我已经实现了Spark自定义接收器，以从http / REST接收DStream，如下所示：
val mem1Total:ReceiverInputDStream[String] = ssc.receiverStream(new CustomReceiver("httpURL1"))
val dstreamMem1:DStream[String] = mem1Total.window(Durations.seconds(30), Durations.seconds(10))
val mem2Total:ReceiverInputDStream[String] = ssc.receiverStream(new CustomReceiver("httpURL2"))
val dstreamMem2:DStream[String] = mem2Total.window(Durations.seconds(30), Durations.seconds(10))
每个流具有以下架构
val schema = StructType(Seq(
StructField("status", StringType),
StructField("data", StructType(Seq(
StructField("resultType", StringType),
StructField("result", ArrayType(StructType(Array(
StructField("metric", StructType(Seq(StructField("application", StringType),
StructField("component", StringType),
StructField("instance", StringType)))),
StructField("value", ArrayType(StringType))
))))
)
))))
在这里，我可以走多远才能从dstreamMem1中提取所需的功能。
dstreamMem1.foreachRDD { rdd =>
import sparkSession.implicits._
val df = rdd.toDS()
.selectExpr("cast (value as string) as myData")
.select(from_json($"myData", schema).as("myDataEvent"))
.select($"myDataEvent.data.*")
.select(explode($"result").as("flat"))
.select($"flat.metric.*", $"flat.value".getItem(0).as("value1"), $"flat.value".getItem(1).as("value2"))
}
但是我无法弄清楚如何将dstreamMem1与dstreamMem2结合在一起，同时还要处理复杂的结构。我可以对dstreamMem1和dstreamMem2进行联合操作。但这在我的情况下不起作用，因为“值”字段表示每个流上的不同内容。有什么想法吗？
编辑＃1 基于以下资源 How to create a custom streaming data source? https://github.com/apache/spark/pull/21145 https://github.com/hienluu/structured-streaming-sources/tree/master/streaming-sources/src/main/scala/org/structured_streaming_sources/twitter
我已经能够创建以下类来实现以下内容
class SSPStreamMicroBatchReader(options: DataSourceOptions) extends MicroBatchReader with Logging {
private val httpURL = options.get(SSPStreamingSource.HTTP_URL).orElse("") //.toString()
private val numPartitions = options.get(SSPStreamingSource.NUM_PARTITIONS).orElse("5").toInt
private val queueSize = options.get(SSPStreamingSource.QUEUE_SIZE).orElse("512").toInt
private val debugLevel = options.get(SSPStreamingSource.DEBUG_LEVEL).orElse("debug").toLowerCase
private val NO_DATA_OFFSET = SSPOffset(-1)
private var startOffset: SSPOffset = new SSPOffset(-1)
private var endOffset: SSPOffset = new SSPOffset(-1)
private var currentOffset: SSPOffset = new SSPOffset(-1)
private var lastReturnedOffset: SSPOffset = new SSPOffset(-2)
private var lastOffsetCommitted : SSPOffset = new SSPOffset(-1)
private var incomingEventCounter = 0;
private var stopped:Boolean = false
private var acsURLConn:HttpURLConnection = null
private var worker:Thread = null
private val sspList:ListBuffer[StreamingQueryStatus] = new ListBuffer[StreamingQueryStatus]()
private var sspQueue:BlockingQueue[StreamingQueryStatus] = null
initialize()
private def initialize(): Unit = synchronized {
log.warn(s"Inside initialize ....")
sspQueue = new ArrayBlockingQueue(queueSize)
new Thread("Socket Receiver") { log.warn(s"Inside thread ....")
override def run() {
log.warn(s"Inside run ....")
receive() }
}.start()
}
private def receive(): Unit = {
log.warn(s"Inside recieve() ....")
var userInput: String = null
acsURLConn = new AccessACS(httpURL).getACSConnection();
// Until stopped or connection broken continue reading
val reader = new BufferedReader(
new InputStreamReader(acsURLConn.getInputStream(), java.nio.charset.StandardCharsets.UTF_8))
userInput = reader.readLine()
while(!stopped) {
// poll tweets from queue
val tweet:StreamingQueryStatus = sspQueue.poll(100, TimeUnit.MILLISECONDS)
if (tweet != null) {
sspList.append(tweet);
currentOffset = currentOffset + 1
incomingEventCounter = incomingEventCounter + 1;
}
}
reader.close()
}
override def planInputPartitions(): java.util.List[InputPartition[org.apache.spark.sql.catalyst.InternalRow]] = {
synchronized {
log.warn(s"Inside planInputPartitions ....")
