spark KinesisWordCountASL 源码

spark KinesisWordCountASL 代码

文件路径：/connector/kinesis-asl/src/main/scala/org/apache/spark/examples/streaming/KinesisWordCountASL.scala

/*
 * 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.
 */

// scalastyle:off println
package org.apache.spark.examples.streaming

import java.nio.ByteBuffer

import scala.util.Random

import com.amazonaws.auth.DefaultAWSCredentialsProviderChain
import com.amazonaws.services.kinesis.AmazonKinesisClient
import com.amazonaws.services.kinesis.model.PutRecordRequest
import org.apache.logging.log4j.Level
import org.apache.logging.log4j.core.config.Configurator

import org.apache.spark.SparkConf
import org.apache.spark.internal.Logging
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.{Milliseconds, StreamingContext}
import org.apache.spark.streaming.dstream.DStream.toPairDStreamFunctions
import org.apache.spark.streaming.kinesis.KinesisInitialPositions.Latest
import org.apache.spark.streaming.kinesis.KinesisInputDStream

/**
 * Consumes messages from a Amazon Kinesis streams and does wordcount.
 *
 * This example spins up 1 Kinesis Receiver per shard for the given stream.
 * It then starts pulling from the last checkpointed sequence number of the given stream.
 *
 * Usage: KinesisWordCountASL <app-name> <stream-name> <endpoint-url> <region-name>
 *   <app-name> is the name of the consumer app, used to track the read data in DynamoDB
 *   <stream-name> name of the Kinesis stream (i.e. mySparkStream)
 *   <endpoint-url> endpoint of the Kinesis service
 *     (e.g. https://kinesis.us-east-1.amazonaws.com)
 *
 *
 * Example:
 *      # export AWS keys if necessary
 *      $ export AWS_ACCESS_KEY_ID=<your-access-key>
 *      $ export AWS_SECRET_ACCESS_KEY=<your-secret-key>
 *
 *      # run the example
 *      $ SPARK_HOME/bin/run-example  streaming.KinesisWordCountASL myAppName  mySparkStream \
 *              https://kinesis.us-east-1.amazonaws.com
 *
 * There is a companion helper class called KinesisWordProducerASL which puts dummy data
 * onto the Kinesis stream.
 *
 * This code uses the DefaultAWSCredentialsProviderChain to find credentials
 * in the following order:
 *    Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY
 *    Java System Properties - aws.accessKeyId and aws.secretKey
 *    Credential profiles file - default location (~/.aws/credentials) shared by all AWS SDKs
 *    Instance profile credentials - delivered through the Amazon EC2 metadata service
 * For more information, see
 * http://docs.aws.amazon.com/AWSSdkDocsJava/latest/DeveloperGuide/credentials.html
 *
 * See https://spark.apache.org/docs/latest/streaming-kinesis-integration.html for more details on
 * the Kinesis Spark Streaming integration.
 */
object KinesisWordCountASL extends Logging {
  def main(args: Array[String]): Unit = {
    // Check that all required args were passed in.
    if (args.length != 3) {
      System.err.println(
        """
          |Usage: KinesisWordCountASL <app-name> <stream-name> <endpoint-url>
          |
          |    <app-name> is the name of the consumer app, used to track the read data in DynamoDB
          |    <stream-name> is the name of the Kinesis stream
          |    <endpoint-url> is the endpoint of the Kinesis service
          |                   (e.g. https://kinesis.us-east-1.amazonaws.com)
          |
          |Generate input data for Kinesis stream using the example KinesisWordProducerASL.
          |See https://spark.apache.org/docs/latest/streaming-kinesis-integration.html for more
          |details.
        """.stripMargin)
      System.exit(1)
    }

    StreamingExamples.setStreamingLogLevels()

    // Populate the appropriate variables from the given args
    val Array(appName, streamName, endpointUrl) = args


    // Determine the number of shards from the stream using the low-level Kinesis Client
    // from the AWS Java SDK.
    val credentials = new DefaultAWSCredentialsProviderChain().getCredentials()
    require(credentials != null,
      "No AWS credentials found. Please specify credentials using one of the methods specified " +
        "in http://docs.aws.amazon.com/AWSSdkDocsJava/latest/DeveloperGuide/credentials.html")
    val kinesisClient = new AmazonKinesisClient(credentials)
    kinesisClient.setEndpoint(endpointUrl)
    val numShards = kinesisClient.describeStream(streamName).getStreamDescription().getShards().size


    // In this example, we're going to create 1 Kinesis Receiver/input DStream for each shard.
    // This is not a necessity; if there are less receivers/DStreams than the number of shards,
    // then the shards will be automatically distributed among the receivers and each receiver
    // will receive data from multiple shards.
    val numStreams = numShards

    // Spark Streaming batch interval
    val batchInterval = Milliseconds(2000)

    // Kinesis checkpoint interval is the interval at which the DynamoDB is updated with information
    // on sequence number of records that have been received. Same as batchInterval for this
    // example.
    val kinesisCheckpointInterval = batchInterval

    // Get the region name from the endpoint URL to save Kinesis Client Library metadata in
    // DynamoDB of the same region as the Kinesis stream
    val regionName = KinesisExampleUtils.getRegionNameByEndpoint(endpointUrl)

    // Setup the SparkConfig and StreamingContext
    val sparkConfig = new SparkConf().setAppName("KinesisWordCountASL")
    val ssc = new StreamingContext(sparkConfig, batchInterval)

