Hi Bharath, This is related to SPARK-1112, which we already found the root cause. I will let you know when this is fixed.
Best, Xiangrui On Tue, Jun 17, 2014 at 7:37 PM, Bharath Ravi Kumar <reachb...@gmail.com> wrote: > Couple more points: > 1)The inexplicable stalling of execution with large feature sets appears > similar to that reported with the news-20 dataset: > http://mail-archives.apache.org/mod_mbox/spark-user/201406.mbox/%3c53a03542.1010...@gmail.com%3E > > 2) The NPE trying to call mapToPair convert an RDD<Long, Long, Integer, > Integer> into a JavaPairRDD<Tuple2<Long,Long>, Tuple2<Integer,Integer>> is > unrelated to mllib. > > Thanks, > Bharath > > > > On Wed, Jun 18, 2014 at 7:14 AM, Bharath Ravi Kumar <reachb...@gmail.com> > wrote: >> >> Hi Xiangrui , >> >> I'm using 1.0.0. >> >> Thanks, >> Bharath >> >> On 18-Jun-2014 1:43 am, "Xiangrui Meng" <men...@gmail.com> wrote: >>> >>> Hi Bharath, >>> >>> Thanks for posting the details! Which Spark version are you using? >>> >>> Best, >>> Xiangrui >>> >>> On Tue, Jun 17, 2014 at 6:48 AM, Bharath Ravi Kumar <reachb...@gmail.com> >>> wrote: >>> > Hi, >>> > >>> > (Apologies for the long mail, but it's necessary to provide sufficient >>> > details considering the number of issues faced.) >>> > >>> > I'm running into issues testing LogisticRegressionWithSGD a two node >>> > cluster >>> > (each node with 24 cores and 16G available to slaves out of 24G on the >>> > system). Here's a description of the application: >>> > >>> > The model is being trained based on categorical features x, y, and >>> > (x,y). >>> > The categorical features are mapped to binary features by converting >>> > each >>> > distinct value in the category enum into a binary feature by itself >>> > (i.e >>> > presence of that value in a record implies corresponding feature = 1, >>> > else >>> > feature = 0. So, there'd be as many distinct features as enum values) . >>> > The >>> > training vector is laid out as >>> > [x1,x2...xn,y1,y2....yn,(x1,y1),(x2,y2)...(xn,yn)]. Each record in the >>> > training data has only one combination (Xk,Yk) and a label appearing in >>> > the >>> > record. Thus, the corresponding labeledpoint sparse vector would only >>> > have 3 >>> > values Xk, Yk, (Xk,Yk) set for a record. The total length of the vector >>> > (though parse) would be nearly 614000. The number of records is about >>> > 1.33 >>> > million. The records have been coalesced into 20 partitions across two >>> > nodes. The input data has not been cached. >>> > (NOTE: I do realize the records & features may seem large for a two >>> > node >>> > setup, but given the memory & cpu, and the fact that I'm willing to >>> > give up >>> > some turnaround time, I don't see why tasks should inexplicably fail) >>> > >>> > Additional parameters include: >>> > >>> > spark.executor.memory = 14G >>> > spark.default.parallelism = 1 >>> > spark.cores.max=20 >>> > spark.storage.memoryFraction=0.8 //No cache space required >>> > (Trying to set spark.akka.frameSize to a larger number, say, 20 didn't >>> > help >>> > either) >>> > >>> > The model training was initialized as : new >>> > LogisticRegressionWithSGD(1, >>> > maxIterations, 0.0, 0.05) >>> > >>> > However, after 4 iterations of gradient descent, the entire execution >>> > appeared to stall inexplicably. The corresponding executor details and >>> > details of the stalled stage (number 14) are as follows: >>> > >>> > Metric Min 25th Median 75th >>> > Max >>> > Result serialization time 12 ms 13 ms 14 ms 16 ms 18 ms >>> > Duration 4 s 4 s 5 s 5 s >>> > 5 s >>> > Time spent fetching task 0 ms 0 ms 0 ms 0 ms 0 ms >>> > results >>> > Scheduler delay 6 s 6 s 6 s 6 s >>> > 12 s >>> > >>> > >>> > Stage Id >>> > 14 aggregate at GradientDescent.scala:178 >>> > >>> > Task Index Task ID Status Locality Level Executor >>> > Launch Time Duration GC Result Ser Time Errors >>> > >>> > Time >>> > >>> > 0 600 RUNNING PROCESS_LOCAL serious.dataone.foo.bar.com >>> > 2014/06/17 10:32:27 1.1 h >>> > 1 601 RUNNING PROCESS_LOCAL casual.dataone.foo.bar.com >>> > 2014/06/17 10:32:27 1.1 h >>> > 2 602 RUNNING PROCESS_LOCAL serious.dataone.foo.bar.com >>> > 2014/06/17 10:32:27 1.1 h >>> > 3 603 RUNNING PROCESS_LOCAL casual.dataone.foo.bar.com >>> > 2014/06/17 10:32:27 1.1 h >>> > 4 604 RUNNING PROCESS_LOCAL serious.dataone.foo.bar.com >>> > 2014/06/17 10:32:27 1.1 h >>> > 5 605 SUCCESS PROCESS_LOCAL casual.dataone.foo.bar.com >>> > 2014/06/17 10:32:27 4 s 2 s 12 ms >>> > 6 606 SUCCESS PROCESS_LOCAL serious.dataone.foo.bar.com >>> > 2014/06/17 10:32:27 4 s 1 s 14 ms >>> > 7 607 SUCCESS PROCESS_LOCAL casual.dataone.foo.bar.com >>> > 2014/06/17 10:32:27 4 s 2 s 12 ms >>> > 8 608 SUCCESS PROCESS_LOCAL serious.dataone.foo.bar.com >>> > 2014/06/17 10:32:27 5 s 1 s 15 ms >>> > 9 609 SUCCESS PROCESS_LOCAL casual.dataone.foo.bar.com >>> > 2014/06/17 10:32:27 5 s 1 s 14 ms >>> > 10 610 SUCCESS PROCESS_LOCAL >>> > serious.dataone.foo.bar.com >>> > 2014/06/17 10:32:27 5 s 1 s 15 ms >>> > 11 611 SUCCESS PROCESS_LOCAL casual.dataone.foo.bar.com >>> > 2014/06/17 10:32:27 4 s 1 s 13 ms >>> > 12 612 SUCCESS PROCESS_LOCAL >>> > serious.dataone.foo.bar.com >>> > 2014/06/17 10:32:27 5 s 1 s 18 ms >>> > 13 613 SUCCESS PROCESS_LOCAL casual.dataone.foo.bar.com >>> > 2014/06/17 10:32:27 5 s 1 s 13 ms >>> > 14 614 SUCCESS PROCESS_LOCAL >>> > serious.dataone.foo.bar.com >>> > 2014/06/17 10:32:27 4 s 1 s 14 ms >>> > 15 615 SUCCESS PROCESS_LOCAL casual.dataone.foo.bar.com >>> > 2014/06/17 10:32:27 4 s 1 s 12 ms >>> > 16 616 SUCCESS PROCESS_LOCAL >>> > serious.dataone.foo.bar.com >>> > 2014/06/17 10:32:27 5 s 1 s 15 ms >>> > 17 617 SUCCESS PROCESS_LOCAL casual.dataone.foo.bar.com >>> > 2014/06/17 10:32:27 5 s 1 s 18 ms >>> > 18 618 SUCCESS PROCESS_LOCAL >>> > serious.dataone.foo.bar.com >>> > 2014/06/17 10:32:27 5 s 1 s 16 ms >>> > 19 619 SUCCESS PROCESS_LOCAL casual.dataone.foo.bar.com >>> > 2014/06/17 10:32:27 4 s 1 s 18 ms >>> > >>> > Executor stats: >>> > >>> > RDD Blocks Memory Used Disk Used Active Tasks Failed Tasks >>> > Complete Tasks Total Tasks Task Time Shuffle Read Shuffle >>> > Write >>> > 0 0.0 B / 6.7 GB 0.0 B 2 0 >>> > 307 309 23.2 m 0.0 B 0.0 B >>> > 0 0.0 B / 6.7 GB 0.0 B 3 0 >>> > 308 311 22.4 m 0.0 B 0.0 B >>> > >>> > >>> > Executor jmap output: >>> > >>> > Server compiler detected. >>> > JVM version is 24.55-b03 >>> > >>> > using thread-local object allocation. >>> > Parallel GC with 18 thread(s) >>> > >>> > Heap Configuration: >>> > MinHeapFreeRatio = 40 >>> > MaxHeapFreeRatio = 70 >>> > MaxHeapSize = 10737418240 (10240.0MB) >>> > NewSize = 1310720 (1.25MB) >>> > MaxNewSize = 17592186044415 MB >>> > OldSize = 5439488 (5.1875MB) >>> > NewRatio = 2 >>> > SurvivorRatio = 8 >>> > PermSize = 21757952 (20.75MB) >>> > MaxPermSize = 134217728 (128.0MB) >>> > G1HeapRegionSize = 0 (0.0MB) >>> > >>> > Heap Usage: >>> > PS Young Generation >>> > Eden Space: >>> > capacity = 2783969280 (2655.0MB) >>> > used = 192583816 (183.66223907470703MB) >>> > free = 2591385464 (2471.337760925293MB) >>> > 6.917598458557704% used >>> > From Space: >>> > capacity = 409993216 (391.0MB) >>> > used = 1179808 (1.125152587890625MB) >>> > free = 408813408 (389.8748474121094MB) >>> > 0.2877628102022059% used >>> > To Space: >>> > capacity = 385351680 (367.5MB) >>> > used = 0 (0.0MB) >>> > free = 385351680 (367.5MB) >>> > 0.0% used >>> > PS Old Generation >>> > capacity = 7158628352 (6827.0MB) >>> > used = 4455093024 (4248.707794189453MB) >>> > free = 2703535328 (2578.292205810547MB) >>> > 62.2338918146983% used >>> > PS Perm Generation >>> > capacity = 90701824 (86.5MB) >>> > used = 45348832 (43.248016357421875MB) >>> > free = 45352992 (43.251983642578125MB) >>> > 