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
