Thanks for your help, I retest by disable the object reuse and got the
same result (please see the picture attached).
2016-02-03 10:51 GMT+01:00 Stephan Ewen <se...@apache.org>:
> The definition looks correct.
> Because the windows are by-key, you should get one window result per key
> per second.
>
> Can you turn off object-reuse? That is a pretty experimental thing and
> works with the batch operations quite well, but not so much with the
> streaming windows, yet.
> I would only enable object reuse after the program works well and
> correctly without.
>
> Greetings,
> Stephan
>
>
> On Tue, Feb 2, 2016 at 7:31 PM, yutao sun <yutao.sun...@gmail.com> wrote:
>
>> Hi Flink users,
>>
>> I have a question about Tumbling Windows using Processing Time at Flink
>> ver 0.10.1 :
>>
>> In fact, I want to measure the throughput of my application, the idea is
>> at the last operator, by using a Tumbling processing Time windows with a
>> size of 1 second, I count the message received.
>>
>> The problem is in case of 4 parallelisms, the number of windows should be
>> 4/second, but I got 7 windows/second, I wonder if is there any error the
>> windows is defined?
>>
>> I copy my code here and thanks a lot for your help in advance.
>> [KAFKA partition : 4]
>>
>>
>> *val env = StreamExecutionEnvironment.getExecutionEnvironment*
>>
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>> *val parallelism = 4*
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>> *env.setParallelism(parallelism)env.getConfig.enableObjectReuse()env.setStreamTimeCharacteristic(TimeCharacteristic.ProcessingTime)env.getConfig.setAutoWatermarkInterval(-1L)env.getConfig.disableTimestamps()env.addSource(
>> new FlinkKafkaConsumer082[String]( "test_topic", new
>> SimpleStringSchema, properties for connection KAFKA )) .rebalance
>> .map(do some thing) .map(payload => (payload, 1L)) .keyBy(mappedPayload =>
>> mappedPayload._1.id.hashcode % parallelism) .timeWindow(Time.of(1,
>> TimeUnit.SECONDS)) .reduce((tuple0: (Payload, Long), tuple1: (Payload,
>> Long)) => (tuple._0, tuple._1 + 1L)) .addSink( new
>> FlinkKafkaProducer[(Payload, Long)]( KafkaBootstrapServers,
>> TARGET_TOPIC, new SerializationSchema[(Payload, Long), Array[Byte]] {
>> override def serialize(element: (Payload, Long)): Array[Byte] = {
>> element._2.toString().getBytes } } ))env.execute("test")*
>>
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>
