diff options
71 files changed, 3317 insertions, 1208 deletions
diff --git a/.gitignore b/.gitignore index b87fc1ee79..ae39c52b11 100644 --- a/.gitignore +++ b/.gitignore @@ -36,4 +36,5 @@ streaming-tests.log dependency-reduced-pom.xml .ensime .ensime_lucene +checkpoint derby.log diff --git a/core/pom.xml b/core/pom.xml index d8687bf991..88f0ed70f3 100644 --- a/core/pom.xml +++ b/core/pom.xml @@ -32,8 +32,8 @@ <artifactId>compress-lzf</artifactId> </dependency> <dependency> - <groupId>asm</groupId> - <artifactId>asm-all</artifactId> + <groupId>org.ow2.asm</groupId> + <artifactId>asm</artifactId> </dependency> <dependency> <groupId>com.google.protobuf</groupId> diff --git a/core/src/main/java/spark/network/netty/FileClient.java b/core/src/main/java/spark/network/netty/FileClient.java index 3a62dacbc8..a4bb4bc701 100644 --- a/core/src/main/java/spark/network/netty/FileClient.java +++ b/core/src/main/java/spark/network/netty/FileClient.java @@ -8,15 +8,20 @@ import io.netty.channel.ChannelOption; import io.netty.channel.oio.OioEventLoopGroup; import io.netty.channel.socket.oio.OioSocketChannel; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; class FileClient { + private Logger LOG = LoggerFactory.getLogger(this.getClass().getName()); private FileClientHandler handler = null; private Channel channel = null; private Bootstrap bootstrap = null; + private int connectTimeout = 60*1000; // 1 min - public FileClient(FileClientHandler handler) { + public FileClient(FileClientHandler handler, int connectTimeout) { this.handler = handler; + this.connectTimeout = connectTimeout; } public void init() { @@ -25,25 +30,10 @@ class FileClient { .channel(OioSocketChannel.class) .option(ChannelOption.SO_KEEPALIVE, true) .option(ChannelOption.TCP_NODELAY, true) + .option(ChannelOption.CONNECT_TIMEOUT_MILLIS, connectTimeout) .handler(new FileClientChannelInitializer(handler)); } - public static final class ChannelCloseListener implements ChannelFutureListener { - private FileClient fc = null; - - public ChannelCloseListener(FileClient fc){ - this.fc = fc; - } - - @Override - public void operationComplete(ChannelFuture future) { - if (fc.bootstrap!=null){ - fc.bootstrap.shutdown(); - fc.bootstrap = null; - } - } - } - public void connect(String host, int port) { try { // Start the connection attempt. @@ -58,8 +48,8 @@ class FileClient { public void waitForClose() { try { channel.closeFuture().sync(); - } catch (InterruptedException e){ - e.printStackTrace(); + } catch (InterruptedException e) { + LOG.warn("FileClient interrupted", e); } } diff --git a/core/src/main/java/spark/network/netty/FileClientHandler.java b/core/src/main/java/spark/network/netty/FileClientHandler.java index 2069dee5ca..9fc9449827 100644 --- a/core/src/main/java/spark/network/netty/FileClientHandler.java +++ b/core/src/main/java/spark/network/netty/FileClientHandler.java @@ -9,7 +9,14 @@ abstract class FileClientHandler extends ChannelInboundByteHandlerAdapter { private FileHeader currentHeader = null; + private volatile boolean handlerCalled = false; + + public boolean isComplete() { + return handlerCalled; + } + public abstract void handle(ChannelHandlerContext ctx, ByteBuf in, FileHeader header); + public abstract void handleError(String blockId); @Override public ByteBuf newInboundBuffer(ChannelHandlerContext ctx) { @@ -26,6 +33,7 @@ abstract class FileClientHandler extends ChannelInboundByteHandlerAdapter { // get file if(in.readableBytes() >= currentHeader.fileLen()) { handle(ctx, in, currentHeader); + handlerCalled = true; currentHeader = null; ctx.close(); } diff --git a/core/src/main/scala/spark/BlockStoreShuffleFetcher.scala b/core/src/main/scala/spark/BlockStoreShuffleFetcher.scala index e1fb02157a..3239f4c385 100644 --- a/core/src/main/scala/spark/BlockStoreShuffleFetcher.scala +++ b/core/src/main/scala/spark/BlockStoreShuffleFetcher.scala @@ -58,6 +58,7 @@ private[spark] class BlockStoreShuffleFetcher extends ShuffleFetcher with Loggin CompletionIterator[(K,V), Iterator[(K,V)]](itr, { val shuffleMetrics = new ShuffleReadMetrics + shuffleMetrics.shuffleFinishTime = System.currentTimeMillis shuffleMetrics.remoteFetchTime = blockFetcherItr.remoteFetchTime shuffleMetrics.fetchWaitTime = blockFetcherItr.fetchWaitTime shuffleMetrics.remoteBytesRead = blockFetcherItr.remoteBytesRead diff --git a/core/src/main/scala/spark/ClosureCleaner.scala b/core/src/main/scala/spark/ClosureCleaner.scala index 50d6a1c5c9..d5e7132ff9 100644 --- a/core/src/main/scala/spark/ClosureCleaner.scala +++ b/core/src/main/scala/spark/ClosureCleaner.scala @@ -5,8 +5,7 @@ import java.lang.reflect.Field import scala.collection.mutable.Map import scala.collection.mutable.Set -import org.objectweb.asm.{ClassReader, MethodVisitor, Type} -import org.objectweb.asm.commons.EmptyVisitor +import org.objectweb.asm.{ClassReader, ClassVisitor, MethodVisitor, Type} import org.objectweb.asm.Opcodes._ import java.io.{InputStream, IOException, ByteArrayOutputStream, ByteArrayInputStream, BufferedInputStream} @@ -162,10 +161,10 @@ private[spark] object ClosureCleaner extends Logging { } } -private[spark] class FieldAccessFinder(output: Map[Class[_], Set[String]]) extends EmptyVisitor { +private[spark] class FieldAccessFinder(output: Map[Class[_], Set[String]]) extends ClassVisitor(ASM4) { override def visitMethod(access: Int, name: String, desc: String, sig: String, exceptions: Array[String]): MethodVisitor = { - return new EmptyVisitor { + return new MethodVisitor(ASM4) { override def visitFieldInsn(op: Int, owner: String, name: String, desc: String) { if (op == GETFIELD) { for (cl <- output.keys if cl.getName == owner.replace('/', '.')) { @@ -188,7 +187,7 @@ private[spark] class FieldAccessFinder(output: Map[Class[_], Set[String]]) exten } } -private[spark] class InnerClosureFinder(output: Set[Class[_]]) extends EmptyVisitor { +private[spark] class InnerClosureFinder(output: Set[Class[_]]) extends ClassVisitor(ASM4) { var myName: String = null override def visit(version: Int, access: Int, name: String, sig: String, @@ -198,7 +197,7 @@ private[spark] class InnerClosureFinder(output: Set[Class[_]]) extends EmptyVisi override def visitMethod(access: Int, name: String, desc: String, sig: String, exceptions: Array[String]): MethodVisitor = { - return new EmptyVisitor { + return new MethodVisitor(ASM4) { override def visitMethodInsn(op: Int, owner: String, name: String, desc: String) { val argTypes = Type.getArgumentTypes(desc) diff --git a/core/src/main/scala/spark/PairRDDFunctions.scala b/core/src/main/scala/spark/PairRDDFunctions.scala index 2b0e697337..7630fe7803 100644 --- a/core/src/main/scala/spark/PairRDDFunctions.scala +++ b/core/src/main/scala/spark/PairRDDFunctions.scala @@ -1,5 +1,6 @@ package spark +import java.nio.ByteBuffer import java.util.{Date, HashMap => JHashMap} import java.text.SimpleDateFormat @@ -10,6 +11,8 @@ import scala.collection.JavaConversions._ import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path +import org.apache.hadoop.io.compress.CompressionCodec +import org.apache.hadoop.io.SequenceFile.CompressionType import org.apache.hadoop.mapred.FileOutputCommitter import org.apache.hadoop.mapred.FileOutputFormat import org.apache.hadoop.mapred.HadoopWriter @@ -17,7 +20,7 @@ import org.apache.hadoop.mapred.JobConf import org.apache.hadoop.mapred.OutputFormat import org.apache.hadoop.mapreduce.lib.output.{FileOutputFormat => NewFileOutputFormat} -import org.apache.hadoop.mapreduce.{OutputFormat => NewOutputFormat, RecordWriter => NewRecordWriter, Job => NewAPIHadoopJob, HadoopMapReduceUtil, TaskAttemptID, TaskAttemptContext} +import org.apache.hadoop.mapreduce.{OutputFormat => NewOutputFormat, RecordWriter => NewRecordWriter, Job => NewAPIHadoopJob, HadoopMapReduceUtil} import spark.partial.BoundedDouble import spark.partial.PartialResult @@ -62,8 +65,7 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest]( throw new SparkException("Default partitioner cannot partition array keys.") } } - val aggregator = - new Aggregator[K, V, C](createCombiner, mergeValue, mergeCombiners) + val aggregator = new Aggregator[K, V, C](createCombiner, mergeValue, mergeCombiners) if (self.partitioner == Some(partitioner)) { self.mapPartitions(aggregator.combineValuesByKey(_), true) } else if (mapSideCombine) { @@ -95,7 +97,16 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest]( * list concatenation, 0 for addition, or 1 for multiplication.). */ def foldByKey(zeroValue: V, partitioner: Partitioner)(func: (V, V) => V): RDD[(K, V)] = { - combineByKey[V]({v: V => func(zeroValue, v)}, func, func, partitioner) + // Serialize the zero value to a byte array so that we can get a new clone of it on each key + val zeroBuffer = SparkEnv.get.closureSerializer.newInstance().serialize(zeroValue) + val zeroArray = new Array[Byte](zeroBuffer.limit) + zeroBuffer.get(zeroArray) + + // When deserializing, use a lazy val to create just one instance of the serializer per task + lazy val cachedSerializer = SparkEnv.get.closureSerializer.newInstance() + def createZero() = cachedSerializer.deserialize[V](ByteBuffer.wrap(zeroArray)) + + combineByKey[V]((v: V) => func(createZero(), v), func, func, partitioner) } /** @@ -185,11 +196,13 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest]( * partitioning of the resulting key-value pair RDD by passing a Partitioner. */ def groupByKey(partitioner: Partitioner): RDD[(K, Seq[V])] = { + // groupByKey shouldn't use map side combine because map side combine does not + // reduce the amount of data shuffled and requires all map side data be inserted + // into a hash table, leading to more objects in the old gen. def createCombiner(v: V) = ArrayBuffer(v) def mergeValue(buf: ArrayBuffer[V], v: V) = buf += v - def mergeCombiners(b1: ArrayBuffer[V], b2: ArrayBuffer[V]) = b1 ++= b2 val bufs = combineByKey[ArrayBuffer[V]]( - createCombiner _, mergeValue _, mergeCombiners _, partitioner) + createCombiner _, mergeValue _, null, partitioner, mapSideCombine=false) bufs.asInstanceOf[RDD[(K, Seq[V])]] } @@ -516,6 +529,16 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest]( } /** + * Output the RDD to any Hadoop-supported file system, using a Hadoop `OutputFormat` class + * supporting the key and value types K and V in this RDD. Compress the result with the + * supplied codec. + */ + def saveAsHadoopFile[F <: OutputFormat[K, V]]( + path: String, codec: Class[_ <: CompressionCodec]) (implicit fm: ClassManifest[F]) { + saveAsHadoopFile(path, getKeyClass, getValueClass, fm.erasure.asInstanceOf[Class[F]], codec) + } + + /** * Output the RDD to any Hadoop-supported file system, using a new Hadoop API `OutputFormat` * (mapreduce.OutputFormat) object supporting the key and value types K and V in this RDD. */ @@ -576,6 +599,20 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest]( /** * Output the RDD to any Hadoop-supported file system, using a Hadoop `OutputFormat` class + * supporting the key and value types K and V in this RDD. Compress with the supplied codec. + */ + def saveAsHadoopFile( + path: String, + keyClass: Class[_], + valueClass: Class[_], + outputFormatClass: Class[_ <: OutputFormat[_, _]], + codec: Class[_ <: CompressionCodec]) { + saveAsHadoopFile(path, keyClass, valueClass, outputFormatClass, + new JobConf(self.context.hadoopConfiguration), Some(codec)) + } + + /** + * Output the RDD to any Hadoop-supported file system, using a Hadoop `OutputFormat` class * supporting the key and value types K and V in this RDD. */ def saveAsHadoopFile( @@ -583,11 +620,19 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest]( keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[_ <: OutputFormat[_, _]], - conf: JobConf = new JobConf(self.context.hadoopConfiguration)) { + conf: JobConf = new JobConf(self.context.hadoopConfiguration), + codec: Option[Class[_ <: CompressionCodec]] = None) { conf.setOutputKeyClass(keyClass) conf.setOutputValueClass(valueClass) // conf.setOutputFormat(outputFormatClass) // Doesn't work in Scala 2.9 due to what may be a generics bug conf.set("mapred.output.format.class", outputFormatClass.getName) + for (c <- codec) { + conf.setCompressMapOutput(true) + conf.set("mapred.output.compress", "true") + conf.setMapOutputCompressorClass(c) + conf.set("mapred.output.compression.codec", c.getCanonicalName) + conf.set("mapred.output.compression.type", CompressionType.BLOCK.toString) + } conf.setOutputCommitter(classOf[FileOutputCommitter]) FileOutputFormat.setOutputPath(conf, HadoopWriter.createPathFromString(path, conf)) saveAsHadoopDataset(conf) diff --git a/core/src/main/scala/spark/RDD.scala b/core/src/main/scala/spark/RDD.scala index e6c0438d76..f336c2ea1e 100644 --- a/core/src/main/scala/spark/RDD.scala +++ b/core/src/main/scala/spark/RDD.scala @@ -7,12 +7,14 @@ import scala.collection.JavaConversions.mapAsScalaMap import scala.collection.mutable.ArrayBuffer import org.apache.hadoop.io.BytesWritable +import org.apache.hadoop.io.compress.CompressionCodec import org.apache.hadoop.io.NullWritable import org.apache.hadoop.io.Text import org.apache.hadoop.mapred.TextOutputFormat import it.unimi.dsi.fastutil.objects.{Object2LongOpenHashMap => OLMap} +import spark.broadcast.Broadcast import spark.Partitioner._ import spark.partial.BoundedDouble import spark.partial.CountEvaluator @@ -35,6 +37,7 @@ import spark.rdd.ZippedPartitionsRDD2 import spark.rdd.ZippedPartitionsRDD3 import spark.rdd.ZippedPartitionsRDD4 import spark.storage.StorageLevel +import spark.util.BoundedPriorityQueue import SparkContext._ @@ -114,6 +117,14 @@ abstract class RDD[T: ClassManifest]( this } + /** User-defined generator of this RDD*/ + var generator = Utils.getCallSiteInfo.firstUserClass + + /** Reset generator*/ + def setGenerator(_generator: String) = { + generator = _generator + } + /** * Set this RDD's storage level to persist its values across operations after the first time * it is computed. This can only be used to assign a new storage level if the RDD does not @@ -352,13 +363,36 @@ abstract class RDD[T: ClassManifest]( /** * Return an RDD created by piping elements to a forked external process. */ - def pipe(command: Seq[String]): RDD[String] = new PipedRDD(this, command) + def pipe(command: String, env: Map[String, String]): RDD[String] = + new PipedRDD(this, command, env) + /** * Return an RDD created by piping elements to a forked external process. - */ - def pipe(command: Seq[String], env: Map[String, String]): RDD[String] = - new PipedRDD(this, command, env) + * The print behavior can be customized by providing two functions. + * + * @param command command to run in forked process. + * @param env environment variables to set. + * @param printPipeContext Before piping elements, this function is called as an oppotunity + * to pipe context data. Print line function (like out.println) will be + * passed as printPipeContext's parameter. + * @param printRDDElement Use this function to customize how to pipe elements. This function + * will be called with each RDD element as the 1st parameter, and the + * print line function (like out.println()) as the 2nd parameter. + * An example of pipe the RDD data of groupBy() in a streaming way, + * instead of constructing a huge String to concat all the elements: + * def printRDDElement(record:(String, Seq[String]), f:String=>Unit) = + * for (e <- record._2){f(e)} + * @return the result RDD + */ + def pipe( + command: Seq[String], + env: Map[String, String] = Map(), + printPipeContext: (String => Unit) => Unit = null, + printRDDElement: (T, String => Unit) => Unit = null): RDD[String] = + new PipedRDD(this, command, env, + if (printPipeContext ne null) sc.clean(printPipeContext) else null, + if (printRDDElement ne null) sc.clean(printRDDElement) else null) /** * Return a new RDD by applying a function to each partition of this RDD. @@ -723,6 +757,24 @@ abstract class RDD[T: ClassManifest]( } /** + * Returns the top K elements from this RDD as defined by + * the specified implicit Ordering[T]. + * @param num the number of top elements to return + * @param ord the implicit ordering for T + * @return an array of top elements + */ + def top(num: Int)(implicit ord: Ordering[T]): Array[T] = { + mapPartitions { items => + val queue = new BoundedPriorityQueue[T](num) + queue ++= items + Iterator.single(queue) + }.reduce { (queue1, queue2) => + queue1 ++= queue2 + queue1 + }.toArray + } + + /** * Save this RDD as a text file, using string representations of elements. */ def saveAsTextFile(path: String) { @@ -731,6 +783,14 @@ abstract class RDD[T: ClassManifest]( } /** + * Save this RDD as a compressed text file, using string representations of elements. + */ + def saveAsTextFile(path: String, codec: Class[_ <: CompressionCodec]) { + this.map(x => (NullWritable.get(), new Text(x.toString))) + .saveAsHadoopFile[TextOutputFormat[NullWritable, Text]](path, codec) + } + + /** * Save this RDD as a SequenceFile of serialized objects. */ def saveAsObjectFile(path: String) { @@ -788,7 +848,7 @@ abstract class RDD[T: ClassManifest]( private var storageLevel: StorageLevel = StorageLevel.NONE /** Record user function generating this RDD. */ - private[spark] val origin = Utils.getSparkCallSite + private[spark] val origin = Utils.formatSparkCallSite private[spark] def elementClassManifest: ClassManifest[T] = classManifest[T] diff --git a/core/src/main/scala/spark/SequenceFileRDDFunctions.scala b/core/src/main/scala/spark/SequenceFileRDDFunctions.scala index 518034e07b..2911f9036e 100644 --- a/core/src/main/scala/spark/SequenceFileRDDFunctions.scala +++ b/core/src/main/scala/spark/SequenceFileRDDFunctions.scala @@ -18,6 +18,7 @@ import org.apache.hadoop.mapred.TextOutputFormat import org.apache.hadoop.mapred.SequenceFileOutputFormat import org.apache.hadoop.mapred.OutputCommitter import org.apache.hadoop.mapred.FileOutputCommitter +import org.apache.hadoop.io.compress.CompressionCodec import org.apache.hadoop.io.Writable import org.apache.hadoop.io.NullWritable import org.apache.hadoop.io.BytesWritable @@ -62,7 +63,7 @@ class SequenceFileRDDFunctions[K <% Writable: ClassManifest, V <% Writable : Cla * byte arrays to BytesWritable, and Strings to Text. The `path` can be on any Hadoop-supported * file system. */ - def saveAsSequenceFile(path: String) { + def saveAsSequenceFile(path: String, codec: Option[Class[_ <: CompressionCodec]] = None) { def anyToWritable[U <% Writable](u: U): Writable = u val keyClass = getWritableClass[K] @@ -72,14 +73,18 @@ class SequenceFileRDDFunctions[K <% Writable: ClassManifest, V <% Writable : Cla logInfo("Saving as sequence file of type (" + keyClass.getSimpleName + "," + valueClass.getSimpleName + ")" ) val format = classOf[SequenceFileOutputFormat[Writable, Writable]] + val jobConf = new JobConf(self.context.hadoopConfiguration) if (!convertKey && !convertValue) { - self.saveAsHadoopFile(path, keyClass, valueClass, format) + self.saveAsHadoopFile(path, keyClass, valueClass, format, jobConf, codec) } else if (!convertKey && convertValue) { - self.map(x => (x._1,anyToWritable(x._2))).saveAsHadoopFile(path, keyClass, valueClass, format) + self.map(x => (x._1,anyToWritable(x._2))).saveAsHadoopFile( + path, keyClass, valueClass, format, jobConf, codec) } else if (convertKey && !convertValue) { - self.map(x => (anyToWritable(x._1),x._2)).saveAsHadoopFile(path, keyClass, valueClass, format) + self.map(x => (anyToWritable(x._1),x._2)).saveAsHadoopFile( + path, keyClass, valueClass, format, jobConf, codec) } else if (convertKey && convertValue) { - self.map(x => (anyToWritable(x._1),anyToWritable(x._2))).saveAsHadoopFile(path, keyClass, valueClass, format) + self.map(x => (anyToWritable(x._1),anyToWritable(x._2))).saveAsHadoopFile( + path, keyClass, valueClass, format, jobConf, codec) } } } diff --git a/core/src/main/scala/spark/SparkContext.scala b/core/src/main/scala/spark/SparkContext.scala index bc05d08fd6..70a9d7698c 100644 --- a/core/src/main/scala/spark/SparkContext.scala +++ b/core/src/main/scala/spark/SparkContext.scala @@ -49,7 +49,6 @@ import spark.scheduler.mesos.{CoarseMesosSchedulerBackend, MesosSchedulerBackend import spark.storage.{BlockManagerUI, StorageStatus, StorageUtils, RDDInfo} import spark.util.{MetadataCleaner, TimeStampedHashMap} - /** * Main entry point for Spark functionality. A SparkContext represents the connection to a Spark * cluster, and can be used to create RDDs, accumulators and broadcast variables on that cluster. @@ -630,7 +629,7 @@ class SparkContext( partitions: Seq[Int], allowLocal: Boolean, resultHandler: (Int, U) => Unit) { - val callSite = Utils.getSparkCallSite + val callSite = Utils.formatSparkCallSite logInfo("Starting job: " + callSite) val start = System.nanoTime val result = dagScheduler.runJob(rdd, func, partitions, callSite, allowLocal, resultHandler, localProperties.value) @@ -713,7 +712,7 @@ class SparkContext( func: (TaskContext, Iterator[T]) => U, evaluator: ApproximateEvaluator[U, R], timeout: Long): PartialResult[R] = { - val callSite = Utils.getSparkCallSite + val callSite = Utils.formatSparkCallSite logInfo("Starting job: " + callSite) val start = System.nanoTime val result = dagScheduler.runApproximateJob(rdd, func, evaluator, callSite, timeout, localProperties.value) diff --git a/core/src/main/scala/spark/SparkEnv.scala b/core/src/main/scala/spark/SparkEnv.scala index be1a04d619..7ccde2e818 100644 --- a/core/src/main/scala/spark/SparkEnv.scala +++ b/core/src/main/scala/spark/SparkEnv.scala @@ -1,5 +1,8 @@ package spark +import collection.mutable +import serializer.Serializer + import akka.actor.{Actor, ActorRef, Props, ActorSystemImpl, ActorSystem} import akka.remote.RemoteActorRefProvider @@ -9,6 +12,7 @@ import spark.storage.BlockManagerMaster import spark.network.ConnectionManager import spark.serializer.{Serializer, SerializerManager} import spark.util.AkkaUtils +import spark.api.python.PythonWorkerFactory /** @@ -37,7 +41,10 @@ class SparkEnv ( // If executorId is NOT found, return defaultHostPort var executorIdToHostPort: Option[(String, String) => String]) { + private val pythonWorkers = mutable.HashMap[(String, Map[String, String]), PythonWorkerFactory]() + def stop() { + pythonWorkers.foreach { case(key, worker) => worker.stop() } httpFileServer.stop() mapOutputTracker.stop() shuffleFetcher.stop() @@ -50,6 +57,11 @@ class SparkEnv ( actorSystem.awaitTermination() } + def createPythonWorker(pythonExec: String, envVars: Map[String, String]): java.net.Socket = { + synchronized { + pythonWorkers.getOrElseUpdate((pythonExec, envVars), new PythonWorkerFactory(pythonExec, envVars)).create() + } + } def resolveExecutorIdToHostPort(executorId: String, defaultHostPort: String): String = { val env = SparkEnv.get diff --git a/core/src/main/scala/spark/Utils.scala b/core/src/main/scala/spark/Utils.scala index ec15326014..f3621c6bee 100644 --- a/core/src/main/scala/spark/Utils.scala +++ b/core/src/main/scala/spark/Utils.scala @@ -116,8 +116,8 @@ private object Utils extends Logging { while (dir == null) { attempts += 1 if (attempts > maxAttempts) { - throw new IOException("Failed to create a temp directory after " + maxAttempts + - " attempts!") + throw new IOException("Failed to create a temp directory (under " + root + ") after " + + maxAttempts + " attempts!") } try { dir = new File(root, "spark-" + UUID.randomUUID.toString) @@ -522,13 +522,14 @@ private object Utils extends Logging { execute(command, new File(".")) } - + private[spark] class CallSiteInfo(val lastSparkMethod: String, val firstUserFile: String, + val firstUserLine: Int, val firstUserClass: String) /** * When called inside a class in the spark package, returns the name of the user code class * (outside the spark package) that called into Spark, as well as which Spark method they called. * This is used, for example, to tell users where in their code each RDD got created. */ - def getSparkCallSite: String = { + def getCallSiteInfo: CallSiteInfo = { val trace = Thread.currentThread.getStackTrace().filter( el => (!el.getMethodName.contains("getStackTrace"))) @@ -540,6 +541,7 @@ private object Utils extends Logging { var firstUserFile = "<unknown>" var firstUserLine = 0 var finished = false + var firstUserClass = "<unknown>" for (el <- trace) { if (!finished) { @@ -554,13 +556,19 @@ private object Utils extends Logging { else { firstUserLine = el.getLineNumber firstUserFile = el.getFileName + firstUserClass = el.getClassName finished = true } } } - "%s at %s:%s".format(lastSparkMethod, firstUserFile, firstUserLine) + new CallSiteInfo(lastSparkMethod, firstUserFile, firstUserLine, firstUserClass) } + def formatSparkCallSite = { + val callSiteInfo = getCallSiteInfo + "%s at %s:%s".format(callSiteInfo.lastSparkMethod, callSiteInfo.firstUserFile, + callSiteInfo.firstUserLine) + } /** * Try to find a free port to bind to on the local host. This should ideally never be needed, * except that, unfortunately, some of the networking libraries we currently rely on (e.g. Spray) diff --git a/core/src/main/scala/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/spark/api/java/JavaPairRDD.scala index 30084df4e2..76051597b6 100644 --- a/core/src/main/scala/spark/api/java/JavaPairRDD.scala +++ b/core/src/main/scala/spark/api/java/JavaPairRDD.scala @@ -6,6 +6,7 @@ import java.util.Comparator import scala.Tuple2 import scala.collection.JavaConversions._ +import org.apache.hadoop.io.compress.CompressionCodec import org.apache.hadoop.mapred.JobConf import org.apache.hadoop.mapred.OutputFormat import org.apache.hadoop.mapreduce.{OutputFormat => NewOutputFormat} @@ -459,6 +460,16 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif rdd.saveAsHadoopFile(path, keyClass, valueClass, outputFormatClass) } + /** Output the RDD to any Hadoop-supported file system, compressing with the supplied codec. */ + def saveAsHadoopFile[F <: OutputFormat[_, _]]( + path: String, + keyClass: Class[_], + valueClass: Class[_], + outputFormatClass: Class[F], + codec: Class[_ <: CompressionCodec]) { + rdd.saveAsHadoopFile(path, keyClass, valueClass, outputFormatClass, codec) + } + /** Output the RDD to any Hadoop-supported file system. */ def saveAsNewAPIHadoopFile[F <: NewOutputFormat[_, _]]( path: String, diff --git a/core/src/main/scala/spark/api/java/JavaRDD.scala b/core/src/main/scala/spark/api/java/JavaRDD.scala index eb81ed64cd..626b499454 100644 --- a/core/src/main/scala/spark/api/java/JavaRDD.scala +++ b/core/src/main/scala/spark/api/java/JavaRDD.scala @@ -86,7 +86,6 @@ JavaRDDLike[T, JavaRDD[T]] { */ def subtract(other: JavaRDD[T], p: Partitioner): JavaRDD[T] = wrapRDD(rdd.subtract(other, p)) - } object JavaRDD { diff --git a/core/src/main/scala/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/spark/api/java/JavaRDDLike.scala index 9b74d1226f..b555f2030a 100644 --- a/core/src/main/scala/spark/api/java/JavaRDDLike.scala +++ b/core/src/main/scala/spark/api/java/JavaRDDLike.scala @@ -1,9 +1,10 @@ package spark.api.java -import java.util.{List => JList} +import java.util.{List => JList, Comparator} import scala.Tuple2 import scala.collection.JavaConversions._ +import org.apache.hadoop.io.compress.CompressionCodec import spark.{SparkContext, Partition, RDD, TaskContext} import spark.api.java.JavaPairRDD._ import spark.api.java.function.{Function2 => JFunction2, Function => JFunction, _} @@ -310,6 +311,13 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { */ def saveAsTextFile(path: String) = rdd.saveAsTextFile(path) + + /** + * Save this RDD as a compressed text file, using string representations of elements. + */ + def saveAsTextFile(path: String, codec: Class[_ <: CompressionCodec]) = + rdd.saveAsTextFile(path, codec) + /** * Save this RDD as a SequenceFile of serialized objects. */ @@ -351,4 +359,29 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { def toDebugString(): String = { rdd.toDebugString } + + /** + * Returns the top K elements from this RDD as defined by + * the specified Comparator[T]. + * @param num the number of top elements to return + * @param comp the comparator that defines the order + * @return an array of top elements + */ + def top(num: Int, comp: Comparator[T]): JList[T] = { + import scala.collection.JavaConversions._ + val topElems = rdd.top(num)(Ordering.comparatorToOrdering(comp)) + val arr: java.util.Collection[T] = topElems.toSeq + new java.util.ArrayList(arr) + } + + /** + * Returns the top K elements from this RDD using the + * natural ordering for T. + * @param num the number of top elements to return + * @return an array of top elements + */ + def top(num: Int): JList[T] = { + val comp = com.google.common.collect.Ordering.natural().asInstanceOf[Comparator[T]] + top(num, comp) + } } diff --git a/core/src/main/scala/spark/api/python/PythonRDD.scala b/core/src/main/scala/spark/api/python/PythonRDD.scala index 807119ca8c..63140cf37f 100644 --- a/core/src/main/scala/spark/api/python/PythonRDD.scala +++ b/core/src/main/scala/spark/api/python/PythonRDD.scala @@ -2,10 +2,9 @@ package spark.api.python import java.io._ import java.net._ -import java.util.{List => JList, ArrayList => JArrayList, Collections} +import java.util.{List => JList, ArrayList => JArrayList, Map => JMap, Collections} import scala.collection.JavaConversions._ -import scala.io.Source import spark.api.java.