Spark修炼之道（进阶篇）——Spark入门到精通：第十三节 Spark Streaming—— Spark SQL、DataFrame与Spark Streaming

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本文介绍了一个使用SparkSQL和SparkStreaming进行实时流处理的例子。通过接收网络输入数据，利用SparkStreaming将数据转换为DataFrame，并执行SQL查询来统计词频。展示了如何设置Spark环境、创建流处理任务以及处理结果。

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Spark SQL、DataFrame与Spark Streaming

1. Spark SQL、DataFrame与Spark Streaming

源码直接参照：https://github.com/apache/spark/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/SqlNetworkWordCount.scala

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD
import org.apache.spark.streaming.{Time, Seconds, StreamingContext}
import org.apache.spark.util.IntParam
import org.apache.spark.sql.SQLContext
import org.apache.spark.storage.StorageLevel

object SqlNetworkWordCount {
  def main(args: Array[String]) {
    if (args.length < 2) {
      System.err.println("Usage: NetworkWordCount <hostname> <port>")
      System.exit(1)
    }

    StreamingExamples.setStreamingLogLevels()

    // Create the context with a 2 second batch size
    val sparkConf = new SparkConf().setAppName("SqlNetworkWordCount").setMaster("local[4]")
    val ssc = new StreamingContext(sparkConf, Seconds(2))

    // Create a socket stream on target ip:port and count the
    // words in input stream of \n delimited text (eg. generated by 'nc')
    // Note that no duplication in storage level only for running locally.
    // Replication necessary in distributed scenario for fault tolerance.
    //Socke作为数据源
    val lines = ssc.socketTextStream(args(0), args(1).toInt, StorageLevel.MEMORY_AND_DISK_SER)
    //words DStream
    val words = lines.flatMap(_.split(" "))

    // Convert RDDs of the words DStream to DataFrame and run SQL query
    //调用foreachRDD方法，遍历DStream中的RDD
    words.foreachRDD((rdd: RDD[String], time: Time) => {
      // Get the singleton instance of SQLContext
      val sqlContext = SQLContextSingleton.getInstance(rdd.sparkContext)
      import sqlContext.implicits._

      // Convert RDD[String] to RDD[case class] to DataFrame
      val wordsDataFrame = rdd.map(w => Record(w)).toDF()

      // Register as table
      wordsDataFrame.registerTempTable("words")

      // Do word count on table using SQL and print it
      val wordCountsDataFrame =
        sqlContext.sql("select word, count(*) as total from words group by word")
      println(s"========= $time =========")
      wordCountsDataFrame.show()
    })

    ssc.start()
    ssc.awaitTermination()
  }
}


/** Case class for converting RDD to DataFrame */
case class Record(word: String)


/** Lazily instantiated singleton instance of SQLContext */
object SQLContextSingleton {

  @transient  private var instance: SQLContext = _

  def getInstance(sparkContext: SparkContext): SQLContext = {
    if (instance == null) {
      instance = new SQLContext(sparkContext)
    }
    instance
  }
}

运行程序后，再运行下列命令

root@sparkmaster:~# nc -lk 9999
Spark is a fast and general cluster computing system for Big Data
Spark is a fast and general cluster computing system for Big Data
Spark is a fast and general cluster computing system for Big Data
Spark is a fast and general cluster computing system for Big Data
Spark is a fast and general cluster computing system for Big Data
Spark is a fast and general cluster computing system for Big Data
Spark is a fast and general cluster computing system for Big Data

处理结果：


========= 1448783840000 ms =========
+---------+-----+
|     word|total|
+---------+-----+
|    Spark|   12|
|   system|   12|
|  general|   12|
|     fast|   12|
|      and|   12|
|computing|   12|
|        a|   12|
|       is|   12|
|      for|   12|
|      Big|   12|
|  cluster|   12|
|     Data|   12|
+---------+-----+

========= 1448783842000 ms =========
+----+-----+
|word|total|
+----+-----+
+----+-----+

========= 1448783844000 ms =========
+----+-----+
|word|total|
+----+-----+
+----+-----+