Great, this is the best way to use the APIs. The big win with CCO, the algo you are using is with multiple user actions. Be aware that when you go to this methods the input IndexedDatasets must be coerced to have compatible dimensionality, in this case the primary action defines the user-set used in calculating the model—not the one for making queries, which can use anonymous user history. But that is for later and outside Mahout.
1) 4x max parallelism is a rule of thumb since the cores may not need 100% duty cycle, if they are already at 100% the 4x does no good. 2) you have found a long running task but there will always be one, if it weren’t this one it would be another. Different types of tasks use resources differently. For instance the collects, which must eventually use a the memory of the Driver to instantiate an in-memory data structure. There is no magic choice to make this work differently but it avoid several joins, which are much slower. I’m not quite sure what your question is. On Aug 15, 2017, at 6:21 AM, Scruggs, Matt <matt.scru...@bronto.com> wrote: Hi Pat, I've taken some screenshots of my Spark UI to hopefully shed some light on the behavior I'm seeing. Do you mind if I send you a link via direct email (would rather not post it here)? It's just a shared Dropbox folder. Thanks, Matt On 8/14/17, 11:34 PM, "Scruggs, Matt" <matt.scru...@bronto.com> wrote: > I'm running a custom Scala app (distributed in a shaded jar) directly calling > SimilarityAnalysis.cooccurrenceIDSs(), not using the CLI. > > The input data already gets explicitly repartitioned to spark.cores.max > (defaultParallelism) in our code. I'll try increasing that by the factor of 4 > that you suggest, but all our cores are already utilized so I'm not sure that > will help. It gets bogged down in the post-shuffle (shuffle read / combine / > reduce) phase even with all cores busy the whole time, which is why I've been > playing around with various values for spark.sql.shuffle.partitions. The > O(log n) operations I mentioned seem to take >95% of runtime. > > Thanks, > Matt > ________________________________ > From: Pat Ferrel <p...@occamsmachete.com> > Sent: Monday, August 14, 2017 11:02:42 PM > To: user@mahout.apache.org > Subject: Re: spark-itemsimilarity scalability / Spark parallelism issues > (SimilarityAnalysis.cooccurrencesIDSs) > > Are you using the CLI? If so it’s likely that there is only one partition of > the data. If you use Mahout in the Spark shell or using it as a lib, do a > repartition on the input data before passing it into > SimilarityAnalysis.cooccurrencesIDSs. I repartition to 4*total cores to start > with and set max parallelism for spark to the same. The CLI isn’t really > production worthy, just for super easy experiments with CSVs. > > > On Aug 14, 2017, at 2:31 PM, Scruggs, Matt <matt.scru...@bronto.com> wrote: > > Howdy, > > I'm running SimilarityAnalysis.cooccurrencesIDSs on a fairly small dataset > (about 870k [user, item] rows in the primary action IDS…no cross > co-occurrence IDS) and I noticed it scales strangely. This is with Mahout > 0.13.0 although the same behavior happens in 0.12.x as well (haven't tested > it before that). > > TLDR - regardless of the Spark parallelism (CPUs) I throw at this routine, > every Spark task within the final / busy stage seems to take the same amount > of time, which leads me to guess that every shuffle partition contains the > same amount of data (perhaps the full dataset matrix in shape/size, albeit > with different values). I'm reaching out to see if this is a known > algorithmic complexity issue in this routine, or if my config is to blame (or > both). > > Regarding our hardware, we have identical physical machines in a Mesos > cluster with 6 workers and a few masters. Each worker has ~500GB of SSD, 32 > cores and 128g RAM. We run lots of Spark jobs and have generally ironed out > the kinks in terms of hardware and cluster config, so I don't suspect any > hardware-related issues. > > Here are some timings for SimilarityAnalysis.cooccurrencesIDSs on this > dataset with maxNumInteractions = 500, maxInterestingItemsPerThing = 20, > randomSeed = default, parOpts = default (there's lots of other Spark config, > this is just what I'm varying to check for effects). In particular, notice > how the ratio of (spark.sql.shuffle.partitions / spark.cores.max) affects the > runtime: > > * 8 executors w/8 cores each, takes about 45 minutes > * note that spark.sql.shuffle.partitions > spark.cores.max > spark.cores.max = 64 > spark.executor.cores = 8 > spark.sql.shuffle.partitions = 200 (default) > > * 1 executors w/24 cores, takes about 65 minutes > * note that spark.sql.shuffle.partitions >>> spark.cores.max > spark.cores.max = 24 > spark.executor.cores = 24 > spark.sql.shuffle.partitions = 200 (default) > > * 1 executor w/8 cores, takes about 8 minutes > * note that spark.sql.shuffle.partitions = spark.cores.max > spark.cores.max = 8 > spark.executor.cores = 8 (1 executor w/8 cores) > spark.sql.shuffle.partitions = 8 > > * 1 executor w/24 cores, takes about 8 minutes (same as 8 cores!) > * note that spark.sql.shuffle.partitions = spark.cores.max > spark.cores.max = 24 > spark.executor.cores = 24 (1 executor w/24 cores) > spark.sql.shuffle.partitions = 24 > > * 32 executors w/2 cores each, takes about 8 minutes (same as 8 cores!) > * note that spark.sql.shuffle.partitions = spark.cores.max > spark.cores.max = 64 > spark.executor.cores = 2 > spark.sql.shuffle.partitions = 88 (results in 64 tasks for final stage) > > Adjusting the "maxNumInteractions" parameter down to 100 and 50 results in a > minor improvement (5-10%). I've also played around with removing [user, item] > rows from the input dataset for users with only 1 interaction…I read to try > that in another thread…that yielded maybe a 