Question

https://spark.apache.org/docs/latest/streaming-programming-guide.html#output-operations-on-dstreams的火花流媒体网站提到了以下代码：

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
  }
}

我试图使用org.apache.commons.pool2来实现它，但是运行应用程序失败了，带有预期的java.io.NotSerializableException：

15/05/26 08:06:21 ERROR OneForOneStrategy: org.apache.commons.pool2.impl.GenericObjectPool
java.io.NotSerializableException: org.apache.commons.pool2.impl.GenericObjectPool
        at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1184)
 ...

我想知道实现可序列化的连接池是多么现实。有没有人成功过这个？

谢谢。

Answer 1

为了解决这个“本地资源”问题，我们需要的是一个单例对象 - 即保证在JVM中只实例化一次且仅一次实例化的对象。幸运的是，Scala object提供了开箱即用的功能。

要考虑的第二件事是，这个单例将为在托管它的同一个JVM上运行的所有任务提供服务，因此，必须负责并发和资源管理。

让我们尝试草拟（*）这样的服务：

class ManagedSocket(private val pool: ObjectPool, val socket:Socket) {
   def release() = pool.returnObject(socket)
}

// singleton object 
object SocketPool {
    var hostPortPool:Map[(String, Int),ObjectPool] = Map()
    sys.addShutdownHook{
        hostPortPool.values.foreach{ // terminate each pool } 
    }

    // factory method
    def apply(host:String, port:String): ManagedSocket = {
        val pool = hostPortPool.getOrElse{(host,port), {
            val p = ??? // create new pool for (host, port)
            hostPortPool += (host,port) -> p
            p
        }
        new ManagedSocket(pool, pool.borrowObject)
    }
}

然后用法变为：

val host = ???
val port = ???
stream.foreachRDD { rdd =>
    rdd.foreachPartition { partition => 
        val mSocket = SocketPool(host, port)
        partition.foreach{elem => 
            val os = mSocket.socket.getOutputStream()
            // do stuff with os + elem
        }
        mSocket.release()
    }
}

我假设问题中使用的GenericObjectPool正在处理并发问题。否则，需要通过某种形式的同步来保护对每个pool实例的访问。

（*）代码用于说明如何设计此类对象的想法 - 需要额外的努力才能转换为工作版本。

Answer 2

以下答案错误！ 我在这里留下答案供参考，但答案是错误的，原因如下。 socketPool被声明为lazy val，因此每次第一次访问请求都会对其进行实例化。由于SocketPool案例类不是Serializable，这意味着它将在每个分区中实例化。这使得连接池无用，因为我们希望保持跨分区和RDD的连接。它作为伴侣对象或案例类实现没有区别。底线是：连接池必须是Serializable，而apache commons pool不是。

import java.io.PrintStream
import java.net.Socket

import org.apache.commons.pool2.{PooledObject, BasePooledObjectFactory}
import org.apache.commons.pool2.impl.{DefaultPooledObject, GenericObjectPool}
import org.apache.spark.streaming.dstream.DStream

/**
 * Publish a Spark stream to a socket.
 */
class PooledSocketStreamPublisher[T](host: String, port: Int)
  extends Serializable {

    lazy val socketPool = SocketPool(host, port)

    /**
     * Publish the stream to a socket.
     */
    def publishStream(stream: DStream[T], callback: (T) => String) = {
        stream.foreachRDD { rdd =>

            rdd.foreachPartition { partition =>

                val socket = socketPool.getSocket
                val out = new PrintStream(socket.getOutputStream)

                partition.foreach { event =>
                    val text : String = callback(event)
                    out.println(text)
                    out.flush()
                }

                out.close()
                socketPool.returnSocket(socket)

            }
        }
    }

}

class SocketFactory(host: String, port: Int) extends BasePooledObjectFactory[Socket] {

    def create(): Socket = {
        new Socket(host, port)
    }

    def wrap(socket: Socket): PooledObject[Socket] = {
        new DefaultPooledObject[Socket](socket)
    }

}

case class SocketPool(host: String, port: Int) {

    val socketPool = new GenericObjectPool[Socket](new SocketFactory(host, port))

    def getSocket: Socket = {
        socketPool.borrowObject
    }

    def returnSocket(socket: Socket) = {
        socketPool.returnObject(socket)
    }

}

您可以按如下方式调用：

val socketStreamPublisher = new PooledSocketStreamPublisher[MyEvent](host = "10.10.30.101", port = 29009)
socketStreamPublisher.publishStream(myEventStream, (e: MyEvent) => Json.stringify(Json.toJson(e)))

spark-streaming和连接池实现

2 个答案: