Sliding Window Count: Tricky Edge Case / Count Zero Problem

[Jan Brusch]

Hi,
I was recently working on a problem where we wanted to implement a
simple count on a sliding window, e.g. "how many messages of a certain
type were emitted by a certain type of sensor in the last n minutes".
Which sounds simple enough in theory:

messageStream
     .keyBy(//EmitterType + MessageType)
     .assignWindow(SlidingProcessingTimeWindows.of(Time.minutes(n),
Time.seconds(30)))
     .map(_ => 1)
     .reduce((x,y) => x + y)
     .addSink(...)

But there is a tricky edge case: The downstream systems will never know
when the count for a certain key goes back to 0, which is important for
our use case. The technical reason being that flink doesn't open a
window if there are no entries, i.e. a window with count 0 doesn't exist
in flink.

We came up with the following solution for the time being:

messageStream
     .keyBy(//EmitterType + MessageType)
     .window(GlobalWindows.create())
     .trigger(ContinuousEventTimeTrigger.of(Time.seconds(30)))
     .evictor(// CustomEvictor: Evict all messages older than n minutes
BEFORE processing the window)
     .process(// CustomCounter: Count all Messages in Window State);
     .addSink(...)

In the case of zero messages in the last n minutes, all messages will be
evicted from the window and the process-function will get triggered one
last time on the now empty window, so we can produce a count of 0.

I have two problems, though, with this solution:
1) It is computationally inefficient for a simple count, as custom
process functions will always keep all messages in state. And, on every
trigger all elements will have to be touched twice: To compare the
timestamp and to count.
2) It does seem like a very roundabout solution to a simple problem.

So, I was wondering if there was a more efficient or "flink-like"
approach to this. Sorry for the long writeup, but I would love to hear
your takes.


Best regards
Jan

--
neuland  – Büro für Informatik GmbH
Konsul-Smidt-Str. 8g, 28217 Bremen

Telefon [phone]
Fax [phone]
https://www.neuland-bfi.de

https://twitter.com/neuland
https://facebook.com/neulandbfi
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Geschäftsführer: Thomas Gebauer, Jan Zander
Registergericht: Amtsgericht Bremen, HRB 23395 HB
USt-ID. DE 246585501

[Yun Gao]

Hi Jan,

From my view, I think in Flink Window should be as a "high-level" operation for some kind

of aggregation operation and if it could not satisfy the requirements, we could at least turn to

using the "low-level" api by using KeyedProcessFunction[1].

In this case, we could use a ValueState to store the current value for each key, and increment

the value on each element. Then we could also register time for each key on receiving the first 

element for this key,  and in the onTimer callback, we could send the current state value, update

the value to 0 and register another timer for this key after 30s.

Best,

 Yun

[1] https://ci.apache.org/projects/flink/flink-docs-stable/dev/stream/operators/process_function.html#the-keyedprocessfunction

------------------Original Mail ------------------

Sender:Jan Brusch <[hidden email]>

Send Date:Sat Feb 6 23:44:00 2021

Recipients:user <[hidden email]>

Subject:Sliding Window Count: Tricky Edge Case / Count Zero Problem

Hi,
I was recently working on a problem where we wanted to implement a 
simple count on a sliding window, e.g. "how many messages of a certain 
type were emitted by a certain type of sensor in the last n minutes". 
Which sounds simple enough in theory:

messageStream
     .keyBy(//EmitterType + MessageType)
     .assignWindow(SlidingProcessingTimeWindows.of(Time.minutes(n), 
Time.seconds(30)))
     .map(_ => 1)
     .reduce((x,y) => x + y)
     .addSink(...)

But there is a tricky edge case: The downstream systems will never know 
when the count for a certain key goes back to 0, which is important for 
our use case. The technical reason being that flink doesn't open a 
window if there are no entries, i.e. a window with count 0 doesn't exist 
in flink.

We came up with the following solution for the time being:

messageStream
     .keyBy(//EmitterType + MessageType)
     .window(GlobalWindows.create())
     .trigger(ContinuousEventTimeTrigger.of(Time.seconds(30)))
     .evictor(// CustomEvictor: Evict all messages older than n minutes 
BEFORE processing the window)
     .process(// CustomCounter: Count all Messages in Window State);
     .addSink(...)

In the case of zero messages in the last n minutes, all messages will be 
evicted from the window and the process-function will get triggered one 
last time on the now empty window, so we can produce a count of 0.

I have two problems, though, with this solution:
1) It is computationally inefficient for a simple count, as custom 
process functions will always keep all messages in state. And, on every 
trigger all elements will have to be touched twice: To compare the 
timestamp and to count.
2) It does seem like a very roundabout solution to a simple problem.

So, I was wondering if there was a more efficient or "flink-like" 
approach to this. Sorry for the long writeup, but I would love to hear 
your takes.


Best regards
Jan

-- 
neuland  – Büro für Informatik GmbH
Konsul-Smidt-Str. 8g, 28217 Bremen

Telefon [phone]
Fax [phone]
https://www.neuland-bfi.de

https://twitter.com/neuland
https://facebook.com/neulandbfi
https://xing.com/company/neulandbfi


Geschäftsführer: Thomas Gebauer, Jan Zander
Registergericht: Amtsgericht Bremen, HRB 23395 HB
USt-ID. DE 246585501

[Jan Brusch]

Hi Yun,

thanks for your reply.

I do agree with your point about standard windows being for high level operations and the lower-level apis offering a rich toolset for most advanced use cases.

I have tried to solve my problem with keyedProcessFunctions also but was not able to get it to work for two reasons:

1) I was not able to set up a combination of ValueState, Timers and Triggers that emulated a sliding window with a rising and falling count (including 0) good enough.

2) Memory Leak: States / Windows should be cleared after a certain time of being at count 0 in order to prevent an infinitely rising of ValueStates (that are not needed anymore)

Can you maybe please elaborate in pseudocode how you would envision your solution?

Best regards

Jan

On 08.02.21 05:31, Yun Gao wrote:

Hi Jan,

From my view, I think in Flink Window should be as a "high-level" operation for some kind

of aggregation operation and if it could not satisfy the requirements, we could at least turn to

using the "low-level" api by using KeyedProcessFunction[1].

In this case, we could use a ValueState to store the current value for each key, and increment

the value on each element. Then we could also register time for each key on receiving the first 

element for this key,  and in the onTimer callback, we could send the current state value, update

the value to 0 and register another timer for this key after 30s.

Best,

 Yun

[1] https://ci.apache.org/projects/flink/flink-docs-stable/dev/stream/operators/process_function.html#the-keyedprocessfunction

------------------Original Mail ------------------

Sender:Jan Brusch [hidden email]

Send Date:Sat Feb 6 23:44:00 2021

Recipients:user [hidden email]

Subject:Sliding Window Count: Tricky Edge Case / Count Zero Problem

Hi,
I was recently working on a problem where we wanted to implement a 
simple count on a sliding window, e.g. "how many messages of a certain 
type were emitted by a certain type of sensor in the last n minutes". 
Which sounds simple enough in theory:

messageStream
     .keyBy(//EmitterType + MessageType)
     .assignWindow(SlidingProcessingTimeWindows.of(Time.minutes(n), 
Time.seconds(30)))
     .map(_ => 1)
     .reduce((x,y) => x + y)
     .addSink(...)

But there is a tricky edge case: The downstream systems will never know 
when the count for a certain key goes back to 0, which is important for 
our use case. The technical reason being that flink doesn't open a 
window if there are no entries, i.e. a window with count 0 doesn't exist 
in flink.

We came up with the following solution for the time being:

messageStream
     .keyBy(//EmitterType + MessageType)
     .window(GlobalWindows.create())
     .trigger(ContinuousEventTimeTrigger.of(Time.seconds(30)))
     .evictor(// CustomEvictor: Evict all messages older than n minutes 
BEFORE processing the window)
     .process(// CustomCounter: Count all Messages in Window State);
     .addSink(...)

In the case of zero messages in the last n minutes, all messages will be 
evicted from the window and the process-function will get triggered one 
last time on the now empty window, so we can produce a count of 0.

I have two problems, though, with this solution:
1) It is computationally inefficient for a simple count, as custom 
process functions will always keep all messages in state. And, on every 
trigger all elements will have to be touched twice: To compare the 
timestamp and to count.
2) It does seem like a very roundabout solution to a simple problem.

So, I was wondering if there was a more efficient or "flink-like" 
approach to this. Sorry for the long writeup, but I would love to hear 
your takes.


Best regards
Jan

-- 
neuland  – Büro für Informatik GmbH
Konsul-Smidt-Str. 8g, 28217 Bremen

Telefon [phone]
Fax [phone]
https://www.neuland-bfi.de

https://twitter.com/neuland
https://facebook.com/neulandbfi
https://xing.com/company/neulandbfi


Geschäftsführer: Thomas Gebauer, Jan Zander
Registergericht: Amtsgericht Bremen, HRB 23395 HB
USt-ID. DE 246585501

[r_khachatryan]

Hi,

Probably another solution would be to register a timer (using KeyedProcessFunction) once we see an element after keyBy. The timer will fire in windowIntervalMs. Upon firing, it will emit a dummy element which will be ignored (or subtracted) in the end.

Upon receiving each new element, the function will shift the timer accordingly.

On Mon, Feb 8, 2021 at 10:50 AM Jan Brusch <[hidden email]> wrote:

Hi Yun,

thanks for your reply.

I do agree with your point about standard windows being for high level operations and the lower-level apis offering a rich toolset for most advanced use cases.

I have tried to solve my problem with keyedProcessFunctions also but was not able to get it to work for two reasons:

1) I was not able to set up a combination of ValueState, Timers and Triggers that emulated a sliding window with a rising and falling count (including 0) good enough.

2) Memory Leak: States / Windows should be cleared after a certain time of being at count 0 in order to prevent an infinitely rising of ValueStates (that are not needed anymore)

Can you maybe please elaborate in pseudocode how you would envision your solution?

Best regards

Jan

On 08.02.21 05:31, Yun Gao wrote:

Hi Jan,

From my view, I think in Flink Window should be as a "high-level" operation for some kind

of aggregation operation and if it could not satisfy the requirements, we could at least turn to

using the "low-level" api by using KeyedProcessFunction[1].