{JavaSparkContext, JavaPairRDD, JavaRDD} import spark.broadcast.Broadcast @@ -16,7 +15,7 @@ import spark.rdd.PipedRDD private[spark] class PythonRDD[T: ClassManifest]( parent: RDD[T], command: Seq[String], - envVars: java.util.Map[String, String], + envVars: JMap[String, String], preservePartitoning: Boolean, pythonExec: String, broadcastVars: JList[Broadcast[Array[Byte]]], @@ -25,7 +24,7 @@ private[spark] class PythonRDD[T: ClassManifest]( // Similar to Runtime.exec(), if we are given a single string, split it into words // using a standard StringTokenizer (i.e. by spaces) - def this(parent: RDD[T], command: String, envVars: java.util.Map[String, String], + def this(parent: RDD[T], command: String, envVars: JMap[String, String], preservePartitoning: Boolean, pythonExec: String, broadcastVars: JList[Broadcast[Array[Byte]]], accumulator: Accumulator[JList[Array[Byte]]]) = @@ -36,35 +35,18 @@ private[spark] class PythonRDD[T: ClassManifest]( override val partitioner = if (preservePartitoning) parent.partitioner else None - override def compute(split: Partition, context: TaskContext): Iterator[Array[Byte]] = { - val SPARK_HOME = new ProcessBuilder().environment().get("SPARK_HOME") - - val pb = new ProcessBuilder(Seq(pythonExec, SPARK_HOME + "/python/pyspark/worker.py")) - // Add the environmental variables to the process. - val currentEnvVars = pb.environment() - - for ((variable, value) <- envVars) { - currentEnvVars.put(variable, value) - } - val proc = pb.start() + override def compute(split: Partition, context: TaskContext): Iterator[Array[Byte]] = { + val startTime = System.currentTimeMillis val env = SparkEnv.get - - // Start a thread to print the process's stderr to ours - new Thread("stderr reader for " + pythonExec) { - override def run() { - for (line <- Source.fromInputStream(proc.getErrorStream).getLines) { - System.err.println(line) - } - } - }.start() + val worker = env.createPythonWorker(pythonExec, envVars.toMap) // Start a thread to feed the process input from our parent's iterator new Thread("stdin writer for " + pythonExec) { override def run() { SparkEnv.set(env) - val out = new PrintWriter(proc.getOutputStream) - val dOut = new DataOutputStream(proc.getOutputStream) + val out = new PrintWriter(worker.getOutputStream) + val dOut = new DataOutputStream(worker.getOutputStream) // Partition index dOut.writeInt(split.index) // sparkFilesDir @@ -88,16 +70,21 @@ private[spark] class PythonRDD[T: ClassManifest]( } dOut.flush() out.flush() - proc.getOutputStream.close() + worker.shutdownOutput() } }.start() // Return an iterator that read lines from the process's stdout - val stream = new DataInputStream(proc.getInputStream) + val stream = new DataInputStream(worker.getInputStream) return new Iterator[Array[Byte]] { def next(): Array[Byte] = { val obj = _nextObj - _nextObj = read() + if (hasNext) { + // FIXME: can deadlock if worker is waiting for us to + // respond to current message (currently irrelevant because + // output is shutdown before we read any input) + _nextObj = read() + } obj } @@ -108,6 +95,17 @@ private[spark] class PythonRDD[T: ClassManifest]( val obj = new Array[Byte](length) stream.readFully(obj) obj + case -3 => + // Timing data from worker + val bootTime = stream.readLong() + val initTime = stream.readLong() + val finishTime = stream.readLong() + val boot = bootTime - startTime + val init = initTime - bootTime + val finish = finishTime - initTime + val total = finishTime - startTime + logInfo("Times: total = %s, boot = %s, init = %s, finish = %s".format(total, boot, init, finish)) + read case -2 => // Signals that an exception has been thrown in python val exLength = stream.readInt() @@ -115,23 +113,21 @@ private[spark] class PythonRDD[T: ClassManifest]( stream.readFully(obj) throw new PythonException(new String(obj)) case -1 => - // We've finished the data section of the output, but we can still read some - // accumulator updates; let's do that, breaking when we get EOFException - while (true) { - val len2 = stream.readInt() + // We've finished the data section of the output, but we can still + // read some accumulator updates; let's do that, breaking when we + // get a negative length record. + var len2 = stream.readInt() + while (len2 >= 0) { val update = new Array[Byte](len2) stream.readFully(update) accumulator += Collections.singletonList(update) + len2 = stream.readInt() } new Array[Byte](0) } } catch { case eof: EOFException => { - val exitStatus = proc.waitFor() - if (exitStatus != 0) { - throw new Exception("Subprocess exited with status " + exitStatus) - } - new Array[Byte](0) + throw new SparkException("Python worker exited unexpectedly (crashed)", eof) } case e => throw e } @@ -159,7 +155,7 @@ private class PairwiseRDD(prev: RDD[Array[Byte]]) extends override def compute(split: Partition, context: TaskContext) = prev.iterator(split, context).grouped(2).map { case Seq(a, b) => (a, b) - case x => throw new Exception("PairwiseRDD: unexpected value: " + x) + case x => throw new SparkException("PairwiseRDD: unexpected value: " + x) } val asJavaPairRDD : JavaPairRDD[Array[Byte], Array[Byte]] = JavaPairRDD.fromRDD(this) } @@ -215,7 +211,7 @@ private[spark] object PythonRDD { dOut.write(s) dOut.writeByte(Pickle.STOP) } else { - throw new Exception("Unexpected RDD type") + throw new SparkException("Unexpected RDD type") } } diff --git a/core/src/main/scala/spark/api/python/PythonWorkerFactory.scala b/core/src/main/scala/spark/api/python/PythonWorkerFactory.scala new file mode 100644 index 0000000000..8844411d73 --- /dev/null +++ b/core/src/main/scala/spark/api/python/PythonWorkerFactory.scala @@ -0,0 +1,95 @@ +package spark.api.python + +import java.io.{DataInputStream, IOException} +import java.net.{Socket, SocketException, InetAddress} + +import scala.collection.JavaConversions._ + +import spark._ + +private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String, String]) + extends Logging { + var daemon: Process = null + val daemonHost = InetAddress.getByAddress(Array(127, 0, 0, 1)) + var daemonPort: Int = 0 + + def create(): Socket = { + synchronized { + // Start the daemon if it hasn't been started + startDaemon() + + // Attempt to connect, restart and retry once if it fails + try { + new Socket(daemonHost, daemonPort) + } catch { + case exc: SocketException => { + logWarning("Python daemon unexpectedly quit, attempting to restart") + stopDaemon() + startDaemon() + new Socket(daemonHost, daemonPort) + } + case e => throw e + } + } + } + + def stop() { + stopDaemon() + } + + private def startDaemon() { + synchronized { + // Is it already running? + if (daemon != null) { + return + } + + try { + // Create and start the daemon + val sparkHome = new ProcessBuilder().environment().get("SPARK_HOME") + val pb = new ProcessBuilder(Seq(pythonExec, sparkHome + "/python/pyspark/daemon.py")) + val workerEnv = pb.environment() + workerEnv.putAll(envVars) + daemon = pb.start() + daemonPort = new DataInputStream(daemon.getInputStream).readInt() + + // Redirect the stderr to ours + new Thread("stderr reader for " + pythonExec) { + override def run() { + scala.util.control.Exception.ignoring(classOf[IOException]) { + // FIXME HACK: We copy the stream on the level of bytes to + // attempt to dodge encoding problems. + val in = daemon.getErrorStream + var buf = new Array[Byte](1024) + var len = in.read(buf) + while (len != -1) { + System.err.write(buf, 0, len) + len = in.read(buf) + } + } + } + }.start() + } catch { + case e => { + stopDaemon() + throw e + } + } + + // Important: don't close daemon's stdin (daemon.getOutputStream) so it can correctly + // detect our disappearance. + } + } + + private def stopDaemon() { + synchronized { + // Request shutdown of existing daemon by sending SIGTERM + if (daemon != null) { + daemon.destroy() + } + + daemon = null + daemonPort = 0 + } + } +} diff --git a/core/src/main/scala/spark/executor/Executor.scala b/core/src/main/scala/spark/executor/Executor.scala index 890938d48b..2bf55ea9a9 100644 --- a/core/src/main/scala/spark/executor/Executor.scala +++ b/core/src/main/scala/spark/executor/Executor.scala @@ -42,7 +42,8 @@ private[spark] class Executor(executorId: String, slaveHostname: String, propert // Create our ClassLoader and set it on this thread private val urlClassLoader = createClassLoader() - Thread.currentThread.setContextClassLoader(urlClassLoader) + private val replClassLoader = addReplClassLoaderIfNeeded(urlClassLoader) + Thread.currentThread.setContextClassLoader(replClassLoader) // Make any thread terminations due to uncaught exceptions kill the entire // executor process to avoid surprising stalls. @@ -88,7 +89,7 @@ private[spark] class Executor(executorId: String, slaveHostname: String, propert override def run() { val startTime = System.currentTimeMillis() SparkEnv.set(env) - Thread.currentThread.setContextClassLoader(urlClassLoader) + Thread.currentThread.setContextClassLoader(replClassLoader) val ser = SparkEnv.get.closureSerializer.newInstance() logInfo("Running task ID " + taskId) context.statusUpdate(taskId, TaskState.RUNNING, EMPTY_BYTE_BUFFER) @@ -104,6 +105,7 @@ private[spark] class Executor(executorId: String, slaveHostname: String, propert val value = task.run(taskId.toInt) val taskFinish = System.currentTimeMillis() task.metrics.foreach{ m => + m.hostname = Utils.localHostName m.executorDeserializeTime = (taskStart - startTime).toInt m.executorRunTime = (taskFinish - taskStart).toInt } @@ -152,26 +154,31 @@ private[spark] class Executor(executorId: String, slaveHostname: String, propert val urls = currentJars.keySet.map { uri => new File(uri.split("/").last).toURI.toURL }.toArray - loader = new URLClassLoader(urls, loader) + new ExecutorURLClassLoader(urls, loader) + } - // If the REPL is in use, add another ClassLoader that will read - // new classes defined by the REPL as the user types code + /** + * If the REPL is in use, add another ClassLoader that will read + * new classes defined by the REPL as the user types code + */ + private def addReplClassLoaderIfNeeded(parent: ClassLoader): ClassLoader = { val classUri = System.getProperty("spark.repl.class.uri") if (classUri != null) { logInfo("Using REPL class URI: " + classUri) - loader = { - try { - val klass = Class.forName("spark.repl.ExecutorClassLoader") - .asInstanceOf[Class[_ <: ClassLoader]] - val constructor = klass.getConstructor(classOf[String], classOf[ClassLoader]) - constructor.newInstance(classUri, loader) - } catch { - case _: ClassNotFoundException => loader - } + try { + val klass = Class.forName("spark.repl.ExecutorClassLoader") + .asInstanceOf[Class[_ <: ClassLoader]] + val constructor = klass.getConstructor(classOf[String], classOf[ClassLoader]) + return constructor.newInstance(classUri, parent) + } catch { + case _: ClassNotFoundException => + logError("Could not find spark.repl.ExecutorClassLoader on classpath!") + System.exit(1) + null } + } else { + return parent } - - return new ExecutorURLClassLoader(Array(), loader) } /** diff --git a/core/src/main/scala/spark/executor/TaskMetrics.scala b/core/src/main/scala/spark/executor/TaskMetrics.scala index a7c56c2371..1dc13754f9 100644 --- a/core/src/main/scala/spark/executor/TaskMetrics.scala +++ b/core/src/main/scala/spark/executor/TaskMetrics.scala @@ -2,6 +2,11 @@ package spark.executor class TaskMetrics extends Serializable { /** + * Host's name the task runs on + */ + var hostname: String = _ + + /** * Time taken on the executor to deserialize this task */ var executorDeserializeTime: Int = _ @@ -34,9 +39,14 @@ object TaskMetrics { class ShuffleReadMetrics extends Serializable { /** + * Time when shuffle finishs + */ + var shuffleFinishTime: Long = _ + + /** * Total number of blocks fetched in a shuffle (remote or local) */ - var totalBlocksFetched : Int = _ + var totalBlocksFetched: Int = _ /** * Number of remote blocks fetched in a shuffle diff --git a/core/src/main/scala/spark/network/netty/ShuffleCopier.scala b/core/src/main/scala/spark/network/netty/ShuffleCopier.scala index a91f5a886d..8d5194a737 100644 --- a/core/src/main/scala/spark/network/netty/ShuffleCopier.scala +++ b/core/src/main/scala/spark/network/netty/ShuffleCopier.scala @@ -9,19 +9,36 @@ import io.netty.util.CharsetUtil import spark.Logging import spark.network.ConnectionManagerId +import scala.collection.JavaConverters._ + private[spark] class ShuffleCopier extends Logging { - def getBlock(cmId: ConnectionManagerId, blockId: String, + def getBlock(host: String, port: Int, blockId: String, resultCollectCallback: (String, Long, ByteBuf) => Unit) { val handler = new ShuffleCopier.ShuffleClientHandler(resultCollectCallback) - val fc = new FileClient(handler) - fc.init() - fc.connect(cmId.host, cmId.port) - fc.sendRequest(blockId) - fc.waitForClose() - fc.close() + val connectTimeout = System.getProperty("spark.shuffle.netty.connect.timeout", "60000").toInt + val fc = new FileClient(handler, connectTimeout) + + try { + fc.init() + fc.connect(host, port) + fc.sendRequest(blockId) + fc.waitForClose() + fc.close() + } catch { + // Handle any socket-related exceptions in FileClient + case e: Exception => { + logError("Shuffle copy of block " + blockId + " from " + host + ":" + port + " failed", e) + handler.handleError(blockId) + } + } + } + + def getBlock(cmId: ConnectionManagerId, blockId: String, + resultCollectCallback: (String, Long, ByteBuf) => Unit) { + getBlock(cmId.host, cmId.port, blockId, resultCollectCallback) } def getBlocks(cmId: ConnectionManagerId, @@ -44,20 +61,18 @@ private[spark] object ShuffleCopier extends Logging { logDebug("Received Block: " + header.blockId + " (" + header.fileLen + "B)"); resultCollectCallBack(header.blockId, header.fileLen.toLong, in.readBytes(header.fileLen)) } - } - def echoResultCollectCallBack(blockId: String, size: Long, content: ByteBuf) { - logInfo("File: " + blockId + " content is : \" " + content.toString(CharsetUtil.UTF_8) + "\"") + override def handleError(blockId: String) { + if (!isComplete) { + resultCollectCallBack(blockId, -1, null) + } + } } - def runGetBlock(host:String, port:Int, file:String){ - val handler = new ShuffleClientHandler(echoResultCollectCallBack) - val fc = new FileClient(handler) - fc.init(); - fc.connect(host, port) - fc.sendRequest(file) - fc.waitForClose(); - fc.close() + def echoResultCollectCallBack(blockId: String, size: Long, content: ByteBuf) { + if (size != -1) { + logInfo("File: " + blockId + " content is : \" " + content.toString(CharsetUtil.UTF_8) + "\"") + } } def main(args: Array[String]) { @@ -71,14 +86,16 @@ private[spark] object ShuffleCopier extends Logging { val threads = if (args.length > 3) args(3).toInt else 10 val copiers = Executors.newFixedThreadPool(80) - for (i <- Range(0, threads)) { - val runnable = new Runnable() { + val tasks = (for (i <- Range(0, threads)) yield { + Executors.callable(new Runnable() { def run() { - runGetBlock(host, port, file) + val copier = new ShuffleCopier() + copier.getBlock(host, port, file, echoResultCollectCallBack) } - } - copiers.execute(runnable) - } + }) + }).asJava + copiers.invokeAll(tasks) copiers.shutdown + System.exit(0) } } diff --git a/core/src/main/scala/spark/rdd/CoGroupedRDD.scala b/core/src/main/scala/spark/rdd/CoGroupedRDD.scala index 7599ba1a02..8966f9f86e 100644 --- a/core/src/main/scala/spark/rdd/CoGroupedRDD.scala +++ b/core/src/main/scala/spark/rdd/CoGroupedRDD.scala @@ -6,7 +6,7 @@ import java.util.{HashMap => JHashMap} import scala.collection.JavaConversions import scala.collection.mutable.ArrayBuffer -import spark.{Aggregator, Logging, Partition, Partitioner, RDD, SparkEnv, TaskContext} +import spark.{Aggregator, Partition, Partitioner, RDD, SparkEnv, TaskContext} import spark.{Dependency, OneToOneDependency, ShuffleDependency} @@ -49,12 +49,16 @@ private[spark] class CoGroupAggregator * * @param rdds parent RDDs. * @param part partitioner used to partition the shuffle output. - * @param mapSideCombine flag indicating whether to merge values before shuffle step. + * @param mapSideCombine flag indicating whether to merge values before shuffle step. If the flag + * is on, Spark does an extra pass over the data on the map side to merge + * all values belonging to the same key together. This can reduce the amount + * of data shuffled if and only if the number of distinct keys is very small, + * and the ratio of key size to value size is also very small. */ class CoGroupedRDD[K]( @transient var rdds: Seq[RDD[(K, _)]], part: Partitioner, - val mapSideCombine: Boolean = true, + val mapSideCombine: Boolean = false, val serializerClass: String = null) extends RDD[(K, Seq[Seq[_]])](rdds.head.context, Nil) { diff --git a/core/src/main/scala/spark/rdd/PipedRDD.scala b/core/src/main/scala/spark/rdd/PipedRDD.scala index 962a1b21ad..c0baf43d43 100644 --- a/core/src/main/scala/spark/rdd/PipedRDD.scala +++ b/core/src/main/scala/spark/rdd/PipedRDD.scala @@ -9,6 +9,7 @@ import scala.collection.mutable.ArrayBuffer import scala.io.Source import spark.{RDD, SparkEnv, Partition, TaskContext} +import spark.broadcast.Broadcast /** @@ -18,14 +19,21 @@ import spark.{RDD, SparkEnv, Partition, TaskContext} class PipedRDD[T: ClassManifest]( prev: RDD[T], command: Seq[String], - envVars: Map[String, String]) + envVars: Map[String, String], + printPipeContext: (String => Unit) => Unit, + printRDDElement: (T, String => Unit) => Unit) extends RDD[String](prev) { - def this(prev: RDD[T], command: Seq[String]) = this(prev, command, Map()) - // Similar to Runtime.exec(), if we are given a single string, split it into words // using a standard StringTokenizer (i.e. by spaces) - def this(prev: RDD[T], command: String) = this(prev, PipedRDD.tokenize(command)) + def this( + prev: RDD[T], + command: String, + envVars: Map[String, String] = Map(), + printPipeContext: (String => Unit) => Unit = null, + printRDDElement: (T, String => Unit) => Unit = null) = + this(prev, PipedRDD.tokenize(command), envVars, printPipeContext, printRDDElement) + override def getPartitions: Array[Partition] = firstParent[T].partitions @@ -52,8 +60,17 @@ class PipedRDD[T: ClassManifest]( override def run() { SparkEnv.set(env) val out = new PrintWriter(proc.getOutputStream) + + // input the pipe context firstly + if (printPipeContext != null) { + printPipeContext(out.println(_)) + } for (elem <- firstParent[T].iterator(split, context)) { - out.println(elem) + if (printRDDElement != null) { + printRDDElement(elem, out.println(_)) + } else { + out.println(elem) + } } out.close() } diff --git a/core/src/main/scala/spark/rdd/ZippedPartitionsRDD.scala b/core/src/main/scala/spark/rdd/ZippedPartitionsRDD.scala index dd9f3c2680..b234428ab2 100644 --- a/core/src/main/scala/spark/rdd/ZippedPartitionsRDD.scala +++ b/core/src/main/scala/spark/rdd/ZippedPartitionsRDD.scala @@ -53,14 +53,10 @@ abstract class ZippedPartitionsBaseRDD[V: ClassManifest]( val exactMatchLocations = exactMatchPreferredLocations.reduce((x, y) => x.intersect(y)) // Remove exact match and then do host local match. - val otherNodePreferredLocations = rddSplitZip.map(x => { - x._1.preferredLocations(x._2).map(hostPort => { - val host = Utils.parseHostPort(hostPort)._1 - - if (exactMatchLocations.contains(host)) null else host - }).filter(_ != null) - }) - val otherNodeLocalLocations = otherNodePreferredLocations.reduce((x, y) => x.intersect(y)) + val exactMatchHosts = exactMatchLocations.map(Utils.parseHostPort(_)._1) + val matchPreferredHosts = exactMatchPreferredLocations.map(locs => locs.map(Utils.parseHostPort(_)._1)) + .reduce((x, y) => x.intersect(y)) + val otherNodeLocalLocations = matchPreferredHosts.filter { s => !exactMatchHosts.contains(s) } otherNodeLocalLocations ++ exactMatchLocations } diff --git a/core/src/main/scala/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/spark/scheduler/DAGScheduler.scala index 7feeb97542..f7d60be5db 100644 --- a/core/src/main/scala/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/spark/scheduler/DAGScheduler.scala @@ -298,6 +298,7 @@ class DAGScheduler( // Compute very short actions like first() or take() with no parent stages locally. runLocally(job) } else { + sparkListeners.foreach(_.onJobStart(SparkListenerJobStart(job, properties))) idToActiveJob(runId) = job activeJobs += job resultStageToJob(finalStage) = job @@ -311,6 +312,8 @@ class DAGScheduler( handleExecutorLost(execId) case completion: CompletionEvent => + sparkListeners.foreach(_.onTaskEnd(SparkListenerTaskEnd(completion.task, + completion.reason, completion.taskInfo, completion.taskMetrics))) handleTaskCompletion(completion) case TaskSetFailed(taskSet, reason) => @@ -321,6 +324,7 @@ class DAGScheduler( for (job <- activeJobs) { val error = new SparkException("Job cancelled because SparkContext was shut down") job.listener.jobFailed(error) + sparkListeners.foreach(_.onJobEnd(SparkListenerJobEnd(job, JobFailed(error)))) } return true } @@ -468,6 +472,7 @@ class DAGScheduler( } } if (tasks.size > 0) { + sparkListeners.foreach(_.onStageSubmitted(SparkListenerStageSubmitted(stage, tasks.size))) logInfo("Submitting " + tasks.size + " missing tasks from " + stage + " (" + stage.rdd + ")") myPending ++= tasks logDebug("New pending tasks: " + myPending) @@ -522,6 +527,7 @@ class DAGScheduler( activeJobs -= job resultStageToJob -= stage markStageAsFinished(stage) + sparkListeners.foreach(_.onJobEnd(SparkListenerJobEnd(job, JobSucceeded))) } job.listener.taskSucceeded(rt.outputId, event.result) } @@ -662,7 +668,9 @@ class DAGScheduler( val dependentStages = resultStageToJob.keys.filter(x => stageDependsOn(x, failedStage)).toSeq for (resultStage <- dependentStages) { val job = resultStageToJob(resultStage) - job.listener.jobFailed(new SparkException("Job failed: " + reason)) + val error = new SparkException("Job failed: " + reason) + job.listener.jobFailed(error) + sparkListeners.foreach(_.onJobEnd(SparkListenerJobEnd(job, JobFailed(error)))) activeJobs -= job resultStageToJob -= resultStage } diff --git a/core/src/main/scala/spark/scheduler/JobLogger.scala b/core/src/main/scala/spark/scheduler/JobLogger.scala new file mode 100644 index 0000000000..178bfaba3d --- /dev/null +++ b/core/src/main/scala/spark/scheduler/JobLogger.scala @@ -0,0 +1,306 @@ +package spark.scheduler
+
+import java.io.PrintWriter
+import java.io.File
+import java.io.FileNotFoundException
+import java.text.SimpleDateFormat
+import java.util.{Date, Properties}
+import java.util.concurrent.LinkedBlockingQueue
+import scala.collection.mutable.{Map, HashMap, ListBuffer}
+import scala.io.Source
+import spark._
+import spark.executor.TaskMetrics
+import spark.scheduler.cluster.TaskInfo
+
+// Used to record runtime information for each job, including RDD graph
+// tasks' start/stop shuffle information and information from outside
+
+class JobLogger(val logDirName: String) extends SparkListener with Logging {
+ private val logDir =
+ if (System.getenv("SPARK_LOG_DIR") != null)
+ System.getenv("SPARK_LOG_DIR")
+ else
+ "/tmp/spark"
+ private val jobIDToPrintWriter = new HashMap[Int, PrintWriter]
+ private val stageIDToJobID = new HashMap[Int, Int]
+ private val jobIDToStages = new HashMap[Int, ListBuffer[Stage]]
+ private val DATE_FORMAT = new SimpleDateFormat("yyyy/MM/dd HH:mm:ss")
+ private val eventQueue = new LinkedBlockingQueue[SparkListenerEvents]
+
+ createLogDir()
+ def this() = this(String.valueOf(System.currentTimeMillis()))
+
+ def getLogDir = logDir
+ def getJobIDtoPrintWriter = jobIDToPrintWriter
+ def getStageIDToJobID = stageIDToJobID
+ def getJobIDToStages = jobIDToStages
+ def getEventQueue = eventQueue
+
+ new Thread("JobLogger") {
+ setDaemon(true)
+ override def run() {
+ while (true) {
+ val event = eventQueue.take
+ logDebug("Got event of type " + event.getClass.getName)
+ event match {
+ case SparkListenerJobStart(job, properties) =>
+ processJobStartEvent(job, properties)
+ case SparkListenerStageSubmitted(stage, taskSize) =>
+ processStageSubmittedEvent(stage, taskSize)
+ case StageCompleted(stageInfo) =>
+ processStageCompletedEvent(stageInfo)
+ case SparkListenerJobEnd(job, result) =>
+ processJobEndEvent(job, result)
+ case SparkListenerTaskEnd(task, reason, taskInfo, taskMetrics) =>
+ processTaskEndEvent(task, reason, taskInfo, taskMetrics)
+ case _ =>
+ }
+ }
+ }
+ }.start()
+
+ // Create a folder for log files, the folder's name is the creation time of the jobLogger
+ protected def createLogDir() {
+ val dir = new File(logDir + "/" + logDirName + "/")
+ if (dir.exists()) {
+ return
+ }
+ if (dir.mkdirs() == false) {
+ logError("create log directory error:" + logDir + "/" + logDirName + "/")
+ }
+ }
+
+ // Create a log file for one job, the file name is the jobID
+ protected def createLogWriter(jobID: Int) {
+ try{
+ val fileWriter = new PrintWriter(logDir + "/" + logDirName + "/" + jobID)
+ jobIDToPrintWriter += (jobID -> fileWriter)
+ } catch {
+ case e: FileNotFoundException => e.printStackTrace()
+ }
+ }
+
+ // Close log file, and clean the stage relationship in stageIDToJobID
+ protected def closeLogWriter(jobID: Int) =
+ jobIDToPrintWriter.get(jobID).foreach { fileWriter =>
+ fileWriter.close()
+ jobIDToStages.get(jobID).foreach(_.foreach{ stage =>
+ stageIDToJobID -= stage.id
+ })
+ jobIDToPrintWriter -= jobID
+ jobIDToStages -= jobID
+ }
+
+ // Write log information to log file, withTime parameter controls whether to recored
+ // time stamp for the information
+ protected def jobLogInfo(jobID: Int, info: String, withTime: Boolean = true) {
+ var writeInfo = info
+ if (withTime) {
+ val date = new Date(System.currentTimeMillis())
+ writeInfo = DATE_FORMAT.format(date) + ": " +info
+ }
+ jobIDToPrintWriter.get(jobID).foreach(_.println(writeInfo))
+ }
+
+ protected def stageLogInfo(stageID: Int, info: String, withTime: Boolean = true) =
+ stageIDToJobID.get(stageID).foreach(jobID => jobLogInfo(jobID, info, withTime))
+
+ protected def buildJobDep(jobID: Int, stage: Stage) {
+ if (stage.priority == jobID) {
+ jobIDToStages.get(jobID) match {
+ case Some(stageList) => stageList += stage
+ case None => val stageList = new ListBuffer[Stage]
+ stageList += stage
+ jobIDToStages += (jobID -> stageList)
+ }
+ stageIDToJobID += (stage.id -> jobID)
+ stage.parents.foreach(buildJobDep(jobID, _))
+ }
+ }
+
+ protected def recordStageDep(jobID: Int) {
+ def getRddsInStage(rdd: RDD[_]): ListBuffer[RDD[_]] = {
+ var rddList = new ListBuffer[RDD[_]]
+ rddList += rdd
+ rdd.dependencies.foreach{ dep => dep match {
+ case shufDep: ShuffleDependency[_,_] =>
+ case _ => rddList ++= getRddsInStage(dep.rdd)
+ }
+ }
+ rddList
+ }
+ jobIDToStages.get(jobID).foreach {_.foreach { stage =>
+ var depRddDesc: String = ""
+ getRddsInStage(stage.rdd).foreach { rdd =>
+ depRddDesc += rdd.id + ","
+ }
+ var depStageDesc: String = ""
+ stage.parents.foreach { stage =>
+ depStageDesc += "(" + stage.id + "," + stage.shuffleDep.get.shuffleId + ")"
+ }
+ jobLogInfo(jobID, "STAGE_ID=" + stage.id + " RDD_DEP=(" +
+ depRddDesc.substring(0, depRddDesc.length - 1) + ")" +
+ " STAGE_DEP=" + depStageDesc, false)
+ }
+ }
+ }
+
+ // Generate indents and convert to String
+ protected def indentString(indent: Int) = {
+ val sb = new StringBuilder()
+ for (i <- 1 to indent) {
+ sb.append(" ")
+ }
+ sb.toString()
+ }
+
+ protected def getRddName(rdd: RDD[_]) = {
+ var rddName = rdd.getClass.getName
+ if (rdd.name != null) {
+ rddName = rdd.name
+ }
+ rddName
+ }
+
+ protected def recordRddInStageGraph(jobID: Int, rdd: RDD[_], indent: Int) {
+ val rddInfo = "RDD_ID=" + rdd.id + "(" + getRddName(rdd) + "," + rdd.generator + ")"
+ jobLogInfo(jobID, indentString(indent) + rddInfo, false)
+ rdd.dependencies.foreach{ dep => dep match {
+ case shufDep: ShuffleDependency[_,_] =>
+ val depInfo = "SHUFFLE_ID=" + shufDep.shuffleId
+ jobLogInfo(jobID, indentString(indent + 1) + depInfo, false)
+ case _ => recordRddInStageGraph(jobID, dep.rdd, indent + 1)
+ }
+ }
+ }
+
+ protected def recordStageDepGraph(jobID: Int, stage: Stage, indent: Int = 0) {
+ var stageInfo: String = ""
+ if (stage.isShuffleMap) {
+ stageInfo = "STAGE_ID=" + stage.id + " MAP_STAGE SHUFFLE_ID=" +
+ stage.shuffleDep.get.shuffleId
+ }else{
+ stageInfo = "STAGE_ID=" + stage.id + " RESULT_STAGE"
+ }
+ if (stage.priority == jobID) {
+ jobLogInfo(jobID, indentString(indent) + stageInfo, false)
+ recordRddInStageGraph(jobID, stage.rdd, indent)
+ stage.parents.foreach(recordStageDepGraph(jobID, _, indent + 2))
+ } else
+ jobLogInfo(jobID, indentString(indent) + stageInfo + " JOB_ID=" + stage.priority, false)
+ }
+
+ // Record task metrics into job log files
+ protected def recordTaskMetrics(stageID: Int, status: String,
+ taskInfo: TaskInfo, taskMetrics: TaskMetrics) {
+ val info = " TID=" + taskInfo.taskId + " STAGE_ID=" + stageID +
+ " START_TIME=" + taskInfo.launchTime + " FINISH_TIME=" + taskInfo.finishTime +
+ " EXECUTOR_ID=" + taskInfo.executorId + " HOST=" + taskMetrics.hostname
+ val executorRunTime = " EXECUTOR_RUN_TIME=" + taskMetrics.executorRunTime
+ val readMetrics =
+ taskMetrics.shuffleReadMetrics match {
+ case Some(metrics) =>
+ " SHUFFLE_FINISH_TIME=" + metrics.shuffleFinishTime +
+ " BLOCK_FETCHED_TOTAL=" + metrics.totalBlocksFetched +
+ " BLOCK_FETCHED_LOCAL=" + metrics.localBlocksFetched +
+ " BLOCK_FETCHED_REMOTE=" + metrics.remoteBlocksFetched +
+ " REMOTE_FETCH_WAIT_TIME=" + metrics.fetchWaitTime +
+ " REMOTE_FETCH_TIME=" + metrics.remoteFetchTime +
+ " REMOTE_BYTES_READ=" + metrics.remoteBytesRead
+ case None => ""
+ }
+ val writeMetrics =
+ taskMetrics.shuffleWriteMetrics match {
+ case Some(metrics) =>
+ " SHUFFLE_BYTES_WRITTEN=" + metrics.shuffleBytesWritten
+ case None => ""
+ }
+ stageLogInfo(stageID, status + info + executorRunTime + readMetrics + writeMetrics)
+ }
+
+ override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted) {
+ eventQueue.put(stageSubmitted)
+ }
+
+ protected def processStageSubmittedEvent(stage: Stage, taskSize: Int) {
+ stageLogInfo(stage.id, "STAGE_ID=" + stage.id + " STATUS=SUBMITTED" + " TASK_SIZE=" + taskSize)
+ }
+
+ override def onStageCompleted(stageCompleted: StageCompleted) {
+ eventQueue.put(stageCompleted)
+ }
+
+ protected def processStageCompletedEvent(stageInfo: StageInfo) {
+ stageLogInfo(stageInfo.stage.id, "STAGE_ID=" +
+ stageInfo.stage.id + " STATUS=COMPLETED")
+
+ }
+
+ override def onTaskEnd(taskEnd: SparkListenerTaskEnd) {
+ eventQueue.put(taskEnd)
+ }
+
+ protected def processTaskEndEvent(task: Task[_], reason: TaskEndReason,
+ taskInfo: TaskInfo, taskMetrics: TaskMetrics) {
+ var taskStatus = ""
+ task match {
+ case resultTask: ResultTask[_, _] => taskStatus = "TASK_TYPE=RESULT_TASK"
+ case shuffleMapTask: ShuffleMapTask => taskStatus = "TASK_TYPE=SHUFFLE_MAP_TASK"
+ }
+ reason match {
+ case Success => taskStatus += " STATUS=SUCCESS"
+ recordTaskMetrics(task.stageId, taskStatus, taskInfo, taskMetrics)
+ case Resubmitted =>
+ taskStatus += " STATUS=RESUBMITTED TID=" + taskInfo.taskId +
+ " STAGE_ID=" + task.stageId
+ stageLogInfo(task.stageId, taskStatus)
+ case FetchFailed(bmAddress, shuffleId, mapId, reduceId) =>
+ taskStatus += " STATUS=FETCHFAILED TID=" + taskInfo.taskId + " STAGE_ID=" +
+ task.stageId + " SHUFFLE_ID=" + shuffleId + " MAP_ID=" +
+ mapId + " REDUCE_ID=" + reduceId
+ stageLogInfo(task.stageId, taskStatus)
+ case OtherFailure(message) =>
+ taskStatus += " STATUS=FAILURE TID=" + taskInfo.taskId +
+ " STAGE_ID=" + task.stageId + " INFO=" + message
+ stageLogInfo(task.stageId, taskStatus)
+ case _ =>
+ }
+ }
+
+ override def onJobEnd(jobEnd: SparkListenerJobEnd) {
+ eventQueue.put(jobEnd)
+ }
+
+ protected def processJobEndEvent(job: ActiveJob, reason: JobResult) {
+ var info = "JOB_ID=" + job.runId
+ reason match {
+ case JobSucceeded => info += " STATUS=SUCCESS"
+ case JobFailed(exception) =>
+ info += " STATUS=FAILED REASON="
+ exception.getMessage.split("\\s+").foreach(info += _ + "_")
+ case _ =>
+ }
+ jobLogInfo(job.runId, info.substring(0, info.length - 1).toUpperCase)
+ closeLogWriter(job.runId)
+ }
+
+ protected def recordJobProperties(jobID: Int, properties: Properties) {
+ if(properties != null) {
+ val annotation = properties.getProperty("spark.job.annotation", "")
+ jobLogInfo(jobID, annotation, false)
+ }
+ }
+
+ override def onJobStart(jobStart: SparkListenerJobStart) {
+ eventQueue.put(jobStart)
+ }
+
+ protected def processJobStartEvent(job: ActiveJob, properties: Properties) {
+ createLogWriter(job.runId)
+ recordJobProperties(job.runId, properties)
+ buildJobDep(job.runId, job.finalStage)
+ recordStageDep(job.runId)
+ recordStageDepGraph(job.runId, job.finalStage)
+ jobLogInfo(job.runId, "JOB_ID=" + job.runId + " STATUS=STARTED")
+ }
+}
diff --git a/core/src/main/scala/spark/scheduler/SparkListener.scala b/core/src/main/scala/spark/scheduler/SparkListener.scala index a65140b145..bac984b5c9 100644 --- a/core/src/main/scala/spark/scheduler/SparkListener.scala +++ b/core/src/main/scala/spark/scheduler/SparkListener.scala @@ -1,27 +1,59 @@ package spark.scheduler +import java.util.Properties import spark.scheduler.cluster.TaskInfo import spark.util.Distribution -import spark.{Utils, Logging} +import spark.{Logging, SparkContext, TaskEndReason, Utils} import spark.executor.TaskMetrics -trait SparkListener { - /** - * called when a stage is completed, with information on the completed stage - */ - def onStageCompleted(stageCompleted: StageCompleted) -} - sealed trait SparkListenerEvents +case class SparkListenerStageSubmitted(stage: Stage, taskSize: Int) extends SparkListenerEvents + case class StageCompleted(val stageInfo: StageInfo) extends SparkListenerEvents +case class SparkListenerTaskEnd(task: Task[_], reason: TaskEndReason, taskInfo: TaskInfo, + taskMetrics: TaskMetrics) extends SparkListenerEvents + +case class SparkListenerJobStart(job: ActiveJob, properties: Properties = null) + extends SparkListenerEvents + +case class SparkListenerJobEnd(job: ActiveJob, jobResult: JobResult) + extends SparkListenerEvents + +trait SparkListener { + /** + * Called when a stage is completed, with information on the completed stage + */ + def onStageCompleted(stageCompleted: StageCompleted) { } + + /** + * Called when a stage is submitted + */ + def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted) { } + + /** + * Called when a task ends + */ + def onTaskEnd(taskEnd: SparkListenerTaskEnd) { } + + /** + * Called when a job starts + */ + def onJobStart(jobStart: SparkListenerJobStart) { } + + /** + * Called when a job ends + */ + def onJobEnd(jobEnd: SparkListenerJobEnd) { } + +} /** * Simple SparkListener that logs a few summary statistics when each stage completes */ class StatsReportListener extends SparkListener with Logging { - def onStageCompleted(stageCompleted: StageCompleted) { + override def onStageCompleted(stageCompleted: StageCompleted) { import spark.scheduler.StatsReportListener._ implicit val sc = stageCompleted this.logInfo("Finished stage: " + stageCompleted.stageInfo) diff --git a/core/src/main/scala/spark/scheduler/cluster/ClusterScheduler.scala b/core/src/main/scala/spark/scheduler/cluster/ClusterScheduler.scala index 053d4b8e4a..3a0c29b27f 100644 --- a/core/src/main/scala/spark/scheduler/cluster/ClusterScheduler.scala +++ b/core/src/main/scala/spark/scheduler/cluster/ClusterScheduler.scala @@ -177,7 +177,7 @@ private[spark] class ClusterScheduler(val sc: SparkContext) val tasks = taskSet.tasks logInfo("Adding task set " + taskSet.id + " with " + tasks.length + " tasks") this.synchronized { - val manager = new TaskSetManager(this, taskSet) + val manager = new ClusterTaskSetManager(this, taskSet) activeTaskSets(taskSet.id) = manager schedulableBuilder.addTaskSetManager(manager, manager.taskSet.properties) taskSetTaskIds(taskSet.id) = new HashSet[Long]() diff --git a/core/src/main/scala/spark/scheduler/cluster/ClusterTaskSetManager.scala b/core/src/main/scala/spark/scheduler/cluster/ClusterTaskSetManager.scala new file mode 100644 index 0000000000..d72b0bfc9f --- /dev/null +++ b/core/src/main/scala/spark/scheduler/cluster/ClusterTaskSetManager.scala @@ -0,0 +1,747 @@ +package spark.scheduler.cluster + +import java.util.{HashMap => JHashMap, NoSuchElementException, Arrays} + +import scala.collection.mutable.ArrayBuffer +import scala.collection.mutable.HashMap +import scala.collection.mutable.HashSet +import scala.math.max +import scala.math.min + +import spark._ +import spark.scheduler._ +import spark.TaskState.TaskState +import java.nio.ByteBuffer + +private[spark] object TaskLocality extends Enumeration("PROCESS_LOCAL", "NODE_LOCAL", "RACK_LOCAL", "ANY") with Logging { + + // process local is expected to be used ONLY within tasksetmanager for now. + val PROCESS_LOCAL, NODE_LOCAL, RACK_LOCAL, ANY = Value + + type TaskLocality = Value + + def isAllowed(constraint: TaskLocality, condition: TaskLocality): Boolean = { + + // Must not be the constraint. + assert (constraint != TaskLocality.PROCESS_LOCAL) + + constraint match { + case TaskLocality.NODE_LOCAL => condition == TaskLocality.NODE_LOCAL + case TaskLocality.RACK_LOCAL => condition == TaskLocality.NODE_LOCAL || condition == TaskLocality.RACK_LOCAL + // For anything else, allow + case _ => true + } + } + + def parse(str: String): TaskLocality = { + // better way to do this ? + try { + val retval = TaskLocality.withName(str) + // Must not specify PROCESS_LOCAL ! + assert (retval != TaskLocality.PROCESS_LOCAL) + + retval + } catch { + case nEx: NoSuchElementException => { + logWarning("Invalid task locality specified '" + str + "', defaulting to NODE_LOCAL"); + // default to preserve earlier behavior + NODE_LOCAL + } + } + } +} + +/** + * Schedules the tasks within a single TaskSet in the ClusterScheduler. + */ +private[spark] class ClusterTaskSetManager( + sched: ClusterScheduler, + val taskSet: TaskSet) + extends TaskSetManager + with Logging { + + // Maximum time to wait to run a task in a preferred location (in ms) + val LOCALITY_WAIT = System.getProperty("spark.locality.wait", "3000").toLong + + // CPUs to request per task + val CPUS_PER_TASK = System.getProperty("spark.task.cpus", "1").toDouble + + // Maximum times a task is allowed to fail before failing the job + val MAX_TASK_FAILURES = 4 + + // Quantile of tasks at which to start speculation + val SPECULATION_QUANTILE = System.getProperty("spark.speculation.quantile", "0.75").toDouble + val SPECULATION_MULTIPLIER = System.getProperty("spark.speculation.multiplier", "1.5").toDouble + + // Serializer for closures and tasks. + val ser = SparkEnv.get.closureSerializer.newInstance() + + val tasks = taskSet.tasks + val numTasks = tasks.length + val copiesRunning = new Array[Int](numTasks) + val finished = new Array[Boolean](numTasks) + val numFailures = new Array[Int](numTasks) + val taskAttempts = Array.fill[List[TaskInfo]](numTasks)(Nil) + var tasksFinished = 0 + + var weight = 1 + var minShare = 0 + var runningTasks = 0 + var priority = taskSet.priority + var stageId = taskSet.stageId + var name = "TaskSet_"+taskSet.stageId.toString + var parent:Schedulable = null + + // Last time when we launched a preferred task (for delay scheduling) + var lastPreferredLaunchTime = System.currentTimeMillis + + // List of pending tasks for each node (process local to container). These collections are actually + // treated as stacks, in which new tasks are added to the end of the + // ArrayBuffer and removed from the end. This makes it faster to detect + // tasks that repeatedly fail because whenever a task failed, it is put + // back at the head of the stack. They are also only cleaned up lazily; + // when a task is launched, it remains in all the pending lists except + // the one that it was launched from, but gets removed from them later. + private val pendingTasksForHostPort = new HashMap[String, ArrayBuffer[Int]] + + // List of pending tasks for each node. + // Essentially, similar to pendingTasksForHostPort, except at host level + private val pendingTasksForHost = new HashMap[String, ArrayBuffer[Int]] + + // List of pending tasks for each node based on rack locality. + // Essentially, similar to pendingTasksForHost, except at rack level + private val pendingRackLocalTasksForHost = new HashMap[String, ArrayBuffer[Int]] + + // List containing pending tasks with no locality preferences + val pendingTasksWithNoPrefs = new ArrayBuffer[Int] + + // List containing all pending tasks (also used as a stack, as above) + val allPendingTasks = new ArrayBuffer[Int] + + // Tasks that can be speculated. Since these will be a small fraction of total + // tasks, we'll just hold them in a HashSet. + val speculatableTasks = new HashSet[Int] + + // Task index, start and finish time for each task attempt (indexed by task ID) + val taskInfos = new HashMap[Long, TaskInfo] + + // Did the job fail? + var failed = false + var causeOfFailure = "" + + // How frequently to reprint duplicate exceptions in full, in milliseconds + val EXCEPTION_PRINT_INTERVAL = + System.getProperty("spark.logging.exceptionPrintInterval", "10000").toLong + // Map of recent exceptions (identified by string representation and + // top stack frame) to duplicate count (how many times the same + // exception has appeared) and time the full exception was + // printed. This should ideally be an LRU map that can drop old + // exceptions automatically. + val recentExceptions = HashMap[String, (Int, Long)]() + + // Figure out the current map output tracker generation and set it on all tasks + val generation = sched.mapOutputTracker.getGeneration + logDebug("Generation for " + taskSet.id + ": " + generation) + for (t <- tasks) { + t.generation = generation + } + + // Add all our tasks to the pending lists. We do this in reverse order + // of task index so that tasks with low indices get launched first. + for (i <- (0 until numTasks).reverse) { + addPendingTask(i) + } + + // Note that it follows the hierarchy. + // if we search for NODE_LOCAL, the output will include PROCESS_LOCAL and + // if we search for RACK_LOCAL, it will include PROCESS_LOCAL & NODE_LOCAL + private def findPreferredLocations(_taskPreferredLocations: Seq[String], scheduler: ClusterScheduler, + taskLocality: TaskLocality.TaskLocality): HashSet[String] = { + + if (TaskLocality.PROCESS_LOCAL == taskLocality) { + // straight forward comparison ! Special case it. + val retval = new HashSet[String]() + scheduler.synchronized { + for (location <- _taskPreferredLocations) { + if (scheduler.isExecutorAliveOnHostPort(location)) { + retval += location + } + } + } + + return retval + } + + val taskPreferredLocations = + if (TaskLocality.NODE_LOCAL == taskLocality) { + _taskPreferredLocations + } else { + assert (TaskLocality.RACK_LOCAL == taskLocality) + // Expand set to include all 'seen' rack local hosts. + // This works since container allocation/management happens within master - so any rack locality information is updated in msater. + // Best case effort, and maybe sort of kludge for now ... rework it later ? + val hosts = new HashSet[String] + _taskPreferredLocations.foreach(h => { + val rackOpt = scheduler.getRackForHost(h) + if (rackOpt.isDefined) { + val hostsOpt = scheduler.getCachedHostsForRack(rackOpt.get) + if (hostsOpt.isDefined) { + hosts ++= hostsOpt.get + } + } + + // Ensure that irrespective of what scheduler says, host is always added ! + hosts += h + }) + + hosts + } + + val retval = new HashSet[String] + scheduler.synchronized { + for (prefLocation <- taskPreferredLocations) { + val aliveLocationsOpt = scheduler.getExecutorsAliveOnHost(Utils.parseHostPort(prefLocation)._1) + if (aliveLocationsOpt.isDefined) { + retval ++= aliveLocationsOpt.get + } + } + } + + retval + } + + // Add a task to all the pending-task lists that it should be on. + private def addPendingTask(index: Int) { + // We can infer hostLocalLocations from rackLocalLocations by joining it against tasks(index).preferredLocations (with appropriate + // hostPort <-> host conversion). But not doing it for simplicity sake. If this becomes a performance issue, modify it. + val processLocalLocations = findPreferredLocations(tasks(index).preferredLocations, sched, TaskLocality.PROCESS_LOCAL) + val hostLocalLocations = findPreferredLocations(tasks(index).preferredLocations, sched, TaskLocality.NODE_LOCAL) + val rackLocalLocations = findPreferredLocations(tasks(index).preferredLocations, sched, TaskLocality.RACK_LOCAL) + + if (rackLocalLocations.size == 0) { + // Current impl ensures this. + assert (processLocalLocations.size == 0) + assert (hostLocalLocations.size == 0) + pendingTasksWithNoPrefs += index + } else { + + // process local locality + for (hostPort <- processLocalLocations) { + // DEBUG Code + Utils.checkHostPort(hostPort) + + val hostPortList = pendingTasksForHostPort.getOrElseUpdate(hostPort, ArrayBuffer()) + hostPortList += index + } + + // host locality (includes process local) + for (hostPort <- hostLocalLocations) { + // DEBUG Code + Utils.checkHostPort(hostPort) + + val host = Utils.parseHostPort(hostPort)._1 + val hostList = pendingTasksForHost.getOrElseUpdate(host, ArrayBuffer()) + hostList += index + } + + // rack locality (includes process local and host local) + for (rackLocalHostPort <- rackLocalLocations) { + // DEBUG Code + Utils.checkHostPort(rackLocalHostPort) + + val rackLocalHost = Utils.parseHostPort(rackLocalHostPort)._1 + val list = pendingRackLocalTasksForHost.getOrElseUpdate(rackLocalHost, ArrayBuffer()) + list += index + } + } + + allPendingTasks += index + } + + // Return the pending tasks list for a given host port (process local), or an empty list if + // there is no map entry for that host + private def getPendingTasksForHostPort(hostPort: String): ArrayBuffer[Int] = { + // DEBUG Code + Utils.checkHostPort(hostPort) + pendingTasksForHostPort.getOrElse(hostPort, ArrayBuffer()) + } + + // Return the pending tasks list for a given host, or an empty list if + // there is no map entry for that host + private def getPendingTasksForHost(hostPort: String): ArrayBuffer[Int] = { + val host = Utils.parseHostPort(hostPort)._1 + pendingTasksForHost.getOrElse(host, ArrayBuffer()) + } + + // Return the pending tasks (rack level) list for a given host, or an empty list if + // there is no map entry for that host + private def getRackLocalPendingTasksForHost(hostPort: String): ArrayBuffer[Int] = { + val host = Utils.parseHostPort(hostPort)._1 + pendingRackLocalTasksForHost.getOrElse(host, ArrayBuffer()) + } + + // Number of pending tasks for a given host Port (which would be process local) + def numPendingTasksForHostPort(hostPort: String): Int = { + getPendingTasksForHostPort(hostPort).count( index => copiesRunning(index) == 0 && !finished(index) ) + } + + // Number of pending tasks for a given host (which would be data local) + def numPendingTasksForHost(hostPort: String): Int = { + getPendingTasksForHost(hostPort).count( index => copiesRunning(index) == 0 && !finished(index) ) + } + + // Number of pending rack local tasks for a given host + def numRackLocalPendingTasksForHost(hostPort: String): Int = { + getRackLocalPendingTasksForHost(hostPort).count( index => copiesRunning(index) == 0 && !finished(index) ) + } + + + // Dequeue a pending task from the given list and return its index. + // Return None if the list is empty. + // This method also cleans up any tasks in the list that have already + // been launched, since we want that to happen lazily. + private def findTaskFromList(list: ArrayBuffer[Int]): Option[Int] = { + while (!list.isEmpty) { + val index = list.last + list.trimEnd(1) + if (copiesRunning(index) == 0 && !finished(index)) { + return Some(index) + } + } + return None + } + + // Return a speculative task for a given host if any are available. The task should not have an + // attempt running on this host, in case the host is slow. In addition, if locality is set, the + // task must have a preference for this host/rack/no preferred locations at all. + private def findSpeculativeTask(hostPort: String, locality: TaskLocality.TaskLocality): Option[Int] = { + + assert (TaskLocality.isAllowed(locality, TaskLocality.NODE_LOCAL)) + speculatableTasks.retain(index => !finished(index)) // Remove finished tasks from set + + if (speculatableTasks.size > 0) { + val localTask = speculatableTasks.find { + index => + val locations = findPreferredLocations(tasks(index).preferredLocations, sched, TaskLocality.NODE_LOCAL) + val attemptLocs = taskAttempts(index).map(_.hostPort) + (locations.size == 0 || locations.contains(hostPort)) && !attemptLocs.contains(hostPort) + } + + if (localTask != None) { + speculatableTasks -= localTask.get + return localTask + } + + // check for rack locality + if (TaskLocality.isAllowed(locality, TaskLocality.RACK_LOCAL)) { + val rackTask = speculatableTasks.find { + index => + val locations = findPreferredLocations(tasks(index).preferredLocations, sched, TaskLocality.RACK_LOCAL) + val attemptLocs = taskAttempts(index).map(_.hostPort) + locations.contains(hostPort) && !attemptLocs.contains(hostPort) + } + + if (rackTask != None) { + speculatableTasks -= rackTask.get + return rackTask + } + } + + // Any task ... + if (TaskLocality.isAllowed(locality, TaskLocality.ANY)) { + // Check for attemptLocs also ? + val nonLocalTask = speculatableTasks.find(i => !taskAttempts(i).map(_.hostPort).contains(hostPort)) + if (nonLocalTask != None) { + speculatableTasks -= nonLocalTask.get + return nonLocalTask + } + } + } + return None + } + + // Dequeue a pending task for a given node and return its index. + // If localOnly is set to false, allow non-local tasks as well. + private def findTask(hostPort: String, locality: TaskLocality.TaskLocality): Option[Int] = { + val processLocalTask = findTaskFromList(getPendingTasksForHostPort(hostPort)) + if (processLocalTask != None) { + return processLocalTask + } + + val localTask = findTaskFromList(getPendingTasksForHost(hostPort)) + if (localTask != None) { + return localTask + } + + if (TaskLocality.isAllowed(locality, TaskLocality.RACK_LOCAL)) { + val rackLocalTask = findTaskFromList(getRackLocalPendingTasksForHost(hostPort)) + if (rackLocalTask != None) { + return rackLocalTask + } + } + + // Look for no pref tasks AFTER rack local tasks - this has side effect that we will get to failed tasks later rather than sooner. + // TODO: That code path needs to be revisited (adding to no prefs list when host:port goes down). + val noPrefTask = findTaskFromList(pendingTasksWithNoPrefs) + if (noPrefTask != None) { + return noPrefTask + } + + if (TaskLocality.isAllowed(locality, TaskLocality.ANY)) { + val nonLocalTask = findTaskFromList(allPendingTasks) + if (nonLocalTask != None) { + return nonLocalTask + } + } + + // Finally, if all else has failed, find a speculative task + return findSpeculativeTask(hostPort, locality) + } + + private def isProcessLocalLocation(task: Task[_], hostPort: String): Boolean = { + Utils.checkHostPort(hostPort) + + val locs = task.preferredLocations + + locs.contains(hostPort) + } + + private def isHostLocalLocation(task: Task[_], hostPort: String): Boolean = { + val locs = task.preferredLocations + + // If no preference, consider it as host local + if (locs.isEmpty) return true + + val host = Utils.parseHostPort(hostPort)._1 + locs.find(h => Utils.parseHostPort(h)._1 == host).isDefined + } + + // Does a host count as a rack local preferred location for a task? (assumes host is NOT preferred location). + // This is true if either the task has preferred locations and this host is one, or it has + // no preferred locations (in which we still count the launch as preferred). + private def isRackLocalLocation(task: Task[_], hostPort: String): Boolean = { + + val locs = task.preferredLocations + + val preferredRacks = new HashSet[String]() + for (preferredHost <- locs) { + val rack = sched.getRackForHost(preferredHost) + if (None != rack) preferredRacks += rack.get + } + + if (preferredRacks.isEmpty) return false + + val hostRack = sched.getRackForHost(hostPort) + + return None != hostRack && preferredRacks.contains(hostRack.get) + } + + // Respond to an offer of a single slave from the scheduler by finding a task + def slaveOffer(execId: String, hostPort: String, availableCpus: Double, overrideLocality: TaskLocality.TaskLocality = null): Option[TaskDescription] = { + + if (tasksFinished < numTasks && availableCpus >= CPUS_PER_TASK) { + // If explicitly specified, use that + val locality = if (overrideLocality != null) overrideLocality else { + // expand only if we have waited for more than LOCALITY_WAIT for a host local task ... + val time = System.currentTimeMillis + if (time - lastPreferredLaunchTime < LOCALITY_WAIT) TaskLocality.NODE_LOCAL else TaskLocality.ANY + } + + findTask(hostPort, locality) match { + case Some(index) => { + // Found a task; do some bookkeeping and return a Mesos task for it + val task = tasks(index) + val taskId = sched.newTaskId() + // Figure out whether this should count as a preferred launch + val taskLocality = + if (isProcessLocalLocation(task, hostPort)) TaskLocality.PROCESS_LOCAL else + if (isHostLocalLocation(task, hostPort)) TaskLocality.NODE_LOCAL else + if (isRackLocalLocation(task, hostPort)) TaskLocality.RACK_LOCAL else + TaskLocality.ANY + val prefStr = taskLocality.toString + logInfo("Starting task %s:%d as TID %s on slave %s: %s (%s)".format( + taskSet.id, index, taskId, execId, hostPort, prefStr)) + // Do various bookkeeping + copiesRunning(index) += 1 + val time = System.currentTimeMillis + val info = new TaskInfo(taskId, index, time, execId, hostPort, taskLocality) + taskInfos(taskId) = info + taskAttempts(index) = info :: taskAttempts(index) + if (TaskLocality.NODE_LOCAL == taskLocality) { + lastPreferredLaunchTime = time + } + // Serialize and return the task + val startTime = System.currentTimeMillis + val serializedTask = Task.serializeWithDependencies( + task, sched.sc.addedFiles, sched.sc.addedJars, ser) + val timeTaken = System.currentTimeMillis - startTime + increaseRunningTasks(1) + logInfo("Serialized task %s:%d as %d bytes in %d ms".format( + taskSet.id, index, serializedTask.limit, timeTaken)) + val taskName = "task %s:%d".format(taskSet.id, index) + return Some(new TaskDescription(taskId, execId, taskName, serializedTask)) + } + case _ => + } + } + return None + } + + def statusUpdate(tid: Long, state: TaskState, serializedData: ByteBuffer) { + state match { + case TaskState.FINISHED => + taskFinished(tid, state, serializedData) + case TaskState.LOST => + taskLost(tid, state, serializedData) + case TaskState.FAILED => + taskLost(tid, state, serializedData) + case TaskState.KILLED => + taskLost(tid, state, serializedData) + case _ => + } + } + + def taskFinished(tid: Long, state: TaskState, serializedData: ByteBuffer) { + val info = taskInfos(tid) + if (info.failed) { + // We might get two task-lost messages for the same task in coarse-grained Mesos mode, + // or even from Mesos itself when acks get delayed. + return + } + val index = info.index + info.markSuccessful() + decreaseRunningTasks(1) + if (!finished(index)) { + tasksFinished += 1 + logInfo("Finished TID %s in %d ms (progress: %d/%d)".format( + tid, info.duration, tasksFinished, numTasks)) + // Deserialize task result and pass it to the scheduler + try { + val result = ser.deserialize[TaskResult[_]](serializedData) + result.metrics.resultSize = serializedData.limit() + sched.listener.taskEnded(tasks(index), Success, result.value, result.accumUpdates, info, result.metrics) + } catch { + case cnf: ClassNotFoundException => + val loader = Thread.currentThread().getContextClassLoader + throw new SparkException("ClassNotFound with classloader: " + loader, cnf) + case ex => throw ex + } + // Mark finished and stop if we've finished all the tasks + finished(index) = true + if (tasksFinished == numTasks) { + sched.taskSetFinished(this) + } + } else { + logInfo("Ignoring task-finished event for TID " + tid + + " because task " + index + " is already finished") + } + } + + def taskLost(tid: Long, state: TaskState, serializedData: ByteBuffer) { + val info = taskInfos(tid) + if (info.failed) { + // We might get two task-lost messages for the same task in coarse-grained Mesos mode, + // or even from Mesos itself when acks get delayed. + return + } + val index = info.index + info.markFailed() + decreaseRunningTasks(1) + if (!finished(index)) { + logInfo("Lost TID %s (task %s:%d)".format(tid, taskSet.id, index)) + copiesRunning(index) -= 1 + // Check if the problem is a map output fetch failure. In that case, this + // task will never succeed on any node, so tell the scheduler about it. + if (serializedData != null && serializedData.limit() > 0) { + val reason = ser.deserialize[TaskEndReason](serializedData, getClass.getClassLoader) + reason match { + case fetchFailed: FetchFailed => + logInfo("Loss was due to fetch failure from " + fetchFailed.bmAddress) + sched.listener.taskEnded(tasks(index), fetchFailed, null, null, info, null) + finished(index) = true + tasksFinished += 1 + sched.taskSetFinished(this) + decreaseRunningTasks(runningTasks) + return + + case taskResultTooBig: TaskResultTooBigFailure => + logInfo("Loss was due to task %s result exceeding Akka frame size; " + + "aborting job".format(tid)) + abort("Task %s result exceeded Akka frame size".format(tid)) + return + + case ef: ExceptionFailure => + val key = ef.description + val now = System.currentTimeMillis + val (printFull, dupCount) = { + if (recentExceptions.contains(key)) { + val (dupCount, printTime) = recentExceptions(key) + if (now - printTime > EXCEPTION_PRINT_INTERVAL) { + recentExceptions(key) = (0, now) + (true, 0) + } else { + recentExceptions(key) = (dupCount + 1, printTime) + (false, dupCount + 1) + } + } else { + recentExceptions(key) = (0, now) + (true, 0) + } + } + if (printFull) { + val locs = ef.stackTrace.map(loc => "\tat %s".format(loc.toString)) + logInfo("Loss was due to %s\n%s\n%s".format( + ef.className, ef.description, locs.mkString("\n"))) + } else { + logInfo("Loss was due to %s [duplicate %d]".format(ef.description, dupCount)) + } + + case _ => {} + } + } + // On non-fetch failures, re-enqueue the task as pending for a max number of retries + addPendingTask(index) + // Count failed attempts only on FAILED and LOST state (not on KILLED) + if (state == TaskState.FAILED || state == TaskState.LOST) { + numFailures(index) += 1 + if (numFailures(index) > MAX_TASK_FAILURES) { + logError("Task %s:%d failed more than %d times; aborting job".format( + taskSet.id, index, MAX_TASK_FAILURES)) + abort("Task %s:%d failed more than %d times".format(taskSet.id, index, MAX_TASK_FAILURES)) + } + } + } else { + logInfo("Ignoring task-lost event for TID " + tid + + " because task " + index + " is already finished") + } + } + + def error(message: String) { + // Save the error message + abort("Error: " + message) + } + + def abort(message: String) { + failed = true + causeOfFailure = message + // TODO: Kill running tasks if we were not terminated due to a Mesos error + sched.listener.taskSetFailed(taskSet, message) + decreaseRunningTasks(runningTasks) + sched.taskSetFinished(this) + } + + override def increaseRunningTasks(taskNum: Int) { + runningTasks += taskNum + if (parent != null) { + parent.increaseRunningTasks(taskNum) + } + } + + override def decreaseRunningTasks(taskNum: Int) { + runningTasks -= taskNum + if (parent != null) { + parent.decreaseRunningTasks(taskNum) + } + } + + //TODO: for now we just find Pool not TaskSetManager, we can extend this function in future if needed + override def getSchedulableByName(name: String): Schedulable = { + return null + } + + override def addSchedulable(schedulable:Schedulable) { + //nothing + } + + override def removeSchedulable(schedulable:Schedulable) { + //nothing + } + + override def getSortedTaskSetQueue(): ArrayBuffer[TaskSetManager] = { + var sortedTaskSetQueue = new ArrayBuffer[TaskSetManager] + sortedTaskSetQueue += this + return sortedTaskSetQueue + } + + override def executorLost(execId: String, hostPort: String) { + logInfo("Re-queueing tasks for " + execId + " from TaskSet " + taskSet.id) + + // If some task has preferred locations only on hostname, and there are no more executors there, + // put it in the no-prefs list to avoid the wait from delay scheduling + + // host local tasks - should we push this to rack local or no pref list ? For now, preserving behavior and moving to + // no prefs list. Note, this was done due to impliations related to 'waiting' for data local tasks, etc. + // Note: NOT checking process local list - since host local list is super set of that. We need to ad to no prefs only if + // there is no host local node for the task (not if there is no process local node for the task) + for (index <- getPendingTasksForHost(Utils.parseHostPort(hostPort)._1)) { + // val newLocs = findPreferredLocations(tasks(index).preferredLocations, sched, TaskLocality.RACK_LOCAL) + val newLocs = findPreferredLocations(tasks(index).preferredLocations, sched, TaskLocality.NODE_LOCAL) + if (newLocs.isEmpty) { + pendingTasksWithNoPrefs += index + } + } + + // Re-enqueue any tasks that ran on the failed executor if this is a shuffle map stage + if (tasks(0).isInstanceOf[ShuffleMapTask]) { + for ((tid, info) <- taskInfos if info.executorId == execId) { + val index = taskInfos(tid).index + if (finished(index)) { + finished(index) = false + copiesRunning(index) -= 1 + tasksFinished -= 1 + addPendingTask(index) + // Tell the DAGScheduler that this task was resubmitted so that it doesn't think our + // stage finishes when a total of tasks.size tasks finish. + sched.listener.taskEnded(tasks(index), Resubmitted, null, null, info, null) + } + } + } + // Also re-enqueue any tasks that were running on the node + for ((tid, info) <- taskInfos if info.running && info.executorId == execId) { + taskLost(tid, TaskState.KILLED, null) + } + } + + /** + * Check for tasks to be speculated and return true if there are any. This is called periodically + * by the ClusterScheduler. + * + * TODO: To make this scale to large jobs, we need to maintain a list of running tasks, so that + * we don't scan the whole task set. It might also help to make this sorted by launch time. + */ + override def checkSpeculatableTasks(): Boolean = { + // Can't speculate if we only have one task, or if all tasks have finished. + if (numTasks == 1 || tasksFinished == numTasks) { + return false + } + var foundTasks = false + val minFinishedForSpeculation = (SPECULATION_QUANTILE * numTasks).floor.toInt + logDebug("Checking for speculative tasks: minFinished = " + minFinishedForSpeculation) + if (tasksFinished >= minFinishedForSpeculation) { + val time = System.currentTimeMillis() + val durations = taskInfos.values.filter(_.successful).map(_.duration).toArray + Arrays.sort(durations) + val medianDuration = durations(min((0.5 * numTasks).round.toInt, durations.size - 1)) + val threshold = max(SPECULATION_MULTIPLIER * medianDuration, 100) + // TODO: Threshold should also look at standard deviation of task durations and have a lower + // bound based on that. + logDebug("Task length threshold for speculation: " + threshold) + for ((tid, info) <- taskInfos) { + val index = info.index + if (!finished(index) && copiesRunning(index) == 1 && info.timeRunning(time) > threshold && + !speculatableTasks.contains(index)) { + logInfo( + "Marking task %s:%d (on %s) as speculatable because it ran more than %.0f ms".format( + taskSet.id, index, info.hostPort, threshold)) + speculatableTasks += index + foundTasks = true + } + } + } + return foundTasks + } + + override def hasPendingTasks(): Boolean = { + numTasks > 0 && tasksFinished < numTasks + } +} diff --git a/core/src/main/scala/spark/scheduler/cluster/SchedulingAlgorithm.scala b/core/src/main/scala/spark/scheduler/cluster/SchedulingAlgorithm.scala index a5d6285c99..f33310a34a 100644 --- a/core/src/main/scala/spark/scheduler/cluster/SchedulingAlgorithm.scala +++ b/core/src/main/scala/spark/scheduler/cluster/SchedulingAlgorithm.scala @@ -13,11 +13,11 @@ private[spark] class FIFOSchedulingAlgorithm extends SchedulingAlgorithm { override def comparator(s1: Schedulable, s2: Schedulable): Boolean = { val priority1 = s1.priority val priority2 = s2.priority - var res = Math.signum(priority1 - priority2) + var res = math.signum(priority1 - priority2) if (res == 0) { val stageId1 = s1.stageId val stageId2 = s2.stageId - res = Math.signum(stageId1 - stageId2) + res = math.signum(stageId1 - stageId2) } if (res < 0) { return true @@ -35,22 +35,30 @@ private[spark] class FairSchedulingAlgorithm extends SchedulingAlgorithm { val runningTasks2 = s2.runningTasks val s1Needy = runningTasks1 < minShare1 val s2Needy = runningTasks2 < minShare2 - val minShareRatio1 = runningTasks1.toDouble / Math.max(minShare1, 1.0).toDouble - val minShareRatio2 = runningTasks2.toDouble / Math.max(minShare2, 1.0).toDouble + val minShareRatio1 = runningTasks1.toDouble / math.max(minShare1, 1.0).toDouble + val minShareRatio2 = runningTasks2.toDouble / math.max(minShare2, 1.0).toDouble val taskToWeightRatio1 = runningTasks1.toDouble / s1.weight.toDouble val taskToWeightRatio2 = runningTasks2.toDouble / s2.weight.toDouble var res:Boolean = true + var compare:Int = 0 if (s1Needy && !s2Needy) { - res = true + return true } else if (!s1Needy && s2Needy) { - res = false + return false } else if (s1Needy && s2Needy) { - res = minShareRatio1 <= minShareRatio2 + compare = minShareRatio1.compareTo(minShareRatio2) + } else { + compare = taskToWeightRatio1.compareTo(taskToWeightRatio2) + } + + if (compare < 0) { + return true + } else if (compare > 0) { + return false } else { - res = taskToWeightRatio1 <= taskToWeightRatio2 + return s1.name < s2.name } - return res } } diff --git a/core/src/main/scala/spark/scheduler/cluster/TaskSetManager.scala b/core/src/main/scala/spark/scheduler/cluster/TaskSetManager.scala index f1c6266bac..b4dd75d90f 100644 --- a/core/src/main/scala/spark/scheduler/cluster/TaskSetManager.scala +++ b/core/src/main/scala/spark/scheduler/cluster/TaskSetManager.scala @@ -1,747 +1,17 @@ package spark.scheduler.cluster -import java.util.{HashMap => JHashMap, NoSuchElementException, Arrays} - import scala.collection.mutable.ArrayBuffer -import scala.collection.mutable.HashMap -import scala.collection.mutable.HashSet -import scala.math.max -import scala.math.min - -import spark._ import spark.scheduler._ import spark.TaskState.TaskState import java.nio.ByteBuffer -private[spark] object TaskLocality extends Enumeration("PROCESS_LOCAL", "NODE_LOCAL", "RACK_LOCAL", "ANY") with Logging { - - // process local is expected to be used ONLY within tasksetmanager for now. - val PROCESS_LOCAL, NODE_LOCAL, RACK_LOCAL, ANY = Value - - type TaskLocality = Value - - def isAllowed(constraint: TaskLocality, condition: TaskLocality): Boolean = { - - // Must not be the constraint. - assert (constraint != TaskLocality.PROCESS_LOCAL) - - constraint match { - case TaskLocality.NODE_LOCAL => condition == TaskLocality.NODE_LOCAL - case TaskLocality.RACK_LOCAL => condition == TaskLocality.NODE_LOCAL || condition == TaskLocality.RACK_LOCAL - // For anything else, allow - case _ => true - } - } - - def parse(str: String): TaskLocality = { - // better way to do this ? - try { - val retval = TaskLocality.withName(str) - // Must not specify PROCESS_LOCAL ! - assert (retval != TaskLocality.PROCESS_LOCAL) - - retval - } catch { - case nEx: NoSuchElementException => { - logWarning("Invalid task locality specified '" + str + "', defaulting to NODE_LOCAL"); - // default to preserve earlier behavior - NODE_LOCAL - } - } - } -} - -/** - * Schedules the tasks within a single TaskSet in the ClusterScheduler. - */ -private[spark] class TaskSetManager( - sched: ClusterScheduler, - val taskSet: TaskSet) - extends Schedulable - with Logging { - - // Maximum time to wait to run a task in a preferred location (in ms) - val LOCALITY_WAIT = System.getProperty("spark.locality.wait", "3000").toLong - - // CPUs to request per task - val CPUS_PER_TASK = System.getProperty("spark.task.cpus", "1").toDouble - - // Maximum times a task is allowed to fail before failing the job - val MAX_TASK_FAILURES = 4 - - // Quantile of tasks at which to start speculation - val SPECULATION_QUANTILE = System.getProperty("spark.speculation.quantile", "0.75").toDouble - val SPECULATION_MULTIPLIER = System.getProperty("spark.speculation.multiplier", "1.5").toDouble - - // Serializer for closures and tasks. - val ser = SparkEnv.get.closureSerializer.newInstance() - - val tasks = taskSet.tasks - val numTasks = tasks.length - val copiesRunning = new Array[Int](numTasks) - val finished = new Array[Boolean](numTasks) - val numFailures = new Array[Int](numTasks) - val taskAttempts = Array.fill[List[TaskInfo]](numTasks)(Nil) - var tasksFinished = 0 - - var weight = 1 - var minShare = 0 - var runningTasks = 0 - var priority = taskSet.priority - var stageId = taskSet.stageId - var name = "TaskSet_"+taskSet.stageId.toString - var parent:Schedulable = null - - // Last time when we launched a preferred task (for delay scheduling) - var lastPreferredLaunchTime = System.currentTimeMillis - - // List of pending tasks for each node (process local to container). These collections are actually - // treated as stacks, in which new tasks are added to the end of the - // ArrayBuffer and removed from the end. This makes it faster to detect - // tasks that repeatedly fail because whenever a task failed, it is put - // back at the head of the stack. They are also only cleaned up lazily; - // when a task is launched, it remains in all the pending lists except - // the one that it was launched from, but gets removed from them later. - private val pendingTasksForHostPort = new HashMap[String, ArrayBuffer[Int]] - - // List of pending tasks for each node. - // Essentially, similar to pendingTasksForHostPort, except at host level - private val pendingTasksForHost = new HashMap[String, ArrayBuffer[Int]] - - // List of pending tasks for each node based on rack locality. - // Essentially, similar to pendingTasksForHost, except at rack level - private val pendingRackLocalTasksForHost = new HashMap[String, ArrayBuffer[Int]] - - // List containing pending tasks with no locality preferences - val pendingTasksWithNoPrefs = new ArrayBuffer[Int] - - // List containing all pending tasks (also used as a stack, as above) - val allPendingTasks = new ArrayBuffer[Int] - - // Tasks that can be speculated. Since these will be a small fraction of total - // tasks, we'll just hold them in a HashSet. - val speculatableTasks = new HashSet[Int] - - // Task index, start and finish time for each task attempt (indexed by task ID) - val taskInfos = new HashMap[Long, TaskInfo] - - // Did the job fail? - var failed = false - var causeOfFailure = "" - - // How frequently to reprint duplicate exceptions in full, in milliseconds - val EXCEPTION_PRINT_INTERVAL = - System.getProperty("spark.logging.exceptionPrintInterval", "10000").toLong - // Map of recent exceptions (identified by string representation and - // top stack frame) to duplicate count (how many times the same - // exception has appeared) and time the full exception was - // printed. This should ideally be an LRU map that can drop old - // exceptions automatically. - val recentExceptions = HashMap[String, (Int, Long)]() - - // Figure out the current map output tracker generation and set it on all tasks - val generation = sched.mapOutputTracker.getGeneration - logDebug("Generation for " + taskSet.id + ": " + generation) - for (t <- tasks) { - t.generation = generation - } - - // Add all our tasks to the pending lists. We do this in reverse order - // of task index so that tasks with low indices get launched first. - for (i <- (0 until numTasks).reverse) { - addPendingTask(i) - } - - // Note that it follows the hierarchy. - // if we search for NODE_LOCAL, the output will include PROCESS_LOCAL and - // if we search for RACK_LOCAL, it will include PROCESS_LOCAL & NODE_LOCAL - private def findPreferredLocations(_taskPreferredLocations: Seq[String], scheduler: ClusterScheduler, - taskLocality: TaskLocality.TaskLocality): HashSet[String] = { - - if (TaskLocality.PROCESS_LOCAL == taskLocality) { - // straight forward comparison ! Special case it. - val retval = new HashSet[String]() - scheduler.synchronized { - for (location <- _taskPreferredLocations) { - if (scheduler.isExecutorAliveOnHostPort(location)) { - retval += location - } - } - } - - return retval - } - - val taskPreferredLocations = - if (TaskLocality.NODE_LOCAL == taskLocality) { - _taskPreferredLocations - } else { - assert (TaskLocality.RACK_LOCAL == taskLocality) - // Expand set to include all 'seen' rack local hosts. - // This works since container allocation/management happens within master - so any rack locality information is updated in msater. - // Best case effort, and maybe sort of kludge for now ... rework it later ? - val hosts = new HashSet[String] - _taskPreferredLocations.foreach(h => { - val rackOpt = scheduler.getRackForHost(h) - if (rackOpt.isDefined) { - val hostsOpt = scheduler.getCachedHostsForRack(rackOpt.get) - if (hostsOpt.isDefined) { - hosts ++= hostsOpt.get - } - } - - // Ensure that irrespective of what scheduler says, host is always added ! - hosts += h - }) - - hosts - } - - val retval = new HashSet[String] - scheduler.synchronized { - for (prefLocation <- taskPreferredLocations) { - val aliveLocationsOpt = scheduler.getExecutorsAliveOnHost(Utils.parseHostPort(prefLocation)._1) - if (aliveLocationsOpt.isDefined) { - retval ++= aliveLocationsOpt.get - } - } - } - - retval - } - - // Add a task to all the pending-task lists that it should be on. - private def addPendingTask(index: Int) { - // We can infer hostLocalLocations from rackLocalLocations by joining it against tasks(index).preferredLocations (with appropriate - // hostPort <-> host conversion). But not doing it for simplicity sake. If this becomes a performance issue, modify it. - val processLocalLocations = findPreferredLocations(tasks(index).preferredLocations, sched, TaskLocality.PROCESS_LOCAL) - val hostLocalLocations = findPreferredLocations(tasks(index).preferredLocations, sched, TaskLocality.NODE_LOCAL) - val rackLocalLocations = findPreferredLocations(tasks(index).preferredLocations, sched, TaskLocality.RACK_LOCAL) - - if (rackLocalLocations.size == 0) { - // Current impl ensures this. - assert (processLocalLocations.size == 0) - assert (hostLocalLocations.size == 0) - pendingTasksWithNoPrefs += index - } else { - - // process local locality - for (hostPort <- processLocalLocations) { - // DEBUG Code - Utils.checkHostPort(hostPort) - - val hostPortList = pendingTasksForHostPort.getOrElseUpdate(hostPort, ArrayBuffer()) - hostPortList += index - } - - // host locality (includes process local) - for (hostPort <- hostLocalLocations) { - // DEBUG Code - Utils.checkHostPort(hostPort) - - val host = Utils.parseHostPort(hostPort)._1 - val hostList = pendingTasksForHost.getOrElseUpdate(host, ArrayBuffer()) - hostList += index - } - - // rack locality (includes process local and host local) - for (rackLocalHostPort <- rackLocalLocations) { - // DEBUG Code - Utils.checkHostPort(rackLocalHostPort) - - val rackLocalHost = Utils.parseHostPort(rackLocalHostPort)._1 - val list = pendingRackLocalTasksForHost.getOrElseUpdate(rackLocalHost, ArrayBuffer()) - list += index - } - } - - allPendingTasks += index - } - - // Return the pending tasks list for a given host port (process local), or an empty list if - // there is no map entry for that host - private def getPendingTasksForHostPort(hostPort: String): ArrayBuffer[Int] = { - // DEBUG Code - Utils.checkHostPort(hostPort) - pendingTasksForHostPort.getOrElse(hostPort, ArrayBuffer()) - } - - // Return the pending tasks list for a given host, or an empty list if - // there is no map entry for that host - private def getPendingTasksForHost(hostPort: String): ArrayBuffer[Int] = { - val host = Utils.parseHostPort(hostPort)._1 - pendingTasksForHost.getOrElse(host, ArrayBuffer()) - } - - // Return the pending tasks (rack level) list for a given host, or an empty list if - // there is no map entry for that host - private def getRackLocalPendingTasksForHost(hostPort: String): ArrayBuffer[Int] = { - val host = Utils.parseHostPort(hostPort)._1 - pendingRackLocalTasksForHost.getOrElse(host, ArrayBuffer()) - } - - // Number of pending tasks for a given host Port (which would be process local) - def numPendingTasksForHostPort(hostPort: String): Int = { - getPendingTasksForHostPort(hostPort).count( index => copiesRunning(index) == 0 && !finished(index) ) - } - - // Number of pending tasks for a given host (which would be data local) - def numPendingTasksForHost(hostPort: String): Int = { - getPendingTasksForHost(hostPort).count( index => copiesRunning(index) == 0 && !finished(index) ) - } - - // Number of pending rack local tasks for a given host - def numRackLocalPendingTasksForHost(hostPort: String): Int = { - getRackLocalPendingTasksForHost(hostPort).count( index => copiesRunning(index) == 0 && !finished(index) ) - } - - - // Dequeue a pending task from the given list and return its index. - // Return None if the list is empty. - // This method also cleans up any tasks in the list that have already - // been launched, since we want that to happen lazily. - private def findTaskFromList(list: ArrayBuffer[Int]): Option[Int] = { - while (!list.isEmpty) { - val index = list.last - list.trimEnd(1) - if (copiesRunning(index) == 0 && !finished(index)) { - return Some(index) - } - } - return None - } - - // Return a speculative task for a given host if any are available. The task should not have an - // attempt running on this host, in case the host is slow. In addition, if locality is set, the - // task must have a preference for this host/rack/no preferred locations at all. - private def findSpeculativeTask(hostPort: String, locality: TaskLocality.TaskLocality): Option[Int] = { - - assert (TaskLocality.isAllowed(locality, TaskLocality.NODE_LOCAL)) - speculatableTasks.retain(index => !finished(index)) // Remove finished tasks from set - - if (speculatableTasks.size > 0) { - val localTask = speculatableTasks.find { - index => - val locations = findPreferredLocations(tasks(index).preferredLocations, sched, TaskLocality.NODE_LOCAL) - val attemptLocs = taskAttempts(index).map(_.hostPort) - (locations.size == 0 || locations.contains(hostPort)) && !attemptLocs.contains(hostPort) - } - - if (localTask != None) { - speculatableTasks -= localTask.get - return localTask - } - - // check for rack locality - if (TaskLocality.isAllowed(locality, TaskLocality.RACK_LOCAL)) { - val rackTask = speculatableTasks.find { - index => - val locations = findPreferredLocations(tasks(index).preferredLocations, sched, TaskLocality.RACK_LOCAL) - val attemptLocs = taskAttempts(index).map(_.hostPort) - locations.contains(hostPort) && !attemptLocs.contains(hostPort) - } - - if (rackTask != None) { - speculatableTasks -= rackTask.get - return rackTask - } - } - - // Any task ... - if (TaskLocality.isAllowed(locality, TaskLocality.ANY)) { - // Check for attemptLocs also ? - val nonLocalTask = speculatableTasks.find(i => !taskAttempts(i).map(_.hostPort).contains(hostPort)) - if (nonLocalTask != None) { - speculatableTasks -= nonLocalTask.get - return nonLocalTask - } - } - } - return None - } - - // Dequeue a pending task for a given node and return its index. - // If localOnly is set to false, allow non-local tasks as well. - private def findTask(hostPort: String, locality: TaskLocality.TaskLocality): Option[Int] = { - val processLocalTask = findTaskFromList(getPendingTasksForHostPort(hostPort)) - if (processLocalTask != None) { - return processLocalTask - } - - val localTask = findTaskFromList(getPendingTasksForHost(hostPort)) - if (localTask != None) { - return localTask - } - - if (TaskLocality.isAllowed(locality, TaskLocality.RACK_LOCAL)) { - val rackLocalTask = findTaskFromList(getRackLocalPendingTasksForHost(hostPort)) - if (rackLocalTask != None) { - return rackLocalTask - } - } - - // Look for no pref tasks AFTER rack local tasks - this has side effect that we will get to failed tasks later rather than sooner. - // TODO: That code path needs to be revisited (adding to no prefs list when host:port goes down). - val noPrefTask = findTaskFromList(pendingTasksWithNoPrefs) - if (noPrefTask != None) { - return noPrefTask - } - - if (TaskLocality.isAllowed(locality, TaskLocality.ANY)) { - val nonLocalTask = findTaskFromList(allPendingTasks) - if (nonLocalTask != None) { - return nonLocalTask - } - } - - // Finally, if all else has failed, find a speculative task - return findSpeculativeTask(hostPort, locality) - } - - private def isProcessLocalLocation(task: Task[_], hostPort: String): Boolean = { - Utils.checkHostPort(hostPort) - - val locs = task.preferredLocations - - locs.contains(hostPort) - } - - private def isHostLocalLocation(task: Task[_], hostPort: String): Boolean = { - val locs = task.preferredLocations - - // If no preference, consider it as host local - if (locs.isEmpty) return true - - val host = Utils.parseHostPort(hostPort)._1 - locs.find(h => Utils.parseHostPort(h)._1 == host).isDefined - } - - // Does a host count as a rack local preferred location for a task? (assumes host is NOT preferred location). - // This is true if either the task has preferred locations and this host is one, or it has - // no preferred locations (in which we still count the launch as preferred). - private def isRackLocalLocation(task: Task[_], hostPort: String): Boolean = { - - val locs = task.preferredLocations - - val preferredRacks = new HashSet[String]() - for (preferredHost <- locs) { - val rack = sched.getRackForHost(preferredHost) - if (None != rack) preferredRacks += rack.get - } - - if (preferredRacks.isEmpty) return false - - val hostRack = sched.getRackForHost(hostPort) - - return None != hostRack && preferredRacks.contains(hostRack.get) - } - - // Respond to an offer of a single slave from the scheduler by finding a task - def slaveOffer(execId: String, hostPort: String, availableCpus: Double, overrideLocality: TaskLocality.TaskLocality = null): Option[TaskDescription] = { - - if (tasksFinished < numTasks && availableCpus >= CPUS_PER_TASK) { - // If explicitly specified, use that - val locality = if (overrideLocality != null) overrideLocality else { - // expand only if we have waited for more than LOCALITY_WAIT for a host local task ... - val time = System.currentTimeMillis - if (time - lastPreferredLaunchTime < LOCALITY_WAIT) TaskLocality.NODE_LOCAL else TaskLocality.ANY - } - - findTask(hostPort, locality) match { - case Some(index) => { - // Found a task; do some bookkeeping and return a Mesos task for it - val task = tasks(index) - val taskId = sched.newTaskId() - // Figure out whether this should count as a preferred launch - val taskLocality = - if (isProcessLocalLocation(task, hostPort)) TaskLocality.PROCESS_LOCAL else - if (isHostLocalLocation(task, hostPort)) TaskLocality.NODE_LOCAL else - if (isRackLocalLocation(task, hostPort)) TaskLocality.RACK_LOCAL else - TaskLocality.ANY - val prefStr = taskLocality.toString - logInfo("Starting task %s:%d as TID %s on slave %s: %s (%s)".format( - taskSet.id, index, taskId, execId, hostPort, prefStr)) - // Do various bookkeeping - copiesRunning(index) += 1 - val time = System.currentTimeMillis - val info = new TaskInfo(taskId, index, time, execId, hostPort, taskLocality) - taskInfos(taskId) = info - taskAttempts(index) = info :: taskAttempts(index) - if (TaskLocality.NODE_LOCAL == taskLocality) { - lastPreferredLaunchTime = time - } - // Serialize and return the task - val startTime = System.currentTimeMillis - val serializedTask = Task.serializeWithDependencies( - task, sched.sc.addedFiles, sched.sc.addedJars, ser) - val timeTaken = System.currentTimeMillis - startTime - increaseRunningTasks(1) - logInfo("Serialized task %s:%d as %d bytes in %d ms".format( - taskSet.id, index, serializedTask.limit, timeTaken)) - val taskName = "task %s:%d".format(taskSet.id, index) - return Some(new TaskDescription(taskId, execId, taskName, serializedTask)) - } - case _ => - } - } - return None - } - - def statusUpdate(tid: Long, state: TaskState, serializedData: ByteBuffer) { - state match { - case TaskState.FINISHED => - taskFinished(tid, state, serializedData) - case TaskState.LOST => - taskLost(tid, state, serializedData) - case TaskState.FAILED => - taskLost(tid, state, serializedData) - case TaskState.KILLED => - taskLost(tid, state, serializedData) - case _ => - } - } - - def taskFinished(tid: Long, state: TaskState, serializedData: ByteBuffer) { - val info = taskInfos(tid) - if (info.failed) { - // We might get two task-lost messages for the same task in coarse-grained Mesos mode, - // or even from Mesos itself when acks get delayed. - return - } - val index = info.index - info.markSuccessful() - decreaseRunningTasks(1) - if (!finished(index)) { - tasksFinished += 1 - logInfo("Finished TID %s in %d ms (progress: %d/%d)".format( - tid, info.duration, tasksFinished, numTasks)) - // Deserialize task result and pass it to the scheduler - try { - val result = ser.deserialize[TaskResult[_]](serializedData) - result.metrics.resultSize = serializedData.limit() - sched.listener.taskEnded(tasks(index), Success, result.value, result.accumUpdates, info, result.metrics) - } catch { - case cnf: ClassNotFoundException => - val loader = Thread.currentThread().getContextClassLoader - throw new SparkException("ClassNotFound with classloader: " + loader, cnf) - case ex => throw ex - } - // Mark finished and stop if we've finished all the tasks - finished(index) = true - if (tasksFinished == numTasks) { - sched.taskSetFinished(this) - } - } else { - logInfo("Ignoring task-finished event for TID " + tid + - " because task " + index + " is already finished") - } - } - - def taskLost(tid: Long, state: TaskState, serializedData: ByteBuffer) { - val info = taskInfos(tid) - if (info.failed) { - // We might get two task-lost messages for the same task in coarse-grained Mesos mode, - // or even from Mesos itself when acks get delayed. - return - } - val index = info.index - info.markFailed() - decreaseRunningTasks(1) - if (!finished(index)) { - logInfo("Lost TID %s (task %s:%d)".format(tid, taskSet.id, index)) - copiesRunning(index) -= 1 - // Check if the problem is a map output fetch failure. In that case, this - // task will never succeed on any node, so tell the scheduler about it. - if (serializedData != null && serializedData.limit() > 0) { - val reason = ser.deserialize[TaskEndReason](serializedData, getClass.getClassLoader) - reason match { - case fetchFailed: FetchFailed => - logInfo("Loss was due to fetch failure from " + fetchFailed.bmAddress) - sched.listener.taskEnded(tasks(index), fetchFailed, null, null, info, null) - finished(index) = true - tasksFinished += 1 - sched.taskSetFinished(this) - decreaseRunningTasks(runningTasks) - return - - case taskResultTooBig: TaskResultTooBigFailure => - logInfo("Loss was due to task %s result exceeding Akka frame size;" + - "aborting job".format(tid)) - abort("Task %s result exceeded Akka frame size".format(tid)) - return - - case ef: ExceptionFailure => - val key = ef.description - val now = System.currentTimeMillis - val (printFull, dupCount) = { - if (recentExceptions.contains(key)) { - val (dupCount, printTime) = recentExceptions(key) - if (now - printTime > EXCEPTION_PRINT_INTERVAL) { - recentExceptions(key) = (0, now) - (true, 0) - } else { - recentExceptions(key) = (dupCount + 1, printTime) - (false, dupCount + 1) - } - } else { - recentExceptions(key) = (0, now) - (true, 0) - } - } - if (printFull) { - val locs = ef.stackTrace.map(loc => "\tat %s".format(loc.toString)) - logInfo("Loss was due to %s\n%s\n%s".format( - ef.className, ef.description, locs.mkString("\n"))) - } else { - logInfo("Loss was due to %s [duplicate %d]".format(ef.description, dupCount)) - } - - case _ => {} - } - } - // On non-fetch failures, re-enqueue the task as pending for a max number of retries - addPendingTask(index) - // Count failed attempts only on FAILED and LOST state (not on KILLED) - if (state == TaskState.FAILED || state == TaskState.LOST) { - numFailures(index) += 1 - if (numFailures(index) > MAX_TASK_FAILURES) { - logError("Task %s:%d failed more than %d times; aborting job".format( - taskSet.id, index, MAX_TASK_FAILURES)) - abort("Task %s:%d failed more than %d times".format(taskSet.id, index, MAX_TASK_FAILURES)) - } - } - } else { - logInfo("Ignoring task-lost event for TID " + tid + - " because task " + index + " is already finished") - } - } - - def error(message: String) { - // Save the error message - abort("Error: " + message) - } - - def abort(message: String) { - failed = true - causeOfFailure = message - // TODO: Kill running tasks if we were not terminated due to a Mesos error - sched.listener.taskSetFailed(taskSet, message) - decreaseRunningTasks(runningTasks) - sched.taskSetFinished(this) - } - - override def increaseRunningTasks(taskNum: Int) { - runningTasks += taskNum - if (parent != null) { - parent.increaseRunningTasks(taskNum) - } - } - - override def decreaseRunningTasks(taskNum: Int) { - runningTasks -= taskNum - if (parent != null) { - parent.decreaseRunningTasks(taskNum) - } - } - - //TODO: for now we just find Pool not TaskSetManager, we can extend this function in future if needed - override def getSchedulableByName(name: String): Schedulable = { - return null - } - - override def addSchedulable(schedulable:Schedulable) { - //nothing - } - - override def removeSchedulable(schedulable:Schedulable) { - //nothing - } - - override def getSortedTaskSetQueue(): ArrayBuffer[TaskSetManager] = { - var sortedTaskSetQueue = new ArrayBuffer[TaskSetManager] - sortedTaskSetQueue += this - return sortedTaskSetQueue - } - - override def executorLost(execId: String, hostPort: String) { - logInfo("Re-queueing tasks for " + execId + " from TaskSet " + taskSet.id) - - // If some task has preferred locations only on hostname, and there are no more executors there, - // put it in the no-prefs list to avoid the wait from delay scheduling - - // host local tasks - should we push this to rack local or no pref list ? For now, preserving behavior and moving to - // no prefs list. Note, this was done due to impliations related to 'waiting' for data local tasks, etc. - // Note: NOT checking process local list - since host local list is super set of that. We need to ad to no prefs only if - // there is no host local node for the task (not if there is no process local node for the task) - for (index <- getPendingTasksForHost(Utils.parseHostPort(hostPort)._1)) { - // val newLocs = findPreferredLocations(tasks(index).preferredLocations, sched, TaskLocality.RACK_LOCAL) - val newLocs = findPreferredLocations(tasks(index).preferredLocations, sched, TaskLocality.NODE_LOCAL) - if (newLocs.isEmpty) { - pendingTasksWithNoPrefs += index - } - } - - // Re-enqueue any tasks that ran on the failed executor if this is a shuffle map stage - if (tasks(0).isInstanceOf[ShuffleMapTask]) { - for ((tid, info) <- taskInfos if info.executorId == execId) { - val index = taskInfos(tid).index - if (finished(index)) { - finished(index) = false - copiesRunning(index) -= 1 - tasksFinished -= 1 - addPendingTask(index) - // Tell the DAGScheduler that this task was resubmitted so that it doesn't think our - // stage finishes when a total of tasks.size tasks finish. - sched.listener.taskEnded(tasks(index), Resubmitted, null, null, info, null) - } - } - } - // Also re-enqueue any tasks that were running on the node - for ((tid, info) <- taskInfos if info.running && info.executorId == execId) { - taskLost(tid, TaskState.KILLED, null) - } - } - - /** - * Check for tasks to be speculated and return true if there are any. This is called periodically - * by the ClusterScheduler. - * - * TODO: To make this scale to large jobs, we need to maintain a list of running tasks, so that - * we don't scan the whole task set. It might also help to make this sorted by launch time. - */ - override def checkSpeculatableTasks(): Boolean = { - // Can't speculate if we only have one task, or if all tasks have finished. - if (numTasks == 1 || tasksFinished == numTasks) { - return false - } - var foundTasks = false - val minFinishedForSpeculation = (SPECULATION_QUANTILE * numTasks).floor.toInt - logDebug("Checking for speculative tasks: minFinished = " + minFinishedForSpeculation) - if (tasksFinished >= minFinishedForSpeculation) { - val time = System.currentTimeMillis() - val durations = taskInfos.values.filter(_.successful).map(_.duration).toArray - Arrays.sort(durations) - val medianDuration = durations(min((0.5 * numTasks).round.toInt, durations.size - 1)) - val threshold = max(SPECULATION_MULTIPLIER * medianDuration, 100) - // TODO: Threshold should also look at standard deviation of task durations and have a lower - // bound based on that. - logDebug("Task length threshold for speculation: " + threshold) - for ((tid, info) <- taskInfos) { - val index = info.index - if (!finished(index) && copiesRunning(index) == 1 && info.timeRunning(time) > threshold && - !speculatableTasks.contains(index)) { - logInfo( - "Marking task %s:%d (on %s) as speculatable because it ran more than %.0f ms".format( - taskSet.id, index, info.hostPort, threshold)) - speculatableTasks += index - foundTasks = true - } - } - } - return foundTasks - } - - override def hasPendingTasks(): Boolean = { - numTasks > 0 && tasksFinished < numTasks - } +private[spark] trait TaskSetManager extends Schedulable { + def taskSet: TaskSet + def slaveOffer(execId: String, hostPort: String, availableCpus: Double, + overrideLocality: TaskLocality.TaskLocality = null): Option[TaskDescription] + def numPendingTasksForHostPort(hostPort: String): Int + def numRackLocalPendingTasksForHost(hostPort :String): Int + def numPendingTasksForHost(hostPort: String): Int + def statusUpdate(tid: Long, state: TaskState, serializedData: ByteBuffer) + def error(message: String) } diff --git a/core/src/main/scala/spark/scheduler/local/LocalScheduler.scala b/core/src/main/scala/spark/scheduler/local/LocalScheduler.scala index 37a67f9b1b..93d4318b29 100644 --- a/core/src/main/scala/spark/scheduler/local/LocalScheduler.scala +++ b/core/src/main/scala/spark/scheduler/local/LocalScheduler.scala @@ -2,19 +2,50 @@ package spark.scheduler.local import java.io.File import java.util.concurrent.atomic.AtomicInteger +import java.nio.ByteBuffer +import scala.collection.mutable.ArrayBuffer import scala.collection.mutable.HashMap +import scala.collection.mutable.HashSet import spark._ +import spark.TaskState.TaskState import spark.executor.ExecutorURLClassLoader import spark.scheduler._ -import spark.scheduler.cluster.{TaskLocality, TaskInfo} +import spark.scheduler.cluster._ +import akka.actor._ /** - * A simple TaskScheduler implementation that runs tasks locally in a thread pool. Optionally + * A FIFO or Fair TaskScheduler implementation that runs tasks locally in a thread pool. Optionally * the scheduler also allows each task to fail up to maxFailures times, which is useful for * testing fault recovery. */ -private[spark] class LocalScheduler(threads: Int, maxFailures: Int, sc: SparkContext) + +private[spark] case class LocalReviveOffers() +private[spark] case class LocalStatusUpdate(taskId: Long, state: TaskState, serializedData: ByteBuffer) + +private[spark] class LocalActor(localScheduler: LocalScheduler, var freeCores: Int) extends Actor with Logging { + def receive = { + case LocalReviveOffers => + launchTask(localScheduler.resourceOffer(freeCores)) + case LocalStatusUpdate(taskId, state, serializeData) => + freeCores += 1 + localScheduler.statusUpdate(taskId, state, serializeData) + launchTask(localScheduler.resourceOffer(freeCores)) + } + + def launchTask(tasks : Seq[TaskDescription]) { + for (task <- tasks) { + freeCores -= 1 + localScheduler.threadPool.submit(new Runnable { + def run() { + localScheduler.runTask(task.taskId,task.serializedTask) + } + }) + } + } +} + +private[spark] class LocalScheduler(threads: Int, val maxFailures: Int, val sc: SparkContext) extends TaskScheduler with Logging { @@ -30,89 +61,127 @@ private[spark] class LocalScheduler(threads: Int, maxFailures: Int, sc: SparkCon val classLoader = new ExecutorURLClassLoader(Array(), Thread.currentThread.getContextClassLoader) - // TODO: Need to take into account stage priority in scheduling + var schedulableBuilder: SchedulableBuilder = null + var rootPool: Pool = null + val activeTaskSets = new HashMap[String, TaskSetManager] + val taskIdToTaskSetId = new HashMap[Long, String] + val taskSetTaskIds = new HashMap[String, HashSet[Long]] + + var localActor: ActorRef = null + + override def start() { + //default scheduler is FIFO + val schedulingMode = System.getProperty("spark.cluster.schedulingmode", "FIFO") + //temporarily set rootPool name to empty + rootPool = new Pool("", SchedulingMode.withName(schedulingMode), 0, 0) + schedulableBuilder = { + schedulingMode match { + case "FIFO" => + new FIFOSchedulableBuilder(rootPool) + case "FAIR" => + new FairSchedulableBuilder(rootPool) + } + } + schedulableBuilder.buildPools() - override def start() { } + localActor = env.actorSystem.actorOf(Props(new LocalActor(this, threads)), "Test") + } override def setListener(listener: TaskSchedulerListener) { this.listener = listener } override def submitTasks(taskSet: TaskSet) { - val tasks = taskSet.tasks - val failCount = new Array[Int](tasks.size) - - def submitTask(task: Task[_], idInJob: Int) { - val myAttemptId = attemptId.getAndIncrement() - threadPool.submit(new Runnable { - def run() { - runTask(task, idInJob, myAttemptId) - } - }) + synchronized { + var manager = new LocalTaskSetManager(this, taskSet) + schedulableBuilder.addTaskSetManager(manager, manager.taskSet.properties) + activeTaskSets(taskSet.id) = manager + taskSetTaskIds(taskSet.id) = new HashSet[Long]() + localActor ! LocalReviveOffers } + } + + def resourceOffer(freeCores: Int): Seq[TaskDescription] = { + synchronized { + var freeCpuCores = freeCores + val tasks = new ArrayBuffer[TaskDescription](freeCores) + val sortedTaskSetQueue = rootPool.getSortedTaskSetQueue() + for (manager <- sortedTaskSetQueue) { + logDebug("parentName:%s,name:%s,runningTasks:%s".format(manager.parent.name, manager.name, manager.runningTasks)) + } - def runTask(task: Task[_], idInJob: Int, attemptId: Int) { - logInfo("Running " + task) - val info = new TaskInfo(attemptId, idInJob, System.currentTimeMillis(), "local", "local:1", TaskLocality.NODE_LOCAL) - // Set the Spark execution environment for the worker thread - SparkEnv.set(env) - try { - Accumulators.clear() - Thread.currentThread().setContextClassLoader(classLoader) - - // Serialize and deserialize the task so that accumulators are changed to thread-local ones; - // this adds a bit of unnecessary overhead but matches how the Mesos Executor works. - val ser = SparkEnv.get.closureSerializer.newInstance() - val bytes = Task.serializeWithDependencies(task, sc.addedFiles, sc.addedJars, ser) - logInfo("Size of task " + idInJob + " is " + bytes.limit + " bytes") - val (taskFiles, taskJars, taskBytes) = Task.deserializeWithDependencies(bytes) - updateDependencies(taskFiles, taskJars) // Download any files added with addFile - val deserStart = System.currentTimeMillis() - val deserializedTask = ser.deserialize[Task[_]]( - taskBytes, Thread.currentThread.getContextClassLoader) - val deserTime = System.currentTimeMillis() - deserStart - - // Run it - val result: Any = deserializedTask.run(attemptId) - - // Serialize and deserialize the result to emulate what the Mesos - // executor does. This is useful to catch serialization errors early - // on in development (so when users move their local Spark programs - // to the cluster, they don't get surprised by serialization errors). - val serResult = ser.serialize(result) - deserializedTask.metrics.get.resultSize = serResult.limit() - val resultToReturn = ser.deserialize[Any](serResult) - val accumUpdates = ser.deserialize[collection.mutable.Map[Long, Any]]( - ser.serialize(Accumulators.values)) - logInfo("Finished " + task) - info.markSuccessful() - deserializedTask.metrics.get.executorRunTime = info.duration.toInt //close enough - deserializedTask.metrics.get.executorDeserializeTime = deserTime.toInt - - // If the threadpool has not already been shutdown, notify DAGScheduler - if (!Thread.currentThread().isInterrupted) - listener.taskEnded(task, Success, resultToReturn, accumUpdates, info, deserializedTask.metrics.getOrElse(null)) - } catch { - case t: Throwable => { - logError("Exception in task " + idInJob, t) - failCount.synchronized { - failCount(idInJob) += 1 - if (failCount(idInJob) <= maxFailures) { - submitTask(task, idInJob) - } else { - // TODO: Do something nicer here to return all the way to the user - if (!Thread.currentThread().isInterrupted) { - val failure = new ExceptionFailure(t.getClass.getName, t.toString, t.getStackTrace) - listener.taskEnded(task, failure, null, null, info, null) - } + var launchTask = false + for (manager <- sortedTaskSetQueue) { + do { + launchTask = false + manager.slaveOffer(null,null,freeCpuCores) match { + case Some(task) => + tasks += task + taskIdToTaskSetId(task.taskId) = manager.taskSet.id + taskSetTaskIds(manager.taskSet.id) += task.taskId + freeCpuCores -= 1 + launchTask = true + case None => {} } - } - } + } while(launchTask) } + return tasks } + } - for ((task, i) <- tasks.zipWithIndex) { - submitTask(task, i) + def taskSetFinished(manager: TaskSetManager) { + synchronized { + activeTaskSets -= manager.taskSet.id + manager.parent.removeSchedulable(manager) + logInfo("Remove TaskSet %s from pool %s".format(manager.taskSet.id, manager.parent.name)) + taskIdToTaskSetId --= taskSetTaskIds(manager.taskSet.id) + taskSetTaskIds -= manager.taskSet.id + } + } + + def runTask(taskId: Long, bytes: ByteBuffer) { + logInfo("Running " + taskId) + val info = new TaskInfo(taskId, 0, System.currentTimeMillis(), "local", "local:1", TaskLocality.NODE_LOCAL) + // Set the Spark execution environment for the worker thread + SparkEnv.set(env) + val ser = SparkEnv.get.closureSerializer.newInstance() + try { + Accumulators.clear() + Thread.currentThread().setContextClassLoader(classLoader) + + // Serialize and deserialize the task so that accumulators are changed to thread-local ones; + // this adds a bit of unnecessary overhead but matches how the Mesos Executor works. + val (taskFiles, taskJars, taskBytes) = Task.deserializeWithDependencies(bytes) + updateDependencies(taskFiles, taskJars) // Download any files added with addFile + val deserStart = System.currentTimeMillis() + val deserializedTask = ser.deserialize[Task[_]]( + taskBytes, Thread.currentThread.getContextClassLoader) + val deserTime = System.currentTimeMillis() - deserStart + + // Run it + val result: Any = deserializedTask.run(taskId) + + // Serialize and deserialize the result to emulate what the Mesos + // executor does. This is useful to catch serialization errors early + // on in development (so when users move their local Spark programs + // to the cluster, they don't get surprised by serialization errors). + val serResult = ser.serialize(result) + deserializedTask.metrics.get.resultSize = serResult.limit() + val resultToReturn = ser.deserialize[Any](serResult) + val accumUpdates = ser.deserialize[collection.mutable.Map[Long, Any]]( + ser.serialize(Accumulators.values)) + logInfo("Finished " + taskId) + deserializedTask.metrics.get.executorRunTime = deserTime.toInt//info.duration.toInt //close enough + deserializedTask.metrics.get.executorDeserializeTime = deserTime.toInt + + val taskResult = new TaskResult(result, accumUpdates, deserializedTask.metrics.getOrElse(null)) + val serializedResult = ser.serialize(taskResult) + localActor ! LocalStatusUpdate(taskId, TaskState.FINISHED, serializedResult) + } catch { + case t: Throwable => { + val failure = new ExceptionFailure(t.getClass.getName, t.toString, t.getStackTrace) + localActor ! LocalStatusUpdate(taskId, TaskState.FAILED, ser.serialize(failure)) + } } } @@ -128,6 +197,7 @@ private[spark] class LocalScheduler(threads: Int, maxFailures: Int, sc: SparkCon Utils.fetchFile(name, new File(SparkFiles.getRootDirectory)) currentFiles(name) = timestamp } + for ((name, timestamp) <- newJars if currentJars.getOrElse(name, -1L) < timestamp) { logInfo("Fetching " + name + " with timestamp " + timestamp) Utils.fetchFile(name, new File(SparkFiles.getRootDirectory)) @@ -143,7 +213,16 @@ private[spark] class LocalScheduler(threads: Int, maxFailures: Int, sc: SparkCon } } - override def stop() { + def statusUpdate(taskId :Long, state: TaskState, serializedData: ByteBuffer) { + synchronized { + val taskSetId = taskIdToTaskSetId(taskId) + val taskSetManager = activeTaskSets(taskSetId) + taskSetTaskIds(taskSetId) -= taskId + taskSetManager.statusUpdate(taskId, state, serializedData) + } + } + + override def stop() { threadPool.shutdownNow() } diff --git a/core/src/main/scala/spark/scheduler/local/LocalTaskSetManager.scala b/core/src/main/scala/spark/scheduler/local/LocalTaskSetManager.scala new file mode 100644 index 0000000000..70b69bb26f --- /dev/null +++ b/core/src/main/scala/spark/scheduler/local/LocalTaskSetManager.scala @@ -0,0 +1,172 @@ +package spark.scheduler.local + +import java.io.File +import java.util.concurrent.atomic.AtomicInteger +import java.nio.ByteBuffer +import scala.collection.mutable.ArrayBuffer +import scala.collection.mutable.HashMap +import scala.collection.mutable.HashSet + +import spark._ +import spark.TaskState.TaskState +import spark.scheduler._ +import spark.scheduler.cluster._ + +private[spark] class LocalTaskSetManager(sched: LocalScheduler, val taskSet: TaskSet) extends TaskSetManager with Logging { + var parent: Schedulable = null + var weight: Int = 1 + var minShare: Int = 0 + var runningTasks: Int = 0 + var priority: Int = taskSet.priority + var stageId: Int = taskSet.stageId + var name: String = "TaskSet_"+taskSet.stageId.toString + + + var failCount = new Array[Int](taskSet.tasks.size) + val taskInfos = new HashMap[Long, TaskInfo] + val numTasks = taskSet.tasks.size + var numFinished = 0 + val ser = SparkEnv.get.closureSerializer.newInstance() + val copiesRunning = new Array[Int](numTasks) + val finished = new Array[Boolean](numTasks) + val numFailures = new Array[Int](numTasks) + val MAX_TASK_FAILURES = sched.maxFailures + + def increaseRunningTasks(taskNum: Int): Unit = { + runningTasks += taskNum + if (parent != null) { + parent.increaseRunningTasks(taskNum) + } + } + + def decreaseRunningTasks(taskNum: Int): Unit = { + runningTasks -= taskNum + if (parent != null) { + parent.decreaseRunningTasks(taskNum) + } + } + + def addSchedulable(schedulable: Schedulable): Unit = { + //nothing + } + + def removeSchedulable(schedulable: Schedulable): Unit = { + //nothing + } + + def getSchedulableByName(name: String): Schedulable = { + return null + } + + def executorLost(executorId: String, host: String): Unit = { + //nothing + } + + def checkSpeculatableTasks(): Boolean = { + return true + } + + def getSortedTaskSetQueue(): ArrayBuffer[TaskSetManager] = { + var sortedTaskSetQueue = new ArrayBuffer[TaskSetManager] + sortedTaskSetQueue += this + return sortedTaskSetQueue + } + + def hasPendingTasks(): Boolean = { + return true + } + + def findTask(): Option[Int] = { + for (i <- 0 to numTasks-1) { + if (copiesRunning(i) == 0 && !finished(i)) { + return Some(i) + } + } + return None + } + + def slaveOffer(execId: String, hostPort: String, availableCpus: Double, overrideLocality: TaskLocality.TaskLocality = null): Option[TaskDescription] = { + SparkEnv.set(sched.env) + logDebug("availableCpus:%d,numFinished:%d,numTasks:%d".format(availableCpus.toInt, numFinished, numTasks)) + if (availableCpus > 0 && numFinished < numTasks) { + findTask() match { + case Some(index) => + val taskId = sched.attemptId.getAndIncrement() + val task = taskSet.tasks(index) + val info = new TaskInfo(taskId, index, System.currentTimeMillis(), "local", "local:1", TaskLocality.NODE_LOCAL) + taskInfos(taskId) = info + val bytes = Task.serializeWithDependencies(task, sched.sc.addedFiles, sched.sc.addedJars, ser) + logInfo("Size of task " + taskId + " is " + bytes.limit + " bytes") + val taskName = "task %s:%d".format(taskSet.id, index) + copiesRunning(index) += 1 + increaseRunningTasks(1) + return Some(new TaskDescription(taskId, null, taskName, bytes)) + case None => {} + } + } + return None + } + + def numPendingTasksForHostPort(hostPort: String): Int = { + return 0 + } + + def numRackLocalPendingTasksForHost(hostPort :String): Int = { + return 0 + } + + def numPendingTasksForHost(hostPort: String): Int = { + return 0 + } + + def statusUpdate(tid: Long, state: TaskState, serializedData: ByteBuffer) { + state match { + case TaskState.FINISHED => + taskEnded(tid, state, serializedData) + case TaskState.FAILED => + taskFailed(tid, state, serializedData) + case _ => {} + } + } + + def taskEnded(tid: Long, state: TaskState, serializedData: ByteBuffer) { + val info = taskInfos(tid) + val index = info.index + val task = taskSet.tasks(index) + info.markSuccessful() + val result = ser.deserialize[TaskResult[_]](serializedData, getClass.getClassLoader) + result.metrics.resultSize = serializedData.limit() + sched.listener.taskEnded(task, Success, result.value, result.accumUpdates, info, result.metrics) + numFinished += 1 + decreaseRunningTasks(1) + finished(index) = true + if (numFinished == numTasks) { + sched.taskSetFinished(this) + } + } + + def taskFailed(tid: Long, state: TaskState, serializedData: ByteBuffer) { + val info = taskInfos(tid) + val index = info.index + val task = taskSet.tasks(index) + info.markFailed() + decreaseRunningTasks(1) + val reason: ExceptionFailure = ser.deserialize[ExceptionFailure](serializedData, getClass.getClassLoader) + if (!finished(index)) { + copiesRunning(index) -= 1 + numFailures(index) += 1 + val locs = reason.stackTrace.map(loc => "\tat %s".format(loc.toString)) + logInfo("Loss was due to %s\n%s\n%s".format(reason.className, reason.description, locs.mkString("\n"))) + if (numFailures(index) > MAX_TASK_FAILURES) { + val errorMessage = "Task %s:%d failed more than %d times; aborting job %s".format(taskSet.id, index, 4, reason.description) + decreaseRunningTasks(runningTasks) + sched.listener.taskSetFailed(taskSet, errorMessage) + // need to delete failed Taskset from schedule queue + sched.taskSetFinished(this) + } + } + } + + def error(message: String) { + } +} diff --git a/core/src/main/scala/spark/storage/BlockFetcherIterator.scala b/core/src/main/scala/spark/storage/BlockFetcherIterator.scala index 1d69d658f7..bec876213e 100644 --- a/core/src/main/scala/spark/storage/BlockFetcherIterator.scala +++ b/core/src/main/scala/spark/storage/BlockFetcherIterator.scala @@ -67,11 +67,20 @@ object BlockFetcherIterator { throw new IllegalArgumentException("BlocksByAddress is null") } - protected var _totalBlocks = blocksByAddress.map(_._2.size).sum - logDebug("Getting " + _totalBlocks + " blocks") + // Total number blocks fetched (local + remote). Also number of FetchResults expected + protected var _numBlocksToFetch = 0 + protected var startTime = System.currentTimeMillis - protected val localBlockIds = new ArrayBuffer[String]() - protected val remoteBlockIds = new HashSet[String]() + + // This represents the number of local blocks, also counting zero-sized blocks + private var numLocal = 0 + // BlockIds for local blocks that need to be fetched. Excludes zero-sized blocks + protected val localBlocksToFetch = new ArrayBuffer[String]() + + // This represents the number of remote blocks, also counting zero-sized blocks + private var numRemote = 0 + // BlockIds for remote blocks that need to be fetched. Excludes zero-sized blocks + protected val remoteBlocksToFetch = new HashSet[String]() // A queue to hold our results. protected val results = new LinkedBlockingQueue[FetchResult] @@ -124,13 +133,15 @@ object BlockFetcherIterator { protected def splitLocalRemoteBlocks(): ArrayBuffer[FetchRequest] = { // Split local and remote blocks. Remote blocks are further split into FetchRequests of size // at most maxBytesInFlight in order to limit the amount of data in flight. - val originalTotalBlocks = _totalBlocks val remoteRequests = new ArrayBuffer[FetchRequest] for ((address, blockInfos) <- blocksByAddress) { if (address == blockManagerId) { - localBlockIds ++= blockInfos.map(_._1) + numLocal = blockInfos.size + // Filter out zero-sized blocks + localBlocksToFetch ++= blockInfos.filter(_._2 != 0).map(_._1) + _numBlocksToFetch += localBlocksToFetch.size } else { - remoteBlockIds ++= blockInfos.map(_._1) + numRemote += blockInfos.size // Make our requests at least maxBytesInFlight / 5 in length; the reason to keep them // smaller than maxBytesInFlight is to allow multiple, parallel fetches from up to 5 // nodes, rather than blocking on reading output from one node. @@ -144,10 +155,10 @@ object BlockFetcherIterator { // Skip empty blocks if (size > 0) { curBlocks += ((blockId, size)) + remoteBlocksToFetch += blockId + _numBlocksToFetch += 1 curRequestSize += size - } else if (size == 0) { - _totalBlocks -= 1 - } else { + } else if (size < 0) { throw new BlockException(blockId, "Negative block size " + size) } if (curRequestSize >= minRequestSize) { @@ -163,8 +174,8 @@ object BlockFetcherIterator { } } } - logInfo("Getting " + _totalBlocks + " non-zero-bytes blocks out of " + - originalTotalBlocks + " blocks") + logInfo("Getting " + _numBlocksToFetch + " non-zero-bytes blocks out of " + + totalBlocks + " blocks") remoteRequests } @@ -172,7 +183,7 @@ object BlockFetcherIterator { // Get the local blocks while remote blocks are being fetched. Note that it's okay to do // these all at once because they will just memory-map some files, so they won't consume // any memory that might exceed our maxBytesInFlight - for (id <- localBlockIds) { + for (id <- localBlocksToFetch) { getLocalFromDisk(id, serializer) match { case Some(iter) => { // Pass 0 as size since it's not in flight @@ -198,7 +209,7 @@ object BlockFetcherIterator { sendRequest(fetchRequests.dequeue()) } - val numGets = remoteBlockIds.size - fetchRequests.size + val numGets = remoteRequests.size - fetchRequests.size logInfo("Started " + numGets + " remote gets in " + Utils.getUsedTimeMs(startTime)) // Get Local Blocks @@ -210,7 +221,7 @@ object BlockFetcherIterator { //an iterator that will read fetched blocks off the queue as they arrive. @volatile protected var resultsGotten = 0 - override def hasNext: Boolean = resultsGotten < _totalBlocks + override def hasNext: Boolean = resultsGotten < _numBlocksToFetch override def next(): (String, Option[Iterator[Any]]) = { resultsGotten += 1 @@ -227,9 +238,9 @@ object BlockFetcherIterator { } // Implementing BlockFetchTracker trait. - override def totalBlocks: Int = _totalBlocks - override def numLocalBlocks: Int = localBlockIds.size - override def numRemoteBlocks: Int = remoteBlockIds.size + override def totalBlocks: Int = numLocal + numRemote + override def numLocalBlocks: Int = numLocal + override def numRemoteBlocks: Int = numRemote override def remoteFetchTime: Long = _remoteFetchTime override def fetchWaitTime: Long = _fetchWaitTime override def remoteBytesRead: Long = _remoteBytesRead @@ -265,7 +276,7 @@ object BlockFetcherIterator { }).toList } - //keep this to interrupt the threads when necessary + // keep this to interrupt the threads when necessary private def stopCopiers() { for (copier <- copiers) { copier.interrupt() @@ -291,7 +302,7 @@ object BlockFetcherIterator { private var copiers: List[_ <: Thread] = null override def initialize() { - // Split Local Remote Blocks and adjust totalBlocks to include only the non 0-byte blocks + // Split Local Remote Blocks and set numBlocksToFetch val remoteRequests = splitLocalRemoteBlocks() // Add the remote requests into our queue in a random order for (request <- Utils.randomize(remoteRequests)) { @@ -311,10 +322,7 @@ object BlockFetcherIterator { override def next(): (String, Option[Iterator[Any]]) = { resultsGotten += 1 val result = results.take() - // if all the results has been retrieved, shutdown the copiers - if (resultsGotten == _totalBlocks && copiers != null) { - stopCopiers() - } + // If all the results has been retrieved, copiers will exit automatically (result.blockId, if (result.failed) None else Some(result.deserialize())) } } diff --git a/core/src/main/scala/spark/storage/DiskStore.scala b/core/src/main/scala/spark/storage/DiskStore.scala index c7281200e7..da859eebcb 100644 --- a/core/src/main/scala/spark/storage/DiskStore.scala +++ b/core/src/main/scala/spark/storage/DiskStore.scala @@ -35,21 +35,25 @@ private class DiskStore(blockManager: BlockManager, rootDirs: String) private var bs: OutputStream = null private var objOut: SerializationStream = null private var lastValidPosition = 0L + private var initialized = false override def open(): DiskBlockObjectWriter = { val fos = new FileOutputStream(f, true) channel = fos.getChannel() - bs = blockManager.wrapForCompression(blockId, new FastBufferedOutputStream(fos)) + bs = blockManager.wrapForCompression(blockId, new FastBufferedOutputStream(fos, bufferSize)) objOut = serializer.newInstance().serializeStream(bs) + initialized = true this } override def close() { - objOut.close() - bs.close() - channel = null - bs = null - objOut = null + if (initialized) { + objOut.close() + bs.close() + channel = null + bs = null + objOut = null + } // Invoke the close callback handler. super.close() } @@ -59,38 +63,48 @@ private class DiskStore(blockManager: BlockManager, rootDirs: String) // Flush the partial writes, and set valid length to be the length of the