungGen: 9326617K->333519K(10704896K)] > 9326705K->333615K(35171840K), 0.0774990 secs] [Times: user=0.35 sys=1.28, > real=0.08 secs] > 92.974: [GC [PSYoungGen: 9509583K->193370K(10704896K)] > 9509679K->193474K(35171840K), 0.0241590 secs] [Times: user=0.35 sys=0.11, > real=0.02 secs] > 114.842: [GC [PSYoungGen: 9369434K->123577K(10704896K)] > 9369538K->123689K(35171840K), 0.0201000 secs] [Times: user=0.31 sys=0.00, > real=0.02 secs] > 117.277: [GC [PSYoungGen: 9299641K->135459K(11918336K)] > 9299753K->135579K(36385280K), 0.0244820 secs] [Times: user=0.19 sys=0.25, > real=0.02 secs] > > So ~9GB is getting GC'ed every few seconds. Which seems like a lot. > > Question: The filter() is removing 99% of the data. Does this 99% of the > data get GC'ed? > > Now, I was able to finally get to reduceByKey() by reducing the number of > executor-cores (to 2), based on suggestions at > http://apache-spark-user-list.1001560.n3.nabble.com/java-lang-OutOfMemoryError-java-lang-OutOfMemoryError-GC-overhead-limit-exceeded-td9036.html > . This makes everything before reduceByKey() run pretty smoothly. > > I ran this with more executor-memory and less executors (most important > thing was fewer executor-cores): > > --num-executors 150 \ > --driver-memory 15g \ > --executor-memory 110g \ > --executor-cores 32 \ > > But then, reduceByKey() fails with: > > java.lang.OutOfMemoryError: Java heap space > > > > > On Sat, Feb 28, 2015 at 12:09 PM, Arun Luthra <arun.lut...@gmail.com> > wrote: > >> The Spark UI names the line number and name of the operation (repartition >> in this case) that it is performing. Only if this information is wrong >> (just a possibility), could it have started groupByKey already. >> >> I will try to analyze the amount of skew in the data by using reduceByKey >> (or simply countByKey) which is relatively inexpensive. For the purposes of >> this algorithm I can simply log and remove keys with huge counts, before >> doing groupByKey. >> >> On Sat, Feb 28, 2015 at 11:38 AM, Aaron Davidson <ilike...@gmail.com> >> wrote: >> >>> All stated symptoms are consistent with GC pressure (other nodes timeout >>> trying to connect because of a long stop-the-world), quite possibly due to >>> groupByKey. groupByKey is a very expensive operation as it may bring all >>> the data for a particular partition into memory (in particular, it cannot >>> spill values for a single key, so if you have a single very skewed key you >>> can get behavior like this). >>> >>> On Sat, Feb 28, 2015 at 11:33 AM, Paweł Szulc <paul.sz...@gmail.com> >>> wrote: >>> >>>> But groupbykey will repartition according to numer of keys as I >>>> understand how it works. How do you know that you haven't reached the >>>> groupbykey phase? Are you using a profiler or do yoi base that assumption >>>> only on logs? >>>> >>>> sob., 28 lut 2015, 8:12 PM Arun Luthra użytkownik < >>>> arun.lut...@gmail.com> napisał: >>>> >>>> A correction to my first post: >>>>> >>>>> There is also a repartition right before groupByKey to help avoid >>>>> too-many-open-files error: >>>>> >>>>> >>>>> rdd2.union(rdd1).map(...).filter(...).repartition(15000).groupByKey().map(...).flatMap(...).saveAsTextFile() >>>>> >>>>> On Sat, Feb 28, 2015 at 11:10 AM, Arun Luthra <arun.lut...