Thank you, Fabian.

Maybe there's also some buffers sit between data source and the first operator? I observed that in my implementation of SourceFunction (using a socket server, as listed in the previous email), for receiving two messages, in terms of event time, it took 0.2 ms before the SourceFunction receives the first message but then it took 97 ms to receive the second message. The interval between the two sends is 0.07 ms at the sending side, which is a java socket client.


Or could it be that there is a timeout setting for scheduling data source in Flink?


Thanks,

Chao


On 08/08/2017 02:58 AM, Fabian Hueske wrote:
One pointer is the StreamExecutionEnvironment.setBufferTimeout() parameter. Flink's network stack collects records in buffers to send them over the network. A buffer is sent when it is completely filled or after a configurable timeout. So if your program does not process many records, these records might "get stuck" in the buffers and be emitted after the timeout flushes the buffer.
The default timeout is 100ms. Try to reduce it.

Best, Fabian

2017-08-08 1:06 GMT+02:00 Chao Wang <chaow...@wustl.edu <mailto:chaow...@wustl.edu>>:

    Following the original post, I've tried stripping down my Flink
    app to only the following, and then it still exhibits long
    latencies: after the second source socket write, it took 90+
    milliseconds from data source to the socket-front in Flink. I
    would like to ask for pointers about how to investigate the
    latency issue like this, and in general how to properly benchmark
    Flink latencies. Thank you very much!


    The main method:


      public static void main(String[] args) throws Exception {
        final StreamExecutionEnvironment env =
    StreamExecutionEnvironment.getExecutionEnvironment();
        DataStream<EventGroup> inEventGroupStream = env.addSource(new
    SocketEventGroupStreamFunction(6065, 512));
        inEventGroupStream.writeToSocket("DestHost", 6066, new
    MySeGroup<EventGroup>());
        env.execute("event processing");
     }


    where all the custom classes are as follows (for
    serialization/deserialization and socket server functionality):


      public static class MySeGroup<T> implements
    SerializationSchema<EventGroup> {

        @Override
        public byte[] serialize(EventGroup arg0) {
          int tLength = EKFFFTAES.getSizeTimepoint();
          //Note: report error if tLength != arg0.getT().length
          if (tLength != arg0.getT().length) {
            System.out.println ("Serialization error: Timepoint size
    discrepancy.");
            System.out.println ("tLength = " + tLength);
            System.out.println ("arg0.getT().length = " +
    arg0.getT().length);
          }
          byte[] buffer = new byte[1 + arg0.getT().length +
    arg0.getP().length];
          buffer[0] = arg0.type;
          System.arraycopy(arg0.getT(), 0, buffer, 1, tLength);
          System.arraycopy(arg0.getP(), 0, buffer, 1 + tLength,
    arg0.getP().length);
          return buffer;
        }
      }

      public static class Event extends
    SimpleImmutableEntry<byte[],byte[]> {

        Event(byte[] timestamp, byte[] payload){
          super(timestamp, payload);
        }
        public byte[] getT() { // get the timestamp
          return getKey();
        }
        public byte[] getP() { // get the payload
          return getValue();
        }
      }

      public static class EventGroup extends Event {
        public byte type;
        EventGroup(byte type, byte[] timestamp, byte[] payload){
          super(timestamp, payload);
          this.type = type;
        }
      }


      public static class SocketEventGroupStreamFunction implements
    SourceFunction<EventGroup> {

        private transient ServerSocket serverSocket;
        private int serverPort;
        private int dataLength;
        private byte[] inbuf;
        private byte[] timestamp;
        private byte[] payload;
        private int tLength = EKFFFTAES.getSizeTimepoint();
        private volatile boolean isRunning = true;

        public SocketEventGroupStreamFunction(int port, int length) {
          serverPort = port;
          dataLength = length;
          inbuf = new byte[1 + dataLength + tLength];
          timestamp = new byte[tLength];
          payload = new byte[dataLength];
        }

