soundcloud / cosine-lsh-join-spark Goto Github PK

hi there,
I tried to save result by using the following command in the end of Main.scala:

result.saveAsTextFile(output)

It worked on my laptop, but was stuck on my cluster. Can you help me with that?

Thanks a lot.
rubing

In the construction of signatures in matrixToBitSet, the vector given here is the one from the multiplication of inputMatrix and localRandomMatrix and this vector will be used to calculate the similarity of words, which implies that cos(θ(u, v)) = cos(θ(u * r, v * r)) (Here r is the random matrix). According to the original paper, it's suggested that cos(θ(u, v)) = cos((1 − Pr[hr(u) = hr(v)])π) = cos(hamming_distance / d * π) and I think it doesn't mean cos(hamming_distance / d * π) = cos(θ(u * r, v * r)) ? (Correct me if I'm wrong)

So I think we either

1. change indexedRow.vector in matrixToBitSet to be the corresponding row in inputMatrix; or
2. change Cosine(x.vector, y.vector) in neighbours() to be hamming(x.bitSet, y.bitSet)

I have a matrix with a dimension of 2M x 30K. It contains binary data and is highly sparse.
So I want to use LSH approach to find similar rows.

On the main.scala there is a part where a dense matrix is used. e.g.

val rows = indexed.map {
case ((word, features), index) =>
IndexedRow(index, Vectors.dense(features))
}

How could I implement it with a sparse matrix?

Thanks in advance.

Hi

My environment spark 2.3.0 with spark-mllib_2.11:2.3.0
When I include com.soundcloud:cosine-lsh-join-spark_2.11:1.0.4 in my shaowJar , it will cause the following exception.

[2018-06-22 08:20:37,962][ERROR] TransportRequestHandler : Error while invoking RpcHandler#receive() for one-way message.
java.lang.ClassCastException: cannot assign instance of scala.concurrent.duration.FiniteDuration to field org.apache.spark.rpc.RpcTimeout.duration of type scala.concurrent.duration.FiniteDuration in instance of org.apache.spark.rpc.RpcTimeout
at java.io.ObjectStreamClass$FieldReflector.setObjFieldValues(ObjectStreamClass.java:2233)
at java.io.ObjectStreamClass.setObjFieldValues(ObjectStreamClass.java:1405)
at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2284)
at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:2202)
at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:2060)
at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1567)
at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2278)
at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:2202)
at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:2060)
at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1567)
at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2278)
at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:2202)
at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:2060)
at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1567)
at java.io.ObjectInputStream.readObject(ObjectInputStream.java:427)
at org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:75)
at org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:108)
at org.apache.spark.rpc.netty.NettyRpcEnv$$anonfun$deserialize$1$$anonfun$apply$1.apply(NettyRpcEnv.scala:271)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
at org.apache.spark.rpc.netty.NettyRpcEnv.deserialize(NettyRpcEnv.scala:320)
at org.apache.spark.rpc.netty.NettyRpcEnv$$anonfun$deserialize$1.apply(NettyRpcEnv.scala:270)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
at org.apache.spark.rpc.netty.NettyRpcEnv.deserialize(NettyRpcEnv.scala:269)
at org.apache.spark.rpc.netty.RequestMessage$.apply(NettyRpcEnv.scala:611)
at org.apache.spark.rpc.netty.NettyRpcHandler.internalReceive(NettyRpcEnv.scala:662)
at org.apache.spark.rpc.netty.NettyRpcHandler.receive(NettyRpcEnv.scala:654)
at org.apache.spark.network.server.TransportRequestHandler.processOneWayMessage(TransportRequestHandler.java:208)
at org.apache.spark.network.server.TransportRequestHandler.handle(TransportRequestHandler.java:113)
at org.apache.spark.network.server.TransportChannelHandler.channelRead(TransportChannelHandler.java:118)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:362)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:348)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:340)
at io.netty.handler.timeout.IdleStateHandler.channelRead(IdleStateHandler.java:286)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:362)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:348)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:340)
at io.netty.handler.codec.MessageToMessageDecoder.channelRead(MessageToMessageDecoder.java:102)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:362)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:348)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:340)
at org.apache.spark.network.util.TransportFrameDecoder.channelRead(TransportFrameDecoder.java:85)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:362)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:348)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:340)
at io.netty.channel.DefaultChannelPipeline$HeadContext.channelRead(DefaultChannelPipeline.java:1359)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:362)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:348)
at io.netty.channel.DefaultChannelPipeline.fireChannelRead(DefaultChannelPipeline.java:935)
at io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:138)
at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:645)
at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:580)
at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:497)
at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:459)
at io.netty.util.concurrent.SingleThreadEventExecutor$5.run(SingleThreadEventExecutor.java:858)
at io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:138)
at java.lang.Thread.run(Thread.java:748)

Hi,
I know that the library is tested with Spark 2.0.1 but I decided to give Spark 2.1.0 a try and I wanted to report my experience.