//initialize()
val startOrdinal = startOffset.offset.toInt + 1
val endOrdinal = endOffset.offset.toInt + 1
internalLog(s"createDataReaderFactories: sOrd: $startOrdinal, eOrd: $endOrdinal, " +
s"lastOffsetCommitted: $lastOffsetCommitted")
val newBlocks = synchronized {
val sliceStart = startOrdinal - lastOffsetCommitted.offset.toInt - 1
val sliceEnd = endOrdinal - lastOffsetCommitted.offset.toInt - 1
assert(sliceStart <= sliceEnd, s"sliceStart: $sliceStart sliceEnd: $sliceEnd")
sspList.slice(sliceStart, sliceEnd)
}
newBlocks.grouped(numPartitions).map { block =>
new SSPStreamBatchTask(block).asInstanceOf[InputPartition[org.apache.spark.sql.catalyst.InternalRow]]
}.toList.asJava
}
}
override def setOffsetRange(start: Optional[Offset],
end: Optional[Offset]): Unit = {
if (start.isPresent && start.get().asInstanceOf[SSPOffset].offset != currentOffset.offset) {
internalLog(s"setOffsetRange: start: $start, end: $end currentOffset: $currentOffset")
}
this.startOffset = start.orElse(NO_DATA_OFFSET).asInstanceOf[SSPOffset]
this.endOffset = end.orElse(currentOffset).asInstanceOf[SSPOffset]
}
override def getStartOffset(): Offset = {
internalLog("getStartOffset was called")
if (startOffset.offset == -1) {
throw new IllegalStateException("startOffset is -1")
}
startOffset
}
override def getEndOffset(): Offset = {
if (endOffset.offset == -1) {
currentOffset
} else {
if (lastReturnedOffset.offset < endOffset.offset) {
internalLog(s"** getEndOffset => $endOffset)")
lastReturnedOffset = endOffset
}
endOffset
}
}
override def commit(end: Offset): Unit = {
internalLog(s"** commit($end) lastOffsetCommitted: $lastOffsetCommitted")
val newOffset = SSPOffset.convert(end).getOrElse(
sys.error(s"SSPStreamMicroBatchReader.commit() received an offset ($end) that did not " +
s"originate with an instance of this class")
)
val offsetDiff = (newOffset.offset - lastOffsetCommitted.offset).toInt
if (offsetDiff < 0) {
sys.error(s"Offsets committed out of order: $lastOffsetCommitted followed by $end")
}
sspList.trimStart(offsetDiff)
lastOffsetCommitted = newOffset
}
override def stop(): Unit = {
log.warn(s"There is a total of $incomingEventCounter events that came in")
stopped = true
if (acsURLConn != null) {
try {
//acsURLConn.disconnect()
} catch {
case e: IOException =>
}
}
}
override def deserializeOffset(json: String): Offset = {
SSPOffset(json.toLong)
}
override def readSchema(): StructType = {
SSPStreamingSource.SCHEMA
}
private def internalLog(msg:String): Unit = {
debugLevel match {
case "warn" => log.warn(msg)
case "info" => log.info(msg)
case "debug" => log.debug(msg)
case _ =>
}
}
}
object SSPStreamingSource {
val HTTP_URL = "httpURL"
val DEBUG_LEVEL = "debugLevel"
val NUM_PARTITIONS = "numPartitions"
val QUEUE_SIZE = "queueSize"
val SCHEMA = StructType(Seq(
StructField("status", StringType),
StructField("data", StructType(Seq(
StructField("resultType", StringType),
StructField("result", ArrayType(StructType(Array(
StructField("application", StringType),
StructField("component", StringType),
StructField("instance", StringType)))),
StructField("value", ArrayType(StringType))
))))
)
))))
}
class SSPStreamBatchTask(sspList:ListBuffer[StreamingQueryStatus]) extends InputPartition[Row] {
override def createPartitionReader(): InputPartitionReader[Row] = new SSPStreamBatchReader(sspList)
}
class SSPStreamBatchReader(sspList:ListBuffer[StreamingQueryStatus]) extends InputPartitionReader[Row] {
private var currentIdx = -1
override def next(): Boolean = {
// Return true as long as the new index is in the seq.
