Scala Apache Spark 中 DStream 的输出内容

Question

下面的 Spark 代码似乎没有对文件 example.txt 执行任何操作

val conf = new org.apache.spark.SparkConf()
  .setMaster("local")
  .setAppName("filter")
  .setSparkHome("C:\\spark\\spark-1.2.1-bin-hadoop2.4")
  .set("spark.executor.memory", "2g");

val ssc = new StreamingContext(conf, Seconds(1))
val dataFile: DStream[String] = ssc.textFileStream("C:\\example.txt")

dataFile.print()
ssc.start() // Start the computation
ssc.awaitTermination() // Wait for the computation to terminate

我正在尝试使用 dataFile.print() 打印文件的前 10 个元素

一些生成的输出：

15/03/12 12:23:53 INFO JobScheduler: Started JobScheduler
15/03/12 12:23:54 INFO FileInputDStream: Finding new files took 105 ms
15/03/12 12:23:54 INFO FileInputDStream: New files at time 1426163034000 ms:

15/03/12 12:23:54 INFO JobScheduler: Added jobs for time 1426163034000 ms
15/03/12 12:23:54 INFO JobScheduler: Starting job streaming job 1426163034000 ms.0 from job set of time 1426163034000 ms
-------------------------------------------
Time: 1426163034000 ms
-------------------------------------------

15/03/12 12:23:54 INFO JobScheduler: Finished job streaming job 1426163034000 ms.0 from job set of time 1426163034000 ms
15/03/12 12:23:54 INFO JobScheduler: Total delay: 0.157 s for time 1426163034000 ms (execution: 0.006 s)
15/03/12 12:23:54 INFO FileInputDStream: Cleared 0 old files that were older than 1426162974000 ms: 
15/03/12 12:23:54 INFO ReceivedBlockTracker: Deleting batches ArrayBuffer()
15/03/12 12:23:55 INFO FileInputDStream: Finding new files took 2 ms
15/03/12 12:23:55 INFO FileInputDStream: New files at time 1426163035000 ms:

15/03/12 12:23:55 INFO JobScheduler: Added jobs for time 1426163035000 ms
15/03/12 12:23:55 INFO JobScheduler: Starting job streaming job 1426163035000 ms.0 from job set of time 1426163035000 ms
-------------------------------------------
Time: 1426163035000 ms
-------------------------------------------

15/03/12 12:23:55 INFO JobScheduler: Finished job streaming job 1426163035000 ms.0 from job set of time 1426163035000 ms
15/03/12 12:23:55 INFO JobScheduler: Total delay: 0.011 s for time 1426163035000 ms (execution: 0.001 s)
15/03/12 12:23:55 INFO MappedRDD: Removing RDD 1 from persistence list
15/03/12 12:23:55 INFO BlockManager: Removing RDD 1
15/03/12 12:23:55 INFO FileInputDStream: Cleared 0 old files that were older than 1426162975000 ms: 
15/03/12 12:23:55 INFO ReceivedBlockTracker: Deleting batches ArrayBuffer()
15/03/12 12:23:56 INFO FileInputDStream: Finding new files took 3 ms
15/03/12 12:23:56 INFO FileInputDStream: New files at time 1426163036000 ms:

example.txt 的格式为：

gdaeicjdcg,194,155,98,107
jhbcfbdigg,73,20,122,172
ahdjfgccgd,28,47,40,178
afeidjjcef,105,164,37,53
afeiccfdeg,29,197,128,85
aegddbbcii,58,126,89,28
fjfdbfaeid,80,89,180,82

正如print 文档所述：

/** * 打印此 DStream 中生成的每个 RDD 的前十个元素。这是一个输出 * 操作符，所以这个 DStream 将被注册为输出流并在那里实现。 */

这是否意味着已经为这个流生成了 0 个 RDD？如果想查看 RDD 的内容，使用 Apache Spark 将使用 RDD 的收集功能。这些是 Streams 的类似方法吗？那么简而言之，如何打印到 Stream 的控制台内容？

更新：

根据@0x0FFF 注释更新了代码。 http://spark.apache.org/docs/1.2.0/streaming-programming-guide.html 似乎没有给出从本地文件系统读取的示例。这不像使用 Spark 核心那样常见吗，那里有从文件中读取数据的明确示例？

这里是更新的代码：

val conf = new org.apache.spark.SparkConf()
  .setMaster("local[2]")
  .setAppName("filter")
  .setSparkHome("C:\\spark\\spark-1.2.1-bin-hadoop2.4")
  .set("spark.executor.memory", "2g");

val ssc = new StreamingContext(conf, Seconds(1))
val dataFile: DStream[String] = ssc.textFileStream("file:///c:/data/")

dataFile.print()
ssc.start() // Start the computation
ssc.awaitTermination() // Wait for the computation to terminate

