Scala ProducerConfig类代码示例

//设置package包名称以及导入依赖的类
package com.rockiey.kafka

import java.util.{Date, Properties, Random}

import kafka.producer.{KeyedMessage, Producer, ProducerConfig}
import org.junit.{After, Before, Test}

class TestProducer {

  val brokers = "localhost:9092"
  val topic = "test"

  val rnd = new Random()
  val props = new Properties()
  props.put("metadata.broker.list", brokers)
  props.put("serializer.class", "kafka.serializer.StringEncoder")
  //props.put("partitioner.class", "com.colobu.kafka.SimplePartitioner")
  props.put("producer.type", "async")
  //props.put("request.required.acks", "1")

  var producer: Producer[String, String] = null

  @Before
  def before: Unit = {

    val config = new ProducerConfig(props)
    producer = new Producer[String, String](config)
  }

  @After
  def after: Unit = {
    producer.close()
  }

  def produce(events: Int): Unit = {
    val t = System.currentTimeMillis()
    for (nEvents <- Range(0, events)) {
      val runtime = new Date().getTime()
      val ip = "192.168.2." + rnd.nextInt(255)
      val msg = runtime + "," + nEvents + ",www.example.com," + ip
      val data = new KeyedMessage[String, String](topic, ip, msg)
      producer.send(data)
    }

    System.out.println("sent per second: " + events * 1000 / (System.currentTimeMillis() - t))
  }

  @Test
  def testProducer: Unit = {
    produce(100)
  }

  @Test
  def testConsumer {

  }
} 

//设置package包名称以及导入依赖的类
package com.fortysevendeg.log.utils

import java.util.Properties

import kafka.admin.AdminUtils
import kafka.producer.{KeyedMessage, Producer, ProducerConfig}
import kafka.utils.ZkUtils
import org.I0Itec.zkclient.ZkConnection
import org.apache.kafka.clients.consumer.KafkaConsumer

object KafkaUtilities {

  def createKafkaProducer(): Producer[String, String] = {
    val props = new Properties()
    props.put("metadata.broker.list", "localhost:9092")
    props.put("serializer.class", "kafka.serializer.StringEncoder")
//    props.put("partitioner.class", "com.fortysevendeg.biglog.SimplePartitioner")
//    props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer")
//    props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer")
    props.put("producer.type", "async")
    props.put("request.required.acks", "1")

    val config = new ProducerConfig(props)
    new Producer[String, String](config)
  }

  def createKafkaConsumer(): KafkaConsumer[String, String] = {
    val props = new Properties()
    props.put("bootstrap.servers", "localhost:9092")
    props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer")
    props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer")

    new KafkaConsumer[String, String](props)
  }

  def createTopicIntoKafka(topic: String, numPartitions: Int, replicationFactor: Int): Unit = {
    val zookeeperConnect = "localhost:2181"
    val sessionTimeoutMs = 10 * 1000
    val connectionTimeoutMs = 8 * 1000

    val zkClient = ZkUtils.createZkClient(zookeeperConnect, sessionTimeoutMs, connectionTimeoutMs)
    val zkUtils = new ZkUtils(zkClient, zkConnection = new ZkConnection(zookeeperConnect), isSecure = false)
    AdminUtils.createTopic(zkUtils, topic, numPartitions, replicationFactor, new Properties)
    zkClient.close()
  }

  def d(kafkaProducer: Producer[String, String], topic: String, message: String) = {
    kafkaProducer.send(new KeyedMessage[String, String](topic, message))
  }

} 

//设置package包名称以及导入依赖的类
package com.groupon.dse.kafka.cluster.impl

import com.groupon.dse.configs.KafkaServerConfig
import com.groupon.dse.testutils.{EmbeddedKafka, TestDefaults}
import com.groupon.dse.zookeeper.ZkClientBuilder
import kafka.producer.{Producer, ProducerConfig}
import org.I0Itec.zkclient.ZkClient
import org.scalatest.{BeforeAndAfter, FlatSpec}

class ClusterImplTest extends FlatSpec with BeforeAndAfter {

  val kafkaTopic = TestDefaults.TestTopic
  val zkConnTimeout = 10000
  val zkSessionTimeout = 10000
  var producer: Producer[String, Array[Byte]] = _
  var embeddedKafka: EmbeddedKafka = _
  var cluster: ClusterImpl = _
  var zkConnect: String = _
  var kafkaServerConfigs: KafkaServerConfig = _
  var zkClient: ZkClient = _

  before {
    embeddedKafka = new EmbeddedKafka
    embeddedKafka.startCluster()
    producer = new Producer[String, Array[Byte]](new ProducerConfig(embeddedKafka.kafkaProducerProperties))
    zkConnect = embeddedKafka.zkServer.connectString
    kafkaServerConfigs = TestDefaults.testKafkaServerConfig(zkConnect)
    cluster = new ClusterImpl(kafkaServerConfigs)
    zkClient = ZkClientBuilder(zkConnect, zkConnTimeout, zkSessionTimeout)
  }

  after {
    zkClient.close()
    embeddedKafka.stopCluster()
  }

  "The topic list" must "have size 0 before producing" in {
    assert(cluster.topics(zkClient).size == 0)
  }