currentIdx += 1
currentIdx < sspList.size
}
override def get(): Row = {
val tweet = sspList(currentIdx)
Row(tweet.json)
}
override def close(): Unit = {}
}
该类的用法如下
val a = sparkSession.readStream
.format(providerClassName)
.option(SSPStreamingSource.HTTP_URL, httpMemTotal)
.load()
a.printSchema()
a.writeStream
.outputMode(OutputMode.Append())
.option("checkpointLocation", "/home/localCheckpoint1") //local
.start("/home/sparkoutput/aa00a01")
这是错误。我还没有破解：（
18/11/29 13:33:28 INFO StateStoreCoordinatorRef: Registered StateStoreCoordinator endpoint
18/11/29 13:33:28 WARN SSPStreamingSource: Inside createMicroBatchReader() ....
18/11/29 13:33:28 WARN SSPStreamMicroBatchReader: Inside initialize ....
18/11/29 13:33:28 WARN SSPStreamMicroBatchReader: Inside thread ....
18/11/29 13:33:28 WARN SSPStreamMicroBatchReader: There is a total of 0 events that came in
18/11/29 13:33:28 WARN SSPStreamMicroBatchReader: Inside run ....
18/11/29 13:33:28 WARN SSPStreamMicroBatchReader: Inside recieve() ....
root
|-- status: string (nullable = true)
|-- data: struct (nullable = true)
| |-- resultType: string (nullable = true)
| |-- result: array (nullable = true)
| | |-- element: struct (containsNull = true)
| | | |-- metric: struct (nullable = true)
| | | | |-- application: string (nullable = true)
| | | | |-- component: string (nullable = true)
| | | | |-- instance: string (nullable = true)
| | | |-- value: array (nullable = true)
| | | | |-- element: string (containsNull = true)
18/11/29 13:33:30 INFO MicroBatchExecution: Starting [id = f15252df-96d8-45b4-a6db-83fd4c7aed71, runId = 65a6dc28-5eb4-468a-80c3-f547504689d7]. Use file:///home/localCheckpoint1 to store the query checkpoint.
18/11/29 13:33:30 WARN SSPStreamingSource: Inside createMicroBatchReader() ....
18/11/29 13:33:30 WARN SSPStreamMicroBatchReader: Inside initialize ....
18/11/29 13:33:30 ERROR StreamingContext: Error starting the context, marking it as stopped
java.lang.IllegalArgumentException: requirement failed: No output operations registered, so nothing to execute
at scala.Predef$.require(Predef.scala:224)
at org.apache.spark.streaming.DStreamGraph.validate(DStreamGraph.scala:168)
at org.apache.spark.streaming.StreamingContext.validate(StreamingContext.scala:513)
at org.apache.spark.streaming.StreamingContext.liftedTree1$1(StreamingContext.scala:573)
at org.apache.spark.streaming.StreamingContext.start(StreamingContext.scala:572)
at myproject.spark.predictive_monitoring.predictmyproject$.run(predictmyproject.scala:99)
at myproject.spark.predictive_monitoring.predictmyproject$.main(predictmyproject.scala:31)
at myproject.spark.predictive_monitoring.predictmyproject.main(predictmyproject.scala)
Exception in thread "main" java.lang.IllegalArgumentException: requirement failed: No output operations registered, so nothing to execute
at scala.Predef$.require(Predef.scala:224)
at org.apache.spark.streaming.DStreamGraph.validate(DStreamGraph.scala:168)
at org.apache.spark.streaming.StreamingContext.validate(StreamingContext.scala:513)
at org.apache.spark.streaming.StreamingContext.liftedTree1$1(StreamingContext.scala:573)
at org.apache.spark.streaming.StreamingContext.start(StreamingContext.scala:572)
at myproject.spark.predictive_monitoring.predictmyproject$.run(predictmyproject.scala:99)
at myproject.spark.predictive_monitoring.predictmyproject$.main(predictmyproject.scala:31)
at myproject.spark.predictive_monitoring.predictmyproject.main(predictmyproject.scala)
18/11/29 13:33:30 INFO SparkContext: Invoking stop() from shutdown hook
18/11/29 13:33:30 WARN SSPStreamMicroBatchReader: Inside thread ....