entire file. // Return the number of bytes written for this commit. override def commit(): Long = { - // NOTE: Flush the serializer first and then the compressed/buffered output stream - objOut.flush() - bs.flush() - val prevPos = lastValidPosition - lastValidPosition = channel.position() - lastValidPosition - prevPos + if (initialized) { + // NOTE: Flush the serializer first and then the compressed/buffered output stream + objOut.flush() + bs.flush() + val prevPos = lastValidPosition + lastValidPosition = channel.position() + lastValidPosition - prevPos + } else { + // lastValidPosition is zero if stream is uninitialized + lastValidPosition + } } override def revertPartialWrites() { - // Discard current writes. We do this by flushing the outstanding writes and - // truncate the file to the last valid position. - objOut.flush() - bs.flush() - channel.truncate(lastValidPosition) + if (initialized) { + // Discard current writes. We do this by flushing the outstanding writes and + // truncate the file to the last valid position. + objOut.flush() + bs.flush() + channel.truncate(lastValidPosition) + } } override def write(value: Any) { + if (!initialized) { + open() + } objOut.writeObject(value) } override def size(): Long = lastValidPosition } - val MAX_DIR_CREATION_ATTEMPTS: Int = 10 - val subDirsPerLocalDir = System.getProperty("spark.diskStore.subDirectories", "64").toInt + private val MAX_DIR_CREATION_ATTEMPTS: Int = 10 + private val subDirsPerLocalDir = System.getProperty("spark.diskStore.subDirectories", "64").toInt - var shuffleSender : ShuffleSender = null + private var shuffleSender : ShuffleSender = null // Create one local directory for each path mentioned in spark.local.dir; then, inside this // directory, create multiple subdirectories that we will hash files into, in order to avoid // having really large inodes at the top level. - val localDirs = createLocalDirs() - val subDirs = Array.fill(localDirs.length)(new Array[File](subDirsPerLocalDir)) + private val localDirs: Array[File] = createLocalDirs() + private val subDirs = Array.fill(localDirs.length)(new Array[File](subDirsPerLocalDir)) addShutdownHook() @@ -99,7 +113,6 @@ private class DiskStore(blockManager: BlockManager, rootDirs: String) new DiskBlockObjectWriter(blockId, serializer, bufferSize) } - override def getSize(blockId: String): Long = { getFile(blockId).length() } @@ -197,7 +210,10 @@ private class DiskStore(blockManager: BlockManager, rootDirs: String) private def createFile(blockId: String, allowAppendExisting: Boolean = false): File = { val file = getFile(blockId) if (!allowAppendExisting && file.exists()) { - throw new Exception("File for block " + blockId + " already exists on disk: " + file) + // NOTE(shivaram): Delete the file if it exists. This might happen if a ShuffleMap task + // was rescheduled on the same machine as the old task. + logWarning("File for block " + blockId + " already exists on disk: " + file + ". Deleting") + file.delete() } file } @@ -232,8 +248,8 @@ private class DiskStore(blockManager: BlockManager, rootDirs: String) private def createLocalDirs(): Array[File] = { logDebug("Creating local directories at root dirs '" + rootDirs + "'") val dateFormat = new SimpleDateFormat("yyyyMMddHHmmss") - rootDirs.split(",").map(rootDir => { - var foundLocalDir: Boolean = false + rootDirs.split(",").map { rootDir => + var foundLocalDir = false var localDir: File = null var localDirId: String = null var tries = 0 @@ -248,7 +264,7 @@ private class DiskStore(blockManager: BlockManager, rootDirs: String) } } catch { case e: Exception => - logWarning("Attempt " + tries + " to create local dir failed", e) + logWarning("Attempt " + tries + " to create local dir " + localDir + " failed", e) } } if (!foundLocalDir) { @@ -258,7 +274,7 @@ private class DiskStore(blockManager: BlockManager, rootDirs: String) } logInfo("Created local directory at " + localDir) localDir - }) + } } private def addShutdownHook() { @@ -266,15 +282,16 @@ private class DiskStore(blockManager: BlockManager, rootDirs: String) Runtime.getRuntime.addShutdownHook(new Thread("delete Spark local dirs") { override def run() { logDebug("Shutdown hook called") - try { - localDirs.foreach { localDir => + localDirs.foreach { localDir => + try { if (!Utils.hasRootAsShutdownDeleteDir(localDir)) Utils.deleteRecursively(localDir) + } catch { + case t: Throwable => + logError("Exception while deleting local spark dir: " + localDir, t) } - if (shuffleSender != null) { - shuffleSender.stop - } - } catch { - case t: Throwable => logError("Exception while deleting local spark dirs", t) + } + if (shuffleSender != null) { + shuffleSender.stop } } }) diff --git a/core/src/main/scala/spark/storage/ShuffleBlockManager.scala b/core/src/main/scala/spark/storage/ShuffleBlockManager.scala index 49eabfb0d2..44638e0c2d 100644 --- a/core/src/main/scala/spark/storage/ShuffleBlockManager.scala +++ b/core/src/main/scala/spark/storage/ShuffleBlockManager.scala @@ -24,7 +24,7 @@ class ShuffleBlockManager(blockManager: BlockManager) { val bufferSize = System.getProperty("spark.shuffle.file.buffer.kb", "100").toInt * 1024 val writers = Array.tabulate[BlockObjectWriter](numBuckets) { bucketId => val blockId = ShuffleBlockManager.blockId(shuffleId, bucketId, mapId) - blockManager.getDiskBlockWriter(blockId, serializer, bufferSize).open() + blockManager.getDiskBlockWriter(blockId, serializer, bufferSize) } new ShuffleWriterGroup(mapId, writers) } diff --git a/core/src/main/scala/spark/storage/StorageUtils.scala b/core/src/main/scala/spark/storage/StorageUtils.scala index 8f52168c24..950c0cdf35 100644 --- a/core/src/main/scala/spark/storage/StorageUtils.scala +++ b/core/src/main/scala/spark/storage/StorageUtils.scala @@ -55,21 +55,21 @@ object StorageUtils { }.mapValues(_.values.toArray) // For each RDD, generate an RDDInfo object - val rddInfos = groupedRddBlocks.map { case(rddKey, rddBlocks) => - + val rddInfos = groupedRddBlocks.map { case (rddKey, rddBlocks) => // Add up memory and disk sizes val memSize = rddBlocks.map(_.memSize).reduce(_ + _) val diskSize = rddBlocks.map(_.diskSize).reduce(_ + _) // Find the id of the RDD, e.g. rdd_1 => 1 val rddId = rddKey.split("_").last.toInt - // Get the friendly name for the rdd, if available. - val rdd = sc.persistentRdds(rddId) - val rddName = Option(rdd.name).getOrElse(rddKey) - val rddStorageLevel = rdd.getStorageLevel - RDDInfo(rddId, rddName, rddStorageLevel, rddBlocks.length, rdd.partitions.size, memSize, diskSize) - }.toArray + // Get the friendly name and storage level for the RDD, if available + sc.persistentRdds.get(rddId).map { r => + val rddName = Option(r.name).getOrElse(rddKey) + val rddStorageLevel = r.getStorageLevel + RDDInfo(rddId, rddName, rddStorageLevel, rddBlocks.length, r.partitions.size, memSize, diskSize) + } + }.flatten.toArray scala.util.Sorting.quickSort(rddInfos) diff --git a/core/src/main/scala/spark/util/BoundedPriorityQueue.scala b/core/src/main/scala/spark/util/BoundedPriorityQueue.scala new file mode 100644 index 0000000000..4bc5db8bb7 --- /dev/null +++ b/core/src/main/scala/spark/util/BoundedPriorityQueue.scala @@ -0,0 +1,45 @@ +package spark.util + +import java.io.Serializable +import java.util.{PriorityQueue => JPriorityQueue} +import scala.collection.generic.Growable +import scala.collection.JavaConverters._ + +/** + * Bounded priority queue. This class wraps the original PriorityQueue + * class and modifies it such that only the top K elements are retained. + * The top K elements are defined by an implicit Ordering[A]. + */ +class BoundedPriorityQueue[A](maxSize: Int)(implicit ord: Ordering[A]) + extends Iterable[A] with Growable[A] with Serializable { + + private val underlying = new JPriorityQueue[A](maxSize, ord) + + override def iterator: Iterator[A] = underlying.iterator.asScala + + override def ++=(xs: TraversableOnce[A]): this.type = { + xs.foreach { this += _ } + this + } + + override def +=(elem: A): this.type = { + if (size < maxSize) underlying.offer(elem) + else maybeReplaceLowest(elem) + this + } + + override def +=(elem1: A, elem2: A, elems: A*): this.type = { + this += elem1 += elem2 ++= elems + } + + override def clear() { underlying.clear() } + + private def maybeReplaceLowest(a: A): Boolean = { + val head = underlying.peek() + if (head != null && ord.gt(a, head)) { + underlying.poll() + underlying.offer(a) + } else false + } +} + diff --git a/core/src/main/scala/spark/util/StatCounter.scala b/core/src/main/scala/spark/util/StatCounter.scala index 5f80180339..2b980340b7 100644 --- a/core/src/main/scala/spark/util/StatCounter.scala +++ b/core/src/main/scala/spark/util/StatCounter.scala @@ -37,17 +37,23 @@ class StatCounter(values: TraversableOnce[Double]) extends Serializable { if (other == this) { merge(other.copy()) // Avoid overwriting fields in a weird order } else { - val delta = other.mu - mu - if (other.n * 10 < n) { - mu = mu + (delta * other.n) / (n + other.n) - } else if (n * 10 < other.n) { - mu = other.mu - (delta * n) / (n + other.n) - } else { - mu = (mu * n + other.mu * other.n) / (n + other.n) + if (n == 0) { + mu = other.mu + m2 = other.m2 + n = other.n + } else if (other.n != 0) { + val delta = other.mu - mu + if (other.n * 10 < n) { + mu = mu + (delta * other.n) / (n + other.n) + } else if (n * 10 < other.n) { + mu = other.mu - (delta * n) / (n + other.n) + } else { + mu = (mu * n + other.mu * other.n) / (n + other.n) + } + m2 += other.m2 + (delta * delta * n * other.n) / (n + other.n) + n += other.n } - m2 += other.m2 + (delta * delta * n * other.n) / (n + other.n) - n += other.n - this + this } } diff --git a/core/src/test/scala/spark/FileSuite.scala b/core/src/test/scala/spark/FileSuite.scala index 91b48c7456..e61ff7793d 100644 --- a/core/src/test/scala/spark/FileSuite.scala +++ b/core/src/test/scala/spark/FileSuite.scala @@ -7,6 +7,8 @@ import scala.io.Source import com.google.common.io.Files import org.scalatest.FunSuite import org.apache.hadoop.io._ +import org.apache.hadoop.io.compress.{DefaultCodec, CompressionCodec, GzipCodec} + import SparkContext._ @@ -26,6 +28,28 @@ class FileSuite extends FunSuite with LocalSparkContext { assert(sc.textFile(outputDir).collect().toList === List("1", "2", "3", "4")) } + test("text files (compressed)") { + sc = new SparkContext("local", "test") + val tempDir = Files.createTempDir() + val normalDir = new File(tempDir, "output_normal").getAbsolutePath + val compressedOutputDir = new File(tempDir, "output_compressed").getAbsolutePath + val codec = new DefaultCodec() + + val data = sc.parallelize("a" * 10000, 1) + data.saveAsTextFile(normalDir) + data.saveAsTextFile(compressedOutputDir, classOf[DefaultCodec]) + + val normalFile = new File(normalDir, "part-00000") + val normalContent = sc.textFile(normalDir).collect + assert(normalContent === Array.fill(10000)("a")) + + val compressedFile = new File(compressedOutputDir, "part-00000" + codec.getDefaultExtension) + val compressedContent = sc.textFile(compressedOutputDir).collect + assert(compressedContent === Array.fill(10000)("a")) + + assert(compressedFile.length < normalFile.length) + } + test("SequenceFiles") { sc = new SparkContext("local", "test") val tempDir = Files.createTempDir() @@ -37,6 +61,28 @@ class FileSuite extends FunSuite with LocalSparkContext { assert(output.map(_.toString).collect().toList === List("(1,a)", "(2,aa)", "(3,aaa)")) } + test("SequenceFile (compressed)") { + sc = new SparkContext("local", "test") + val tempDir = Files.createTempDir() + val normalDir = new File(tempDir, "output_normal").getAbsolutePath + val compressedOutputDir = new File(tempDir, "output_compressed").getAbsolutePath + val codec = new DefaultCodec() + + val data = sc.parallelize(Seq.fill(100)("abc"), 1).map(x => (x, x)) + data.saveAsSequenceFile(normalDir) + data.saveAsSequenceFile(compressedOutputDir, Some(classOf[DefaultCodec])) + + val normalFile = new File(normalDir, "part-00000") + val normalContent = sc.sequenceFile[String, String](normalDir).collect + assert(normalContent === Array.fill(100)("abc", "abc")) + + val compressedFile = new File(compressedOutputDir, "part-00000" + codec.getDefaultExtension) + val compressedContent = sc.sequenceFile[String, String](compressedOutputDir).collect + assert(compressedContent === Array.fill(100)("abc", "abc")) + + assert(compressedFile.length < normalFile.length) + } + test("SequenceFile with writable key") { sc = new SparkContext("local", "test") val tempDir = Files.createTempDir() diff --git a/core/src/test/scala/spark/JavaAPISuite.java b/core/src/test/scala/spark/JavaAPISuite.java index 93bb69b41c..d306124fca 100644 --- a/core/src/test/scala/spark/JavaAPISuite.java +++ b/core/src/test/scala/spark/JavaAPISuite.java @@ -8,6 +8,7 @@ import java.util.*; import scala.Tuple2; import com.google.common.base.Charsets; +import org.apache.hadoop.io.compress.DefaultCodec; import com.google.common.io.Files; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; @@ -474,6 +475,19 @@ public class JavaAPISuite implements Serializable { } @Test + public void textFilesCompressed() throws IOException { + File tempDir = Files.createTempDir(); + String outputDir = new File(tempDir, "output").getAbsolutePath(); + JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4)); + rdd.saveAsTextFile(outputDir, DefaultCodec.class); + + // Try reading it in as a text file RDD + List<String> expected = Arrays.asList("1", "2", "3", "4"); + JavaRDD<String> readRDD = sc.textFile(outputDir); + Assert.assertEquals(expected, readRDD.collect()); + } + + @Test public void sequenceFile() { File tempDir = Files.createTempDir(); String outputDir = new File(tempDir, "output").getAbsolutePath(); @@ -620,6 +634,37 @@ public class JavaAPISuite implements Serializable { } @Test + public void hadoopFileCompressed() { + File tempDir = Files.createTempDir(); + String outputDir = new File(tempDir, "output_compressed").getAbsolutePath(); + List<Tuple2<Integer, String>> pairs = Arrays.asList( + new Tuple2<Integer, String>(1, "a"), + new Tuple2<Integer, String>(2, "aa"), + new Tuple2<Integer, String>(3, "aaa") + ); + JavaPairRDD<Integer, String> rdd = sc.parallelizePairs(pairs); + + rdd.map(new PairFunction<Tuple2<Integer, String>, IntWritable, Text>() { + @Override + public Tuple2<IntWritable, Text> call(Tuple2<Integer, String> pair) { + return new Tuple2<IntWritable, Text>(new IntWritable(pair._1()), new Text(pair._2())); + } + }).saveAsHadoopFile(outputDir, IntWritable.class, Text.class, SequenceFileOutputFormat.class, + DefaultCodec.class); + + JavaPairRDD<IntWritable, Text> output = sc.hadoopFile(outputDir, + SequenceFileInputFormat.class, IntWritable.class, Text.class); + + Assert.assertEquals(pairs.toString(), output.map(new Function<Tuple2<IntWritable, Text>, + String>() { + @Override + public String call(Tuple2<IntWritable, Text> x) { + return x.toString(); + } + }).collect().toString()); + } + + @Test public void zip() { JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5)); JavaDoubleRDD doubles = rdd.map(new DoubleFunction<Integer>() { diff --git a/core/src/test/scala/spark/PartitioningSuite.scala b/core/src/test/scala/spark/PartitioningSuite.scala index 60db759c25..16f93e71a3 100644 --- a/core/src/test/scala/spark/PartitioningSuite.scala +++ b/core/src/test/scala/spark/PartitioningSuite.scala @@ -1,10 +1,10 @@ package spark import org.scalatest.FunSuite - import scala.collection.mutable.ArrayBuffer - import SparkContext._ +import spark.util.StatCounter +import scala.math.abs class PartitioningSuite extends FunSuite with LocalSparkContext { @@ -120,4 +120,21 @@ class PartitioningSuite extends FunSuite with LocalSparkContext { assert(intercept[SparkException]{ arrPairs.reduceByKeyLocally(_ + _) }.getMessage.contains("array")) assert(intercept[SparkException]{ arrPairs.reduceByKey(_ + _) }.getMessage.contains("array")) } + + test("Zero-length partitions should be correctly handled") { + // Create RDD with some consecutive empty partitions (including the "first" one) + sc = new SparkContext("local", "test") + val rdd: RDD[Double] = sc + .parallelize(Array(-1.0, -1.0, -1.0, -1.0, 2.0, 4.0, -1.0, -1.0), 8) + .filter(_ >= 0.0) + + // Run the partitions, including the consecutive empty ones, through StatCounter + val stats: StatCounter = rdd.stats(); + assert(abs(6.0 - stats.sum) < 0.01); + assert(abs(6.0/2 - rdd.mean) < 0.01); + assert(abs(1.0 - rdd.variance) < 0.01); + assert(abs(1.0 - rdd.stdev) < 0.01); + + // Add other tests here for classes that should be able to handle empty partitions correctly + } } diff --git a/core/src/test/scala/spark/PipedRDDSuite.scala b/core/src/test/scala/spark/PipedRDDSuite.scala index a6344edf8f..ed075f93ec 100644 --- a/core/src/test/scala/spark/PipedRDDSuite.scala +++ b/core/src/test/scala/spark/PipedRDDSuite.scala @@ -19,6 +19,45 @@ class PipedRDDSuite extends FunSuite with LocalSparkContext { assert(c(3) === "4") } + test("advanced pipe") { + sc = new SparkContext("local", "test") + val nums = sc.makeRDD(Array(1, 2, 3, 4), 2) + val bl = sc.broadcast(List("0")) + + val piped = nums.pipe(Seq("cat"), + Map[String, String](), + (f: String => Unit) => {bl.value.map(f(_));f("\u0001")}, + (i:Int, f: String=> Unit) => f(i + "_")) + + val c = piped.collect() + + assert(c.size === 8) + assert(c(0) === "0") + assert(c(1) === "\u0001") + assert(c(2) === "1_") + assert(c(3) === "2_") + assert(c(4) === "0") + assert(c(5) === "\u0001") + assert(c(6) === "3_") + assert(c(7) === "4_") + + val nums1 = sc.makeRDD(Array("a\t1", "b\t2", "a\t3", "b\t4"), 2) + val d = nums1.groupBy(str=>str.split("\t")(0)). + pipe(Seq("cat"), + Map[String, String](), + (f: String => Unit) => {bl.value.map(f(_));f("\u0001")}, + (i:Tuple2[String, Seq[String]], f: String=> Unit) => {for (e <- i._2){ f(e + "_")}}).collect() + assert(d.size === 8) + assert(d(0) === "0") + assert(d(1) === "\u0001") + assert(d(2) === "b\t2_") + assert(d(3) === "b\t4_") + assert(d(4) === "0") + assert(d(5) === "\u0001") + assert(d(6) === "a\t1_") + assert(d(7) === "a\t3_") + } + test("pipe with env variable") { sc = new SparkContext("local", "test") val nums = sc.makeRDD(Array(1, 2, 3, 4), 2) diff --git a/core/src/test/scala/spark/RDDSuite.scala b/core/src/test/scala/spark/RDDSuite.scala index 3f69e99780..67f3332d44 100644 --- a/core/src/test/scala/spark/RDDSuite.scala +++ b/core/src/test/scala/spark/RDDSuite.scala @@ -317,4 +317,23 @@ class RDDSuite extends FunSuite with LocalSparkContext { assert(sample.size === checkSample.size) for (i <- 0 until sample.size) assert(sample(i) === checkSample(i)) } + + test("top with predefined ordering") { + sc = new SparkContext("local", "test") + val nums = Array.range(1, 100000) + val ints = sc.makeRDD(scala.util.Random.shuffle(nums), 2) + val topK = ints.top(5) + assert(topK.size === 5) + assert(topK.sorted === nums.sorted.takeRight(5)) + } + + test("top with custom ordering") { + sc = new SparkContext("local", "test") + val words = Vector("a", "b", "c", "d") + implicit val ord = implicitly[Ordering[String]].reverse + val rdd = sc.makeRDD(words, 2) + val topK = rdd.top(2) + assert(topK.size === 2) + assert(topK.sorted === Array("b", "a")) + } } diff --git a/core/src/test/scala/spark/ShuffleSuite.scala b/core/src/test/scala/spark/ShuffleSuite.scala index b967016cf7..0c1ec29f96 100644 --- a/core/src/test/scala/spark/ShuffleSuite.scala +++ b/core/src/test/scala/spark/ShuffleSuite.scala @@ -367,6 +367,53 @@ class ShuffleSuite extends FunSuite with ShouldMatchers with LocalSparkContext { assert(nonEmptyBlocks.size <= 4) } + test("zero sized blocks without kryo") { + // Use a local cluster with 2 processes to make sure there are both local and remote blocks + sc = new SparkContext("local-cluster[2,1,512]", "test") + + // 10 partitions from 4 keys + val NUM_BLOCKS = 10 + val a = sc.parallelize(1 to 4, NUM_BLOCKS) + val b = a.map(x => (x, x*2)) + + // NOTE: The default Java serializer should create zero-sized blocks + val c = new ShuffledRDD(b, new HashPartitioner(10)) + + val shuffleId = c.dependencies.head.asInstanceOf[ShuffleDependency[Int, Int]].shuffleId + assert(c.count === 4) + + val blockSizes = (0 until NUM_BLOCKS).flatMap { id => + val statuses = SparkEnv.get.mapOutputTracker.getServerStatuses(shuffleId, id) + statuses.map(x => x._2) + } + val nonEmptyBlocks = blockSizes.filter(x => x > 0) + + // We should have at most 4 non-zero sized partitions + assert(nonEmptyBlocks.size <= 4) + } + + test("foldByKey") { + sc = new SparkContext("local", "test") + val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1))) + val sums = pairs.foldByKey(0)(_+_).collect() + assert(sums.toSet === Set((1, 7), (2, 1))) + } + + test("foldByKey with mutable result type") { + sc = new SparkContext("local", "test") + val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1))) + val bufs = pairs.mapValues(v => ArrayBuffer(v)).cache() + // Fold the values using in-place mutation + val sums = bufs.foldByKey(new ArrayBuffer[Int])(_ ++= _).collect() + assert(sums.toSet === Set((1, ArrayBuffer(1, 2, 3, 1)), (2, ArrayBuffer(1)))) + // Check that the mutable objects in the original RDD were not changed + assert(bufs.collect().toSet === Set( + (1, ArrayBuffer(1)), + (1, ArrayBuffer(2)), + (1, ArrayBuffer(3)), + (1, ArrayBuffer(1)), + (2, ArrayBuffer(1)))) + } } object ShuffleSuite { diff --git a/core/src/test/scala/spark/scheduler/ClusterSchedulerSuite.scala b/core/src/test/scala/spark/scheduler/ClusterSchedulerSuite.scala index a39418b716..8e1ad27e14 100644 --- a/core/src/test/scala/spark/scheduler/ClusterSchedulerSuite.scala +++ b/core/src/test/scala/spark/scheduler/ClusterSchedulerSuite.scala @@ -16,7 +16,7 @@ class DummyTaskSetManager( initNumTasks: Int, clusterScheduler: ClusterScheduler, taskSet: TaskSet) - extends TaskSetManager(clusterScheduler,taskSet) { + extends ClusterTaskSetManager(clusterScheduler,taskSet) { parent = null weight = 1 @@ -88,7 +88,7 @@ class DummyTask(stageId: Int) extends Task[Int](stageId) } } -class ClusterSchedulerSuite extends FunSuite with LocalSparkContext { +class ClusterSchedulerSuite extends FunSuite with LocalSparkContext with Logging { def createDummyTaskSetManager(priority: Int, stage: Int, numTasks: Int, cs: ClusterScheduler, taskSet: TaskSet): DummyTaskSetManager = { new DummyTaskSetManager(priority, stage, numTasks, cs , taskSet) @@ -96,8 +96,11 @@ class ClusterSchedulerSuite extends FunSuite with LocalSparkContext { def resourceOffer(rootPool: Pool): Int = { val taskSetQueue = rootPool.getSortedTaskSetQueue() - for (taskSet <- taskSetQueue) - { + /* Just for Test*/ + for (manager <- taskSetQueue) { + logInfo("parentName:%s, parent running tasks:%d, name:%s,runningTasks:%d".format(manager.parent.name, manager.parent.runningTasks, manager.name, manager.runningTasks)) + } + for (taskSet <- taskSetQueue) { taskSet.slaveOffer("execId_1", "hostname_1", 1) match { case Some(task) => return taskSet.stageId diff --git a/core/src/test/scala/spark/scheduler/JobLoggerSuite.scala b/core/src/test/scala/spark/scheduler/JobLoggerSuite.scala new file mode 100644 index 0000000000..4000c4d520 --- /dev/null +++ b/core/src/test/scala/spark/scheduler/JobLoggerSuite.scala @@ -0,0 +1,105 @@ +package spark.scheduler + +import java.util.Properties +import java.util.concurrent.LinkedBlockingQueue +import org.scalatest.FunSuite +import org.scalatest.matchers.ShouldMatchers +import scala.collection.mutable +import spark._ +import spark.SparkContext._ + + +class JobLoggerSuite extends FunSuite with LocalSparkContext with ShouldMatchers { + + test("inner method") { + sc = new SparkContext("local", "joblogger") + val joblogger = new JobLogger { + def createLogWriterTest(jobID: Int) = createLogWriter(jobID) + def closeLogWriterTest(jobID: Int) = closeLogWriter(jobID) + def getRddNameTest(rdd: RDD[_]) = getRddName(rdd) + def buildJobDepTest(jobID: Int, stage: Stage) = buildJobDep(jobID, stage) + } + type MyRDD = RDD[(Int, Int)] + def makeRdd( + numPartitions: Int, + dependencies: List[Dependency[_]] + ): MyRDD = { + val maxPartition = numPartitions - 1 + return new MyRDD(sc, dependencies) { + override def compute(split: Partition, context: TaskContext): Iterator[(Int, Int)] = + throw new RuntimeException("should not be reached") + override def getPartitions = (0 to maxPartition).map(i => new Partition { + override def index = i + }).toArray + } + } + val jobID = 5 + val parentRdd = makeRdd(4, Nil) + val shuffleDep = new ShuffleDependency(parentRdd, null) + val rootRdd = makeRdd(4, List(shuffleDep)) + val shuffleMapStage = new Stage(1, parentRdd, Some(shuffleDep), Nil, jobID) + val rootStage = new Stage(0, rootRdd, None, List(shuffleMapStage), jobID) + + joblogger.onStageSubmitted(SparkListenerStageSubmitted(rootStage, 4)) + joblogger.getEventQueue.size should be (1) + joblogger.getRddNameTest(parentRdd) should be (parentRdd.getClass.getName) + parentRdd.setName("MyRDD") + joblogger.getRddNameTest(parentRdd) should be ("MyRDD") + joblogger.createLogWriterTest(jobID) + joblogger.getJobIDtoPrintWriter.size should be (1) + joblogger.buildJobDepTest(jobID, rootStage) + joblogger.getJobIDToStages.get(jobID).get.size should be (2) + joblogger.getStageIDToJobID.get(0) should be (Some(jobID)) + joblogger.getStageIDToJobID.get(1) should be (Some(jobID)) + joblogger.closeLogWriterTest(jobID) + joblogger.getStageIDToJobID.size should be (0) + joblogger.getJobIDToStages.size should be (0) + joblogger.getJobIDtoPrintWriter.size should be (0) + } + + test("inner variables") { + sc = new SparkContext("local[4]", "joblogger") + val joblogger = new JobLogger { + override protected def closeLogWriter(jobID: Int) = + getJobIDtoPrintWriter.get(jobID).foreach { fileWriter => + fileWriter.close() + } + } + sc.addSparkListener(joblogger) + val rdd = sc.parallelize(1 to 1e2.toInt, 4).map{ i => (i % 12, 2 * i) } + rdd.reduceByKey(_+_).collect() + + joblogger.getLogDir should be ("/tmp/spark") + joblogger.getJobIDtoPrintWriter.size should be (1) + joblogger.getStageIDToJobID.size should be (2) + joblogger.getStageIDToJobID.get(0) should be (Some(0)) + joblogger.getStageIDToJobID.get(1) should be (Some(0)) + joblogger.getJobIDToStages.size should be (1) + } + + + test("interface functions") { + sc = new SparkContext("local[4]", "joblogger") + val joblogger = new JobLogger { + var onTaskEndCount = 0 + var onJobEndCount = 0 + var onJobStartCount = 0 + var onStageCompletedCount = 0 + var onStageSubmittedCount = 0 + override def onTaskEnd(taskEnd: SparkListenerTaskEnd) = onTaskEndCount += 1 + override def onJobEnd(jobEnd: SparkListenerJobEnd) = onJobEndCount += 1 + override def onJobStart(jobStart: SparkListenerJobStart) = onJobStartCount += 1 + override def onStageCompleted(stageCompleted: StageCompleted) = onStageCompletedCount += 1 + override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted) = onStageSubmittedCount += 1 + } + sc.addSparkListener(joblogger) + val rdd = sc.parallelize(1 to 1e2.toInt, 4).map{ i => (i % 12, 2 * i) } + rdd.reduceByKey(_+_).collect() + + joblogger.onJobStartCount should be (1) + joblogger.onJobEndCount should be (1) + joblogger.onTaskEndCount should be (8) + joblogger.onStageSubmittedCount should be (2) + joblogger.onStageCompletedCount should be (2) + } +} diff --git a/core/src/test/scala/spark/scheduler/LocalSchedulerSuite.scala b/core/src/test/scala/spark/scheduler/LocalSchedulerSuite.scala new file mode 100644 index 0000000000..8bd813fd14 --- /dev/null +++ b/core/src/test/scala/spark/scheduler/LocalSchedulerSuite.scala @@ -0,0 +1,206 @@ +package spark.scheduler + +import org.scalatest.FunSuite +import org.scalatest.BeforeAndAfter + +import spark._ +import spark.scheduler._ +import spark.scheduler.cluster._ +import scala.collection.mutable.ArrayBuffer +import scala.collection.mutable.