Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/807#discussion_r13838518
--- Diff:
external/flume-sink/src/main/scala/org/apache/spark/flume/sink/SparkSink.scala
---
@@ -0,0 +1,432 @@
+/*
+ * 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 java.net.InetSocketAddress
+import java.nio.ByteBuffer
+import java.util
+import java.util.concurrent._
+import java.util.concurrent.atomic.AtomicLong
+
+import scala.util.control.Breaks
+
+import com.google.common.util.concurrent.ThreadFactoryBuilder
+import org.apache.avro.ipc.NettyServer
+import org.apache.avro.ipc.specific.SpecificResponder
+import org.apache.commons.lang.RandomStringUtils
+import org.apache.flume.Sink.Status
+import org.apache.flume.conf.{ConfigurationException, Configurable}
+import org.apache.flume.sink.AbstractSink
+import org.apache.flume.{Channel, Transaction, FlumeException, Context}
+import org.slf4j.LoggerFactory
+
+import org.apache.spark.flume.{SparkSinkEvent, EventBatch,
SparkFlumeProtocol}
+
+/**
+ * A sink that uses Avro RPC to run a server that can be polled by Spark's
+ * FlumePollingInputDStream. This sink has the following configuration
parameters:
+ *
+ * hostname - The hostname to bind to. Default: 0.0.0.0
+ * port - The port to bind to. (No default - mandatory)
+ * timeout - Time in seconds after which a transaction is rolled back,
+ * if an ACK is not received from Spark within that time
+ * threads - Number of threads to use to receive requests from Spark
(Default: 10)
+ *
+ */
+// Flume forces transactions to be thread-local. So each transaction
*must* be committed, or
+// rolled back from the thread it was originally created in. So each
getEvents call from Spark
+// creates a TransactionProcessor which runs in a new thread, in which the
transaction is created
+// and events are pulled off the channel. Once the events are sent to
spark,
+// that thread is blocked and the TransactionProcessor is saved in a map,
+// until an ACK or NACK comes back or the transaction times out (after the
specified timeout).
+// When the response comes, the TransactionProcessor is retrieved and then
unblocked,
+// at which point the transaction is committed or rolled back.
+class SparkSink extends AbstractSink with Configurable {
+
+ // Size of the pool to use for holding transaction processors.
+ private var poolSize: Integer = SparkSinkConfig.DEFAULT_THREADS
+
+ // Timeout for each transaction. If spark does not respond in this much
time,
+ // rollback the transaction
+ private var transactionTimeout =
SparkSinkConfig.DEFAULT_TRANSACTION_TIMEOUT
+
+ // Address info to bind on
+ private var hostname: String = SparkSinkConfig.DEFAULT_HOSTNAME
+ private var port: Int = 0
+
+ // Handle to the server
+ private var serverOpt: Option[NettyServer] = None
+
+ // The handler that handles the callback from Avro
+ private var handler: Option[SparkAvroCallbackHandler] = None
+
+ // Latch that blocks off the Flume framework from wasting 1 thread.
+ private val blockingLatch = new CountDownLatch(1)
+
+ override def start() {
+ handler = Option(new SparkAvroCallbackHandler(poolSize, getChannel,
transactionTimeout))
+ val responder = new SpecificResponder(classOf[SparkFlumeProtocol],
handler.get)
+ // 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()
+ })
+ super.start()
+ }
+
+ override def stop() {
+ handler.map(callbackHandler => {
+ callbackHandler.shutdown()
+ })
+ serverOpt.map(server => {
+ server.close()
+ server.join()
+ })
+ blockingLatch.countDown()
+ super.stop()
+ }
+
+ /**
+ * @param ctx
+ */
+ override def configure(ctx: Context) {
+ import SparkSinkConfig._
+ hostname = ctx.getString(CONF_HOSTNAME, DEFAULT_HOSTNAME)
+ port = Option(ctx.getInteger(CONF_PORT)).
+ getOrElse(throw new ConfigurationException("The port to bind to must
be specified"))
+ poolSize = ctx.getInteger(THREADS, DEFAULT_THREADS)
+ transactionTimeout = ctx.getInteger(CONF_TRANSACTION_TIMEOUT,
DEFAULT_TRANSACTION_TIMEOUT)
+ }
+
+ 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. The sink runner will interrupt this
thread when the sink is
+ // being shut down.
+ blockingLatch.await()
+ Status.BACKOFF
+ }
+}
+
+/**
+ * Class that implements the SparkFlumeProtocol, that is used by the Avro
Netty Server to process
+ * requests. Each getEvents, ack and nack call is forwarded to an instance
of this class.
+ * @param threads Number of threads to use to process requests.
+ * @param channel The channel that the sink pulls events from
+ * @param transactionTimeout Timeout in millis after which the transaction
if not acked by Spark
+ * is rolled back.