40-50% speed improvement, but I'd > rather not toss out data (unless it truly is totally useless, of course :D ). > > When I look at the thread dump within the Spark UI's Executors -> thread dump > pages, it seems all the executors are very busy in the code pasted below for > >95% of the run. GC throughput is very good so we're not bogged down > there...it's just super busy doing running the code below. I am intrigued > about the comments on the SequentialAccessSparseVector methods I see being > called (getQuick and setQuick), which state they take O(log n) time > (https://github.com/apache/mahout/blob/08e02602e947ff945b9bd73ab5f0b45863df3e53/math/src/main/java/org/apache/mahout/math/SequentialAccessSparseVector.java). > > > Thanks all for your time and feedback! > > Matt Scruggs > > org.apache.mahout.math.OrderedIntDoubleMapping.find(OrderedIntDoubleMapping.java:105) > org.apache.mahout.math.OrderedIntDoubleMapping.get(OrderedIntDoubleMapping.java:110) > org.apache.mahout.math.SequentialAccessSparseVector.getQuick(SequentialAccessSparseVector.java:157) > org.apache.mahout.math.SparseRowMatrix.getQuick(SparseRowMatrix.java:90) > org.apache.mahout.math.AbstractMatrix.assign(AbstractMatrix.java:240) > org.apache.mahout.math.scalabindings.MatrixOps.$plus$eq(MatrixOps.scala:45) > org.apache.mahout.sparkbindings.blas.AtA$$anonfun$19.apply(AtA.scala:258) > org.apache.mahout.sparkbindings.blas.AtA$$anonfun$19.apply(AtA.scala:258) > org.apache.spark.util.collection.ExternalAppendOnlyMap$$anonfun$3.apply(ExternalAppendOnlyMap.scala:151) > org.apache.spark.util.collection.ExternalAppendOnlyMap$$anonfun$3.apply(ExternalAppendOnlyMap.scala:150) > org.apache.spark.util.collection.AppendOnlyMap.changeValue(AppendOnlyMap.scala:144) > org.apache.spark.util.collection.SizeTrackingAppendOnlyMap.changeValue(SizeTrackingAppendOnlyMap.scala:32) > org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:163) > org.apache.spark.Aggregator.combineCombinersByKey(Aggregator.scala:50) > org.apache.spark.shuffle.BlockStoreShuffleReader.read(BlockStoreShuffleReader.scala:85) > org.apache.spark.rdd.ShuffledRDD.compute(ShuffledRDD.scala:109) > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) > org.apache.spark.rdd.RDD.iterator(RDD.scala:283) > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) > org.apache.spark.rdd.RDD.iterator(RDD.scala:283) > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) > org.apache.spark.rdd.RDD.iterator(RDD.scala:283) > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) > org.apache.spark.rdd.RDD.iterator(RDD.scala:283) > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) > org.apache.spark.rdd.RDD.iterator(RDD.scala:283) > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) > org.apache.spark.rdd.RDD.iterator(RDD.scala:283) > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) > org.apache.spark.rdd.RDD.iterator(RDD.scala:283) > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) > org.apache.spark.rdd.RDD.iterator(RDD.scala:283) > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) > org.apache.spark.rdd.RDD.iterator(RDD.scala:283) > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79) > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47) > org.apache.spark.scheduler.Task.run(Task.scala:86) > org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274) > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) > java.lang.Thread.run(Thread.java:745) > > ……or this code…… > > org.apache.mahout.math.SparseRowMatrix.setQuick(SparseRowMatrix.java:105) > org.apache.mahout.math.AbstractMatrix.assign(AbstractMatrix.java:240) > org.apache.mahout.math.scalabindings.MatrixOps.$plus$eq(MatrixOps.scala:45) > org.apache.mahout.sparkbindings.blas.AtA$$anonfun$19.apply(AtA.scala:258) > org.apache.mahout.sparkbindings.blas.AtA$$anonfun$19.apply(AtA.scala:258) > org.apache.spark.util.collection.ExternalAppendOnlyMap$$anonfun$3.apply(ExternalAppendOnlyMap.scala:151) > org.apache.spark.util.collection.ExternalAppendOnlyMap$$anonfun$3.apply(ExternalAppendOnlyMap.scala:150) > org.apache.spark.util.collection.AppendOnlyMap.changeValue(AppendOnlyMap.scala:144) > org.apache.spark.util.collection.SizeTrackingAppendOnlyMap.changeValue(SizeTrackingAppendOnlyMap.scala:32) > org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:163) > org.apache.spark.Aggregator.combineCombinersByKey(Aggregator.scala:50) > org.apache.spark.shuffle.BlockStoreShuffleReader.read(BlockStoreShuffleReader.scala:85) > org.apache.spark.rdd.ShuffledRDD.compute(ShuffledRDD.scala:109) > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) > org.apache.spark.rdd.RDD.iterator(RDD.scala:283) > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) > org.apache.spark.rdd.RDD.iterator(RDD.scala:283) > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) > org.apache.spark.rdd.RDD.iterator(RDD.scala:283) > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) > org.apache.spark.rdd.RDD.iterator(RDD.scala:283) > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) > org.apache.spark.rdd.RDD.iterator(RDD.scala:283) > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) > org.apache.spark.rdd.RDD.iterator(RDD.scala:283) > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) > org.apache.spark.rdd.RDD.iterator(RDD.scala:283) > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) > org.apache.spark.rdd.RDD.iterator(RDD.scala:283) > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) > org.apache.spark.rdd.RDD.iterator(RDD.scala:283) > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79) > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47) > org.apache.spark.scheduler.Task.run(Task.scala:86) > org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274) > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) > java.lang.Thread.run(Thread.java:745) >
