spark-itemsimilarity scalability / Spark parallelism issues (SimilarityAnalysis.cooccurrencesIDSs)

Discussion:

Scruggs, Matt

2017-08-14 21:31:15 UTC

Howdy,

I'm running SimilarityAnalysis.cooccurrencesIDSs on a fairly small dataset (about 870k [user, item] rows in the primary action IDSâŠno 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 interactionâŠI read to try that in another threadâŠthat 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)

âŠâŠor this codeâŠâŠ

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)

Pat Ferrel

2017-08-15 03:02:42 UTC

Permalink

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 <***@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)

Scruggs, Matt

2017-08-15 03:34:18 UTC

Permalink

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 <***@occamsmachete.com>
Sent: Monday, August 14, 2017 11:02:42 PM
To: ***@mahout.apache.org
Subject: Re: spark-itemsimilarity scalability / Spark parallelism issues (SimilarityAnalysis.cooccurrencesIDSs)

Are you using the CLI? If so its 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 isnt really production worthy, just for super easy experiments with CSVs.

On Aug 14, 2017, at 2:31 PM, Scruggs, Matt <***@bronto.com> wrote:

Howdy,

I'm running SimilarityAnalysis.cooccurrencesIDSs on a fairly small dataset (about 870k [user, item] rows in the primary action IDSno 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 interactionI read to try that in another threadthat 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)

Scruggs, Matt

2017-08-15 13:21:44 UTC

Permalink

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

Post by Scruggs, Matt
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
________________________________
Sent: Monday, August 14, 2017 11:02:42 PM
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.
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.
* 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.

Pat Ferrel

2017-08-15 14:15:44 UTC

Permalink

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 <***@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

Scruggs, Matt

2017-08-15 18:13:13 UTC

Permalink

Thanks Pat, that's good to know!

This is the "reduce" step (which gets its own stage in my Spark jobs...this stage takes almost all the runtime) where most of the work is being done, and takes longer the more shuffle partitions there are (relative to # of CPUs):

https://github.com/apache/mahout/blob/08e02602e947ff945b9bd73ab5f0b45863df3e53/spark/src/main/scala/org/apache/mahout/sparkbindings/blas/AtA.scala#L258

Why does the runtime of this reduce stage (that ultimately calls SequentialAccessSparseVector.getQuick() and setQuick() a lot) depend on the ratio of (# Spark CPUs / spark.sql.shuffle.partitions)? Essentially that ratio determines how many "chunks" of shuffle partition (reduce) tasks must run, and each of those chunks always takes the same amount of time, so the stage finishes in less time when that ratio is low (preferably 1).

EXAMPLES - using 32 cores and 200 shuffle partitions, this stage requires ceil(145 tasks / 32 cores) = 5 "chunks" of work (145 tasks instead of 200 because of the estimateProductPartitions call in AtA). Each chunk takes ~8 minutes, so (5 chunks * 8 min) = ~40 mins. For a job with 32 cores and 32 shuffle partitions (same CPU resources, still 100% utilized), this stage requires only ceil(23 tasks / 32 cores) = 1 chunk of work, which takes the same 8 minutes, so the job finishes ~5x faster. You can take this to the extreme with just 1 core and 1 shuffle partition, and the stage still takes the same amount of time! I'd love to know if you can reproduce this behavior.

This goes against most advice and experience I've had with Spark, where you want to *increase* your partitioning in many cases (or at least leave it at the default 200, not lower it dramatically) to utilize CPUs better (and shrink each individual partition's task). There seems to be no reduction in computational complexity *per task* (within this stage I'm talking about) even with high values for spark.sql.shuffle.partitions (so it seems the data isn't actually being partitioned by the shuffle process). Refer back to the timings w/various configs in my first message.

Also...is there a possibility of using a faster hash-based implementation instead of the setQuick() / getQuick() methods of SequentialAccessSparseVector? The javadoc on those methods mentions they shouldn't be used unless absolutely necessary due to their O(log n) complexity.