+ */
+private class SparkAvroCallbackHandler(val threads: Int, val channel:
Channel,
+ val transactionTimeout: Int) extends SparkFlumeProtocol {
+ private val LOG =
LoggerFactory.getLogger(classOf[SparkAvroCallbackHandler])
+ val transactionExecutorOpt = Option(Executors.newFixedThreadPool(threads,
+ new ThreadFactoryBuilder().setDaemon(true)
+ .setNameFormat("Spark Sink Processor Thread - %d").build()))
+ private val processorMap = new ConcurrentHashMap[CharSequence,
TransactionProcessor]()
+ // 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)
+ private val seqCounter = new AtomicLong(0)
+
+ /**
+ * Returns a bunch of events to Spark over Avro RPC.
+ * @param n Maximum number of events to return in a batch
+ * @return [[EventBatch]] instance that has a sequence number and an
array of at most n events
+ */
+ override def getEventBatch(n: Int): EventBatch = {
+ val sequenceNumber = seqBase + seqCounter.incrementAndGet()
+ val processor = new TransactionProcessor(channel, sequenceNumber,
+ n, transactionTimeout, this)
+ transactionExecutorOpt.map(executor => {
+ executor.submit(processor)
+ })
+ // Wait until a batch is available - can be null if some error was
thrown
+ processor.getEventBatch match {
+ case ErrorEventBatch => throw new FlumeException("Something went
wrong. No events" +
+ " retrieved from channel.")
+ case eventBatch: EventBatch =>
+ processorMap.put(sequenceNumber, processor)
+ if (LOG.isDebugEnabled()) {
+ LOG.debug("Sent " + eventBatch.getEvents.size() +
+ " events with sequence number: " +
eventBatch.getSequenceNumber)
+ }
+ eventBatch
+ }
+ }
+
+ /**
+ * Called by Spark to indicate successful commit of a batch
+ * @param sequenceNumber The sequence number of the event batch that was
successful
+ */
+ override def ack(sequenceNumber: CharSequence): Void = {
+ completeTransaction(sequenceNumber, success = true)
+ null
+ }
+
+ /**
+ * Called by Spark to indicate failed commit of a batch
+ * @param sequenceNumber The sequence number of the event batch that
failed
+ * @return
+ */
+ override def nack(sequenceNumber: CharSequence): Void = {
+ completeTransaction(sequenceNumber, success = false)
+ LOG.info("Spark failed to commit transaction. Will reattempt events.")
+ null
+ }
+
+ /**
+ * Helper method to commit or rollback a transaction.
+ * @param sequenceNumber The sequence number of the batch that was
completed
+ * @param success Whether the batch was successful or not.
+ */
+ private def completeTransaction(sequenceNumber: CharSequence, success:
Boolean) {
+ Option(removeAndGetProcessor(sequenceNumber)).map(processor => {
+ processor.batchProcessed(success)
+ })
+ }
+
+ /**
+ * Helper method to remove the TxnProcessor for a Sequence Number. Can
be used to avoid a leak.
+ * @param sequenceNumber
+ * @return The transaction processor for the corresponding batch. Note
that this instance is no
+ * longer tracked and the caller is responsible for that txn
processor.
+ */
+ private[flume] def removeAndGetProcessor(sequenceNumber: CharSequence):
TransactionProcessor = {
+ processorMap.remove(sequenceNumber.toString) // The toString is
required!
+ }
+
+ /**
+ * Shuts down the executor used to process transactions.
+ */
+ def shutdown() {
+ transactionExecutorOpt.map(executor => {
+ executor.shutdownNow()
+ })
+ }
+}
+
+/**
+ * Object representing an empty batch returned by the txn processor due to
some error.
+ */
+case object ErrorEventBatch extends EventBatch
+
+// 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. The thread is blocked until the success
call for that transaction
+ * comes back with an ACK or NACK.
+ * @param channel The channel from which to pull events
+ * @param seqNum The sequence number to use for the transaction. Must be
unique
+ * @param maxBatchSize The maximum number of events to process per batch
+ * @param transactionTimeout Time in seconds after which a transaction
must be rolled back
+ * without waiting for an ACK from Spark
+ * @param parent The parent [[SparkAvroCallbackHandler]] instance, for
reporting timeouts
+ */
+private class TransactionProcessor(val channel: Channel, val seqNum:
String,
+ var maxBatchSize: Int, val transactionTimeout: Int,
+ val parent: SparkAvroCallbackHandler) extends Callable[Void] {
+
+ private val LOG = LoggerFactory.getLogger(classOf[TransactionProcessor])
+
+ // If a real batch is not returned, we always have to return an error
batch.