    // Create the Kinesis DStreams
    val kinesisStreams = (0 until numStreams).map { i =>
      KinesisInputDStream.builder
        .streamingContext(ssc)
        .streamName(streamName)
        .endpointUrl(endpointUrl)
        .regionName(regionName)
        .initialPosition(new Latest())
        .checkpointAppName(appName)
        .checkpointInterval(kinesisCheckpointInterval)
        .storageLevel(StorageLevel.MEMORY_AND_DISK_2)
        .build()
    }

    // Union all the streams
    val unionStreams = ssc.union(kinesisStreams)

    // Convert each line of Array[Byte] to String, and split into words
    val words = unionStreams.flatMap(byteArray => new String(byteArray).split(" "))

    // Map each word to a (word, 1) tuple so we can reduce by key to count the words
    val wordCounts = words.map(word => (word, 1)).reduceByKey(_ + _)

    // Print the first 10 wordCounts
    wordCounts.print()

    // Start the streaming context and await termination
    ssc.start()
    ssc.awaitTermination()
  }
}

/**
 * Usage: KinesisWordProducerASL <stream-name> <endpoint-url> \
 *   <records-per-sec> <words-per-record>
 *
 *   <stream-name> is the name of the Kinesis stream (i.e. mySparkStream)
 *   <endpoint-url> is the endpoint of the Kinesis service
 *     (i.e. https://kinesis.us-east-1.amazonaws.com)
 *   <records-per-sec> is the rate of records per second to put onto the stream
 *   <words-per-record> is the number of words per record
 *
 * Example:
 *    $ SPARK_HOME/bin/run-example streaming.KinesisWordProducerASL mySparkStream \
 *         https://kinesis.us-east-1.amazonaws.com 10 5
 */
object KinesisWordProducerASL {
  def main(args: Array[String]): Unit = {
    if (args.length != 4) {
      System.err.println(
        """
          |Usage: KinesisWordProducerASL <stream-name> <endpoint-url> <records-per-sec>
          |                              <words-per-record>
          |
          |    <stream-name> is the name of the Kinesis stream
          |    <endpoint-url> is the endpoint of the Kinesis service
          |                   (e.g. https://kinesis.us-east-1.amazonaws.com)
          |    <records-per-sec> is the rate of records per second to put onto the stream
          |    <words-per-record> is the number of words per record
          |
        """.stripMargin)

      System.exit(1)
    }

    // Set default log4j logging level to WARN to hide Spark logs
    StreamingExamples.setStreamingLogLevels()

    // Populate the appropriate variables from the given args
    val Array(stream, endpoint, recordsPerSecond, wordsPerRecord) = args

    // Generate the records and return the totals
    val totals = generate(stream, endpoint, recordsPerSecond.toInt,
        wordsPerRecord.toInt)

    // Print the array of (word, total) tuples
    println("Totals for the words sent")
    totals.foreach(println(_))
  }

  def generate(stream: String,
      endpoint: String,
      recordsPerSecond: Int,
      wordsPerRecord: Int): Seq[(String, Int)] = {

    val randomWords = List("spark", "you", "are", "my", "father")
    val totals = scala.collection.mutable.Map[String, Int]()

    // Create the low-level Kinesis Client from the AWS Java SDK.
    val kinesisClient = new AmazonKinesisClient(new DefaultAWSCredentialsProviderChain())
    kinesisClient.setEndpoint(endpoint)

    println(s"Putting records onto stream $stream and endpoint $endpoint at a rate of" +
        s" $recordsPerSecond records per second and $wordsPerRecord words per record")

    // Iterate and put records onto the stream per the given recordPerSec and wordsPerRecord
    for (i <- 1 to 10) {
      // Generate recordsPerSec records to put onto the stream
      val records = (1 to recordsPerSecond.toInt).foreach { recordNum =>
        // Randomly generate wordsPerRecord number of words
        val data = (1 to wordsPerRecord.toInt).map(x => {
          // Get a random index to a word
          val randomWordIdx = Random.nextInt(randomWords.size)
          val randomWord = randomWords(randomWordIdx)

          // Increment total count to compare to server counts later
          totals(randomWord) = totals.getOrElse(randomWord, 0) + 1

          randomWord
        }).mkString(" ")

        // Create a partitionKey based on recordNum
        val partitionKey = s"partitionKey-$recordNum"

        // Create a PutRecordRequest with an Array[Byte] version of the data
        val putRecordRequest = new PutRecordRequest().withStreamName(stream)
            .withPartitionKey(partitionKey)
            .withData(ByteBuffer.wrap(data.getBytes()))

        // Put the record onto the stream and capture the PutRecordResult
        val putRecordResult = kinesisClient.putRecord(putRecordRequest)
      }

      // Sleep for a second
      Thread.sleep(1000)
      println("Sent " + recordsPerSecond + " records")
    }
     // Convert the totals to (index, total) tuple
    totals.toSeq.sortBy(_._1)
  }
}

/**
 *  Utility functions for Spark Streaming examples.
 *  This has been lifted from the examples/ project to remove the circular dependency.
 */
private[streaming] object StreamingExamples extends Logging {
  // Set reasonable logging levels for streaming if the user has not configured log4j.
  def setStreamingLogLevels(): Unit = {
    if (Logging.islog4j2DefaultConfigured()) {
      // We first log something to initialize Spark's default logging, then we override the
      // logging level.
      logInfo("Setting log level to [WARN] for streaming example." +
        " To override add a custom log4j.properties to the classpath.")
      Configurator.setRootLevel(Level.WARN)
    }
  }
}
// scalastyle:on println

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