Github user tdas commented on a diff in the pull request: https://github.com/apache/spark/pull/807#discussion_r13833797 --- 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 --- End diff -- Do we need a ErrorEventBatch? The `getEventBatch` can simply return an `Option[EventBatch]` to signify whether the batch was successfully got or not. The only reason I see that there is a reason for ErrorEventBatch is if you want to pass on extra error info about it, which is not the case here.
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