Thanks for your time...this is fun stuff!
Matt

Post by Pat Ferrel
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.
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

Pat Ferrel

2017-08-16 23:47:46 UTC

Permalink

This uses the Mahout blas optimizing solver, which I just use and do not know well. Mahout virtualizes some things having to do with partitioning and I’ve never quite understood how they work. There is a .par() on one of the matrix classes that has a similar function to partition but in all cases I’ve used .par(auto) and use normal spark repartitioning based on parallelism. Mahout implements a mapBlock function, which (all things being equal) looks at a partition at a time in memory. The reduce is not part of the code I wrote.

The reduce is not part of the code I wrote. Maybe someone else can explain what blas is doing.

BTW hashmap is O(log n) on average for large n—caveats apply. We use fastutils for many things (I thought this was one case) because they are faster than JVM implementations but feel free to dig in further. We use downsampling to maintain an overall rough O(n) calculation speed where n = # rows (users). As the model gets more dense there are greatly diminishing returns for the density so after the elements per row threshold is reached we don’t use more in the model creation math.

Still feel free to dig into what the optimizer is doing.

On Aug 15, 2017, at 11:13 AM, Scruggs, Matt <***@bronto.com> wrote:

Thanks Pat, that's good to know!

This is the "reduce" step (which gets its own stage in my Spark jobs...this stage takes almost all the runtime) where most of the work is being done, and takes longer the more shuffle partitions there are (relative to # of CPUs):

https://github.com/apache/mahout/blob/08e02602e947ff945b9bd73ab5f0b45863df3e53/spark/src/main/scala/org/apache/mahout/sparkbindings/blas/AtA.scala#L258

Why does the runtime of this reduce stage (that ultimately calls SequentialAccessSparseVector.getQuick() and setQuick() a lot) depend on the ratio of (# Spark CPUs / spark.sql.shuffle.partitions)? Essentially that ratio determines how many "chunks" of shuffle partition (reduce) tasks must run, and each of those chunks always takes the same amount of time, so the stage finishes in less time when that ratio is low (preferably 1).

EXAMPLES - using 32 cores and 200 shuffle partitions, this stage requires ceil(145 tasks / 32 cores) = 5 "chunks" of work (145 tasks instead of 200 because of the estimateProductPartitions call in AtA). Each chunk takes ~8 minutes, so (5 chunks * 8 min) = ~40 mins. For a job with 32 cores and 32 shuffle partitions (same CPU resources, still 100% utilized), this stage requires only ceil(23 tasks / 32 cores) = 1 chunk of work, which takes the same 8 minutes, so the job finishes ~5x faster. You can take this to the extreme with just 1 core and 1 shuffle partition, and the stage still takes the same amount of time! I'd love to know if you can reproduce this behavior.

This goes against most advice and experience I've had with Spark, where you want to *increase* your partitioning in many cases (or at least leave it at the default 200, not lower it dramatically) to utilize CPUs better (and shrink each individual partition's task). There seems to be no reduction in computational complexity *per task* (within this stage I'm talking about) even with high values for spark.sql.shuffle.partitions (so it seems the data isn't actually being partitioned by the shuffle process). Refer back to the timings w/various configs in my first message.

Also...is there a possibility of using a faster hash-based implementation instead of the setQuick() / getQuick() methods of SequentialAccessSparseVector? The javadoc on those methods mentions they shouldn't be used unless absolutely necessary due to their O(log n) complexity.

Thanks for your time...this is fun stuff!
Matt

Ted Dunning

2017-08-17 00:29:35 UTC

Permalink

It is common with large numerical codes that things run faster in memory on
just a few cores if the communication required outweighs the parallel
speedup.

The issue is that memory bandwidth is slower than the arithmetic speed by a
very good amount. If you just have to move stuff into the CPU and munch on
it a bit it is one thing, but if you have to move the data to CPU and back
to memory to distributed it around possibly multiple times, you may wind up
with something much slower than you would have had if you were to attack
the problem directly.

