Question

我正在尝试使用火花流式传输库org.apache.spark.streaming.kinesis.KinesisUtils来使用Kinesis Stream。我可以使用python脚本验证Stream中是否有数据。但是，在尝试用scala编写消费者时，我一直在获取空数据。这是我的代码：

def getKinesisData = {

    val endpointUrl = "https://kinesis.us-west-2.amazonaws.com"
    val streamName = "myAwesomeStream"
    val credentials = new DefaultAWSCredentialsProviderChain().getCredentials()
    require(credentials != null, "No AWS credentials found.")

    val kinesisClient = new AmazonKinesisClient(credentials)
    kinesisClient.setEndpoint(endpointUrl)

    val numShards = kinesisClient.describeStream(streamName).getStreamDescription().getShards().size    
    val numStreams = numShards
    val batchInterval = Milliseconds(2000)
    val kinesisCheckpointInterval = batchInterval

    val sparkConfig = new SparkConf().setAppName("myAwesomeApp").setMaster("local")
    val ssc = new StreamingContext(sparkConfig, batchInterval)

    val kinesisStreams = (0 until numStreams).map { i =>
      println(i)
      KinesisUtils.createStream(ssc, "myAwesomeApp", streamName, endpointUrl, regionName,
        InitialPositionInStream.LATEST, kinesisCheckpointInterval, StorageLevel.MEMORY_AND_DISK_2
      )
    }

    val unionStreams = ssc.union(kinesisStreams)

    // Convert each line of Array[Byte] to String, and split into words
    val words = unionStreams.flatMap(byteArray => new String(byteArray).split(" "))

    val wordCounts = words.map(word => (word, 1)).reduceByKey(_ + _)
    wordCounts.print()
  }

我从github那里得到了这个代码作为一个例子，我并不真正关心所有的联合，以及在代码的后半部分已经完成的平面图和文字计数。我只需要知道如何从流中获取实际数据。

更新它在我运行时在控制台上打印以下内容

16/12/16 14:57:01 INFO SparkContext: Running Spark version 2.0.0
16/12/16 14:57:02 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
16/12/16 14:57:02 INFO SecurityManager: Changing view acls to: 
16/12/16 14:57:02 INFO SecurityManager: Changing modify acls to: 
16/12/16 14:57:02 INFO SecurityManager: Changing view acls groups to: 
16/12/16 14:57:02 INFO SecurityManager: Changing modify acls groups to: 
16/12/16 14:57:02 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users  with view permissions: Set(username); groups with view permissions: Set(); users  with modify permissions: Set(username); groups with modify permissions: Set()
16/12/16 14:57:02 INFO Utils: Successfully started service 'sparkDriver' on port 54774.
16/12/16 14:57:02 INFO SparkEnv: Registering MapOutputTracker
16/12/16 14:57:02 INFO SparkEnv: Registering BlockManagerMaster
16/12/16 14:57:02 INFO DiskBlockManager: Created local directory at 
16/12/16 14:57:02 INFO MemoryStore: MemoryStore started with capacity 2004.6 MB
16/12/16 14:57:02 INFO SparkEnv: Registering OutputCommitCoordinator
16/12/16 14:57:02 INFO Utils: Successfully started service 'SparkUI' on port 4040.
16/12/16 14:57:02 INFO SparkUI: Bound SparkUI to 0.0.0.0, and started at http://<I masked this IP address and port>
16/12/16 14:57:03 INFO Executor: Starting executor ID driver on host localhost
16/12/16 14:57:03 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 54775.
16/12/16 14:57:03 INFO NettyBlockTransferService: Server created on <I masked this IP address and port>
16/12/16 14:57:03 INFO BlockManagerMaster: Registering BlockManager BlockManagerId(driver, <I masked this IP address and port>)
16/12/16 14:57:03 INFO BlockManagerMasterEndpoint: Registering block manager <I masked this IP address and port> with 2004.6 MB RAM, BlockManagerId(driver, <I masked this IP address and port>)
16/12/16 14:57:03 INFO BlockManagerMaster: Registered BlockManager BlockManagerId(driver, <I masked this IP address and port>)
16/12/16 14:57:03 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.

0 <-- printing shard 
1 <-- printing shard
#### PRINTING kinesisStreams ###### 
Vector(org.apache.spark.streaming.kinesis.KinesisInputDStream@2650f79,    org.apache.spark.streaming.kinesis.KinesisInputDStream@75fc1992)
#### PRINTING unionStreams ######
()
#### words######
org.apache.spark.streaming.dstream.FlatMappedDStream@6fd12c5
#### PRINTING wordCounts######
org.apache.spark.streaming.dstream.ShuffledDStream@790a251b

16/12/16 14:57:03 INFO SparkContext: Invoking stop() from shutdown hook
16/12/16 14:57:03 INFO SparkUI: Stopped Spark web UI at http://<I masked this IP address and port>
16/12/16 14:57:03 INFO MapOutputTrackerMasterEndpoint: MapOutputTrackerMasterEndpoint stopped!
16/12/16 14:57:03 INFO MemoryStore: MemoryStore cleared
16/12/16 14:57:03 INFO BlockManager: BlockManager stopped
16/12/16 14:57:03 INFO BlockManagerMaster: BlockManagerMaster stopped
16/12/16 14:57:03 INFO OutputCommitCoordinator$OutputCommitCoordinatorEndpoint: OutputCommitCoordinator stopped!
16/12/16 14:57:03 INFO SparkContext: Successfully stopped SparkContext
16/12/16 14:57:03 INFO ShutdownHookManager: Shutdown hook called
16/12/16 14:57:03 INFO ShutdownHookManager: Deleting directory

Answer 1

问题出在1.5版本的Spark库中，与Kinesis不兼容。

Answer 2

希望这可以帮助遇到此问题的人。如果您遇到此问题，那不是真正的错误。

Kinesis Kafka Integration使用Receiver API，并且在来自驱动程序或执行程序的不同线程中运行。在最初的滞后阶段，您认为一切都已开始，但Kinesis Receiver仍在运行一些过程，然后才实际从Kinesis下载数据。

解决方案：要等待，在我的情况下，数据会在40-50秒后显示在Spark端

Spark Streaming Kinesis消费者返回空数据

2 个答案: