Flink1.7.2 Source、Window数据交互源码分析
Flink1.7.2 Source、Window数据交互源码分析
源码
- https://github.com/opensourceteams/fink-maven-scala-2
概述
- Source如何按partition发射数据到对应的Window
- Window 如何处理对应的Source发过来的对应的partition数据
- 理解Flink 数据从Source到Window,上下游数据交换的过程
StreamGraph 图
- https://github.com/opensourceteams/fink-maven-scala-2/blob/master/md/images/wordCount/flink-streamGraph-3.png
输入数据
1 2 3 4 5 6 7 8 9 10WordCount程序
package com.opensourceteams.module.bigdata.flink.example.stream.worldcount.nc.parallelism
import org.apache.flink.configuration.Configuration
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.streaming.api.windowing.time.Time
/**
* nc -lk 1234 输入数据
*/
object SocketWindowWordCountLocal {
def main(args: Array[String]): Unit = {
val port = 1234
// get the execution environment
// val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
val configuration : Configuration = getConfiguration(true)
val env:StreamExecutionEnvironment = StreamExecutionEnvironment.createLocalEnvironment(1,configuration)
// get input data by connecting to the socket
val dataStream = env.socketTextStream("localhost", port, '\n')
import org.apache.flink.streaming.api.scala._
val textResult = dataStream.flatMap( w => w.split("\\s") ).map( w => WordWithCount(w,1))
.keyBy("word")
/**
* 每20秒刷新一次，相当于重新开始计数，
* 好处，不需要一直拿所有的数据统计
* 只需要在指定时间间隔内的增量数据，减少了数据规模
*/
.timeWindow(Time.seconds(5))
//.countWindow(3)
//.countWindow(3,1)
//.countWindowAll(3)
.sum("count" )
textResult
.setParallelism(3)
.print()
if(args == null || args.size ==0){
println("==================================以下为执行计划==================================")
println("执行地址(firefox效果更好):https://flink.apache.org/visualizer")
//执行计划
println(env.getExecutionPlan)
println("==================================以上为执行计划 JSON串==================================\n")
//StreamGraph
//println(env.getStreamGraph.getStreamingPlanAsJSON)
//JsonPlanGenerator.generatePlan(jobGraph)
env.execute("默认作业")
}else{
env.execute(args(0))
}
println("结束")
}
// Data type for words with count
case class WordWithCount(word: String, count: Long){
//override def toString: String = Thread.currentThread().getName + word + " : " + count
}
def getConfiguration(isDebug:Boolean = false):Configuration = {
val configuration : Configuration = new Configuration()
if(isDebug){
val timeout = "100000 s"
val timeoutHeartbeatPause = "1000000 s"
configuration.setString("akka.ask.timeout",timeout)
configuration.setString("akka.lookup.timeout",timeout)
configuration.setString("akka.tcp.timeout",timeout)
configuration.setString("akka.transport.heartbeat.interval",timeout)
configuration.setString("akka.transport.heartbeat.pause",timeoutHeartbeatPause)
configuration.setString("akka.watch.heartbeat.pause",timeout)
configuration.setInteger("heartbeat.interval",10000000)
configuration.setInteger("heartbeat.timeout",50000000)
}
configuration
}
}
源码分析(Source)
RecordWriter.emit
- record 数据为: WordWithCount(1,1)
- 也就是Source.通过Socket.connect 得到流数据后，按行拆分，进行flatMap,map函数之后，得到的数据
- numChannels为Source partitions，这个并行度是在DataStream.setParallelism(3)设置的
- channelSelector.selectChannels(record, numChannels)，这个是按key,进行hash,算得当前元素所分的partition,即为change
- 续续调用 RecordWriter.emit()
public void emit(T record) throws IOException, InterruptedException {
emit(record, channelSelector.selectChannels(record, numChannels));
}
RecordWriter.emit
-
经调试，按key,hash % 并行度,分配的数据如下
key:2 partition:0 key:3 partition:0 key:4 partition:0 key:6 partition:0 key:1 partition:1 key:5 partition:1 key:8 partition:1 key:10 partition:1 key:7 partition:2 key:9 partition:2 -
record进行序列化，数据长度写进ByteBuffer lengthBuffer,数据写进ByteBuffer dataBuffer;
serializer.serializeRecord(record); - 调用RecordWriter.copyFromSerializerToTargetChannel(channel)往通道中写数据
private void emit(T record, int[] targetChannels) throws IOException, InterruptedException {
serializer.serializeRecord(record);
boolean pruneAfterCopying = false;
for (int channel : targetChannels) {
if (copyFromSerializerToTargetChannel(channel)) {
pruneAfterCopying = true;
}
}
// Make sure we don't hold onto the large intermediate serialization buffer for too long
if (pruneAfterCopying) {
serializer.prune();
}
}RecordWriter.copyFromSerializerToTargetChannel
-
getBufferBuilder(targetChannel)通过channel,得到BufferBuilder,就是得到当前的partition写入数据对象BufferBuilder,其实就是操作ResultPartition.subPartitions
BufferBuilder bufferBuilder = getBufferBuilder(targetChannel); -
每个partition写入的数据，对应到各自的window,这样就实现了，在source端将数据分区，对应的window处理source对应的分区数据
subpartitions = {ResultSubpartition[3]@5749} 0 = {PipelinedSubpartition@5771} "PipelinedSubpartition#0 [number of buffers: 0 (0 bytes), number of buffers in backlog: 0, finished? false, read view? true]" 1 = {PipelinedSubpartition@5772} "PipelinedSubpartition#1 [number of buffers: 0 (0 bytes), number of buffers in backlog: 0, finished? false, read view? true]" 2 = {PipelinedSubpartition@5773} "PipelinedSubpartition#2 [number of buffers: 0 (0 bytes), number of buffers in backlog: 0, finished? false, read view? true]" - BufferConsumer是消费BufferBuilder中的数据，BufferBuilder.append 是产生数据
/**
* @param targetChannel
* @return true if the intermediate serialization buffer should be pruned
*/
private boolean copyFromSerializerToTargetChannel(int targetChannel) throws IOException, InterruptedException {
// We should reset the initial position of the intermediate serialization buffer before
// copying, so the serialization results can be copied to multiple target buffers.
serializer.reset();
boolean pruneTriggered = false;
BufferBuilder bufferBuilder = getBufferBuilder(targetChannel);
SerializationResult result = serializer.copyToBufferBuilder(bufferBuilder);
while (result.isFullBuffer()) {
numBytesOut.inc(bufferBuilder.finish());
numBuffersOut.inc();
// If this was a full record, we are done. Not breaking out of the loop at this point
// will lead to another buffer request before breaking out (that would not be a
// problem per se, but it can lead to stalls in the pipeline).
if (result.isFullRecord()) {
pruneTriggered = true;
bufferBuilders[targetChannel] = Optional.empty();
break;
}
bufferBuilder = requestNewBufferBuilder(targetChannel);
result = serializer.copyToBufferBuilder(bufferBuilder);
}
checkState(!serializer.hasSerializedData(), "All data should be written at once");
if (flushAlways) {
targetPartition.flush(targetChannel);
}
return pruneTriggered;
}rdWrecoiter.flushAll
- 把bufferBuilders存储着BufferBuilder 数组，数组个数，对应着并行度的个数，每个BufferBuilder对应着ResultSubpartition.subpartitions的partition的PipelinedSubpartition,会对这些PipelinedSubpartition进行Flush,并给对应的Window进行处理
- 调用PipelinedSubpartition.flushAll()
public void flushAll() {
targetPartition.flushAll();
}PipelinedSubpartition.flushAll()
- 调用PipelinedSubpartition.notifyDataAvailable
- 调用PipelinedSubpartitionView.notifyDataAvailable
public void flush() {
final boolean notifyDataAvailable;
synchronized (buffers) {
if (buffers.isEmpty()) {
return;
}
// if there is more then 1 buffer, we already notified the reader
// (at the latest when adding the second buffer)
notifyDataAvailable = !flushRequested && buffers.size() == 1;
flushRequested = true;
}
if (notifyDataAvailable) {
notifyDataAvailable();
}
}PipelinedSubpartitionView.notifyDataAvailable
- 调用LocalInputChannel.notifyDataAvailable
public void notifyDataAvailable() {
