参考http://spark.apache.org/docs/latest/streaming-programming-guide.html

1.运行demo测试

首先
$ nc -lk 9999hello world
然后
$ ./bin/run-example streaming.NetworkWordCount localhost 9999..
---------------Time: 1357008430000 ms-----
(hello,1)
(world,1)
...

import org.apache.spark.SparkContext
import org.apache.spark.SparkConf
import org.apache.spark.streaming._
import org.apache.spark.streaming.StreamingContext._ // not necessary since Spark 1.3
import org.apache.log4j.{Level, Logger}

object SparkStreaming  {
        def main(args :Array[String]): Unit ={  
               Logger.getLogger("org").setLevel(Level.ERROR)
                val conf = new SparkConf().setMaster("local[2]").setAppName("NetworkWordCount")
                val ssc = new StreamingContext(conf, Seconds(1))
               
                // Create a DStream that will connect to hostname:port, like localhost:9999
                val lines = ssc.socketTextStream("localhost", 9999)
                val words = lines.flatMap(_.split(" "))
                // Count each word in each batch
                val pairs = words.map(word => (word, 1))
                val wordCounts = pairs.reduceByKey(_ + _)
                
                // Print the first ten elements of each RDD generated in this DStream to the console
                wordCounts.print()
                ssc.start()             // Start the computation
                ssc.awaitTermination()  // Wait for the computation to terminate
       }
}

spark Streaming 和kafka集成,wordcount实例

import kafka.serializer.StringDecoder
import org.apache.spark._
import org.apache.spark.streaming._
import org.apache.log4j.{Level, Logger}
import org.apache.spark.streaming.StreamingContext._
import org.apache.spark.streaming.kafka._
import org.apache.spark.streaming.kafka.KafkaUtils

object KafkaStreaming {
  def main(args: Array[String]) {
    Logger.getLogger("org").setLevel(Level.ERROR)
    val conf = new SparkConf().setAppName("NetworkWordCount").setMaster("local[2]")
    val ssc = new StreamingContext(conf, Seconds(10))
    val kafkaParams = Map("metadata.broker.list" -> "master:9092,slave1:9092,slave2:9092")
    // topics: Set[String]
    val topics = Set("test")
    val kafkaStream = org.apache.spark.streaming.kafka.KafkaUtils.
      createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, topics).map(_._2)
    kafkaStream.print()
    val wordCountDStream = kafkaStream.flatMap(_.split(" ")).map(word => (word+"dd", 1)).reduceByKey(_ + _)
    wordCountDStream.print()
    ssc.start()
    ssc.awaitTermination()
  }
}