{ConcurrentMap, HashMap} +import java.util.concurrent.Semaphore +import java.util.concurrent.CountDownLatch +import java.util.Properties + +class Lock() { + var finished = false + def jobWait() = { + synchronized { + while(!finished) { + this.wait() + } + } + } + + def jobFinished() = { + synchronized { + finished = true + this.notifyAll() + } + } +} + +object TaskThreadInfo { + val threadToLock = HashMap[Int, Lock]() + val threadToRunning = HashMap[Int, Boolean]() + val threadToStarted = HashMap[Int, CountDownLatch]() +} + +/* + * 1. each thread contains one job. + * 2. each job contains one stage. + * 3. each stage only contains one task. + * 4. each task(launched) must be lanched orderly(using threadToStarted) to make sure + * it will get cpu core resource, and will wait to finished after user manually + * release "Lock" and then cluster will contain another free cpu cores. + * 5. each task(pending) must use "sleep" to make sure it has been added to taskSetManager queue, + * thus it will be scheduled later when cluster has free cpu cores. + */ +class LocalSchedulerSuite extends FunSuite with LocalSparkContext { + + def createThread(threadIndex: Int, poolName: String, sc: SparkContext, sem: Semaphore) { + + TaskThreadInfo.threadToRunning(threadIndex) = false + val nums = sc.parallelize(threadIndex to threadIndex, 1) + TaskThreadInfo.threadToLock(threadIndex) = new Lock() + TaskThreadInfo.threadToStarted(threadIndex) = new CountDownLatch(1) + new Thread { + if (poolName != null) { + sc.addLocalProperties("spark.scheduler.cluster.fair.pool",poolName) + } + override def run() { + val ans = nums.map(number => { + TaskThreadInfo.threadToRunning(number) = true + TaskThreadInfo.threadToStarted(number).countDown() + TaskThreadInfo.threadToLock(number).jobWait() + TaskThreadInfo.threadToRunning(number) = false + number + }).collect() + assert(ans.toList === List(threadIndex)) + sem.release() + } + }.start() + } + + test("Local FIFO scheduler end-to-end test") { + System.setProperty("spark.cluster.schedulingmode", "FIFO") + sc = new SparkContext("local[4]", "test") + val sem = new Semaphore(0) + + createThread(1,null,sc,sem) + TaskThreadInfo.threadToStarted(1).await() + createThread(2,null,sc,sem) + TaskThreadInfo.threadToStarted(2).await() + createThread(3,null,sc,sem) + TaskThreadInfo.threadToStarted(3).await() + createThread(4,null,sc,sem) + TaskThreadInfo.threadToStarted(4).await() + // thread 5 and 6 (stage pending)must meet following two points + // 1. stages (taskSetManager) of jobs in thread 5 and 6 should be add to taskSetManager + // queue before executing TaskThreadInfo.threadToLock(1).jobFinished() + // 2. priority of stage in thread 5 should be prior to priority of stage in thread 6 + // So I just use "sleep" 1s here for each thread. + // TODO: any better solution? + createThread(5,null,sc,sem) + Thread.sleep(1000) + createThread(6,null,sc,sem) + Thread.sleep(1000) + + assert(TaskThreadInfo.threadToRunning(1) === true) + assert(TaskThreadInfo.threadToRunning(2) === true) + assert(TaskThreadInfo.threadToRunning(3) === true) + assert(TaskThreadInfo.threadToRunning(4) === true) + assert(TaskThreadInfo.threadToRunning(5) === false) + assert(TaskThreadInfo.threadToRunning(6) === false) + + TaskThreadInfo.threadToLock(1).jobFinished() + TaskThreadInfo.threadToStarted(5).await() + + assert(TaskThreadInfo.threadToRunning(1) === false) + assert(TaskThreadInfo.threadToRunning(2) === true) + assert(TaskThreadInfo.threadToRunning(3) === true) + assert(TaskThreadInfo.threadToRunning(4) === true) + assert(TaskThreadInfo.threadToRunning(5) === true) + assert(TaskThreadInfo.threadToRunning(6) === false) + + TaskThreadInfo.threadToLock(3).jobFinished() + TaskThreadInfo.threadToStarted(6).await() + + assert(TaskThreadInfo.threadToRunning(1) === false) + assert(TaskThreadInfo.threadToRunning(2) === true) + assert(TaskThreadInfo.threadToRunning(3) === false) + assert(TaskThreadInfo.threadToRunning(4) === true) + assert(TaskThreadInfo.threadToRunning(5) === true) + assert(TaskThreadInfo.threadToRunning(6) === true) + + TaskThreadInfo.threadToLock(2).jobFinished() + TaskThreadInfo.threadToLock(4).jobFinished() + TaskThreadInfo.threadToLock(5).jobFinished() + TaskThreadInfo.threadToLock(6).jobFinished() + sem.acquire(6) + } + + test("Local fair scheduler end-to-end test") { + sc = new SparkContext("local[8]", "LocalSchedulerSuite") + val sem = new Semaphore(0) + System.setProperty("spark.cluster.schedulingmode", "FAIR") + val xmlPath = getClass.getClassLoader.getResource("fairscheduler.xml").getFile() + System.setProperty("spark.fairscheduler.allocation.file", xmlPath) + + createThread(10,"1",sc,sem) + TaskThreadInfo.threadToStarted(10).await() + createThread(20,"2",sc,sem) + TaskThreadInfo.threadToStarted(20).await() + createThread(30,"3",sc,sem) + TaskThreadInfo.threadToStarted(30).await() + + assert(TaskThreadInfo.threadToRunning(10) === true) + assert(TaskThreadInfo.threadToRunning(20) === true) + assert(TaskThreadInfo.threadToRunning(30) === true) + + createThread(11,"1",sc,sem) + TaskThreadInfo.threadToStarted(11).await() + createThread(21,"2",sc,sem) + TaskThreadInfo.threadToStarted(21).await() + createThread(31,"3",sc,sem) + TaskThreadInfo.threadToStarted(31).await() + + assert(TaskThreadInfo.threadToRunning(11) === true) + assert(TaskThreadInfo.threadToRunning(21) === true) + assert(TaskThreadInfo.threadToRunning(31) === true) + + createThread(12,"1",sc,sem) + TaskThreadInfo.threadToStarted(12).await() + createThread(22,"2",sc,sem) + TaskThreadInfo.threadToStarted(22).await() + createThread(32,"3",sc,sem) + + assert(TaskThreadInfo.threadToRunning(12) === true) + assert(TaskThreadInfo.threadToRunning(22) === true) + assert(TaskThreadInfo.threadToRunning(32) === false) + + TaskThreadInfo.threadToLock(10).jobFinished() + TaskThreadInfo.threadToStarted(32).await() + + assert(TaskThreadInfo.threadToRunning(32) === true) + + //1. Similar with above scenario, sleep 1s for stage of 23 and 33 to be added to taskSetManager + // queue so that cluster will assign free cpu core to stage 23 after stage 11 finished. + //2. priority of 23 and 33 will be meaningless as using fair scheduler here. + createThread(23,"2",sc,sem) + createThread(33,"3",sc,sem) + Thread.sleep(1000) + + TaskThreadInfo.threadToLock(11).jobFinished() + TaskThreadInfo.threadToStarted(23).await() + + assert(TaskThreadInfo.threadToRunning(23) === true) + assert(TaskThreadInfo.threadToRunning(33) === false) + + TaskThreadInfo.threadToLock(12).jobFinished() + TaskThreadInfo.threadToStarted(33).await() + + assert(TaskThreadInfo.threadToRunning(33) === true) + + TaskThreadInfo.threadToLock(20).jobFinished() + TaskThreadInfo.threadToLock(21).jobFinished() + TaskThreadInfo.threadToLock(22).jobFinished() + TaskThreadInfo.threadToLock(23).jobFinished() + TaskThreadInfo.threadToLock(30).jobFinished() + TaskThreadInfo.threadToLock(31).jobFinished() + TaskThreadInfo.threadToLock(32).jobFinished() + TaskThreadInfo.threadToLock(33).jobFinished() + + sem.acquire(11) + } +} diff --git a/core/src/test/scala/spark/scheduler/SparkListenerSuite.scala b/core/src/test/scala/spark/scheduler/SparkListenerSuite.scala index 42a87d8b90..48aa67c543 100644 --- a/core/src/test/scala/spark/scheduler/SparkListenerSuite.scala +++ b/core/src/test/scala/spark/scheduler/SparkListenerSuite.scala @@ -77,7 +77,7 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with ShouldMatc class SaveStageInfo extends SparkListener { val stageInfos = mutable.Buffer[StageInfo]() - def onStageCompleted(stage: StageCompleted) { + override def onStageCompleted(stage: StageCompleted) { stageInfos += stage.stageInfo } } diff --git a/docs/configuration.md b/docs/configuration.md index 17fdbf04d1..2de512f896 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -260,6 +260,13 @@ Apart from these, the following properties are also available, and may be useful applications). Note that any RDD that persists in memory for more than this duration will be cleared as well. </td> </tr> +<tr> + <td>spark.streaming.blockInterval</td> + <td>200</td> + <td> + Duration (milliseconds) of how long to batch new objects coming from network receivers. + </td> +</tr> </table> diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md index b0da130fcb..e9cf9ef36f 100644 --- a/docs/scala-programming-guide.md +++ b/docs/scala-programming-guide.md @@ -43,12 +43,18 @@ new SparkContext(master, appName, [sparkHome], [jars]) The `master` parameter is a string specifying a [Spark or Mesos cluster URL](#master-urls) to connect to, or a special "local" string to run in local mode, as described below. `appName` is a name for your application, which will be shown in the cluster web UI. Finally, the last two parameters are needed to deploy your code to a cluster if running in distributed mode, as described later. -In the Spark shell, a special interpreter-aware SparkContext is already created for you, in the variable called `sc`. Making your own SparkContext will not work. You can set which master the context connects to using the `MASTER` environment variable. For example, to run on four cores, use +In the Spark shell, a special interpreter-aware SparkContext is already created for you, in the variable called `sc`. Making your own SparkContext will not work. You can set which master the context connects to using the `MASTER` environment variable, and you can add JARs to the classpath with the `ADD_JARS` variable. For example, to run `spark-shell` on four cores, use {% highlight bash %} $ MASTER=local[4] ./spark-shell {% endhighlight %} +Or, to also add `code.jar` to its classpath, use: + +{% highlight bash %} +$ MASTER=local[4] ADD_JARS=code.jar ./spark-shell +{% endhighlight %} + ### Master URLs The master URL passed to Spark can be in one of the following formats: @@ -78,7 +84,7 @@ If you want to run your job on a cluster, you will need to specify the two optio * `sparkHome`: The path at which Spark is installed on your worker machines (it should be the same on all of them). * `jars`: A list of JAR files on the local machine containing your job's code and any dependencies, which Spark will deploy to all the worker nodes. You'll need to package your job into a set of JARs using your build system. For example, if you're using SBT, the [sbt-assembly](https://github.com/sbt/sbt-assembly) plugin is a good way to make a single JAR with your code and dependencies. -If you run `spark-shell` on a cluster, any classes you define in the shell will automatically be distributed. +If you run `spark-shell` on a cluster, you can add JARs to it by specifying the `ADD_JARS` environment variable before you launch it. This variable should contain a comma-separated list of JARs. For example, `ADD_JARS=a.jar,b.jar ./spark-shell` will launch a shell with `a.jar` and `b.jar` on its classpath. In addition, any new classes you define in the shell will automatically be distributed. # Resilient Distributed Datasets (RDDs) diff --git a/examples/pom.xml b/examples/pom.xml index c42d2bcdb9..3e5271ec2f 100644 --- a/examples/pom.xml +++ b/examples/pom.xml @@ -34,6 +34,41 @@ <artifactId>scalacheck_${scala.version}</artifactId> <scope>test</scope> </dependency> + <dependency> + <groupId>org.apache.cassandra</groupId> + <artifactId>cassandra-all</artifactId> + <version>1.2.5</version> + <exclusions> + <exclusion> + <groupId>com.google.guava</groupId> + <artifactId>guava</artifactId> + </exclusion> + <exclusion> + <groupId>com.googlecode.concurrentlinkedhashmap</groupId> + <artifactId>concurrentlinkedhashmap-lru</artifactId> + </exclusion> + <exclusion> + <groupId>com.ning</groupId> + <artifactId>compress-lzf</artifactId> + </exclusion> + <exclusion> + <groupId>io.netty</groupId> + <artifactId>netty</artifactId> + </exclusion> + <exclusion> + <groupId>jline</groupId> + <artifactId>jline</artifactId> + </exclusion> + <exclusion> + <groupId>log4j</groupId> + <artifactId>log4j</artifactId> + </exclusion> + <exclusion> + <groupId>org.apache.cassandra.deps</groupId> + <artifactId>avro</artifactId> + </exclusion> + </exclusions> + </dependency> </dependencies> <build> <outputDirectory>target/scala-${scala.version}/classes</outputDirectory> @@ -67,6 +102,11 @@ <artifactId>hadoop-core</artifactId> <scope>provided</scope> </dependency> + <dependency> + <groupId>org.apache.hbase</groupId> + <artifactId>hbase</artifactId> + <version>0.94.6</version> + </dependency> </dependencies> <build> <plugins> @@ -105,6 +145,11 @@ <artifactId>hadoop-client</artifactId> <scope>provided</scope> </dependency> + <dependency> + <groupId>org.apache.hbase</groupId> + <artifactId>hbase</artifactId> + <version>0.94.6</version> + </dependency> </dependencies> <build> <plugins> diff --git a/examples/src/main/scala/spark/examples/CassandraTest.scala b/examples/src/main/scala/spark/examples/CassandraTest.scala new file mode 100644 index 0000000000..0fe1833e83 --- /dev/null +++ b/examples/src/main/scala/spark/examples/CassandraTest.scala @@ -0,0 +1,196 @@ +package spark.examples + +import org.apache.hadoop.mapreduce.Job +import org.apache.cassandra.hadoop.ColumnFamilyOutputFormat +import org.apache.cassandra.hadoop.ConfigHelper +import org.apache.cassandra.hadoop.ColumnFamilyInputFormat +import org.apache.cassandra.thrift._ +import spark.SparkContext +import spark.SparkContext._ +import java.nio.ByteBuffer +import java.util.SortedMap +import org.apache.cassandra.db.IColumn +import org.apache.cassandra.utils.ByteBufferUtil +import scala.collection.JavaConversions._ + + +/* + * This example demonstrates using Spark with Cassandra with the New Hadoop API and Cassandra + * support for Hadoop. + * + * To run this example, run this file with the following command params - + * <spark_master> <cassandra_node> <cassandra_port> + * + * So if you want to run this on localhost this will be, + * local[3] localhost 9160 + * + * The example makes some assumptions: + * 1. You have already created a keyspace called casDemo and it has a column family named Words + * 2. There are column family has a column named "para" which has test content. + * + * You can create the content by running the following script at the bottom of this file with + * cassandra-cli. + * + */ +object CassandraTest { + + def main(args: Array[String]) { + + // Get a SparkContext + val sc = new SparkContext(args(0), "casDemo") + + // Build the job configuration with ConfigHelper provided by Cassandra + val job = new Job() + job.setInputFormatClass(classOf[ColumnFamilyInputFormat]) + + val host: String = args(1) + val port: String = args(2) + + ConfigHelper.setInputInitialAddress(job.getConfiguration(), host) + ConfigHelper.setInputRpcPort(job.getConfiguration(), port) + ConfigHelper.setOutputInitialAddress(job.getConfiguration(), host) + ConfigHelper.setOutputRpcPort(job.getConfiguration(), port) + ConfigHelper.setInputColumnFamily(job.getConfiguration(), "casDemo", "Words") + ConfigHelper.setOutputColumnFamily(job.getConfiguration(), "casDemo", "WordCount") + + val predicate = new SlicePredicate() + val sliceRange = new SliceRange() + sliceRange.setStart(Array.empty[Byte]) + sliceRange.setFinish(Array.empty[Byte]) + predicate.setSlice_range(sliceRange) + ConfigHelper.setInputSlicePredicate(job.getConfiguration(), predicate) + + ConfigHelper.setInputPartitioner(job.getConfiguration(), "Murmur3Partitioner") + ConfigHelper.setOutputPartitioner(job.getConfiguration(), "Murmur3Partitioner") + + // Make a new Hadoop RDD + val casRdd = sc.newAPIHadoopRDD( + job.getConfiguration(), + classOf[ColumnFamilyInputFormat], + classOf[ByteBuffer], + classOf[SortedMap[ByteBuffer, IColumn]]) + + // Let us first get all the paragraphs from the retrieved rows + val paraRdd = casRdd.map { + case (key, value) => { + ByteBufferUtil.string(value.get(ByteBufferUtil.bytes("para")).value()) + } + } + + // Lets get the word count in paras + val counts = paraRdd.flatMap(p => p.split(" ")).map(word => (word, 1)).reduceByKey(_ + _) + + counts.collect().foreach { + case (word, count) => println(word + ":" + count) + } + + counts.map { + case (word, count) => { + val colWord = new org.apache.cassandra.thrift.Column() + colWord.setName(ByteBufferUtil.bytes("word")) + colWord.setValue(ByteBufferUtil.bytes(word)) + colWord.setTimestamp(System.currentTimeMillis) + + val colCount = new org.apache.cassandra.thrift.Column() + colCount.setName(ByteBufferUtil.bytes("wcount")) + colCount.setValue(ByteBufferUtil.bytes(count.toLong)) + colCount.setTimestamp(System.currentTimeMillis) + + val outputkey = ByteBufferUtil.bytes(word + "-COUNT-" + System.currentTimeMillis) + + val mutations: java.util.List[Mutation] = new Mutation() :: new Mutation() :: Nil + mutations.get(0).setColumn_or_supercolumn(new ColumnOrSuperColumn()) + mutations.get(0).column_or_supercolumn.setColumn(colWord) + mutations.get(1).setColumn_or_supercolumn(new ColumnOrSuperColumn()) + mutations.get(1).column_or_supercolumn.setColumn(colCount) + (outputkey, mutations) + } + }.saveAsNewAPIHadoopFile("casDemo", classOf[ByteBuffer], classOf[List[Mutation]], + classOf[ColumnFamilyOutputFormat], job.getConfiguration) + } +} + +/* +create keyspace casDemo; +use casDemo; + +create column family WordCount with comparator = UTF8Type; +update column family WordCount with column_metadata = + [{column_name: word, validation_class: UTF8Type}, + {column_name: wcount, validation_class: LongType}]; + +create column family Words with comparator = UTF8Type; +update column family Words with column_metadata = + [{column_name: book, validation_class: UTF8Type}, + {column_name: para, validation_class: UTF8Type}]; + +assume Words keys as utf8; + +set Words['3musk001']['book'] = 'The Three Musketeers'; +set Words['3musk001']['para'] = 'On the first Monday of the month of April, 1625, the market + town of Meung, in which the author of ROMANCE OF THE ROSE was born, appeared to + be in as perfect a state of revolution as if the Huguenots had just made + a second La Rochelle of it. Many citizens, seeing the women flying + toward the High Street, leaving their children crying at the open doors, + hastened to don the cuirass, and supporting their somewhat uncertain + courage with a musket or a partisan, directed their steps toward the + hostelry of the Jolly Miller, before which was gathered, increasing + every minute, a compact group, vociferous and full of curiosity.'; + +set Words['3musk002']['book'] = 'The Three Musketeers'; +set Words['3musk002']['para'] = 'In those times panics were common, and few days passed without + some city or other registering in its archives an event of this kind. There were + nobles, who made war against each other; there was the king, who made + war against the cardinal; there was Spain, which made war against the + king. Then, in addition to these concealed or public, secret or open + wars, there were robbers, mendicants, Huguenots, wolves, and scoundrels, + who made war upon everybody. The citizens always took up arms readily + against thieves, wolves or scoundrels, often against nobles or + Huguenots, sometimes against the king, but never against cardinal or + Spain. It resulted, then, from this habit that on the said first Monday + of April, 1625, the citizens, on hearing the clamor, and seeing neither + the red-and-yellow standard nor the livery of the Duc de Richelieu, + rushed toward the hostel of the Jolly Miller. When arrived there, the + cause of the hubbub was apparent to all'; + +set Words['3musk003']['book'] = 'The Three Musketeers'; +set Words['3musk003']['para'] = 'You ought, I say, then, to husband the means you have, however + large the sum may be; but you ought also to endeavor to perfect yourself in + the exercises becoming a gentleman. I will write a letter today to the + Director of the Royal Academy, and tomorrow he will admit you without + any expense to yourself. Do not refuse this little service. Our + best-born and richest gentlemen sometimes solicit it without being able + to obtain it. You will learn horsemanship, swordsmanship in all its + branches, and dancing. You will make some desirable acquaintances; and + from time to time you can call upon me, just to tell me how you are + getting on, and to say whether I can be of further service to you.'; + + +set Words['thelostworld001']['book'] = 'The Lost World'; +set Words['thelostworld001']['para'] = 'She sat with that proud, delicate profile of hers outlined + against the red curtain. How beautiful she was! And yet how aloof! We had been + friends, quite good friends; but never could I get beyond the same + comradeship which I might have established with one of my + fellow-reporters upon the Gazette,--perfectly frank, perfectly kindly, + and perfectly unsexual. My instincts are all against a woman being too + frank and at her ease with me. It is no compliment to a man. Where + the real sex feeling begins, timidity and distrust are its companions, + heritage from old wicked days when love and violence went often hand in + hand. The bent head, the averted eye, the faltering voice, the wincing + figure--these, and not the unshrinking gaze and frank reply, are the + true signals of passion. Even in my short life I had learned as much + as that--or had inherited it in that race memory which we call instinct.'; + +set Words['thelostworld002']['book'] = 'The Lost World'; +set Words['thelostworld002']['para'] = 'I always liked McArdle, the crabbed, old, round-backed, + red-headed news editor, and I rather hoped that he liked me. Of course, Beaumont was + the real boss; but he lived in the rarefied atmosphere of some Olympian + height from which he could distinguish nothing smaller than an + international crisis or a split in the Cabinet. Sometimes we saw him + passing in lonely majesty to his inner sanctum, with his eyes staring + vaguely and his mind hovering over the Balkans or the Persian Gulf. He + was above and beyond us. But McArdle was his first lieutenant, and it + was he that we knew. The old man nodded as I entered the room, and he + pushed his spectacles far up on his bald forehead.'; + +*/ diff --git a/examples/src/main/scala/spark/examples/HBaseTest.scala b/examples/src/main/scala/spark/examples/HBaseTest.scala new file mode 100644 index 0000000000..6e910154d4 --- /dev/null +++ b/examples/src/main/scala/spark/examples/HBaseTest.scala @@ -0,0 +1,35 @@ +package spark.examples + +import spark._ +import spark.rdd.NewHadoopRDD +import org.apache.hadoop.hbase.{HBaseConfiguration, HTableDescriptor} +import org.apache.hadoop.hbase.client.HBaseAdmin +import org.apache.hadoop.hbase.mapreduce.TableInputFormat + +object HBaseTest { + def main(args: Array[String]) { + val sc = new SparkContext(args(0), "HBaseTest", + System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR"))) + + val conf = HBaseConfiguration.create() + + // Other options for configuring scan behavior are available. More information available at + // http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/TableInputFormat.html + conf.set(TableInputFormat.INPUT_TABLE, args(1)) + + // Initialize hBase table if necessary + val admin = new HBaseAdmin(conf) + if(!admin.isTableAvailable(args(1))) { + val tableDesc = new HTableDescriptor(args(1)) + admin.createTable(tableDesc) + } + + val hBaseRDD = sc.newAPIHadoopRDD(conf, classOf[TableInputFormat], + classOf[org.apache.hadoop.hbase.io.ImmutableBytesWritable], + classOf[org.apache.hadoop.hbase.client.Result]) + + hBaseRDD.count() + + System.exit(0) + } +}
\ No newline at end of file diff --git a/examples/src/main/scala/spark/streaming/examples/KafkaWordCount.scala b/examples/src/main/scala/spark/streaming/examples/KafkaWordCount.scala index c3a9e491ba..9202e65e09 100644 --- a/examples/src/main/scala/spark/streaming/examples/KafkaWordCount.scala +++ b/examples/src/main/scala/spark/streaming/examples/KafkaWordCount.scala @@ -37,7 +37,7 @@ object KafkaWordCount { ssc.checkpoint("checkpoint") val topicpMap = topics.split(",").map((_,numThreads.toInt)).toMap - val lines = ssc.kafkaStream[String](zkQuorum, group, topicpMap) + val lines = ssc.kafkaStream(zkQuorum, group, topicpMap) val words = lines.flatMap(_.split(" ")) val wordCounts = words.map(x => (x, 1l)).reduceByKeyAndWindow(add _, subtract _, Minutes(10), Seconds(2), 2) wordCounts.print() @@ -59,6 +59,9 @@ <slf4j.version>1.6.1</slf4j.version> <cdh.version>4.1.2</cdh.version> <log4j.version>1.2.17</log4j.version> + + <PermGen>64m</PermGen> + <MaxPermGen>512m</MaxPermGen> </properties> <repositories> @@ -187,9 +190,9 @@ <version>0.8.4</version> </dependency> <dependency> - <groupId>asm</groupId> - <artifactId>asm-all</artifactId> - <version>3.3.1</version> + <groupId>org.ow2.asm</groupId> + <artifactId>asm</artifactId> + <version>4.0</version> </dependency> <dependency> <groupId>com.google.protobuf</groupId> @@ -392,6 +395,8 @@ <jvmArgs> <jvmArg>-Xms64m</jvmArg> <jvmArg>-Xmx1024m</jvmArg> + <jvmArg>-XX:PermSize=${PermGen}</jvmArg> + <jvmArg>-XX:MaxPermSize=${MaxPermGen}</jvmArg> </jvmArgs> <javacArgs> <javacArg>-source</javacArg> diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index e3892371e0..481c06ef54 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -54,7 +54,7 @@ object SparkBuild extends Build { // Fork new JVMs for tests and set Java options for those fork := true, - javaOptions += "-Xmx2g", + javaOptions += "-Xmx2500m", // Only allow one test at a time, even across projects, since they run in the same JVM concurrentRestrictions in Global += Tags.limit(Tags.Test, 1), @@ -125,12 +125,13 @@ object SparkBuild extends Build { publishMavenStyle in MavenCompile := true, publishLocal in MavenCompile <<= publishTask(publishLocalConfiguration in MavenCompile, deliverLocal), publishLocalBoth <<= Seq(publishLocal in MavenCompile, publishLocal).dependOn - ) + ) ++ net.virtualvoid.sbt.graph.Plugin.graphSettings - val slf4jVersion = "1.6.1" + val slf4jVersion = "1.7.2" val excludeJackson = ExclusionRule(organization = "org.codehaus.jackson") val excludeNetty = ExclusionRule(organization = "org.jboss.netty") + val excludeAsm = ExclusionRule(organization = "asm") def coreSettings = sharedSettings ++ Seq( name := "spark-core", @@ -148,7 +149,7 @@ object SparkBuild extends Build { "org.slf4j" % "slf4j-log4j12" % slf4jVersion, "commons-daemon" % "commons-daemon" % "1.0.10", "com.ning" % "compress-lzf" % "0.8.4", - "asm" % "asm-all" % "3.3.1", + "org.ow2.asm" % "asm" % "4.0", "com.google.protobuf" % "protobuf-java" % "2.4.1", "de.javakaffee" % "kryo-serializers" % "0.22", "com.typesafe.akka" % "akka-actor" % "2.0.3" excludeAll(excludeNetty), @@ -201,16 +202,32 @@ object SparkBuild extends Build { def examplesSettings = sharedSettings ++ Seq( name := "spark-examples", - libraryDependencies ++= Seq("com.twitter" % "algebird-core_2.9.2" % "0.1.11") + libraryDependencies ++= Seq( + "com.twitter" % "algebird-core_2.9.2" % "0.1.11", + + "org.apache.hbase" % "hbase" % "0.94.6" excludeAll(excludeNetty, excludeAsm), + + "org.apache.cassandra" % "cassandra-all" % "1.2.5" + exclude("com.google.guava", "guava") + exclude("com.googlecode.concurrentlinkedhashmap", "concurrentlinkedhashmap-lru") + exclude("com.ning","compress-lzf") + exclude("io.netty", "netty") + exclude("jline","jline") + exclude("log4j","log4j") + exclude("org.apache.cassandra.deps", "avro") + ) ) def bagelSettings = sharedSettings ++ Seq(name := "spark-bagel") def streamingSettings = sharedSettings ++ Seq( name := "spark-streaming", + resolvers ++= Seq( + "Akka Repository" at "http://repo.akka.io/releases/" + ), libraryDependencies ++= Seq( "org.apache.flume" % "flume-ng-sdk" % "1.2.0" % "compile" excludeAll(excludeNetty), - "com.github.sgroschupf" % "zkclient" % "0.1", + "com.github.sgroschupf" % "zkclient" % "0.1" excludeAll(excludeNetty), "org.twitter4j" % "twitter4j-stream" % "3.0.3" excludeAll(excludeNetty), "com.typesafe.akka" % "akka-zeromq" % "2.0.3" excludeAll(excludeNetty) ) diff --git a/project/plugins.sbt b/project/plugins.sbt index d4f2442872..f806e66481 100644 --- a/project/plugins.sbt +++ b/project/plugins.sbt @@ -16,3 +16,5 @@ addSbtPlugin("io.spray" %% "sbt-twirl" % "0.6.1") //resolvers += Resolver.url("sbt-plugin-releases", new URL("http://scalasbt.artifactoryonline.com/scalasbt/sbt-plugin-releases/"))(Resolver.ivyStylePatterns) //addSbtPlugin("com.jsuereth" % "xsbt-gpg-plugin" % "0.6") + +addSbtPlugin("net.virtual-void" % "sbt-dependency-graph" % "0.7.3") diff --git a/python/pyspark/daemon.py b/python/pyspark/daemon.py new file mode 100644 index 0000000000..78a2da1e18 --- /dev/null +++ b/python/pyspark/daemon.py @@ -0,0 +1,158 @@ +import os +import sys +import multiprocessing +from ctypes import c_bool +from errno import EINTR, ECHILD +from socket import socket, AF_INET, SOCK_STREAM, SOMAXCONN +from signal import signal, SIGHUP, SIGTERM, SIGCHLD, SIG_DFL, SIG_IGN +from pyspark.worker import main as worker_main +from pyspark.serializers import write_int + +try: + POOLSIZE = multiprocessing.cpu_count() +except NotImplementedError: + POOLSIZE = 4 + +exit_flag = multiprocessing.Value(c_bool, False) + + +def should_exit(): + global exit_flag + return exit_flag.value + + +def compute_real_exit_code(exit_code): + # SystemExit's code can be integer or string, but os._exit only accepts integers + import numbers + if isinstance(exit_code, numbers.Integral): + return exit_code + else: + return 1 + + +def worker(listen_sock): + # Redirect stdout to stderr + os.dup2(2, 1) + + # Manager sends SIGHUP to request termination of workers in the pool + def handle_sighup(*args): + assert should_exit() + signal(SIGHUP, handle_sighup) + + # Cleanup zombie children + def handle_sigchld(*args): + pid = status = None + try: + while (pid, status) != (0, 0): + pid, status = os.waitpid(0, os.WNOHANG) + except EnvironmentError as err: + if err.errno == EINTR: + # retry + handle_sigchld() + elif err.errno != ECHILD: + raise + signal(SIGCHLD, handle_sigchld) + + # Handle clients + while not should_exit(): + # Wait until a client arrives or we have to exit + sock = None + while not should_exit() and sock is None: + try: + sock, addr = listen_sock.accept() + except EnvironmentError as err: + if err.errno != EINTR: + raise + + if sock is not None: + # Fork a child to handle the client. + # The client is handled in the child so that the manager + # never receives SIGCHLD unless a worker crashes. + if os.fork() == 0: + # Leave the worker pool + signal(SIGHUP, SIG_DFL) + listen_sock.close() + # Handle the client then exit + sockfile = sock.makefile() + exit_code = 0 + try: + worker_main(sockfile, sockfile) + except SystemExit as exc: + exit_code = exc.code + finally: + sockfile.close() + sock.close() + os._exit(compute_real_exit_code(exit_code)) + else: + sock.close() + + +def launch_worker(listen_sock): + if os.fork() == 0: + try: + worker(listen_sock) + except Exception as err: + import traceback + traceback.print_exc() + os._exit(1) + else: + assert should_exit() + os._exit(0) + + +def manager(): + # Create a new process group to corral our children + os.setpgid(0, 0) + + # Create a listening socket on the AF_INET loopback interface + listen_sock = socket(AF_INET, SOCK_STREAM) + listen_sock.bind(('127.0.0.1', 0)) + listen_sock.listen(max(1024, 2 * POOLSIZE, SOMAXCONN)) + listen_host, listen_port = listen_sock.getsockname() + write_int(listen_port, sys.stdout) + + # Launch initial worker pool + for idx in range(POOLSIZE): + launch_worker(listen_sock) + listen_sock.close() + + def shutdown(): + global exit_flag + exit_flag.value = True + + # Gracefully exit on SIGTERM, don't die on SIGHUP + signal(SIGTERM, lambda signum, frame: shutdown()) + signal(SIGHUP, SIG_IGN) + + # Cleanup zombie children + def handle_sigchld(*args): + try: + pid, status = os.waitpid(0, os.WNOHANG) + if status != 0 and not should_exit(): + raise RuntimeError("worker crashed: %s, %s" % (pid, status)) + except EnvironmentError as err: + if err.errno not in (ECHILD, EINTR): + raise + signal(SIGCHLD, handle_sigchld) + + # Initialization complete + sys.stdout.close() + try: + while not should_exit(): + try: + # Spark tells us to exit by closing stdin + if os.read(0, 512) == '': + shutdown() + except EnvironmentError as err: + if err.errno != EINTR: + shutdown() + raise + finally: + signal(SIGTERM, SIG_DFL) + exit_flag.value = True + # Send SIGHUP to notify workers of shutdown + os.kill(0, SIGHUP) + + +if __name__ == '__main__': + manager() diff --git a/python/pyspark/serializers.py b/python/pyspark/serializers.py index 115cf28cc2..5a95144983 100644 --- a/python/pyspark/serializers.py +++ b/python/pyspark/serializers.py @@ -46,6 +46,10 @@ def read_long(stream): return struct.unpack("!q", length)[0] +def write_long(value, stream): + stream.write(struct.pack("!q", value)) + + def read_int(stream): length = stream.read(4) if length == "": diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py index 6a1962d267..1e34d47365 100644 --- a/python/pyspark/tests.py +++ b/python/pyspark/tests.py @@ -12,6 +12,7 @@ import unittest from pyspark.context import SparkContext from pyspark.files import SparkFiles from pyspark.java_gateway import SPARK_HOME +from pyspark.serializers import read_int class PySparkTestCase(unittest.TestCase): @@ -117,5 +118,47 @@ class TestIO(PySparkTestCase): self.sc.parallelize([1]).foreach(func) +class TestDaemon(unittest.TestCase): + def connect(self, port): + from socket import socket, AF_INET, SOCK_STREAM + sock = socket(AF_INET, SOCK_STREAM) + sock.connect(('127.0.0.1', port)) + # send a split index of -1 to shutdown the worker + sock.send("\xFF\xFF\xFF\xFF") + sock.close() + return True + + def do_termination_test(self, terminator): + from subprocess import Popen, PIPE + from errno import ECONNREFUSED + + # start daemon + daemon_path = os.path.join(os.path.dirname(__file__), "daemon.py") + daemon = Popen([sys.executable, daemon_path], stdin=PIPE, stdout=PIPE) + + # read the port number + port = read_int(daemon.stdout) + + # daemon should accept connections + self.assertTrue(self.connect(port)) + + # request shutdown + terminator(daemon) + time.sleep(1) + + # daemon should no longer accept connections + with self.assertRaises(EnvironmentError) as trap: + self.connect(port) + self.assertEqual(trap.exception.errno, ECONNREFUSED) + + def test_termination_stdin(self): + """Ensure that daemon and workers terminate when stdin is closed.""" + self.do_termination_test(lambda daemon: daemon.stdin.close()) + + def test_termination_sigterm(self): + """Ensure that daemon and workers terminate on SIGTERM.""" + from signal import SIGTERM + self.do_termination_test(lambda daemon: os.kill(daemon.pid, SIGTERM)) + if __name__ == "__main__": unittest.main() diff --git a/python/pyspark/worker.py b/python/pyspark/worker.py index 812e7a9da5..379bbfd4c2 100644 --- a/python/pyspark/worker.py +++ b/python/pyspark/worker.py @@ -3,6 +3,7 @@ Worker that receives input from Piped RDD. """ import os import sys +import time import traceback from base64 import standard_b64decode # CloudPickler needs to be imported so that depicklers are registered using the @@ -12,48 +13,60 @@ from pyspark.broadcast import Broadcast, _broadcastRegistry from pyspark.cloudpickle import CloudPickler from pyspark.files import SparkFiles from pyspark.serializers import write_with_length, read_with_length, write_int, \ - read_long, read_int, dump_pickle, load_pickle, read_from_pickle_file + read_long, write_long, read_int, dump_pickle, load_pickle, read_from_pickle_file -# Redirect stdout to stderr so that users must return values from functions. -old_stdout = os.fdopen(os.dup(1), 'w') -os.dup2(2, 1) +def load_obj(infile): + return load_pickle(standard_b64decode(infile.readline().strip())) -def load_obj(): - return load_pickle(standard_b64decode(sys.stdin.readline().strip())) +def report_times(outfile, boot, init, finish): + write_int(-3, outfile) + write_long(1000 * boot, outfile) + write_long(1000 * init, outfile) + write_long(1000 * finish, outfile) -def main(): - split_index = read_int(sys.stdin) - spark_files_dir = load_pickle(read_with_length(sys.stdin)) +def main(infile, outfile): + boot_time = time.time() + split_index = read_int(infile) + if split_index == -1: # for unit tests + return + spark_files_dir = load_pickle(read_with_length(infile)) SparkFiles._root_directory = spark_files_dir SparkFiles._is_running_on_worker = True sys.path.append(spark_files_dir) - num_broadcast_variables = read_int(sys.stdin) + num_broadcast_variables = read_int(infile) for _ in range(num_broadcast_variables): - bid = read_long(sys.stdin) - value = read_with_length(sys.stdin) + bid = read_long(infile) + value = read_with_length(infile) _broadcastRegistry[bid] = Broadcast(bid, load_pickle(value)) - func = load_obj() - bypassSerializer = load_obj() + func = load_obj(infile) + bypassSerializer = load_obj(infile) if bypassSerializer: dumps = lambda x: x else: dumps = dump_pickle - iterator = read_from_pickle_file(sys.stdin) + init_time = time.time() + iterator = read_from_pickle_file(infile) try: for obj in func(split_index, iterator): - write_with_length(dumps(obj), old_stdout) + write_with_length(dumps(obj), outfile) except Exception as e: - write_int(-2, old_stdout) - write_with_length(traceback.format_exc(), old_stdout) + write_int(-2, outfile) + write_with_length(traceback.format_exc(), outfile) sys.exit(-1) + finish_time = time.time() + report_times(outfile, boot_time, init_time, finish_time) # Mark the beginning of the accumulators section of the output - write_int(-1, old_stdout) + write_int(-1, outfile) for aid, accum in _accumulatorRegistry.items(): - write_with_length(dump_pickle((aid, accum._value)), old_stdout) + write_with_length(dump_pickle((aid, accum._value)), outfile) + write_int(-1, outfile) if __name__ == '__main__': - main() + # Redirect stdout to stderr so that users must return values from functions. + old_stdout = os.fdopen(os.dup(1), 'w') + os.dup2(2, 1) + main(sys.stdin, old_stdout) diff --git a/repl/src/main/scala/spark/repl/ExecutorClassLoader.scala b/repl/src/main/scala/spark/repl/ExecutorClassLoader.scala index 13d81ec1cf..0e9aa863b5 100644 --- a/repl/src/main/scala/spark/repl/ExecutorClassLoader.scala +++ b/repl/src/main/scala/spark/repl/ExecutorClassLoader.scala @@ -8,7 +8,6 @@ import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.{FileSystem, Path} import org.objectweb.asm._ -import org.objectweb.asm.commons.EmptyVisitor import org.objectweb.asm.Opcodes._ @@ -83,7 +82,7 @@ extends ClassLoader(parent) { } class ConstructorCleaner(className: String, cv: ClassVisitor) -extends ClassAdapter(cv) { +extends ClassVisitor(ASM4, cv) { override def visitMethod(access: Int, name: String, desc: String, sig: String, exceptions: Array[String]): MethodVisitor = { val mv = cv.visitMethod(access, name, desc, sig, exceptions) diff --git a/repl/src/main/scala/spark/repl/SparkILoop.scala b/repl/src/main/scala/spark/repl/SparkILoop.scala index 23556dbc8f..59f9d05683 100644 --- a/repl/src/main/scala/spark/repl/SparkILoop.scala +++ b/repl/src/main/scala/spark/repl/SparkILoop.scala @@ -822,7 +822,7 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: spark.repl.Main.interp.out.println("Spark context available as sc."); spark.repl.Main.interp.out.flush(); """) - command("import spark.SparkContext._"); + command("import spark.SparkContext._") } echo("Type in expressions to have them evaluated.") echo("Type :help for more information.") @@ -838,7 +838,10 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: if (prop != null) prop else "local" } } - sparkContext = new SparkContext(master, "Spark shell") + val jars = Option(System.getenv("ADD_JARS")).map(_.split(',')) + .getOrElse(new Array[String](0)) + .map(new java.io.File(_).getAbsolutePath) + sparkContext = new SparkContext(master, "Spark shell", System.getenv("SPARK_HOME"), jars) sparkContext } @@ -850,6 +853,10 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: printWelcome() echo("Initializing interpreter...") + // Add JARS specified in Spark's ADD_JARS variable to classpath + val jars = Option(System.getenv("ADD_JARS")).map(_.split(',')).getOrElse(new Array[String](0)) + jars.foreach(settings.classpath.append(_)) + this.settings = settings createInterpreter() diff --git a/streaming/src/main/scala/spark/streaming/DStream.scala b/streaming/src/main/scala/spark/streaming/DStream.scala index e1be5ef51c..9be7926a4a 100644 --- a/streaming/src/main/scala/spark/streaming/DStream.scala +++ b/streaming/src/main/scala/spark/streaming/DStream.scala @@ -441,7 +441,12 @@ abstract class DStream[T: ClassManifest] ( * Return a new DStream in which each RDD has a single element generated by counting each RDD * of this DStream. */ - def count(): DStream[Long] = this.map(_ => 1L).reduce(_ + _) + def count(): DStream[Long] = { + this.map(_ => (null, 1L)) + .transform(_.union(context.sparkContext.makeRDD(Seq((null, 0L)), 1))) + .reduceByKey(_ + _) + .map(_._2) + } /** * Return a new DStream in which each RDD contains the counts of each distinct value in @@ -457,7 +462,7 @@ abstract class DStream[T: ClassManifest] ( * this DStream will be registered as an output stream and therefore materialized. */ def foreach(foreachFunc: RDD[T] => Unit) { - foreach((r: RDD[T], t: Time) => foreachFunc(r)) + this.foreach((r: RDD[T], t: Time) => foreachFunc(r)) } /** diff --git a/streaming/src/main/scala/spark/streaming/StreamingContext.scala b/streaming/src/main/scala/spark/streaming/StreamingContext.scala index b8b60aab43..f2c4073f22 100644 --- a/streaming/src/main/scala/spark/streaming/StreamingContext.scala +++ b/streaming/src/main/scala/spark/streaming/StreamingContext.scala @@ -28,6 +28,7 @@ import org.apache.hadoop.mapreduce.lib.input.TextInputFormat import org.apache.hadoop.fs.Path import twitter4j.Status + /** * A StreamingContext is the main entry point for Spark Streaming functionality. Besides the basic * information (such as, cluster URL and job name) to internally create a SparkContext, it provides @@ -186,10 +187,11 @@ class StreamingContext private ( * should be same. */ def actorStream[T: ClassManifest]( - props: Props, - name: String, - storageLevel: StorageLevel = StorageLevel.MEMORY_ONLY_SER_2, - supervisorStrategy: SupervisorStrategy = ReceiverSupervisorStrategy.defaultStrategy): DStream[T] = { + props: Props, + name: String, + storageLevel: StorageLevel = StorageLevel.MEMORY_ONLY_SER_2, + supervisorStrategy: SupervisorStrategy = ReceiverSupervisorStrategy.defaultStrategy + ): DStream[T] = { networkStream(new ActorReceiver[T](props, name, storageLevel, supervisorStrategy)) } @@ -197,9 +199,10 @@ class StreamingContext private ( * Create an input stream that receives messages pushed by a zeromq publisher. * @param publisherUrl Url of remote zeromq publisher * @param subscribe topic to subscribe to - * @param bytesToObjects A zeroMQ stream publishes sequence of frames for each topic and each frame has sequence - * of byte thus it needs the converter(which might be deserializer of bytes) - * to translate from sequence of sequence of bytes, where sequence refer to a frame + * @param bytesToObjects A zeroMQ stream publishes sequence of frames for each topic + * and each frame has sequence of byte thus it needs the converter + * (which might be deserializer of bytes) to translate from sequence + * of sequence of bytes, where sequence refer to a frame * and sub sequence refer to its payload. * @param storageLevel RDD storage level. Defaults to memory-only. */ @@ -215,24 +218,39 @@ class StreamingContext private ( } /** - * Create an input stream that pulls messages form a Kafka Broker. + * Create an input stream that pulls messages from a Kafka Broker. * @param zkQuorum Zookeper quorum (hostname:port,hostname:port,..). * @param groupId The group id for this consumer. * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed - * in its own thread. - * @param initialOffsets Optional initial offsets for each of the partitions to consume. - * By default the value is pulled from zookeper. + * in its own thread. * @param storageLevel Storage level to use for storing the received objects * (default: StorageLevel.MEMORY_AND_DISK_SER_2) */ - def kafkaStream[T: ClassManifest]( + def kafkaStream( zkQuorum: String, groupId: String, topics: Map[String, Int], - initialOffsets: Map[KafkaPartitionKey, Long] = Map[KafkaPartitionKey, Long](), storageLevel: StorageLevel = StorageLevel.MEMORY_ONLY_SER_2 + ): DStream[String] = { + val kafkaParams = Map[String, String]( + "zk.connect" -> zkQuorum, "groupid" -> groupId, "zk.connectiontimeout.ms" -> "10000") + kafkaStream[String, kafka.serializer.StringDecoder](kafkaParams, topics, storageLevel) + } + + /** + * Create an input stream that pulls messages from a Kafka Broker. + * @param kafkaParams Map of kafka configuration paramaters. + * See: http://kafka.apache.org/configuration.html + * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed + * in its own thread. + * @param storageLevel Storage level to use for storing the received objects + */ + def kafkaStream[T: ClassManifest, D <: kafka.serializer.Decoder[_]: Manifest]( + kafkaParams: Map[String, String], + topics: Map[String, Int], + storageLevel: StorageLevel ): DStream[T] = { - val inputStream = new KafkaInputDStream[T](this, zkQuorum, groupId, topics, initialOffsets, storageLevel) + val inputStream = new KafkaInputDStream[T, D](this, kafkaParams, topics, storageLevel) registerInputStream(inputStream) inputStream } @@ -397,7 +415,8 @@ class StreamingContext private ( * it will process either one or all of the RDDs returned by the queue. * @param queue Queue of RDDs * @param oneAtATime Whether only one RDD should be consumed from the queue in every interval - * @param defaultRDD Default RDD is returned by the DStream when the queue is empty. Set as null if no RDD should be returned when empty + * @param defaultRDD Default RDD is returned by the DStream when the queue is empty. + * Set as null if no RDD should be returned when empty * @tparam T Type of objects in the RDD */ def queueStream[T: ClassManifest]( diff --git a/streaming/src/main/scala/spark/streaming/api/java/JavaStreamingContext.scala b/streaming/src/main/scala/spark/streaming/api/java/JavaStreamingContext.scala index 3d149a742c..4259d4891c 100644 --- a/streaming/src/main/scala/spark/streaming/api/java/JavaStreamingContext.scala +++ b/streaming/src/main/scala/spark/streaming/api/java/JavaStreamingContext.scala @@ -121,14 +121,15 @@ class JavaStreamingContext(val ssc: StreamingContext) { * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed * in its own thread. */ - def kafkaStream[T]( + def kafkaStream( zkQuorum: String, groupId: String, topics: JMap[String, JInt]) - : JavaDStream[T] = { - implicit val cmt: ClassManifest[T] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]] - ssc.kafkaStream[T](zkQuorum, groupId, Map(topics.mapValues(_.intValue()).toSeq: _*)) + : JavaDStream[String] = { + implicit val cmt: ClassManifest[String] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[String]] + ssc.kafkaStream(zkQuorum, groupId, Map(topics.mapValues(_.intValue()).toSeq: _*), + StorageLevel.MEMORY_ONLY_SER_2) } /** @@ -136,49 +137,45 @@ class JavaStreamingContext(val ssc: StreamingContext) { * @param zkQuorum Zookeper quorum (hostname:port,hostname:port,..). * @param groupId The group id for this consumer. * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed - * in its own thread. - * @param initialOffsets Optional initial offsets for each of the partitions to consume. - * By default the value is pulled from zookeper. + * in its own thread. + * @param storageLevel RDD storage level. Defaults to memory-only + * */ - def kafkaStream[T]( + def kafkaStream( zkQuorum: String, groupId: String, topics: JMap[String, JInt], - initialOffsets: JMap[KafkaPartitionKey, JLong]) - : JavaDStream[T] = { - implicit val cmt: ClassManifest[T] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]] - ssc.kafkaStream[T]( - zkQuorum, - groupId, - Map(topics.mapValues(_.intValue()).toSeq: _*), - Map(initialOffsets.mapValues(_.longValue()).toSeq: _*)) + storageLevel: StorageLevel) + : JavaDStream[String] = { + implicit val cmt: ClassManifest[String] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[String]] + ssc.kafkaStream(zkQuorum, groupId, Map(topics.mapValues(_.intValue()).toSeq: _*), + storageLevel) } /** * Create an input stream that pulls messages form a Kafka Broker. - * @param zkQuorum Zookeper quorum (hostname:port,hostname:port,..). - * @param groupId The group id for this consumer. + * @param typeClass Type of RDD + * @param decoderClass Type of kafka decoder + * @param kafkaParams Map of kafka configuration paramaters. + * See: http://kafka.apache.org/configuration.html * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed * in its own thread. - * @param initialOffsets Optional initial offsets for each of the partitions to consume. - * By default the value is pulled from zookeper. * @param storageLevel RDD storage level. Defaults to memory-only */ - def kafkaStream[T]( - zkQuorum: String, - groupId: String, + def kafkaStream[T, D <: kafka.serializer.Decoder[_]]( + typeClass: Class[T], + decoderClass: Class[D], + kafkaParams: JMap[String, String], topics: JMap[String, JInt], - initialOffsets: JMap[KafkaPartitionKey, JLong], storageLevel: StorageLevel) : JavaDStream[T] = { implicit val cmt: ClassManifest[T] = implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]] - ssc.kafkaStream[T]( - zkQuorum, - groupId, + implicit val cmd: Manifest[D] = implicitly[Manifest[AnyRef]].asInstanceOf[Manifest[D]] + ssc.kafkaStream[T, D]( + kafkaParams.toMap, Map(topics.mapValues(_.intValue()).toSeq: _*), - Map(initialOffsets.mapValues(_.longValue()).toSeq: _*), storageLevel) } diff --git a/streaming/src/main/scala/spark/streaming/dstream/KafkaInputDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/KafkaInputDStream.scala index ddd9becf32..55d2957be4 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/KafkaInputDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/KafkaInputDStream.scala @@ -9,58 +9,51 @@ import java.util.concurrent.Executors import kafka.consumer._ import kafka.message.{Message, MessageSet, MessageAndMetadata} -import kafka.serializer.StringDecoder +import kafka.serializer.Decoder import kafka.utils.{Utils, ZKGroupTopicDirs} import kafka.utils.ZkUtils._ +import kafka.utils.ZKStringSerializer +import org.I0Itec.zkclient._ import scala.collection.Map import scala.collection.mutable.HashMap import scala.collection.JavaConversions._ -// Key for a specific Kafka Partition: (broker, topic, group, part) -case class KafkaPartitionKey(brokerId: Int, topic: String, groupId: String, partId: Int) - /** * Input stream that pulls messages from a Kafka Broker. * - * @param zkQuorum Zookeper quorum (hostname:port,hostname:port,..). - * @param groupId The group id for this consumer. + * @param kafkaParams Map of kafka configuration paramaters. See: http://kafka.apache.org/configuration.html * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed * in its own thread. - * @param initialOffsets Optional initial offsets for each of the partitions to consume. - * By default the value is pulled from zookeper. * @param storageLevel RDD storage level. */ private[streaming] -class KafkaInputDStream[T: ClassManifest]( +class KafkaInputDStream[T: ClassManifest, D <: Decoder[_]: Manifest]( @transient ssc_ : StreamingContext, - zkQuorum: String, - groupId: String, + kafkaParams: Map[String, String], topics: Map[String, Int], - initialOffsets: Map[KafkaPartitionKey, Long], storageLevel: StorageLevel ) extends NetworkInputDStream[T](ssc_ ) with Logging { def getReceiver(): NetworkReceiver[T] = { - new KafkaReceiver(zkQuorum, groupId, topics, initialOffsets, storageLevel) + new KafkaReceiver[T, D](kafkaParams, topics, storageLevel) .asInstanceOf[NetworkReceiver[T]] } } private[streaming] -class KafkaReceiver(zkQuorum: String, groupId: String, - topics: Map[String, Int], initialOffsets: Map[KafkaPartitionKey, Long], - storageLevel: StorageLevel) extends NetworkReceiver[Any] { - - // Timeout for establishing a connection to Zookeper in ms. - val ZK_TIMEOUT = 10000 +class KafkaReceiver[T: ClassManifest, D <: Decoder[_]: Manifest]( + kafkaParams: Map[String, String], + topics: Map[String, Int], + storageLevel: StorageLevel + ) extends NetworkReceiver[Any] { // Handles pushing data into the BlockManager lazy protected val blockGenerator = new BlockGenerator(storageLevel) // Connection to Kafka - var consumerConnector : ZookeeperConsumerConnector = null + var consumerConnector : ConsumerConnector = null def onStop() { blockGenerator.stop() @@ -73,54 +66,59 @@ class KafkaReceiver(zkQuorum: String, groupId: String, // In case we are using multiple Threads to handle Kafka Messages val executorPool = Executors.newFixedThreadPool(topics.values.reduce(_ + _)) - logInfo("Starting Kafka Consumer Stream with group: " + groupId) - logInfo("Initial offsets: " + initialOffsets.toString) + logInfo("Starting Kafka Consumer Stream with group: " + kafkaParams("groupid")) - // Zookeper connection properties + // Kafka connection properties val props = new Properties() - props.put("zk.connect", zkQuorum) - props.put("zk.connectiontimeout.ms", ZK_TIMEOUT.toString) - props.put("groupid", groupId) + kafkaParams.foreach(param => props.put(param._1, param._2)) // Create the connection to the cluster - logInfo("Connecting to Zookeper: " + zkQuorum) + logInfo("Connecting to Zookeper: " + kafkaParams("zk.connect")) val consumerConfig = new ConsumerConfig(props) - consumerConnector = Consumer.create(consumerConfig).asInstanceOf[ZookeeperConsumerConnector] - logInfo("Connected to " + zkQuorum) + consumerConnector = Consumer.create(consumerConfig) + logInfo("Connected to " + kafkaParams("zk.connect")) - // If specified, set the topic offset - setOffsets(initialOffsets) + // When autooffset.reset is defined, it is our responsibility to try and whack the + // consumer group zk node. + if (kafkaParams.contains("autooffset.reset")) { + tryZookeeperConsumerGroupCleanup(kafkaParams("zk.connect"), kafkaParams("groupid")) + } // Create Threads for each Topic/Message Stream we are listening - val topicMessageStreams = consumerConnector.createMessageStreams(topics, new StringDecoder()) + val decoder = manifest[D].erasure.newInstance.asInstanceOf[Decoder[T]] + val topicMessageStreams = consumerConnector.createMessageStreams(topics, decoder) // Start the messages handler for each partition topicMessageStreams.values.foreach { streams => streams.foreach { stream => executorPool.submit(new MessageHandler(stream)) } } - - } - - // Overwrites the offets in Zookeper. - private def setOffsets(offsets: Map[KafkaPartitionKey, Long]) { - offsets.foreach { case(key, offset) => - val topicDirs = new ZKGroupTopicDirs(key.groupId, key.topic) - val partitionName = key.brokerId + "-" + key.partId - updatePersistentPath(consumerConnector.zkClient, - topicDirs.consumerOffsetDir + "/" + partitionName, offset.toString) - } } // Handles Kafka Messages - private class MessageHandler(stream: KafkaStream[String]) extends Runnable { + private class MessageHandler[T: ClassManifest](stream: KafkaStream[T]) extends Runnable { def run() { logInfo("Starting MessageHandler.") - stream.takeWhile { msgAndMetadata => + for (msgAndMetadata <- stream) { blockGenerator += msgAndMetadata.message - // Keep on handling messages - - true } } } + + // It is our responsibility to delete the consumer group when specifying autooffset.reset. This is because + // Kafka 0.7.2 only honors this param when the group is not in zookeeper. + // + // The kafka high level consumer doesn't expose setting offsets currently, this is a trick copied from Kafkas' + // ConsoleConsumer. See code related to 'autooffset.reset' when it is set to 'smallest'/'largest': + // https://github.com/apache/kafka/blob/0.7.2/core/src/main/scala/kafka/consumer/ConsoleConsumer.scala + private def tryZookeeperConsumerGroupCleanup(zkUrl: String, groupId: String) { + try { + val dir = "/consumers/" + groupId + logInfo("Cleaning up temporary zookeeper data under " + dir + ".") + val zk = new ZkClient(zkUrl, 30*1000, 30*1000, ZKStringSerializer) + zk.deleteRecursive(dir) + zk.close() + } catch { + case _ => // swallow + } + } } diff --git a/streaming/src/main/scala/spark/streaming/dstream/NetworkInputDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/NetworkInputDStream.scala index 7385474963..26805e9621 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/NetworkInputDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/NetworkInputDStream.scala @@ -198,7 +198,7 @@ abstract class NetworkReceiver[T: ClassManifest]() extends Serializable with Log case class Block(id: String, iterator: Iterator[T], metadata: Any = null) val clock = new SystemClock() - val blockInterval = 200L + val blockInterval = System.getProperty("spark.streaming.blockInterval", "200").toLong val blockIntervalTimer = new RecurringTimer(clock, blockInterval, updateCurrentBuffer) val blockStorageLevel = storageLevel val blocksForPushing = new ArrayBlockingQueue[Block](1000) diff --git a/streaming/src/test/java/spark/streaming/JavaAPISuite.java b/streaming/src/test/java/spark/streaming/JavaAPISuite.java index 3bed500f73..e5fdbe1b7a 100644 --- a/streaming/src/test/java/spark/streaming/JavaAPISuite.java +++ b/streaming/src/test/java/spark/streaming/JavaAPISuite.java @@ -4,6 +4,7 @@ import com.google.common.base.Optional; import com.google.common.collect.Lists; import com.google.common.collect.Maps; import com.google.common.io.Files; +import kafka.serializer.StringDecoder; import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat; import org.junit.After; import org.junit.Assert; @@ -23,7 +24,6 @@ import spark.streaming.api.java.JavaPairDStream; import spark.streaming.api.java.JavaStreamingContext; import spark.streaming.JavaTestUtils; import spark.streaming.JavaCheckpointTestUtils; -import spark.streaming.dstream.KafkaPartitionKey; import spark.streaming.InputStreamsSuite; import java.io.*; @@ -1203,10 +1203,14 @@ public class JavaAPISuite implements Serializable { @Test public void testKafkaStream() { HashMap<String, Integer> topics = Maps.newHashMap(); - HashMap<KafkaPartitionKey, Long> offsets = Maps.newHashMap(); JavaDStream test1 = ssc.kafkaStream("localhost:12345", "group", topics); - JavaDStream test2 = ssc.kafkaStream("localhost:12345", "group", topics, offsets); - JavaDStream test3 = ssc.kafkaStream("localhost:12345", "group", topics, offsets, + JavaDStream test2 = ssc.kafkaStream("localhost:12345", "group", topics, + StorageLevel.MEMORY_AND_DISK()); + + HashMap<String, String> kafkaParams = Maps.newHashMap(); + kafkaParams.put("zk.connect","localhost:12345"); + kafkaParams.put("groupid","consumer-group"); + JavaDStream test3 = ssc.kafkaStream(String.class, StringDecoder.class, kafkaParams, topics, StorageLevel.MEMORY_AND_DISK()); } diff --git a/streaming/src/test/scala/spark/streaming/BasicOperationsSuite.scala b/streaming/src/test/scala/spark/streaming/BasicOperationsSuite.scala index e7352deb81..565089a853 100644 --- a/streaming/src/test/scala/spark/streaming/BasicOperationsSuite.scala +++ b/streaming/src/test/scala/spark/streaming/BasicOperationsSuite.scala @@ -93,9 +93,9 @@ class BasicOperationsSuite extends TestSuiteBase { test("count") { testOperation( - Seq(1 to 1, 1 to 2, 1 to 3, 1 to 4), + Seq(Seq(), 1 to 1, 1 to 2, 1 to 3, 1 to 4), (s: DStream[Int]) => s.count(), - Seq(Seq(1L), Seq(2L), Seq(3L), Seq(4L)) + Seq(Seq(0L), Seq(1L), Seq(2L), Seq(3L), Seq(4L)) ) } diff --git a/streaming/src/test/scala/spark/streaming/InputStreamsSuite.scala b/streaming/src/test/scala/spark/streaming/InputStreamsSuite.scala index 0acb6db6f2..b024fc9dcc 100644 --- a/streaming/src/test/scala/spark/streaming/InputStreamsSuite.scala +++ b/streaming/src/test/scala/spark/streaming/InputStreamsSuite.scala @@ -243,6 +243,17 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { assert(output(i) === expectedOutput(i)) } } + + test("kafka input stream") { + val ssc = new StreamingContext(master, framework, batchDuration) + val topics = Map("my-topic" -> 1) + val test1 = ssc.kafkaStream("localhost:12345", "group", topics) + val test2 = ssc.kafkaStream("localhost:12345", "group", topics, StorageLevel.MEMORY_AND_DISK) + + // Test specifying decoder + val kafkaParams = Map("zk.connect"->"localhost:12345","groupid"->"consumer-group") + val test3 = ssc.kafkaStream[String, kafka.serializer.StringDecoder](kafkaParams, topics, StorageLevel.MEMORY_AND_DISK) + } } |