Spark Structured Streaming状态操作样例程序（Scala）

功能介绍

在Spark结构流应用中，跨批次统计每个session期间发生了多少次event以及本session的开始和结束timestamp；同时输出本批次被更新状态的session。

代码样例

下面代码片段仅为演示，具体代码参见：com.huawei.bigdata.spark.examples.kafkaSessionization。

当Streaming DataFrame/Dataset中有新的可用数据时，outputMode用于配置写入Streaming接收器的数据。。

object kafkaSessionization {
  def main(args: Array[String]): Unit = {
    if (args.length < 7) {
      System.err.println("Usage: kafkaSessionization <bootstrap-servers> " +
        "<subscribe-type> <protocol> <service> <domain> <topics> <checkpointLocation>")
      System.exit(1)
    }

    val Array(bootstrapServers, subscribeType, protocol, service, domain,topics，checkpointLocation) = args

    val spark = SparkSession
      .builder
      .appName("kafkaSessionization")
      .getOrCreate()

    spark.conf.set("spark.sql.streaming.checkpointLocation", checkpointLocation)

    spark.streams.addListener(new StreamingQueryListener {

      @volatile private var startTime: Long = 0L
      @volatile private var endTime: Long = 0L
      @volatile private var numRecs: Long = 0L

      override def onQueryStarted(event: StreamingQueryListener.QueryStartedEvent): Unit = {
        println("Query started: " + event.id)
        startTime = System.currentTimeMillis
      }

      override def onQueryProgress(event: StreamingQueryListener.QueryProgressEvent): Unit = {
        println("Query made progress: " + event.progress)
        numRecs += event.progress.numInputRows
      }

      override def onQueryTerminated(event: StreamingQueryListener.QueryTerminatedEvent): Unit = {
        println("Query terminated: " + event.id)
        endTime = System.currentTimeMillis
      }
    })


    import spark.implicits._

    val df = spark
      .readStream
      .format("kafka")
      .option("kafka.bootstrap.servers", bootstrapServers)
      .option("kafka.security.protocol", protocol)
      .option("kafka.sasl.kerberos.service.name", service)
      .option("kafka.kerberos.domain.name", domain)
      .option(subscribeType, topics)
      .load()
      .selectExpr("CAST(value AS STRING)")
      .as[String]
      .map { x =>
        val splitStr = x.split(",")
        (splitStr(0), Timestamp.valueOf(splitStr(1)))
      }.as[(String, Timestamp)].flatMap { case(line, timestamp) =>
      line.split(" ").map(word => Event(sessionId = word, timestamp))}

    // Sessionize the events. Track number of events, start and end timestamps of session, and
    // and report session updates.
    val sessionUpdates = df
      .groupByKey(event => event.sessionId)
      .mapGroupsWithState[SessionInfo, SessionUpdate](GroupStateTimeout.ProcessingTimeTimeout) {

      case (sessionId: String, events: Iterator[Event], state: GroupState[SessionInfo]) =>

        // If timed out, then remove session and send final update
        if (state.hasTimedOut) {
          val finalUpdate =
            SessionUpdate(sessionId, state.get.durationMs, state.get.numEvents, expired = true)
          state.remove()
          finalUpdate
        } else {
          // Update start and end timestamps in session
          val timestamps = events.map(_.timestamp.getTime).toSeq
          val updatedSession = if (state.exists) {
            val oldSession = state.get
            SessionInfo(
              oldSession.numEvents + timestamps.size,
              oldSession.startTimestampMs,
              math.max(oldSession.endTimestampMs, timestamps.max))
          } else {
            SessionInfo(timestamps.size, timestamps.min, timestamps.max)
          }
          state.update(updatedSession)

          // Set timeout such that the session will be expired if no data received for 10 seconds
          state.setTimeoutDuration("10 seconds")
          SessionUpdate(sessionId, state.get.durationMs, state.get.numEvents, expired = false)
        }
    }
    // Start running the query that prints the session updates to the console
    val query = sessionUpdates
      .writeStream
      .outputMode("update")
      .format("console")
      .start()

    query.awaitTermination()
  }