availabilityListener.notifyDataAvailable();
}LocalInputChannel.notifyDataAvailable
- 调用InputChannel.notifyChannelNonEmpty()
- 调用SingleInputGate.notifyChannelNonEmpty
public void notifyDataAvailable() {
notifyChannelNonEmpty();
}SingleInputGate.queueChannel
-
通知Window，有数据产生了，可以开始消费了(处理数据)
inputChannelsWithData.notifyAll();
private void queueChannel(InputChannel channel) {
int availableChannels;
synchronized (inputChannelsWithData) {
if (enqueuedInputChannelsWithData.get(channel.getChannelIndex())) {
return;
}
availableChannels = inputChannelsWithData.size();
inputChannelsWithData.add(channel);
enqueuedInputChannelsWithData.set(channel.getChannelIndex());
if (availableChannels == 0) {
inputChannelsWithData.notifyAll();
}
}
if (availableChannels == 0) {
InputGateListener listener = inputGateListener;
if (listener != null) {
listener.notifyInputGateNonEmpty(this);
}
}
}源码分析(Window)
OneInputStreamTask.run()
- 调用StreamInputProcessor.processInput()进行处理，这个函数，会进行阻塞，如果没有读到数据的话
- 注意，这是Window的处理线程，有多少个并行度，就会开多少个Window,就有多少个这个方法在一直处理Source不断发过来的数据
protected void run() throws Exception {
// cache processor reference on the stack, to make the code more JIT friendly
final StreamInputProcessor inputProcessor = this.inputProcessor;
while (running && inputProcessor.processInput()) {
// all the work happens in the "processInput" method
}
} StreamInputProcessor.processInput()
-
这是一个阻塞的方法，读取Source中对应的partition中的数据,调用BarrierTracker.getNextNonBlocked()
final BufferOrEvent bufferOrEvent = barrierHandler.getNextNonBlocked();
public boolean processInput() throws Exception {
if (isFinished) {
return false;
}
if (numRecordsIn == null) {
try {
numRecordsIn = ((OperatorMetricGroup) streamOperator.getMetricGroup()).getIOMetricGroup().getNumRecordsInCounter();
} catch (Exception e) {
LOG.warn("An exception occurred during the metrics setup.", e);
numRecordsIn = new SimpleCounter();
}
}
while (true) {
if (currentRecordDeserializer != null) {
DeserializationResult result = currentRecordDeserializer.getNextRecord(deserializationDelegate);
if (result.isBufferConsumed()) {
currentRecordDeserializer.getCurrentBuffer().recycleBuffer();
currentRecordDeserializer = null;
}
if (result.isFullRecord()) {
StreamElement recordOrMark = deserializationDelegate.getInstance();
if (recordOrMark.isWatermark()) {
// handle watermark
statusWatermarkValve.inputWatermark(recordOrMark.asWatermark(), currentChannel);
continue;
} else if (recordOrMark.isStreamStatus()) {
// handle stream status
statusWatermarkValve.inputStreamStatus(recordOrMark.asStreamStatus(), currentChannel);
continue;
} else if (recordOrMark.isLatencyMarker()) {
// handle latency marker
synchronized (lock) {
streamOperator.processLatencyMarker(recordOrMark.asLatencyMarker());
}
continue;
} else {
// now we can do the actual processing
StreamRecord record = recordOrMark.asRecord();
synchronized (lock) {
numRecordsIn.inc();
streamOperator.setKeyContextElement1(record);
streamOperator.processElement(record);
}
return true;
}
}
}
final BufferOrEvent bufferOrEvent = barrierHandler.getNextNonBlocked();
if (bufferOrEvent != null) {
if (bufferOrEvent.isBuffer()) {
currentChannel = bufferOrEvent.getChannelIndex();
currentRecordDeserializer = recordDeserializers[currentChannel];
currentRecordDeserializer.setNextBuffer(bufferOrEvent.getBuffer());
}
else {
// Event received
final AbstractEvent event = bufferOrEvent.getEvent();
if (event.getClass() != EndOfPartitionEvent.class) {
throw new IOException("Unexpected event: " + event);
}
}
}
else {
isFinished = true;
if (!barrierHandler.isEmpty()) {
throw new IllegalStateException("Trailing data in checkpoint barrier handler.");