Post by Pat Ferrel
This uses the Mahout blas optimizing solver, which I just use and do not
know well. Mahout virtualizes some things having to do with partitioning
and Iâve never quite understood how they work. There is a .par() on one of
the matrix classes that has a similar function to partition but in all
cases Iâve used .par(auto) and use normal spark repartitioning based on
parallelism. Mahout implements a mapBlock function, which (all things being
equal) looks at a partition at a time in memory. The reduce is not part of
the code I wrote.
The reduce is not part of the code I wrote. Maybe someone else can explain
what blas is doing.
BTW hashmap is O(log n) on average for large nâcaveats apply. We use
fastutils for many things (I thought this was one case) because they are
faster than JVM implementations but feel free to dig in further. We use
downsampling to maintain an overall rough O(n) calculation speed where n =
# rows (users). As the model gets more dense there are greatly diminishing
returns for the density so after the elements per row threshold is reached
we donât use more in the model creation math.
Still feel free to dig into what the optimizer is doing.
Thanks Pat, that's good to know!
This is the "reduce" step (which gets its own stage in my Spark
jobs...this stage takes almost all the runtime) where most of the work is
being done, and takes longer the more shuffle partitions there are
https://github.com/apache/mahout/blob/08e02602e947ff945b9bd73ab5f0b4
5863df3e53/spark/src/main/scala/org/apache/mahout/
sparkbindings/blas/AtA.scala#L258
Why does the runtime of this reduce stage (that ultimately calls
SequentialAccessSparseVector.getQuick() and setQuick() a lot) depend on
the ratio of (# Spark CPUs / spark.sql.shuffle.partitions)? Essentially
that ratio determines how many "chunks" of shuffle partition (reduce) tasks
must run, and each of those chunks always takes the same amount of time, so
the stage finishes in less time when that ratio is low (preferably 1).
EXAMPLES - using 32 cores and 200 shuffle partitions, this stage requires
ceil(145 tasks / 32 cores) = 5 "chunks" of work (145 tasks instead of 200
because of the estimateProductPartitions call in AtA). Each chunk takes ~8
minutes, so (5 chunks * 8 min) = ~40 mins. For a job with 32 cores and 32
shuffle partitions (same CPU resources, still 100% utilized), this stage
requires only ceil(23 tasks / 32 cores) = 1 chunk of work, which takes the
same 8 minutes, so the job finishes ~5x faster. You can take this to the
extreme with just 1 core and 1 shuffle partition, and the stage still takes
the same amount of time! I'd love to know if you can reproduce this
behavior.
This goes against most advice and experience I've had with Spark, where
you want to *increase* your partitioning in many cases (or at least leave
it at the default 200, not lower it dramatically) to utilize CPUs better
(and shrink each individual partition's task). There seems to be no
reduction in computational complexity *per task* (within this stage I'm
talking about) even with high values for spark.sql.shuffle.partitions (so
it seems the data isn't actually being partitioned by the shuffle process).
Refer back to the timings w/various configs in my first message.
Also...is there a possibility of using a faster hash-based implementation
instead of the setQuick() / getQuick() methods of
SequentialAccessSparseVector? The javadoc on those methods mentions they
shouldn't be used unless absolutely necessary due to their O(log n)
complexity.
Thanks for your time...this is fun stuff!
Matt

Post by Pat Ferrel
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 modelânot the one for making queries,
which can use anonymous user history. But that is for later and outside
Mahout.

Post by Pat Ferrel
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 werenât
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.

Post by Pat Ferrel
Iâm not quite sure what your question is.
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.

Post by Pat Ferrel
Thanks,
Matt

Post by Scruggs, Matt
I'm running a custom Scala app (distributed in a shaded jar) directly

calling SimilarityAnalysis.cooccurrenceIDSs(), not using the CLI.