In this case, we could use a ValueState to store the current value for each key, and increment

the value on each element. Then we could also register time for each key on receiving the first 

element for this key,  and in the onTimer callback, we could send the current state value, update

the value to 0 and register another timer for this key after 30s.

Best,

 Yun

[1] https://ci.apache.org/projects/flink/flink-docs-stable/dev/stream/operators/process_function.html#the-keyedprocessfunction

------------------Original Mail ------------------

Sender:Jan Brusch [hidden email]

Send Date:Sat Feb 6 23:44:00 2021

Recipients:user [hidden email]

Subject:Sliding Window Count: Tricky Edge Case / Count Zero Problem

Hi,
I was recently working on a problem where we wanted to implement a 
simple count on a sliding window, e.g. "how many messages of a certain 
type were emitted by a certain type of sensor in the last n minutes". 
Which sounds simple enough in theory:

messageStream
     .keyBy(//EmitterType + MessageType)
     .assignWindow(SlidingProcessingTimeWindows.of(Time.minutes(n), 
Time.seconds(30)))
     .map(_ => 1)
     .reduce((x,y) => x + y)
     .addSink(...)

But there is a tricky edge case: The downstream systems will never know 
when the count for a certain key goes back to 0, which is important for 
our use case. The technical reason being that flink doesn't open a 
window if there are no entries, i.e. a window with count 0 doesn't exist 
in flink.

We came up with the following solution for the time being:

messageStream
     .keyBy(//EmitterType + MessageType)
     .window(GlobalWindows.create())
     .trigger(ContinuousEventTimeTrigger.of(Time.seconds(30)))
     .evictor(// CustomEvictor: Evict all messages older than n minutes 
BEFORE processing the window)
     .process(// CustomCounter: Count all Messages in Window State);
     .addSink(...)

In the case of zero messages in the last n minutes, all messages will be 
evicted from the window and the process-function will get triggered one 
last time on the now empty window, so we can produce a count of 0.

I have two problems, though, with this solution:
1) It is computationally inefficient for a simple count, as custom 
process functions will always keep all messages in state. And, on every 
trigger all elements will have to be touched twice: To compare the 
timestamp and to count.
2) It does seem like a very roundabout solution to a simple problem.

So, I was wondering if there was a more efficient or "flink-like" 
approach to this. Sorry for the long writeup, but I would love to hear 
your takes.


Best regards
Jan

-- 
neuland  – Büro für Informatik GmbH
Konsul-Smidt-Str. 8g, 28217 Bremen

Telefon [phone]
Fax [phone]
https://www.neuland-bfi.de

https://twitter.com/neuland
https://facebook.com/neulandbfi
https://xing.com/company/neulandbfi


Geschäftsführer: Thomas Gebauer, Jan Zander
Registergericht: Amtsgericht Bremen, HRB 23395 HB
USt-ID. DE 246585501

-- 
neuland  – Büro für Informatik GmbH
Konsul-Smidt-Str. 8g, 28217 Bremen

Telefon [phone]
Fax [phone]
https://www.neuland-bfi.de

https://twitter.com/neuland
https://facebook.com/neulandbfi
https://xing.com/company/neulandbfi


Geschäftsführer: Thomas Gebauer, Jan Zander
Registergericht: Amtsgericht Bremen, HRB 23395 HB
USt-ID. DE 246585501

[Yun Gao]

Hi,

I also think there should be different ways to achieve the target. For the first option listed previously, 

the pseudo-code roughly like

class MyFunciton extends KeyedProcessFunction {

    ValueState<Integer> count;

    void open() {

       count = ... // Create the value state

   }  

    ​void processElement(T t, Context context, Collector collector) {

            ​Integer current = count.get();

            if (current == null) {

                      context.timeService().registerTimer(30); // Register timer for the first time

                      current = 0;

            }

            count.update(current + 1); // update the count

    }

    void onTimer(...) {

         collector.collect(new Tuple2<>(getCurrentKey(), count.get());

          context.timeService().registerTimer(30);  // register the following timer

    }

}

1. For flink the state and timer are all bound to a key implicitly, thus I think they should

not need to be bound manually.

2. To clear the outdated state, it could be cleared via count.clear(); if it has been 0 

for a long time. There are different ways to count the interval, like register another timer

and clear the timer when received the elements or update the counter to -1, -2... to mark

how much timer it has passed.

Best,

 Yun

------------------Original Mail ------------------

Sender:Khachatryan Roman <[hidden email]>

Send Date:Tue Feb 9 02:35:20 2021

Recipients:Jan Brusch <[hidden email]>

CC:Yun Gao <[hidden email]>, user <[hidden email]>

Subject:Re: Sliding Window Count: Tricky Edge Case / Count Zero Problem

Hi,

Probably another solution would be to register a timer (using KeyedProcessFunction) once we see an element after keyBy. The timer will fire in windowIntervalMs. Upon firing, it will emit a dummy element which will be ignored (or subtracted) in the end.

Upon receiving each new element, the function will shift the timer accordingly.

On Mon, Feb 8, 2021 at 10:50 AM Jan Brusch <[hidden email]> wrote:

Hi Yun,

thanks for your reply.

