Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/807#discussion_r13466028
--- Diff:
external/flume-sink/src/main/scala/org/apache/spark/flume/sink/SparkSink.scala
---
@@ -0,0 +1,392 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.flume.sink
+
+import org.apache.flume.sink.AbstractSink
+import java.util.concurrent.locks.ReentrantLock
+import org.apache.flume.Sink.Status
+import org.apache.spark.flume.{SparkSinkEvent, EventBatch,
SparkFlumeProtocol}
+import scala.util.control.Breaks
+import java.nio.ByteBuffer
+import org.apache.flume.{FlumeException, Context}
+import org.slf4j.LoggerFactory
+import java.util.concurrent.atomic.AtomicLong
+import org.apache.commons.lang.RandomStringUtils
+import java.util.concurrent._
+import java.util
+import org.apache.flume.conf.{ConfigurationException, Configurable}
+import com.google.common.util.concurrent.ThreadFactoryBuilder
+import org.apache.avro.ipc.NettyServer
+import org.apache.avro.ipc.specific.SpecificResponder
+import java.net.InetSocketAddress
+
+class SparkSink() extends AbstractSink with Configurable {
+ private val LOG = LoggerFactory.getLogger(this.getClass)
+ private val lock = new ReentrantLock()
+ private val blockingCondition = lock.newCondition()
+
+ // This sink will not persist sequence numbers and reuses them if it
gets restarted.
+ // So it is possible to commit a transaction which may have been meant
for the sink before the
+ // restart.
+ // Since the new txn may not have the same sequence number we must guard
against accidentally
+ // committing
+ // a new transaction. To reduce the probability of that happening a
random string is prepended
+ // to the sequence number.
+ // Does not change for life of sink
+ private val seqBase = RandomStringUtils.randomAlphanumeric(8)
+ // Incremented for each transaction
+ private val seqNum = new AtomicLong(0)
+
+ private var transactionExecutorOpt: Option[ExecutorService] = None
+
+ private var numProcessors: Integer =
SparkSinkConfig.DEFAULT_PROCESSOR_COUNT
+ private var transactionTimeout =
SparkSinkConfig.DEFAULT_TRANSACTION_TIMEOUT
+
+ private val processorMap = new ConcurrentHashMap[CharSequence,
TransactionProcessor]()
+
+ private var processorFactory: Option[SparkHandlerFactory] = None
+ private var hostname: String = SparkSinkConfig.DEFAULT_HOSTNAME
+ private var port: Int = 0
+ private var maxThreads: Int = SparkSinkConfig.DEFAULT_MAX_THREADS
+ private var serverOpt: Option[NettyServer] = None
+ private var running = false
+
+ override def start() {
+ transactionExecutorOpt =
Option(Executors.newFixedThreadPool(numProcessors,
+ new ThreadFactoryBuilder().setDaemon(true)
+ .setNameFormat("Spark Sink, " + getName + " Processor Thread -
%d").build()))
+
+ processorFactory = Option(new SparkHandlerFactory(numProcessors))
+
+ val responder = new SpecificResponder(classOf[SparkFlumeProtocol], new
AvroCallbackHandler())
+
+ // Using the constructor that takes specific thread-pools requires
bringing in netty
+ // dependencies which are being excluded in the build. In practice,
+ // Netty dependencies are already available on the JVM as Flume would
have pulled them in.
+ serverOpt = Option(new NettyServer(responder, new
InetSocketAddress(hostname, port)))
+
+ serverOpt.map(server => server.start())
+ lock.lock()
+ try {
+ running = true
+ } finally {
+ lock.unlock()
+ }
+ super.start()
+ }
+
+ override def stop() {
+ transactionExecutorOpt.map(executor => executor.shutdownNow())
+ serverOpt.map(server => {
+ server.close()
+ server.join()
+ })
+ lock.lock()
+ try {
+ running = false
+ blockingCondition.signalAll()
+ } finally {
+ lock.unlock()
+ }
+ }
+
+ override def configure(ctx: Context) {
+ import SparkSinkConfig._
+ hostname = ctx.getString(CONF_HOSTNAME, DEFAULT_HOSTNAME)
+ val portOpt = Option(ctx.getInteger(CONF_PORT))
+ if(portOpt.isDefined) {
+ port = portOpt.get
+ } else {
+ throw new ConfigurationException("The Port to bind must be
specified")
+ }
+ numProcessors = ctx.getInteger(PROCESSOR_COUNT,
DEFAULT_PROCESSOR_COUNT)
+ transactionTimeout = ctx.getInteger(CONF_TRANSACTION_TIMEOUT,
DEFAULT_TRANSACTION_TIMEOUT)
+ maxThreads = ctx.getInteger(CONF_MAX_THREADS, DEFAULT_MAX_THREADS)
+ }
+
+ override def process(): Status = {
+ // This method is called in a loop by the Flume framework - block it
until the sink is
+ // stopped to save CPU resources
+ lock.lock()
+ try {
+ while(running) {
+ blockingCondition.await()
+ }
+ } finally {
+ lock.unlock()
+ }
+ Status.BACKOFF
+ }
+
+
+ // Object representing an empty batch returned by the txn processor due
to some error.