@gmail.com> >>>>> wrote: >>>>> >>>>>> The job fails before getting to groupByKey. >>>>>> >>>>>> I see a lot of timeout errors in the yarn logs, like: >>>>>> >>>>>> 15/02/28 12:47:16 WARN util.AkkaUtils: Error sending message in 1 >>>>>> attempts >>>>>> akka.pattern.AskTimeoutException: Timed out >>>>>> >>>>>> and >>>>>> >>>>>> 15/02/28 12:47:49 WARN util.AkkaUtils: Error sending message in 2 >>>>>> attempts >>>>>> java.util.concurrent.TimeoutException: Futures timed out after [30 >>>>>> seconds] >>>>>> >>>>>> and some of these are followed by: >>>>>> >>>>>> 15/02/28 12:48:02 ERROR executor.CoarseGrainedExecutorBackend: Driver >>>>>> Disassociated [akka.tcp://sparkExecutor@...] -> >>>>>> [akka.tcp://sparkDriver@...] disassociated! Shutting down. >>>>>> 15/02/28 12:48:02 ERROR executor.Executor: Exception in task 421027.0 >>>>>> in stage 1.0 (TID 336601) >>>>>> java.io.FileNotFoundException: >>>>>> ..../hadoop/yarn/local/......../spark-local-20150228123450-3a71/36/shuffle_0_421027_0 >>>>>> (No such file or directory) >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> On Sat, Feb 28, 2015 at 9:33 AM, Paweł Szulc <paul.sz...@gmail.com> >>>>>> wrote: >>>>>> >>>>>>> I would first check whether there is any possibility that after >>>>>>> doing groupbykey one of the groups does not fit in one of the executors' >>>>>>> memory. >>>>>>> >>>>>>> To back up my theory, instead of doing groupbykey + map try >>>>>>> reducebykey + mapvalues. >>>>>>> >>>>>>> Let me know if that helped. >>>>>>> >>>>>>> Pawel Szulc >>>>>>> http://rabbitonweb.com >>>>>>> >>>>>>> sob., 28 lut 2015, 6:22 PM Arun Luthra użytkownik < >>>>>>> arun.lut...@gmail.com> napisał: >>>>>>> >>>>>>> So, actually I am removing the persist for now, because there is >>>>>>>> significant filtering that happens after calling textFile()... but I >>>>>>>> will >>>>>>>> keep that option in mind. >>>>>>>> >>>>>>>> I just tried a few different combinations of number of executors, >>>>>>>> executor memory, and more importantly, number of tasks... *all >>>>>>>> three times it failed when approximately 75.1% of the tasks were >>>>>>>> completed >>>>>>>> (no matter how many tasks resulted from repartitioning the data in >>>>>>>> textfile(..., N))*. Surely this is a strong clue to something? >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> On Fri, Feb 27, 2015 at 1:07 PM, Burak Yavuz <brk...@gmail.com> >>>>>>>> wrote: >>>>>>>> >>>>>>>>> Hi, >>>>>>>>> >>>>>>>>> Not sure if it can help, but `StorageLevel.MEMORY_AND_DISK_SER` >>>>>>>>> generates many small objects that lead to very long GC time, causing >>>>>>>>> the >>>>>>>>> executor losts, heartbeat not received, and GC overhead limit exceeded >>>>>>>>> messages. >>>>>>>>> Could you try using `StorageLevel.MEMORY_AND_DISK` instead? You >>>>>>>>> can also try `OFF_HEAP` (and use Tachyon). >>>>>>>>> >>>>>>>>> Burak >>>>>>>>> >>>>>>>>> On Fri, Feb 27, 2015 at 11:39 AM, Arun Luthra < >>>>>>>>> arun.lut...@gmail.com> wrote: >>>>>>>>> >>>>>>>>>> My program in pseudocode looks like this: >>>>>>>>>> >>>>>>>>>> val conf = new SparkConf().setAppName("Test") >>>>>>>>>> .set("spark.storage.memoryFraction","0.2") // default 0.6 >>>>>>>>>> .set("spark.shuffle.memoryFraction","0.12") // default 0.2 >>>>>>>>>> .set("spark.shuffle.manager","SORT") // preferred setting >>>>>>>>>> for optimized joins >>>>>>>>>> .set("spark.shuffle.consolidateFiles","true") // helpful >>>>>>>>>> for "too many files open" >>>>>>>>>> .set("spark.mesos.coarse", "true") // helpful for >>>>>>>>>> MapOutputTracker errors? >>>>>>>>>> .set("spark.akka.frameSize","500") // helpful when using >>>>>>>>>> consildateFiles=true >>>>>>>>>> .set("spark.akka.askTimeout", "30") >>>>>>>>>> .set("spark.shuffle.compress","false") // >>>>>>>>>> http://apache-spark-user-list.1001560.n3.nabble.com/Fetch-Failure-tp20787p20811.html >>>>>>>>>> .set("spark.file.transferTo","false") // >>>>>>>>>> http://apache-spark-user-list.1001560.n3.nabble.com/Fetch-Failure-tp20787p20811.html >>>>>>>>>> .set("spark.core.connection.ack.wait.timeout","600") // >>>>>>>>>> http://apache-spark-user-list.1001560.n3.nabble.com/Fetch-Failure-tp20787p20811.html >>>>>>>>>> .set("spark.speculation","true") >>>>>>>>>> .set("spark.worker.timeout","600") // >>>>>>>>>> http://apache-spark-user-list.1001560.n3.nabble.com/Heartbeat-exceeds-td3798.html >>>>>>>>>> .set("spark.akka.timeout","300") // >>>>>>>>>> http://apache-spark-user-list.1001560.n3.nabble.com/Heartbeat-exceeds-td3798.html >>>>>>>>>> .set("spark.storage.blockManagerSlaveTimeoutMs","120000") >>>>>>>>>> .set("spark.driver.maxResultSize","2048") // in response to >>>>>>>>>> error: Total size of serialized results of 39901 tasks (1024.0 MB) is >>>>>>>>>> bigger than spark.driver.maxResultSize (1024.0 MB) >>>>>>>>>> .set("spark.serializer", >>>>>>>>>> "org.apache.spark.serializer.KryoSerializer") >>>>>>>>>> >>>>>>>>>> .set("spark.kryo.registrator","com.att.bdcoe.cip.ooh.MyRegistrator") >>>>>>>>>> .set("spark.kryo.registrationRequired", "true") >>>>>>>>>> >>>>>>>>>> val rdd1 = sc.textFile(file1).persist(StorageLevel >>>>>>>>>> .MEMORY_AND_DISK_SER).map(_.split("\\|", -1)...filter(...) >>>>>>>>>> >>>>>>>>>> val rdd2 = >>>>>>>>>> sc.textFile(file2).persist(StorageLevel.MEMORY_AND_DISK_SER).map(_.split("\\|", >>>>>>>>>> -1)...filter(...) >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> rdd2.union(rdd1).map(...).filter(...).groupByKey().map(...).flatMap(...).saveAsTextFile() >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> I run the code with: >>>>>>>>>> --num-executors 500 \ >>>>>>>>>> --driver-memory 20g \ >>>>>>>>>> --executor-memory 20g \ >>>>>>>>>> --executor-cores 32 \ >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> I'm using kryo serialization on everything, including broadcast >>>>>>>>>> variables. >>>>>>>>>> >>>>>>>>>> Spark creates 145k tasks, and the first stage includes everything >>>>>>>>>> before groupByKey(). It fails before getting to groupByKey. I have >>>>>>>>>> tried >>>>>>>>>> doubling and tripling the number of partitions when calling >>>>>>>>>> textFile, with >>>>>>>>>> no success. >>>>>>>>>> >>>>>>>>>> Very similar code (trivial changes, to accomodate different >>>>>>>>>> input) worked on a smaller input (~8TB)... Not that it was easy to >>>>>>>>>> get that >>>>>>>>>> working. >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> Errors vary, here is what I am getting right now: >>>>>>>>>> >>>>>>>>>> ERROR SendingConnection: Exception while reading >>>>>>>>>> SendingConnection ... java.nio.channels.ClosedChannelException >>>>>>>>>> (^ guessing that is symptom of something else) >>>>>>>>>> >>>>>>>>>> WARN BlockManagerMasterActor: Removing BlockManager >>>>>>>>>> BlockManagerId(...) with no recent heart beats: 120030ms exceeds >>>>>>>>>> 120000ms >>>>>>>>>> (^ guessing that is symptom of something else) >>>>>>>>>> >>>>>>>>>> ERROR ActorSystemImpl: Uncaught fatal error from thread (...) >>>>>>>>>> shutting down ActorSystem [sparkDriver] >>>>>>>>>> *java.lang.OutOfMemoryError: GC overhead limit exceeded* >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> Other times I will get messages about "executor lost..." about 1 >>>>>>>>>> message per second, after ~~50k tasks complete, until there are >>>>>>>>>> almost no >>>>>>>>>> executors left and progress slows to nothing. >>>>>>>>>> >>>>>>>>>> I ran with verbose GC info; I do see failing yarn containers that >>>>>>>>>> have multiple (like 30) "Full GC" messages but I don't know how to >>>>>>>>>> interpret if that is the problem. Typical Full GC time taken seems >>>>>>>>>> ok: [Times: user=23.30 sys=0.06, real=1.94 secs] >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> Suggestions, please? >>>>>>>>>> >>>>>>>>>> Huge thanks for useful suggestions, >>>>>>>>>> Arun >>>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>> >>>>>> >>>>> >>> >> >