I do agree with your point about standard windows being for high level operations and the lower-level apis offering a rich toolset for most advanced use cases.

I have tried to solve my problem with keyedProcessFunctions also but was not able to get it to work for two reasons:

1) I was not able to set up a combination of ValueState, Timers and Triggers that emulated a sliding window with a rising and falling count (including 0) good enough.

2) Memory Leak: States / Windows should be cleared after a certain time of being at count 0 in order to prevent an infinitely rising of ValueStates (that are not needed anymore)

Can you maybe please elaborate in pseudocode how you would envision your solution?

Best regards

Jan

On 08.02.21 05:31, Yun Gao wrote:

Hi Jan,

From my view, I think in Flink Window should be as a "high-level" operation for some kind

of aggregation operation and if it could not satisfy the requirements, we could at least turn to

using the "low-level" api by using KeyedProcessFunction[1].

In this case, we could use a ValueState to store the current value for each key, and increment

the value on each element. Then we could also register time for each key on receiving the first 

element for this key,  and in the onTimer callback, we could send the current state value, update

the value to 0 and register another timer for this key after 30s.

Best,

 Yun

[1] https://ci.apache.org/projects/flink/flink-docs-stable/dev/stream/operators/process_function.html#the-keyedprocessfunction

------------------Original Mail ------------------

Sender:Jan Brusch [hidden email]

Send Date:Sat Feb 6 23:44:00 2021

Recipients:user [hidden email]

Subject:Sliding Window Count: Tricky Edge Case / Count Zero Problem

Hi,
I was recently working on a problem where we wanted to implement a 
simple count on a sliding window, e.g. "how many messages of a certain 
type were emitted by a certain type of sensor in the last n minutes". 
Which sounds simple enough in theory:

messageStream
     .keyBy(//EmitterType + MessageType)
     .assignWindow(SlidingProcessingTimeWindows.of(Time.minutes(n), 
Time.seconds(30)))
     .map(_ => 1)
     .reduce((x,y) => x + y)
     .addSink(...)

But there is a tricky edge case: The downstream systems will never know 
when the count for a certain key goes back to 0, which is important for 
our use case. The technical reason being that flink doesn't open a 
window if there are no entries, i.e. a window with count 0 doesn't exist 
in flink.

We came up with the following solution for the time being:

messageStream
     .keyBy(//EmitterType + MessageType)
     .window(GlobalWindows.create())
     .trigger(ContinuousEventTimeTrigger.of(Time.seconds(30)))
     .evictor(// CustomEvictor: Evict all messages older than n minutes 
BEFORE processing the window)
     .process(// CustomCounter: Count all Messages in Window State);
     .addSink(...)

In the case of zero messages in the last n minutes, all messages will be 
evicted from the window and the process-function will get triggered one 
last time on the now empty window, so we can produce a count of 0.

I have two problems, though, with this solution:
1) It is computationally inefficient for a simple count, as custom 
process functions will always keep all messages in state. And, on every 
trigger all elements will have to be touched twice: To compare the 
timestamp and to count.
2) It does seem like a very roundabout solution to a simple problem.

So, I was wondering if there was a more efficient or "flink-like" 
approach to this. Sorry for the long writeup, but I would love to hear 
your takes.


Best regards
Jan

-- 
neuland  – Büro für Informatik GmbH
Konsul-Smidt-Str. 8g, 28217 Bremen

Telefon [phone]
Fax [phone]
https://www.neuland-bfi.de

https://twitter.com/neuland
https://facebook.com/neulandbfi
https://xing.com/company/neulandbfi


Geschäftsführer: Thomas Gebauer, Jan Zander
Registergericht: Amtsgericht Bremen, HRB 23395 HB
USt-ID. DE 246585501

-- neuland  – Büro für Informatik GmbHKonsul-Smidt-Str. 8g, 28217 BremenTelefon [phone] 57Fax [phone]https://www.neuland-bfi.dehttps://twitter.com/neulandhttps://facebook.com/neulandbfihttps://xing.com/company/neulandbfiGeschäftsführer: Thomas Gebauer, Jan ZanderRegistergericht: Amtsgericht Bremen, HRB 23395 HBUSt-ID. DE 246585501

[Arvid Heise-4]

Hi Jan,

Another solution is to insert Heartbeat-events at the source for each sensor. The solution is very similar to how to advance watermarks when there are no elements in the respective source partition.

However, it's only easy to implement if you have your own source and know all sensors on application start. It might also be possible to implement if you use a new Source interface.

On Tue, Feb 9, 2021 at 7:20 AM Yun Gao <[hidden email]> wrote:

Hi,

I also think there should be different ways to achieve the target. For the first option listed previously, 

the pseudo-code roughly like

class MyFunciton extends KeyedProcessFunction {

    ValueState<Integer> count;

    void open() {

       count = ... // Create the value state

   }  

    ​void processElement(T t, Context context, Collector collector) {

            ​Integer current = count.get();

            if (current == null) {

                      context.timeService().registerTimer(30); // Register timer for the first time

                      current = 0;

            }

            count.update(current + 1); // update the count

    }

    void onTimer(...) {

         collector.collect(new Tuple2<>(getCurrentKey(), count.get());

          context.timeService().registerTimer(30);  // register the following timer

    }

}

1. For flink the state and timer are all bound to a key implicitly, thus I think they should

not need to be bound manually.