+ case object ErrorEventBatch extends EventBatch
+
+ private class AvroCallbackHandler() extends SparkFlumeProtocol {
+
+ override def getEventBatch(n: Int): EventBatch = {
+ val processor = processorFactory.get.checkOut(n)
+ transactionExecutorOpt.map(executor => executor.submit(processor))
+ // Wait until a batch is available - can be null if some error was
thrown
+ val eventBatch = processor.eventQueue.take()
+ eventBatch match {
+ case ErrorEventBatch => throw new FlumeException("Something went
wrong. No events" +
+ " retrieved from channel.")
+ case events => {
+ processorMap.put(events.getSequenceNumber, processor)
+ if (LOG.isDebugEnabled) {
+ LOG.debug("Sent " + events.getEventBatch.size() +
+ " events with sequence number: " + events.getSequenceNumber)
+ }
+ events
+ }
+ }
+ }
+
+ override def ack(sequenceNumber: CharSequence): Void = {
+ completeTransaction(sequenceNumber, success = true)
+ null
+ }
+
+ override def nack(sequenceNumber: CharSequence): Void = {
+ completeTransaction(sequenceNumber, success = false)
+ LOG.info("Spark failed to commit transaction. Will reattempt
events.")
+ null
+ }
+
+ def completeTransaction(sequenceNumber: CharSequence, success:
Boolean) {
+ val processorOpt = Option(processorMap.remove(sequenceNumber))
+ if (processorOpt.isDefined) {
+ val processor = processorOpt.get
+ processor.resultQueueUpdateLock.lock()
+ try {
+ // Is the sequence number the same as the one the processor is
processing? If not,
+ // don't update {
+ if
(processor.eventBatch.getSequenceNumber.equals(sequenceNumber)) {
+ processor.resultQueue.put(success)
+ }
+ } finally {
+ processor.resultQueueUpdateLock.unlock()
+ }
+ }
+ }
+ }
+
+ // Flume forces transactions to be thread-local (horrible, I know!)
+ // So the sink basically spawns a new thread to pull the events out
within a transaction.
+ // The thread fills in the event batch object that is set before the
thread is scheduled.
+ // After filling it in, the thread waits on a condition - which is
released only
+ // when the success message comes back for the specific sequence number
for that event batch.
+ /**
+ * This class represents a transaction on the Flume channel. This class
runs a separate thread
+ * which owns the transaction. It is blocked until the success call for
that transaction comes
+ * back.
+ * @param maxBatchSize
+ */
+ private class TransactionProcessor(var maxBatchSize: Int) extends
Callable[Void] {
+ // Must be set to a new event batch before scheduling this!!
+ val eventBatch = new EventBatch("", new
util.LinkedList[SparkSinkEvent])
+ val eventQueue = new SynchronousQueue[EventBatch]()
+ val resultQueue = new SynchronousQueue[Boolean]()
+ val resultQueueUpdateLock = new ReentrantLock()
+
+ object Zero {
+ val zero = "0" // Oh, I miss static finals
+ }
+
+
+ override def call(): Void = {
+ val tx = getChannel.getTransaction
+ tx.begin()
+ try {
+ eventBatch.setSequenceNumber(seqBase + seqNum.incrementAndGet())
+ val events = eventBatch.getEventBatch
+ events.clear()
+ val loop = new Breaks
+ var gotEventsInThisTxn = false
+ loop.breakable {
+ var i = 0
+ // Using for here causes the maxBatchSize change to be
ineffective as the Range gets
+ // pregenerated
+ while (i < maxBatchSize) {
+ i += 1
+ val eventOpt = Option(getChannel.take())
+ eventOpt.map(event => {
+ events.add(new SparkSinkEvent(toCharSequenceMap(event
+ .getHeaders),
+ ByteBuffer.wrap(event.getBody)))
+ gotEventsInThisTxn = true
+ })
+ if (eventOpt.isEmpty) {
+ if (!gotEventsInThisTxn) {
+ // To avoid sending empty batches, we wait till events are
available backing off
+ // between attempts to get events. Each attempt to get an
event though causes one
+ // iteration to be lost. To ensure that we still send back
maxBatchSize number of
+ // events, we cheat and increase the maxBatchSize by 1 to
account for the lost
+ // iteration. Even throwing an exception is expensive as
Avro will serialize it
+ // and send it over the wire, which is useless. Before
incrementing though,
+ // ensure that we are not anywhere near INT_MAX.
+ if (maxBatchSize >= Int.MaxValue / 2) {
+ // Random sanity check
+ throw new RuntimeException("Safety exception - polled
too many times, no events!")
