Spark Streaming整合Flume + Kafka wordCount

flume配置文件 flume_to_kafka.conf

a1.sources = r1 
a1.sinks = k1 
a1.channels = c1 

a1.sources.r1.type = spooldir 
a1.sources.r1.channels = c1 
a1.sources.r1.spoolDir = /home/hadoop/logs/ 
a1.sources.r1.fileHeader = true 

a1.channels.c1.type = memory 
a1.channels.c1.capacity = 10000 
a1.channels.c1.transactionCapacity = 10000 
a1.channels.c1.byteCapacityBufferPercentage = 20 
a1.channels.c1.byteCapacity = 800000 

a1.sinks.k1.type = org.apache.flume.sink.kafka.KafkaSink 
a1.sinks.k1.topic = spark 
a1.sinks.k1.brokerList = m1:9092,m2:9092,m3:9092 
a1.sinks.k1.requiredAcks = 1 
a1.sinks.k1.batchSize = 20 
a1.sinks.k1.channel = c1

# Bind the source and sink to the channel 
a1.sources.r1.channels = c1 
a1.sinks.k1.channel = c1 

kafka

1、启动kafka

./bin/kafka-server-start.sh ./config/server.properties

2、创建spark topic

bin/kafka-topics.sh --create --zookeeper m1:2181 --replication-factor 2 --partitions 2 --topic spark

 

启动flume

flume-ng agent -c conf/ -f conf/flume_to_kafka.conf -n a1

测试是否可以正常消费到数据

bin/kafka-console-consumer.sh --bootstrap-server m1:9092,m2:9092,m3:9092 --from-beginning --topic spark

代码实现

object SparkStreamDemo {
  def main(args: Array[String]) {

    val conf = new SparkConf()
    conf.setAppName("spark_streaming")
    conf.setMaster("local[*]")

    val sc = new SparkContext(conf)
    sc.setCheckpointDir("D:/checkpoints")
    sc.setLogLevel("ERROR")

    val ssc = new StreamingContext(sc, Seconds(5))

    val topics = Map("spark" -> 2)
    val lines = KafkaUtils.createStream(ssc, "m1:2181,m2:2181,m3:2181", "spark", topics).map(_._2)

    val ds1 = lines.flatMap(_.split(" ")).map((_, 1))

    val ds2 = ds1.updateStateByKey[Int]((x:Seq[Int], y:Option[Int]) => {
      Some(x.sum + y.getOrElse(0))
    })

    ds2.print()

    ssc.start()
    ssc.awaitTermination()

  }
}