但是输出是一样的。当我将新文件添加到c:\\data dir（与现有数据文件具有相同格式）时，它们不会被处理。我假设dataFile.print 应该将前 10 行打印到控制台？

更新 2：

也许这与我在 Windows 环境中运行此代码有关？

Answer 1

您误解了textFileStream 的用法。以下是 Spark 文档中的描述：

创建一个输入流，用于监控与 Hadoop 兼容的文件系统中的新文件并将它们作为文本文件读取（使用 LongWritable 的键、Text 的值和 TextInputFormat 的输入格式）。

首先，您应该将目录传递给它，其次，该目录应该可以从运行接收器的节点获得，因此最好使用 HDFS 来实现此目的。然后，当您将 new 文件放入此目录时，它将由函数 print() 处理并为其打印前 10 行

更新：

我的代码：

[alex@sparkdemo tmp]$ pyspark --master local[2]
Python 2.6.6 (r266:84292, Nov 22 2013, 12:16:22) 
[GCC 4.4.7 20120313 (Red Hat 4.4.7-4)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
Spark assembly has been built with Hive, including Datanucleus jars on classpath
s15/03/12 06:37:49 WARN Utils: Your hostname, sparkdemo resolves to a loopback address: 127.0.0.1; using 192.168.208.133 instead (on interface eth0)
15/03/12 06:37:49 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address

Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /__ / .__/\_,_/_/ /_/\_\   version 1.2.0
      /_/

Using Python version 2.6.6 (r266:84292, Nov 22 2013 12:16:22)
SparkContext available as sc.
>>> from pyspark.streaming import StreamingContext
>>> ssc = StreamingContext(sc, 30)
>>> dataFile = ssc.textFileStream('file:///tmp')
>>> dataFile.pprint()
>>> ssc.start()
>>> ssc.awaitTermination()
-------------------------------------------
Time: 2015-03-12 06:40:30
-------------------------------------------

-------------------------------------------
Time: 2015-03-12 06:41:00
-------------------------------------------

-------------------------------------------
Time: 2015-03-12 06:41:30
-------------------------------------------
1 2 3
4 5 6
7 8 9

-------------------------------------------
Time: 2015-03-12 06:42:00
-------------------------------------------

Answer 2

这是我编写的一个自定义接收器，它在指定目录监听数据：

package receivers

import java.io.File
import org.apache.spark.{ SparkConf, Logging }
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.{ Seconds, StreamingContext }
import org.apache.spark.streaming.receiver.Receiver

class CustomReceiver(dir: String)
  extends Receiver[String](StorageLevel.MEMORY_AND_DISK_2) with Logging {

  def onStart() {
    // Start the thread that receives data over a connection
    new Thread("File Receiver") {
      override def run() { receive() }
    }.start()
  }

  def onStop() {
    // There is nothing much to do as the thread calling receive()
    // is designed to stop by itself isStopped() returns false
  }

  def recursiveListFiles(f: File): Array[File] = {
    val these = f.listFiles
    these ++ these.filter(_.isDirectory).flatMap(recursiveListFiles)
  }

  private def receive() {

    for (f <- recursiveListFiles(new File(dir))) {

      val source = scala.io.Source.fromFile(f)
      val lines = source.getLines
      store(lines)
      source.close()
      logInfo("Stopped receiving")
      restart("Trying to connect again")

    }
  }
}

我认为需要注意的一件事是文件需要在 batchDuration 的时间内处理。在下面的示例中，它设置为 10 秒，但如果接收方处理文件的时间超过 10 秒，则某些数据文件将不会被处理。我愿意在这一点上进行更正。

这是自定义接收器的实现方式：

val conf = new org.apache.spark.SparkConf()
  .setMaster("local[2]")
  .setAppName("filter")
  .setSparkHome("C:\\spark\\spark-1.2.1-bin-hadoop2.4")
  .set("spark.executor.memory", "2g");

val ssc = new StreamingContext(conf, Seconds(10))

val customReceiverStream: ReceiverInputDStream[String] = ssc.receiverStream(new CustomReceiver("C:\\data\\"))

customReceiverStream.print

customReceiverStream.foreachRDD(m => {
  println("size is " + m.collect.size)
})

ssc.start() // Start the computation
ssc.awaitTermination() // Wait for the computation to terminate

更多信息： http://spark.apache.org/docs/1.2.0/streaming-programming-guide.html & https://spark.apache.org/docs/1.2.0/streaming-custom-receivers.html

Answer 3

我可能发现了您的问题，您的日志中应该有这个：

WARN StreamingContext: spark.master should be set as local[n], n > 1 in local mode if you have receivers to get data, otherwise Spark jobs will not get resources to process the received data.

问题是您需要至少有 2 个内核才能运行 Spark 流应用程序。 所以解决方案应该是简单地替换：

val conf = new org.apache.spark.SparkConf()
 .setMaster("local")

作者：

val conf = new org.apache.spark.SparkConf()
  .setMaster("local[*]")

或者至少不止一个。