+ }
+ maxBatchSize += 1
+ Thread.sleep(500)
+ } else {
+ loop.break()
+ }
+ }
+ }
+ }
+ // Make the data available to the sender thread
+ eventQueue.put(eventBatch)
+
+ // Wait till timeout for the ack/nack
+ val maybeResult = Option(resultQueue.poll(transactionTimeout,
TimeUnit.SECONDS))
+ // There is a race condition here.
+ // 1. This times out.
+ // 2. The result is empty, so timeout exception is thrown.
+ // 3. The ack comes in before the finally block is entered
+ // 4. The thread with the ack has a handle to this processor,
+ // and another thread has the same processor checked out
+ // (since the finally block was executed and the processor checked
back in)
+ // 5. The thread with the ack now updates the result queue,
+ // so the processor thinks it is the ack for the current batch.
+ // To avoid this - update the sequence number to "0" (with or
without a result - does not
+ // matter).
+ // In the ack method, check if the seq number is the same as the
processor's -
+ // if they are then update the result queue. Now if the
+ // processor updates the seq number first - the ack/nack never
updates the result. If the
+ // ack/nack updates the
+ // result after the timeout but before the seq number is updated
to "0" it does not
+ // matter - the processor would
+ // still timeout and the result is cleared before reusing the
processor.
+ // Unfortunately, this needs to be done from within a lock
+ // to make sure that the new sequence number is actually visible
to the ack thread
+ // (happens-before)
+ resultQueueUpdateLock.lock()
+ try {
+ eventBatch.setSequenceNumber(Zero.zero)
+ } finally {
+ resultQueueUpdateLock.unlock()
+ }
+ eventBatch.getEventBatch.clear()
+ // If the batch failed on spark side, throw a FlumeException
+ maybeResult.map(success =>
+ if (!success) {
+ throw new
+ FlumeException("Spark could not accept events. The
transaction will be retried.")
+ }
+ )
+ // If the operation timed out, throw a TimeoutException
+ if (maybeResult.isEmpty) {
+ throw new TimeoutException("Spark did not respond within the
timeout period of " +
+ transactionTimeout + "seconds. Transaction will be retried")
+ }
+ null
+ } catch {
+ case e: Throwable =>
+ try {
+ LOG.warn("Error while attempting to remove events from the
channel.", e)
+ tx.rollback()
+ } catch {
+ case e1: Throwable => LOG.error(
+ "Rollback failed while attempting to rollback due to commit
failure.", e1)
+ }
+ null // No point rethrowing the exception
+ } finally {
+ // Must *always* release the caller thread
+ eventQueue.put(ErrorEventBatch)
+ // In the case of success coming after the timeout, but before
resetting the seq number
+ // remove the event from the map and then clear the value
+ resultQueue.clear()
+ processorMap.remove(eventBatch.getSequenceNumber)
+ processorFactory.get.checkIn(this)
+ tx.close()
+ }
+ }
+
+ def toCharSequenceMap(inMap: java.util.Map[String, String]):
java.util.Map[CharSequence,
+ CharSequence] = {
+ val charSeqMap = new util.HashMap[CharSequence,
CharSequence](inMap.size())
+ charSeqMap.putAll(inMap)
+ charSeqMap
+ }
+ }
+
+ private class SparkHandlerFactory(val maxInstances: Int) {
+ val queue = new scala.collection.mutable.Queue[TransactionProcessor]
+ val queueModificationLock = new ReentrantLock()
+ var currentSize = 0
+ val waitForCheckIn = queueModificationLock.newCondition()
+
+ def checkOut(n: Int): TransactionProcessor = {
+ def getProcessor = {
+ val processor = queue.dequeue()
+ processor.maxBatchSize = n
+ processor
+ }
+ queueModificationLock.lock()
+ try {
+ if (queue.size > 0) {
+ getProcessor
+ }
+ else {
+ if (currentSize < maxInstances) {
+ currentSize += 1
+ new TransactionProcessor(n)
+ } else {
+ // No events in queue and cannot initialize more!
+ // Since currentSize never reduces, queue size increasing is
the only hope
+ while (queue.size == 0 && currentSize >= maxInstances) {
+ waitForCheckIn.await()
+ }
+ getProcessor
+ }
+ }
+ } finally {
+ queueModificationLock.unlock()
+ }
+ }
+
+ def checkIn(processor: TransactionProcessor) {
+ queueModificationLock.lock()
--- End diff --
If the goal of the queue is to buffer items (i.e. `checkin` that can be
picked up by a different thread (the one that calls 'checkout`), then its
simpler to use something like a
[ArrayBlockingQueue](http://docs.oracle.com/javase/7/docs/api/java/util/concurrent/ArrayBlockingQueue.html)
or
[LinkedBlockingQueue](http://docs.oracle.com/javase/7/docs/api/java/util/concurrent/LinkedBlockingQueue.html).
No need for reentrant locks.
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