2. To clear the outdated state, it could be cleared via count.clear(); if it has been 0 

for a long time. There are different ways to count the interval, like register another timer

and clear the timer when received the elements or update the counter to -1, -2... to mark

how much timer it has passed.

Best,

 Yun

------------------Original Mail ------------------

Sender:Khachatryan Roman <[hidden email]>

Send Date:Tue Feb 9 02:35:20 2021

Recipients:Jan Brusch <[hidden email]>

CC:Yun Gao <[hidden email]>, user <[hidden email]>

Subject:Re: Sliding Window Count: Tricky Edge Case / Count Zero Problem

Hi,

Probably another solution would be to register a timer (using KeyedProcessFunction) once we see an element after keyBy. The timer will fire in windowIntervalMs. Upon firing, it will emit a dummy element which will be ignored (or subtracted) in the end.

Upon receiving each new element, the function will shift the timer accordingly.

Regards,
Roman

On Mon, Feb 8, 2021 at 10:50 AM Jan Brusch <[hidden email]> wrote:

Hi Yun,

thanks for your reply.

I do agree with your point about standard windows being for high level operations and the lower-level apis offering a rich toolset for most advanced use cases.

I have tried to solve my problem with keyedProcessFunctions also but was not able to get it to work for two reasons:

1) I was not able to set up a combination of ValueState, Timers and Triggers that emulated a sliding window with a rising and falling count (including 0) good enough.

2) Memory Leak: States / Windows should be cleared after a certain time of being at count 0 in order to prevent an infinitely rising of ValueStates (that are not needed anymore)

Can you maybe please elaborate in pseudocode how you would envision your solution?

Best regards

Jan

On 08.02.21 05:31, Yun Gao wrote:

Hi Jan,

From my view, I think in Flink Window should be as a "high-level" operation for some kind

of aggregation operation and if it could not satisfy the requirements, we could at least turn to

using the "low-level" api by using KeyedProcessFunction[1].

In this case, we could use a ValueState to store the current value for each key, and increment

the value on each element. Then we could also register time for each key on receiving the first 

element for this key,  and in the onTimer callback, we could send the current state value, update

the value to 0 and register another timer for this key after 30s.

Best,

 Yun

[1] https://ci.apache.org/projects/flink/flink-docs-stable/dev/stream/operators/process_function.html#the-keyedprocessfunction

------------------Original Mail ------------------

Sender:Jan Brusch [hidden email]

Send Date:Sat Feb 6 23:44:00 2021

Recipients:user [hidden email]

Subject:Sliding Window Count: Tricky Edge Case / Count Zero Problem

Hi,
I was recently working on a problem where we wanted to implement a 
simple count on a sliding window, e.g. "how many messages of a certain 
type were emitted by a certain type of sensor in the last n minutes". 
Which sounds simple enough in theory:

messageStream
     .keyBy(//EmitterType + MessageType)
     .assignWindow(SlidingProcessingTimeWindows.of(Time.minutes(n), 
Time.seconds(30)))
     .map(_ => 1)
     .reduce((x,y) => x + y)
     .addSink(...)

But there is a tricky edge case: The downstream systems will never know 
when the count for a certain key goes back to 0, which is important for 
our use case. The technical reason being that flink doesn't open a 
window if there are no entries, i.e. a window with count 0 doesn't exist 
in flink.

We came up with the following solution for the time being:

messageStream
     .keyBy(//EmitterType + MessageType)
     .window(GlobalWindows.create())
     .trigger(ContinuousEventTimeTrigger.of(Time.seconds(30)))
     .evictor(// CustomEvictor: Evict all messages older than n minutes 
BEFORE processing the window)
     .process(// CustomCounter: Count all Messages in Window State);
     .addSink(...)

In the case of zero messages in the last n minutes, all messages will be 
evicted from the window and the process-function will get triggered one 
last time on the now empty window, so we can produce a count of 0.

I have two problems, though, with this solution:
1) It is computationally inefficient for a simple count, as custom 
process functions will always keep all messages in state. And, on every 
trigger all elements will have to be touched twice: To compare the 
timestamp and to count.
2) It does seem like a very roundabout solution to a simple problem.

So, I was wondering if there was a more efficient or "flink-like" 
approach to this. Sorry for the long writeup, but I would love to hear 
your takes.