+ @volatile private var eventBatch: EventBatch = ErrorEventBatch
+
+ // Synchronization primitives
+ val batchGeneratedLatch = new CountDownLatch(1)
+ val batchAckLatch = new CountDownLatch(1)
+
+ // Sanity check to ensure we don't loop like crazy
+ val totalAttemptsToRemoveFromChannel = Int.MaxValue / 2
+
+ // OK to use volatile, since the change would only make this true
(otherwise it will be
+ // changed to false - we never apply a negation operation to this) -
which means the transaction
+ // succeeded.
+ @volatile private var batchSuccess = false
+
+ // The transaction that this processor would handle
+ var txOpt: Option[Transaction] = None
+
+ /**
+ * Get an event batch from the channel. This method will block until a
batch of events is
+ * available from the channel. If no events are available after a large
number of attempts of
+ * polling the channel, this method will return [[ErrorEventBatch]].
+ *
+ * @return An [[EventBatch]] instance with sequence number set to
[[seqNum]], filled with a
+ * maximum of [[maxBatchSize]] events
+ */
+ def getEventBatch: EventBatch = {
+ batchGeneratedLatch.await()
+ eventBatch
+ }
+
+ /**
+ * This method is to be called by the sink when it receives an ACK or
NACK from Spark. This
+ * method is a no-op if it is called after [[transactionTimeout]] has
expired since
+ * [[getEventBatch]] returned a batch of events.
+ * @param success True if an ACK was received and the transaction should
be committed, else false.
+ */
+ def batchProcessed(success: Boolean) {
+ if (LOG.isDebugEnabled) {
+ LOG.debug("Batch processed for sequence number: " + seqNum)
+ }
+ batchSuccess = success
+ batchAckLatch.countDown()
+ }
+
+ /**
+ * Populates events into the event batch. If the batch cannot be
populated,
+ * this method will not set the event batch which will stay
[[ErrorEventBatch]]
+ */
+ private def populateEvents() {
+ try {
+ txOpt = Option(channel.getTransaction)
+ txOpt.map(tx => {
+ tx.begin()
+ val events = new util.ArrayList[SparkSinkEvent](maxBatchSize)
+ val loop = new Breaks
+ var gotEventsInThisTxn = false
+ var loopCounter: Int = 0
+ loop.breakable {
+ while (events.size() < maxBatchSize
+ && loopCounter < totalAttemptsToRemoveFromChannel) {
+ loopCounter += 1
+ Option(channel.take()) match {
+ case Some(event) =>
+ events.add(new
SparkSinkEvent(toCharSequenceMap(event.getHeaders),
+ ByteBuffer.wrap(event.getBody)))
+ gotEventsInThisTxn = true
+ case None =>
+ if (!gotEventsInThisTxn) {
+ TimeUnit.MILLISECONDS.sleep(500)
+ } else {
+ loop.break()
+ }
+ }
+ }
+ }
+ if (!gotEventsInThisTxn) {
+ throw new FlumeException("Tried too many times, didn't get any
events from the channel")
+ }
+ // At this point, the events are available, so fill them into the
event batch
+ eventBatch = new EventBatch(seqNum, events)
+ })
+ } catch {
+ case e: Throwable =>
+ LOG.error("Error while processing transaction.", e)
+ try {
+ txOpt.map(tx => {
+ rollbackAndClose(tx, close = true)
+ })
+ } finally {
+ // Avro might serialize the exception and cause a NACK,
+ // so don't bother with the transaction
+ txOpt = None
+ }
+ } finally {
+ batchGeneratedLatch.countDown()
+ }
+ }
+
+ /**
+ * Waits for upto [[transactionTimeout]] seconds for an ACK. If an ACK
comes in,
+ * this method commits the transaction with the channel. If the ACK does
not come in within
+ * that time or a NACK comes in, this method rolls back the transaction.
+ */
+ private def processAckOrNack() {
+ batchAckLatch.await(transactionTimeout, TimeUnit.SECONDS)
+ txOpt.map(tx => {
+ if (batchSuccess) {
+ try {
+ tx.commit()
+ } catch {
+ case e: Throwable =>
+ rollbackAndClose(tx, close = false) // tx will be closed later
anyway
+ } finally {
+ tx.close()
+ }
+ } else {
+ rollbackAndClose(tx, close = true)
+ // This might have been due to timeout or a NACK. Either way the
following call does not
+ // cause issues. This is required to ensure the
TransactionProcessor instance is not leaked
+ parent.removeAndGetProcessor(seqNum)
+ }
+ })
+ }
+
+ /**
+ * Helper method to rollback and optionally close a transaction
+ * @param tx The transaction to rollback
+ * @param close Whether the transaction should be closed or not after
rolling back
+ */
+ private def rollbackAndClose(tx: Transaction, close: Boolean) {
+ try {
+ tx.rollback()
+ LOG.warn("Spark was unable to successfully process the events.
Transaction is being " +
+ "rolled back.")
+ } catch {
+ case e: Throwable =>
+ LOG.error("Error rolling back transaction. Rollback may have
failed!", e)
--- End diff --
Same here, dont catch throwable, only exception
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