        @Override
        public void run(SourceContext<EventGroup> ctx) throws Exception {
          while(isRunning) {
            serverSocket = new ServerSocket(serverPort, 100,
    InetAddress.getByName("192.168.1.13"));
            serverSocket.setSoTimeout(1000000);
            System.out.println("Waiting for incoming connections on
    port " +
              serverSocket.getLocalPort() + "...");
            Socket server = serverSocket.accept();

            System.out.println("Just connected to " +
    server.getRemoteSocketAddress());
            DataInputStream in = new
    DataInputStream(server.getInputStream());

            while(isRunning) {
              in.readFully(inbuf, 0, inbuf.length);
              System.arraycopy(inbuf, 1, timestamp, 0, tLength);
              System.arraycopy(inbuf, 1+tLength, payload, 0, dataLength);

              System.out.print("Got an event " + inbuf[0] + ": ");
              displayElapsedTime(timestamp);

              ctx.collect(new EventGroup(inbuf[0], timestamp, payload));
            }
          }
        }

        @Override
        public void cancel() {
          isRunning = false;
          ServerSocket theSocket = this.serverSocket;
          if (theSocket != null) {
            try {
              theSocket.close();
            }catch(SocketTimeoutException s) {
              System.out.println("Socket timed out!");
            }catch(IOException e) {
              e.printStackTrace();
            }
          }
        }
      }


    and finally, EKFFFTAES is my cpp library implementing the
    timestamping facility:


    int timePointLength = sizeof(std::chrono::system_clock::time_point);

    JNIEXPORT jint JNICALL Java_eventProcessing_EKFFFTAES_getSizeTimepoint
      (JNIEnv *, jclass)
    {
      return ::timePointLength;
    }

    JNIEXPORT void JNICALL
    Java_eventProcessing_EKFFFTAES_displayElapsedTime
      (JNIEnv *env, jclass, jbyteArray inArray)
    {
      std::chrono::system_clock::time_point end =
        std::chrono::system_clock::now();
      jbyte *inCArray = env->GetByteArrayElements(inArray, NULL);
      std::chrono::system_clock::time_point start;
      std::memcpy (&start, inCArray, ::timePointLength);
      std::cout <<
    std::chrono::duration_cast<std::chrono::microseconds>(end -
    start).count() << std::endl;
    }


    Thank you,

    Chao


    On 08/07/2017 03:20 PM, Chao Wang wrote:

        Hi,

        I have been trying to benchmark the end-to-end latency of a
        Flink 1.3.1 application, but got confused regarding the amount
        of time spent in Flink. In my setting, data source and data
        sink dwell in separated machines, like the following topology:

        Machine 1 Machine 2      Machine 3
        data source (via a socket client)   ->      Flink ->    data
        sink (via a socket server)

        I observed 200-400 milliseconds end-to-end latency, while the
        execution time of my stream transformations took no more than
        two milliseconds, and the socket-only networking latency
        between machines is no more than one millisecond, and I used
        ptpd so that the clock offset between machines were also no
        more than one millisecond.

        Question: What took those hundreds of milliseconds?

        Here are the details of my setting and my observation so far:

        On Machine 2, I implemented a socket server as a data source
        to Flink (by implementing SourceFunction), and I splited the
        incoming stream into several streams (by SplitStream) for some
        transformations (implementing MapFuction and
        CoFlatMapFunction), where the results were fed to socket
        (using writeToSocket). I used c++11's chrono time library
        (through JNI) to take timestamps and determine the elapsed
        time, and I have verified that the overhead of timestamping
        this way is no more than one millisecond.

        I observed that for the four consecutive writes from Machine
        1, with the time between two writes no more than 0.3
        milliseconds, on Machine 2 Flink got the first write in 0.2
        milliseconds, but then it took 90 milliseconds for Flink to
        get the next write, and another 4 milliseconds for the third
        write, and yet another 4 milliseconds for the fourth write.

        And then it took more than 70 milliseconds before Flink
        started processing my plan's first stream transformation. And
        after my last transformation, it took more than 70
        milliseconds before the result was received at Machine 3.


        Thank you,

        Chao





Reply via email to