My scenario is quite simple: I have a Dataframe, from which I generate an IndexedRowMatrix.

Everything works fine if the Dataframe consists of a small number of features, while there is an issue with a bigger number of features (about 200).

With Spark 2.1.0 (local mode), the job (started from Spark submit) gets aborted due to stage failure.

The crucial point seems to be this:
...
org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix.numCols(IndexedRowMatrix.scala:57)
at com.soundcloud.lsh.Lsh.join(Lsh.scala:33)
at clustering.ClusteringCmd$$anonfun$28.apply(ClusteringCmd.scala:533)

Row 533 of ClusteringCmd.scala class is:
val similarityMatrix: CoordinateMatrix = lsh.join(matrix)

Row 33 of Lsh.scala class is:
val numFeatures = inputMatrix.numCols().toInt

Things work fine getting back to Spark 2.0.1 (or querying a Dataframe with a smaller feature set).

I report the full stack, hoping that this could be useful for future development.

Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1435)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1423)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1422)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1422)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:802)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1650)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1605)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1594)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:628)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1918)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1931)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1944)
at org.apache.spark.rdd.RDD$$anonfun$take$1.apply(RDD.scala:1353)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
at org.apache.spark.rdd.RDD.take(RDD.scala:1326)
at org.apache.spark.rdd.RDD$$anonfun$first$1.apply(RDD.scala:1367)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
at org.apache.spark.rdd.RDD.first(RDD.scala:1366)
at org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix.numCols(IndexedRowMatrix.scala:57)
at com.soundcloud.lsh.Lsh.join(Lsh.scala:33)
at clustering.ClusteringCmd$$anonfun$28.apply(ClusteringCmd.scala:533)
at clustering.ClusteringCmd$$anonfun$28.apply(ClusteringCmd.scala:527)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:186)
at clustering.ClusteringCmd$.clusteringParallelEnsemble(ClusteringCmd.scala:527)
at clustering.ClusteringCmd$$anonfun$execEnsemble$1.apply(ClusteringCmd.scala:225)
at clustering.ClusteringCmd$$anonfun$execEnsemble$1.apply(ClusteringCmd.scala:212)
at scala.collection.immutable.List.foreach(List.scala:381)
at clustering.ClusteringCmd$.execEnsemble(ClusteringCmd.scala:212)
at MainParams$.main(MainParams.scala:89)
at MainParams.main(MainParams.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:738)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:187)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:212)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:126)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: org.spark_project.guava.util.concurrent.ExecutionError: java.lang.StackOverflowError
at org.spark_project.guava.cache.LocalCache$Segment.get(LocalCache.java:2261)
at org.spark_project.guava.cache.LocalCache.get(LocalCache.java:4000)
at org.spark_project.guava.cache.LocalCache.getOrLoad(LocalCache.java:4004)
at org.spark_project.guava.cache.LocalCache$LocalLoadingCache.get(LocalCache.java:4874)
at org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator$.compile(CodeGenerator.scala:890)
at org.apache.spark.sql.catalyst.expressions.codegen.GenerateOrdering$.create(GenerateOrdering.scala:155)
at org.apache.spark.sql.catalyst.expressions.codegen.GenerateOrdering$.create(GenerateOrdering.scala:43)
at org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator.generate(CodeGenerator.scala:874)
at org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator.generate(CodeGenerator.scala:871)
at org.apache.spark.sql.execution.SparkPlan.newOrdering(SparkPlan.scala:363)
at org.apache.spark.sql.execution.SortExec.createSorter(SortExec.scala:63)
at org.apache.spark.sql.execution.SortExec$$anonfun$1.apply(SortExec.scala:102)
at org.apache.spark.sql.execution.SortExec$$anonfun$1.apply(SortExec.scala:101)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:826)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:826)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.StackOverflowError
at org.codehaus.janino.Descriptor.size(Descriptor.java:86)
at org.codehaus.janino.CodeContext.determineArgumentsSize(CodeContext.java:785)
at org.codehaus.janino.CodeContext.flowAnalysis(CodeContext.java:474)
at org.codehaus.janino.CodeContext.flowAnalysis(CodeContext.java:541)
... (the last line is repeated many times)

Please add support for Scala 2.12 and 2.13. Thank you :)