Best regards
Jan

-- 
neuland  – Büro für Informatik GmbH
Konsul-Smidt-Str. 8g, 28217 Bremen

Telefon [phone]
Fax [phone]
https://www.neuland-bfi.de

https://twitter.com/neuland
https://facebook.com/neulandbfi
https://xing.com/company/neulandbfi


Geschäftsführer: Thomas Gebauer, Jan Zander
Registergericht: Amtsgericht Bremen, HRB 23395 HB
USt-ID. DE 246585501

-- neuland  – Büro für Informatik GmbHKonsul-Smidt-Str. 8g, 28217 BremenTelefon [phone] 57Fax [phone]https://www.neuland-bfi.dehttps://twitter.com/neulandhttps://facebook.com/neulandbfihttps://xing.com/company/neulandbfiGeschäftsführer: Thomas Gebauer, Jan ZanderRegistergericht: Amtsgericht Bremen, HRB 23395 HBUSt-ID. DE 246585501

[Jan Brusch]

Hi everybody,

I just wanted to say thanks again for all your input and share the (surprisingly simple) solution that we came up with in the meantime:

class SensorRecordCounter extends KeyedProcessFunction<String, SensorRecord, SensorCount>{

private ValueState<SensorCount> state;
private long windowSizeMs = 60000L;

 @Override
  public void open(Configuration parameters) throws Exception {
        state = getRuntimeContext().getState(new ValueStateDescriptor<>("sensorCount", SensorCount.class));
  }


@Override
public void processElement(SensorRecord sensorRecord, Context ctx, Collector<SensorCount> out) throws Exception {
        SensorCount count = state.value();
        if (count == null) {
            count = new SensorCount();
            count.setSensorID(sensorRecord.getSensorID());
            count.setCount(0);
        }
        count.increase();
        state.update(count);
        out.collect(count);
       
        ctx.timerService().registerEventTimeTimer(ctx.timestamp() + windowSizeMs);
}

@Override
public void onTimer(long timestamp, OnTimerContext ctx, Collector<SensorCount> out) throws Exception {
        SensorCount count = state.value();
        count.decrease();
        state.update(count);
        out.collect(count);
       
        if (count.getCount() <= 0) {
            state.clear();
        }
}

}

Best regards and a nice weekend

Jan

On 09.02.21 08:28, Arvid Heise wrote:

Hi Jan,

Another solution is to insert Heartbeat-events at the source for each sensor. The solution is very similar to how to advance watermarks when there are no elements in the respective source partition.

However, it's only easy to implement if you have your own source and know all sensors on application start. It might also be possible to implement if you use a new Source interface.

On Tue, Feb 9, 2021 at 7:20 AM Yun Gao <[hidden email]> wrote:

Hi,

I also think there should be different ways to achieve the target. For the first option listed previously, 

the pseudo-code roughly like

class MyFunciton extends KeyedProcessFunction {

    ValueState<Integer> count;

    void open() {

       count = ... // Create the value state

   }  

    ​void processElement(T t, Context context, Collector collector) {

            ​Integer current = count.get();

            if (current == null) {

                      context.timeService().registerTimer(30); // Register timer for the first time

                      current = 0;

            }

            count.update(current + 1); // update the count

    }

    void onTimer(...) {

         collector.collect(new Tuple2<>(getCurrentKey(), count.get());

          context.timeService().registerTimer(30);  // register the following timer

    }

}

1. For flink the state and timer are all bound to a key implicitly, thus I think they should

not need to be bound manually.

2. To clear the outdated state, it could be cleared via count.clear(); if it has been 0 

for a long time. There are different ways to count the interval, like register another timer

and clear the timer when received the elements or update the counter to -1, -2... to mark

how much timer it has passed.

Best,

 Yun

------------------Original Mail ------------------

Sender:Khachatryan Roman <[hidden email]>

Send Date:Tue Feb 9 02:35:20 2021

Recipients:Jan Brusch <[hidden email]>

CC:Yun Gao <[hidden email]>, user <[hidden email]>

Subject:Re: Sliding Window Count: Tricky Edge Case / Count Zero Problem

Hi,

Probably another solution would be to register a timer (using KeyedProcessFunction) once we see an element after keyBy. The timer will fire in windowIntervalMs. Upon firing, it will emit a dummy element which will be ignored (or subtracted) in the end.

Upon receiving each new element, the function will shift the timer accordingly.

Regards,
Roman

On Mon, Feb 8, 2021 at 10:50 AM Jan Brusch <[hidden email]> wrote:

Hi Yun,

thanks for your reply.

I do agree with your point about standard windows being for high level operations and the lower-level apis offering a rich toolset for most advanced use cases.

I have tried to solve my problem with keyedProcessFunctions also but was not able to get it to work for two reasons:

1) I was not able to set up a combination of ValueState, Timers and Triggers that emulated a sliding window with a rising and falling count (including 0) good enough.

2) Memory Leak: States / Windows should be cleared after a certain time of being at count 0 in order to prevent an infinitely rising of ValueStates (that are not needed anymore)

Can you maybe please elaborate in pseudocode how you would envision your solution?

Best regards

Jan

On 08.02.21 05:31, Yun Gao wrote:

Hi Jan,

From my view, I think in Flink Window should be as a "high-level" operation for some kind

of aggregation operation and if it could not satisfy the requirements, we could at least turn to

using the "low-level" api by using KeyedProcessFunction[1].

In this case, we could use a ValueState to store the current value for each key, and increment

the value on each element. Then we could also register time for each key on receiving the first 

element for this key,  and in the onTimer callback, we could send the current state value, update

the value to 0 and register another timer for this key after 30s.