49.99770677158598% used >>> > >>> > 8432 interned Strings occupying 714672 bytes. >>> > >>> > >>> > Executor GC log snippet: >>> > >>> > 168.778: [GC [PSYoungGen: 2702831K->578545K(2916864K)] >>> > 9302453K->7460857K(9907712K), 0.3193550 secs] [Times: user=5.13 >>> > sys=0.39, >>> > real=0.32 secs] >>> > 169.097: [Full GC [PSYoungGen: 578545K->0K(2916864K)] [ParOldGen: >>> > 6882312K->1073297K(6990848K)] 7460857K->1073297K(9907712K) [PSPermGen: >>> > 44248K->44201K(88576K)], 4.5521090 secs] [Times: user=24.22 sys=0.18, >>> > real=4.55 secs] >>> > 174.207: [GC [PSYoungGen: 2338304K->81315K(2544128K)] >>> > 3411653K->1154665K(9534976K), 0.0966280 secs] [Times: user=1.66 >>> > sys=0.00, >>> > real=0.09 secs] >>> > >>> > I tried to map partitions to cores on the nodes. Increasing the number >>> > of >>> > partitions (say to 80 or 100) would result in progress till the 6th >>> > iteration or so, but the next stage would stall as before with apparent >>> > root >>> > cause / logs. With increased partitions, the last stage that completed >>> > had >>> > the following task times: >>> > >>> > Metric Min 25th Median 75th Max >>> > Result serialization time 11 ms 12 ms 13 ms 15 ms 0.4 s >>> > Duration 0.5 s 0.9 s 1 s 3 s 7 s >>> > Time spent fetching 0 ms 0 ms 0 ms 0 ms 0 ms >>> > task results >>> > Scheduler delay 5 s 6 s 6 s 7 s >>> > 12 s >>> > >>> > My hypothesis is that as the coefficient array becomes less sparse >>> > (with >>> > successive iterations), the cost of the aggregate goes up to the point >>> > that >>> > it stalls (which I failed to explain). Reducing the batch fraction to a >>> > very >>> > low number like 0.01 saw the iterations progress further, but the model >>> > failed to converge in that case after a small number of iterations. >>> > >>> > >>> > I also tried reducing the number of records by aggregating on (x,y) as >>> > the >>> > key (i.e. using aggregations instead of training on every raw record), >>> > but >>> > encountered by the following exception: >>> > >>> > Loss was due to java.lang.NullPointerException >>> > java.lang.NullPointerException >>> > at >>> > >>> > org.apache.spark.api.java.JavaPairRDD$$anonfun$pairFunToScalaFun$1.apply(JavaPairRDD.scala:750) >>> > at >>> > >>> > org.apache.spark.api.java.JavaPairRDD$$anonfun$pairFunToScalaFun$1.apply(JavaPairRDD.scala:750) >>> > at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) >>> > at >>> > org.apache.spark.Aggregator.combineValuesByKey(Aggregator.scala:59) >>> > at >>> > >>> > org.apache.spark.rdd.PairRDDFunctions$$anonfun$1.apply(PairRDDFunctions.scala:96) >>> > at >>> > >>> > org.apache.spark.rdd.PairRDDFunctions$$anonfun$1.apply(PairRDDFunctions.scala:95) >>> > at org.apache.spark.rdd.RDD$$anonfun$14.apply(RDD.scala:582) >>> > at org.apache.spark.rdd.RDD$$anonfun$14.apply(RDD.scala:582) >>> > at >>> > >>> > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) >>> > at >>> > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) >>> > at org.apache.spark.rdd.RDD.iterator(RDD.scala:229) >>> > at >>> > >>> > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:158) >>> > at >>> > >>> > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99) >>> > at org.apache.spark.scheduler.Task.run(Task.scala:51) >>> > at >>> > org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:187) >>> > at >>> > >>> > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) >>> > at >>> > >>> > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) >>> > at java.lang.Thread.run(Thread.java:745) >>> > >>> > >>> > I'd appreciate any insights/comments about what may be causing the >>> > execution >>> > to stall. >>> > >>> > If logs/tables appear poorly indented in the email, here's a gist with >>> > relevant details: >>> > https://gist.github.com/reachbach/a418ab2f01b639b624c1 >>> > >>> > Thanks, >>> > Bharath > >