18/11/29 13:33:30 WARN SSPStreamMicroBatchReader: Inside run ....
18/11/29 13:33:30 WARN SSPStreamMicroBatchReader: Inside recieve() ....
18/11/29 13:33:30 INFO MicroBatchExecution: Using MicroBatchReader [myproject.spark.predictive_monitoring.SSPStreamMicroBatchReader@74cc1ddc] from DataSourceV2 named 'myproject.spark.predictive_monitoring.SSPStreamingSource' [myproject.spark.predictive_monitoring.SSPStreamingSource@7e503c3]
18/11/29 13:33:30 INFO SparkUI: Stopped Spark web UI at http://172.16.221.232:4040
18/11/29 13:33:30 ERROR MicroBatchExecution: Query [id = f15252df-96d8-45b4-a6db-83fd4c7aed71, runId = 65a6dc28-5eb4-468a-80c3-f547504689d7] terminated with error
java.lang.IllegalStateException: Cannot call methods on a stopped SparkContext.
This stopped SparkContext was created at:
org.apache.spark.SparkContext.<init>(SparkContext.scala:76)
org.apache.spark.streaming.StreamingContext$.createNewSparkContext(StreamingContext.scala:838)
org.apache.spark.streaming.StreamingContext.<init>(StreamingContext.scala:85)
myproject.spark.predictive_monitoring.predictmyproject$.run(predictmyproject.scala:37)
myproject.spark.predictive_monitoring.predictmyproject$.main(predictmyproject.scala:31)
myproject.spark.predictive_monitoring.predictmyproject.main(predictmyproject.scala)
The currently active SparkContext was created at:
org.apache.spark.SparkContext.<init>(SparkContext.scala:76)
org.apache.spark.streaming.StreamingContext$.createNewSparkContext(StreamingContext.scala:838)
org.apache.spark.streaming.StreamingContext.<init>(StreamingContext.scala:85)
myproject.spark.predictive_monitoring.predictmyproject$.run(predictmyproject.scala:37)
myproject.spark.predictive_monitoring.predictmyproject$.main(predictmyproject.scala:31)
myproject.spark.predictive_monitoring.predictmyproject.main(predictmyproject.scala)
at org.apache.spark.SparkContext.assertNotStopped(SparkContext.scala:100)
at org.apache.spark.sql.SparkSession.<init>(SparkSession.scala:91)
at org.apache.spark.sql.SparkSession.cloneSession(SparkSession.scala:256)
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:268)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:189)
18/11/29 13:33:30 WARN SSPStreamMicroBatchReader: There is a total of 0 events that came in
18/11/29 13:33:30 INFO MapOutputTrackerMasterEndpoint: MapOutputTrackerMasterEndpoint stopped!
18/11/29 13:33:30 INFO MemoryStore: MemoryStore cleared
18/11/29 13:33:30 INFO BlockManager: BlockManager stopped
18/11/29 13:33:30 INFO BlockManagerMaster: BlockManagerMaster stopped
18/11/29 13:33:30 INFO OutputCommitCoordinator$OutputCommitCoordinatorEndpoint: OutputCommitCoordinator stopped!
18/11/29 13:33:31 INFO SparkContext: Successfully stopped SparkContext