Best,

 Yun

[1] https://ci.apache.org/projects/flink/flink-docs-stable/dev/stream/operators/process_function.html#the-keyedprocessfunction

------------------Original Mail ------------------

Sender:Jan Brusch [hidden email]

Send Date:Sat Feb 6 23:44:00 2021

Recipients:user [hidden email]

Subject:Sliding Window Count: Tricky Edge Case / Count Zero Problem

Hi,
I was recently working on a problem where we wanted to implement a 
simple count on a sliding window, e.g. "how many messages of a certain 
type were emitted by a certain type of sensor in the last n minutes". 
Which sounds simple enough in theory:

messageStream
     .keyBy(//EmitterType + MessageType)
     .assignWindow(SlidingProcessingTimeWindows.of(Time.minutes(n), 
Time.seconds(30)))
     .map(_ => 1)
     .reduce((x,y) => x + y)
     .addSink(...)

But there is a tricky edge case: The downstream systems will never know 
when the count for a certain key goes back to 0, which is important for 
our use case. The technical reason being that flink doesn't open a 
window if there are no entries, i.e. a window with count 0 doesn't exist 
in flink.

We came up with the following solution for the time being:

messageStream
     .keyBy(//EmitterType + MessageType)
     .window(GlobalWindows.create())
     .trigger(ContinuousEventTimeTrigger.of(Time.seconds(30)))
     .evictor(// CustomEvictor: Evict all messages older than n minutes 
BEFORE processing the window)
     .process(// CustomCounter: Count all Messages in Window State);
     .addSink(...)

In the case of zero messages in the last n minutes, all messages will be 
evicted from the window and the process-function will get triggered one 
last time on the now empty window, so we can produce a count of 0.

I have two problems, though, with this solution:
1) It is computationally inefficient for a simple count, as custom 
process functions will always keep all messages in state. And, on every 
trigger all elements will have to be touched twice: To compare the 
timestamp and to count.
2) It does seem like a very roundabout solution to a simple problem.

So, I was wondering if there was a more efficient or "flink-like" 
approach to this. Sorry for the long writeup, but I would love to hear 
your takes.


Best regards
Jan

-- 
neuland  – Büro für Informatik GmbH
Konsul-Smidt-Str. 8g, 28217 Bremen

Telefon [phone]
Fax [phone]
https://www.neuland-bfi.de

https://twitter.com/neuland
https://facebook.com/neulandbfi
https://xing.com/company/neulandbfi


Geschäftsführer: Thomas Gebauer, Jan Zander
Registergericht: Amtsgericht Bremen, HRB 23395 HB
USt-ID. DE 246585501

-- neuland  – Büro für Informatik GmbHKonsul-Smidt-Str. 8g, 28217 BremenTelefon [phone] 57Fax [phone]https://www.neuland-bfi.dehttps://twitter.com/neulandhttps://facebook.com/neulandbfihttps://xing.com/company/neulandbfiGeschäftsführer: Thomas Gebauer, Jan ZanderRegistergericht: Amtsgericht Bremen, HRB 23395 HBUSt-ID. DE 246585501

[Roman Khachatryan]

Hi Jan,

Thanks for sharing your solution.

You probably also want to remove previously created timer(s) in processElement; so that you don't end up with a timer per element.

For that, you can store the previous time (in function state).

On Fri, Feb 26, 2021 at 10:29 PM Jan Brusch <[hidden email]> wrote:

Hi everybody,

I just wanted to say thanks again for all your input and share the (surprisingly simple) solution that we came up with in the meantime:

class SensorRecordCounter extends KeyedProcessFunction<String, SensorRecord, SensorCount>{

private ValueState<SensorCount> state;
private long windowSizeMs = 60000L;

 @Override
  public void open(Configuration parameters) throws Exception {
        state = getRuntimeContext().getState(new ValueStateDescriptor<>("sensorCount", SensorCount.class));
  }


@Override
public void processElement(SensorRecord sensorRecord, Context ctx, Collector<SensorCount> out) throws Exception {
        SensorCount count = state.value();
        if (count == null) {
            count = new SensorCount();
            count.setSensorID(sensorRecord.getSensorID());
            count.setCount(0);
        }
        count.increase();
        state.update(count);
        out.collect(count);
       
        ctx.timerService().registerEventTimeTimer(ctx.timestamp() + windowSizeMs);
}

@Override
public void onTimer(long timestamp, OnTimerContext ctx, Collector<SensorCount> out) throws Exception {
        SensorCount count = state.value();
        count.decrease();
        state.update(count);
        out.collect(count);
       
        if (count.getCount() <= 0) {
            state.clear();
        }
}

}

Best regards and a nice weekend

Jan

On 09.02.21 08:28, Arvid Heise wrote:

Hi Jan,

Another solution is to insert Heartbeat-events at the source for each sensor. The solution is very similar to how to advance watermarks when there are no elements in the respective source partition.

However, it's only easy to implement if you have your own source and know all sensors on application start. It might also be possible to implement if you use a new Source interface.

