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 >> >