  "The topic list" must "have size 1 after producing" in {
    embeddedKafka.sendMessage(4, producer, kafkaTopic)
    assert(cluster.topics(zkClient).size == 1)
  }

  "The number of partitions for a topic" should "be 1 for 1 valid topic" in {
    embeddedKafka.sendMessage(4, producer, kafkaTopic)
    assert(cluster.partitions(List(kafkaTopic), zkClient).size == 1)
  }

  "The number of partitions" should "be 0 for an invalid topic" in {
    embeddedKafka.sendMessage(4, producer, kafkaTopic)
    assert(cluster.partitions(List("invalid_topic"), zkClient).size == 0)
  }

  "The number of partitions" should "be 1 for a valid and invalid topic" in {
    embeddedKafka.sendMessage(4, producer, kafkaTopic)
    assert(cluster.partitions(List(kafkaTopic, "invalid_topic"), zkClient).size == 1)
  }

} 

//设置package包名称以及导入依赖的类
package it.agilelab.bigdata.wasp.core.kafka

import java.util.Properties

import it.agilelab.bigdata.wasp.core.WaspEvent
import it.agilelab.bigdata.wasp.core.WaspEvent.WaspMessageEnvelope
import it.agilelab.bigdata.wasp.core.models.configuration.{TinyKafkaConfig, KafkaConfigModel}
import kafka.producer.{DefaultPartitioner, KeyedMessage, Producer, ProducerConfig}
import kafka.serializer.StringEncoder
import kafka.server.KafkaConfig


  def send(topic: String, key: K, message: V): Unit =
    batchSend(topic, key, Seq(message))

  def batchSend(topic: String, key: K, batch: Seq[V]): Unit = {
    val messages = batch map (msg => new KeyedMessage[K, V](topic, key, msg))
    producer.send(messages.toArray: _*)
  }

  def close(): Unit = producer.close()

}


object WaspKafkaWriter {

  def createConfig(brokers: Set[String], batchSize: Int, producerType: String, serializerFqcn: String, keySerializerFqcn: String, partitionerFqcn: String): ProducerConfig = {
    val props = new Properties()
    props.put("metadata.broker.list", brokers.mkString(","))
    props.put("serializer.class", serializerFqcn)
    props.put("key.serializer.class", keySerializerFqcn)
    props.put("partitioner.class", partitionerFqcn)
    props.put("producer.type", producerType)
    props.put("request.required.acks", "1")
    props.put("batch.num.messages", batchSize.toString)
    new ProducerConfig(props)
  }

  def defaultConfig(config: KafkaConfig): ProducerConfig =
    createConfig(Set(s"${config.hostName}:${config.port}"), 100, "async", classOf[StringEncoder].getName, classOf[StringEncoder].getName, classOf[DefaultPartitioner].getName)
} 

//设置package包名称以及导入依赖的类
package controllers

import java.util.Properties
import java.util.concurrent.atomic.AtomicInteger

import kafka.producer.{KeyedMessage, Producer, ProducerConfig}
import model.grid.Flight
import model.kafka.FlightEvent
import model.web.SubmittedFlight
import play.api.libs.json._
import play.api.mvc._

object KafkaEndpoint extends Controller {

  val counter = new AtomicInteger(0)

  implicit val flightsReader = Json.reads[Flight]
  implicit val submittedFlightsReader = Json.reads[SubmittedFlight]

  def submitFlight = Action(parse.json) { request =>
    parseJson(request) { flight: SubmittedFlight =>
      val rowId = counter.incrementAndGet()
      val event = FlightEvent(rowId, flight)
      send(event.toString(), "flights")
      Created(rowId.toString)
    }
  }

  private def parseJson[R](request: Request[JsValue])(block: R => Result)(implicit reads: Reads[R]): Result = {
    request.body.validate[R](reads).fold(
      valid = block,
      invalid = e => {
        val error = e.mkString
        BadRequest(error)
      }
    )
  }

  // hardcoded to simplify the demo code
  lazy val kafkaConfig = {
    val props = new Properties()
    props.put("metadata.broker.list", "localhost:9092")
    props.put("serializer.class", "kafka.serializer.StringEncoder")
    props
  }
  lazy val producer = new Producer[String, String](new ProducerConfig(kafkaConfig))

  private def send(message: String, topic: String) = producer.send(new KeyedMessage[String, String](topic, message))

} 

//设置package包名称以及导入依赖的类
package com.octo.nad.handson.producer

import java.util.Properties
import java.util.concurrent.Executors

import kafka.producer.{KeyedMessage, ProducerConfig, Producer}


object TicketsProducer extends App with AppConf {
  var sleep = if (args.length == 1) args(0).toInt else 1000
  val millisInAnHour = 60 * 60 * 1000
  // On utilise presque tous les coeurs disponibles pour générer des tickets (opérations CPU-bound)
  val cores = Runtime.getRuntime.availableProcessors

  val props = new Properties
  props.put("metadata.broker.list", brokers)
  props.put("serializer.class", "kafka.serializer.StringEncoder")
  props.put("producer.type", "sync")

  val producer = new Producer[String, String](new ProducerConfig(props))
  produce()
  ThroughoutMeter.start
  ThroughputCursor.start

  def produce() = {
    val pool = Executors.newFixedThreadPool(cores)
    val pr = new ProducerRunnable()
    for (i <- Range(0, cores - 1)) pool.submit(new ProducerRunnable)
  }

  class ProducerRunnable extends Runnable {
    override def run(): Unit = {
      while (true) {
        val ticket = TicketsGenerator.generateTicket
        ThroughoutMeter.counter += 1
        // Sinusoïdale de période T=1heure pour générer du chiffre d'affaire de façon non-linéaire
        Thread.sleep((cores * sleep * (1 + 0.5 * Math.cos(System.currentTimeMillis() * 2 * Math.PI / millisInAnHour))).toInt)
        producer.send(new KeyedMessage(topic, ticket.toJson))
    }}
  }


} 

//设置package包名称以及导入依赖的类
package com.project.producer

import akka.actor.Actor
import akka.actor.Props

import java.util.Properties
import kafka.producer.ProducerConfig
import kafka.producer.Producer
import kafka.producer.KeyedMessage

class KafkaProducer(val broker: String, val topic: String) extends Actor {

    val props = new Properties()
    props.put("metadata.broker.list", broker)
    props.put("serializer.class", "kafka.serializer.StringEncoder")
    props.put("producer.type", "async")

    val config = new ProducerConfig(props)
    val producer = new Producer[String, String](config)
    //producer.send(new KeyedMessage[String, String](topic, broker, "test nachricht test"))

    def receive = {
        case x : String => producer.send(new KeyedMessage[String, String](topic, broker, x))
        case _ => ()
    }
}

object KafkaProducer {
    def props(broker: String, topic: String): Props = Props(new KafkaProducer(broker, topic))
} 

//设置package包名称以及导入依赖的类
package twitter

import kafka.producer.{KeyedMessage, Producer, ProducerConfig}
import org.slf4j.LoggerFactory


object KafkaProducer {
  import Configuration._
  private val log = LoggerFactory.getLogger(getClass)
  def main(args: Array[String]) {
    Setup.ssc.start()
    Setup.ssc.awaitTermination()
  }

  log.info("Creating Kafka producer")

  val stream         = Setup.createStream
  val producedTweets = Setup.ssc.sparkContext.accumulator(0L, "Kafka produced Tweets")

  stream.map { tweet =>
    val location = tweet.getGeoLocation match {
      case null => None
      case gl   => Some(Map("lat" -> gl.getLatitude, "lon" -> gl.getLongitude))
    }

    Tweet(tweet.getText, tweet.getCreatedAt, location, tweet.getLang, tweet.getUser.getName)
  }.foreachRDD(rdd => {
    log.info(s"RDD size: ${rdd.count()}")
    log.info(s"Total tweets produced: ${producedTweets.value}")
    rdd.foreachPartition { partition =>
      val producerConfig = new ProducerConfig(p)
      val producer = new Producer[String, String](producerConfig)

      partition.foreach{ tweet =>
        producedTweets += 1
        producer.send(
          new KeyedMessage[String, String](TOPIC, TweetSerializer.toJson(tweet)))
      }

      producer.close()
    }
  })

  log.info("Starting Twitter Kafka producer stream")
} 

//设置package包名称以及导入依赖的类
package org.cg.spark.databroker.example

import java.util.Properties
import com.twitter.bijection.avro.SpecificAvroCodecs.{toJson, toBinary}
import com.typesafe.config.ConfigFactory
import kafka.javaapi.producer.Producer
import kafka.producer.{KeyedMessage, ProducerConfig}
import twitter4j.{Status, FilterQuery}
import twitter4j.TwitterStream
import org.cg.spark.databroker.example.TwitterStream.OnTweetPosted


object TweetsKafkaProducer {
    private val conf = ConfigFactory.load("tweets-kafka")

  val KafkaTopic = "tweets"

  val kafkaProducer = {
    val props = new Properties()
    props.put("metadata.broker.list", conf.getString("kafka.brokers"))
    props.put("request.required.acks", "1")
    val config = new ProducerConfig(props)
    new Producer[String, Array[Byte]](config)
  }

  val filterUsOnly = new FilterQuery().locations(Array(
    Array(-126.562500,30.448674),
    Array(-61.171875,44.087585)))


  def main (args: Array[String]) {
    val twitterStream = TwitterStream.getStream
    twitterStream.addListener(new OnTweetPosted(s => sendToKafka(toTweet(s))))
    twitterStream.filter(filterUsOnly)
  }

  private def toTweet(s: Status): Tweet = {
    new Tweet(s.getUser.getName, s.getText)
  }

  private def sendToKafka(t:Tweet) {
    println(t.toString())
    val tweetEnc = toBinary[Tweet].apply(t)
    val msg = new KeyedMessage[String, Array[Byte]](KafkaTopic, tweetEnc)
    kafkaProducer.send(msg)
  }


}