}
return false;
}
}
} BarrierTracker.getNextNonBlocked()
- 调用inputGate.getNextBufferOrEvent();调到的是SingleInputGate.getNextBufferOrEvent()去读取Source发过来到当前Window中的数据
public BufferOrEvent getNextNonBlocked() throws Exception {
while (true) {
Optional next = inputGate.getNextBufferOrEvent();
if (!next.isPresent()) {
// buffer or input exhausted
return null;
}
BufferOrEvent bufferOrEvent = next.get();
if (bufferOrEvent.isBuffer()) {
return bufferOrEvent;
}
else if (bufferOrEvent.getEvent().getClass() == CheckpointBarrier.class) {
processBarrier((CheckpointBarrier) bufferOrEvent.getEvent(), bufferOrEvent.getChannelIndex());
}
else if (bufferOrEvent.getEvent().getClass() == CancelCheckpointMarker.class) {
processCheckpointAbortBarrier((CancelCheckpointMarker) bufferOrEvent.getEvent(), bufferOrEvent.getChannelIndex());
}
else {
// some other event
return bufferOrEvent;
}
}
} SingleInputGate.getNextBufferOrEvent
- 这个才真正读Source发过来对应的partition,对应当前的Window中的数据，currentChannel.getNextBuffer();
- inputChannelsWithData 这个对象进行线程阻塞，通知，就是一个开关，Source发过来的数据处理完了就关了，Source有新的数据发过来，就打开了，开关打开时，就可以进行Window数据处理了
private Optional getNextBufferOrEvent(boolean blocking) throws IOException, InterruptedException {
if (hasReceivedAllEndOfPartitionEvents) {
return Optional.empty();
}
if (isReleased) {
throw new IllegalStateException("Released");
}
requestPartitions();
InputChannel currentChannel;
boolean moreAvailable;
Optional result = Optional.empty();
do {
synchronized (inputChannelsWithData) {
while (inputChannelsWithData.size() == 0) {
if (isReleased) {
throw new IllegalStateException("Released");
}
if (blocking) {
inputChannelsWithData.wait();
}
else {
return Optional.empty();
}
}
currentChannel = inputChannelsWithData.remove();
enqueuedInputChannelsWithData.clear(currentChannel.getChannelIndex());
moreAvailable = !inputChannelsWithData.isEmpty();
}
result = currentChannel.getNextBuffer();
} while (!result.isPresent());
// this channel was now removed from the non-empty channels queue
// we re-add it in case it has more data, because in that case no "non-empty" notification
// will come for that channel
if (result.get().moreAvailable()) {
queueChannel(currentChannel);
moreAvailable = true;
}
final Buffer buffer = result.get().buffer();
if (buffer.isBuffer()) {
return Optional.of(new BufferOrEvent(buffer, currentChannel.getChannelIndex(), moreAvailable));
}
else {
final AbstractEvent event = EventSerializer.fromBuffer(buffer, getClass().getClassLoader());
if (event.getClass() == EndOfPartitionEvent.class) {
channelsWithEndOfPartitionEvents.set(currentChannel.getChannelIndex());
if (channelsWithEndOfPartitionEvents.cardinality() == numberOfInputChannels) {
// Because of race condition between:
// 1. releasing inputChannelsWithData lock in this method and reaching this place
// 2. empty data notification that re-enqueues a channel
// we can end up with moreAvailable flag set to true, while we expect no more data.
checkState(!moreAvailable || !pollNextBufferOrEvent().isPresent());
moreAvailable = false;
hasReceivedAllEndOfPartitionEvents = true;
}
currentChannel.notifySubpartitionConsumed();
currentChannel.releaseAllResources();
}
return Optional.of(new BufferOrEvent(event, currentChannel.getChannelIndex(), moreAvailable));
}
} 输出数据
- 注意，打印输出的数据，是有规则的，按partition打印的，而且，parition打印的顺序，是在window时按key去重，此时没有排序，然后发送给sink进行打印输出
--------------------------
partition:0
WordWithCount(2,1)
WordWithCount(6,1)
WordWithCount(4,1)
WordWithCount(3,1)
partition:1
WordWithCount(1,1)
WordWithCount(10,1)
WordWithCount(8,1)
WordWithCount(5,1)
partition:2
WordWithCount(7,1)
WordWithCount(9,1)
``