SparkStream：4)foreachRDD详解

foreachRDD通常用来把SparkStream运行得到的结果保存到外部系统比如HDFS、Mysql、Redis等等。了解下面的知识可以帮助我们避免很多误区

误区1：实例化外部连接对象的位置不正确，比如下面代码

dstream.foreachRDD { rdd =>
  val connection = createNewConnection()  // executed at the driver
  rdd.foreach { record =>
    connection.send(record) // executed at the worker
  }
}

其实例化的连接对象在driver中，然后通过序列化的方式发送到各个Worker，但实际上Connection的序列化通常是无法正确序列化的

误区2：为每条记录都创建一个连接对象

dstream.foreachRDD { rdd =>
  rdd.foreach { record =>
    val connection = createNewConnection()
    connection.send(record)
    connection.close()
  }
}

虽然误区1的问题得到了解决，但通常情况下，外部系统如mysql，其连接对象是非常可贵的，如果一条记录就申请一个连接资源，系统性能会非常糟糕

然后，给出了一个比较好的方法，为每一个分区创建一个连接对象，其具体代码如下

dstream.foreachRDD { rdd =>
  rdd.foreachPartition { partitionOfRecords =>
    val connection = createNewConnection()
    partitionOfRecords.foreach(record => connection.send(record))
    connection.close()
  }
}

最后给出一个较优的方案，使用一个连接池来维护连接对象

dstream.foreachRDD { rdd =>
  rdd.foreachPartition { partitionOfRecords =>
    // ConnectionPool is a static, lazily initialized pool of connections
    val connection = ConnectionPool.getConnection()
    partitionOfRecords.foreach(record => connection.send(record))
    ConnectionPool.returnConnection(connection)  // return to the pool for future reuse
  }
}

正如上面代码阐述的，连接对象推荐是使用lazy关键字来修饰，用到的时候才去实例化

下面给出网上一段把SparkStream的结果保存到Mysql中的代码示例

package spark.examples.streaming

import java.sql.{PreparedStatement, Connection, DriverManager}
import java.util.concurrent.atomic.AtomicInteger

import org.apache.spark.SparkConf
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.streaming._
import org.apache.spark.streaming.StreamingContext._

object SparkStreamingForPartition {
  def main(args: Array[String]) {
    val conf = new SparkConf().setAppName("NetCatWordCount")
    conf.setMaster("local[3]")
    val ssc = new StreamingContext(conf, Seconds(5))
    //The DStream is a collection of RDD, which makes the method foreachRDD reasonable
    val dstream = ssc.socketTextStream("192.168.26.140", 9999)
    dstream.foreachRDD(rdd => {
      //embedded function
      def func(records: Iterator[String]) {
        var conn: Connection = null
        var stmt: PreparedStatement = null
        try {
          val url = "jdbc:mysql://192.168.26.140:3306/person";
          val user = "root";
          val password = ""
          conn = DriverManager.getConnection(url, user, password)
          records.flatMap(_.split(" ")).foreach(word => {
            val sql = "insert into TBL_WORDS(word) values (?)";
            stmt = conn.prepareStatement(sql);
            stmt.setString(1, word)
            stmt.executeUpdate();
          })
        } catch {
          case e: Exception => e.printStackTrace()
        } finally {
          if (stmt != null) {
            stmt.close()
          }
          if (conn != null) {
            conn.close()
          }
        }
      }
      val repartitionedRDD = rdd.repartition(3)
      repartitionedRDD.foreachPartition(func)
    })
    ssc.start()
    ssc.awaitTermination()
  }
}

注意的细节：

Dstream和RDD一样是延迟执行，只有遇到action操作才会真正去计算。因此在Dstream的内部RDD必须包含Action操作才能是接受到的数据得到处理。即使代码中包含foreachRDD,但在内部却没有action的RDD，SparkStream只会简单地接受数据数据而不进行处理