On Tue, Feb 9, 2021 at 7:20 AM Yun Gao <[hidden email]> wrote:

Hi,

I also think there should be different ways to achieve the target. For the first option listed previously, 

the pseudo-code roughly like

class MyFunciton extends KeyedProcessFunction {

    ValueState<Integer> count;

    void open() {

       count = ... // Create the value state

   }  

    ​void processElement(T t, Context context, Collector collector) {

            ​Integer current = count.get();

            if (current == null) {

                      context.timeService().registerTimer(30); // Register timer for the first time

                      current = 0;

            }

            count.update(current + 1); // update the count

    }

    void onTimer(...) {

         collector.collect(new Tuple2<>(getCurrentKey(), count.get());

          context.timeService().registerTimer(30);  // register the following timer

    }

}

1. For flink the state and timer are all bound to a key implicitly, thus I think they should

not need to be bound manually.

2. To clear the outdated state, it could be cleared via count.clear(); if it has been 0 

for a long time. There are different ways to count the interval, like register another timer

and clear the timer when received the elements or update the counter to -1, -2... to mark

how much timer it has passed.

Best,

 Yun

------------------Original Mail ------------------

Sender:Khachatryan Roman <[hidden email]>

Send Date:Tue Feb 9 02:35:20 2021

Recipients:Jan Brusch <[hidden email]>

CC:Yun Gao <[hidden email]>, user <[hidden email]>

Subject:Re: Sliding Window Count: Tricky Edge Case / Count Zero Problem

Hi,

Probably another solution would be to register a timer (using KeyedProcessFunction) once we see an element after keyBy. The timer will fire in windowIntervalMs. Upon firing, it will emit a dummy element which will be ignored (or subtracted) in the end.

Upon receiving each new element, the function will shift the timer accordingly.

Regards,
Roman

On Mon, Feb 8, 2021 at 10:50 AM Jan Brusch <[hidden email]> wrote:

Hi Yun,

thanks for your reply.

I do agree with your point about standard windows being for high level operations and the lower-level apis offering a rich toolset for most advanced use cases.

I have tried to solve my problem with keyedProcessFunctions also but was not able to get it to work for two reasons:

1) I was not able to set up a combination of ValueState, Timers and Triggers that emulated a sliding window with a rising and falling count (including 0) good enough.

2) Memory Leak: States / Windows should be cleared after a certain time of being at count 0 in order to prevent an infinitely rising of ValueStates (that are not needed anymore)

Can you maybe please elaborate in pseudocode how you would envision your solution?

Best regards

Jan

On 08.02.21 05:31, Yun Gao wrote:

Hi Jan,

From my view, I think in Flink Window should be as a "high-level" operation for some kind

of aggregation operation and if it could not satisfy the requirements, we could at least turn to

using the "low-level" api by using KeyedProcessFunction[1].

In this case, we could use a ValueState to store the current value for each key, and increment

the value on each element. Then we could also register time for each key on receiving the first 

element for this key,  and in the onTimer callback, we could send the current state value, update

the value to 0 and register another timer for this key after 30s.

Best,

 Yun

[1] https://ci.apache.org/projects/flink/flink-docs-stable/dev/stream/operators/process_function.html#the-keyedprocessfunction

------------------Original Mail ------------------

Sender:Jan Brusch [hidden email]

Send Date:Sat Feb 6 23:44:00 2021

Recipients:user [hidden email]

Subject:Sliding Window Count: Tricky Edge Case / Count Zero Problem

Hi,
I was recently working on a problem where we wanted to implement a 
simple count on a sliding window, e.g. "how many messages of a certain 
type were emitted by a certain type of sensor in the last n minutes". 
Which sounds simple enough in theory:

messageStream
     .keyBy(//EmitterType + MessageType)
     .assignWindow(SlidingProcessingTimeWindows.of(Time.minutes(n), 
Time.seconds(30)))
     .map(_ => 1)
     .reduce((x,y) => x + y)
     .addSink(...)

But there is a tricky edge case: The downstream systems will never know 
when the count for a certain key goes back to 0, which is important for 
our use case. The technical reason being that flink doesn't open a 
window if there are no entries, i.e. a window with count 0 doesn't exist 
in flink.

We came up with the following solution for the time being:

messageStream
     .keyBy(//EmitterType + MessageType)
     .window(GlobalWindows.create())
     .trigger(ContinuousEventTimeTrigger.of(Time.seconds(30)))
     .evictor(// CustomEvictor: Evict all messages older than n minutes 
BEFORE processing the window)
     .process(// CustomCounter: Count all Messages in Window State);
     .addSink(...)

In the case of zero messages in the last n minutes, all messages will be 
evicted from the window and the process-function will get triggered one 
last time on the now empty window, so we can produce a count of 0.

I have two problems, though, with this solution:
1) It is computationally inefficient for a simple count, as custom 
process functions will always keep all messages in state. And, on every 
trigger all elements will have to be touched twice: To compare the 
timestamp and to count.
2) It does seem like a very roundabout solution to a simple problem.

So, I was wondering if there was a more efficient or "flink-like" 
approach to this. Sorry for the long writeup, but I would love to hear 
your takes.


Best regards
Jan

-- 
neuland  – Büro für Informatik GmbH
Konsul-Smidt-Str. 8g, 28217 Bremen

Telefon [phone]
Fax [phone]
https://www.neuland-bfi.de

https://twitter.com/neuland
https://facebook.com/neulandbfi
https://xing.com/company/neulandbfi


Geschäftsführer: Thomas Gebauer, Jan Zander
Registergericht: Amtsgericht Bremen, HRB 23395 HB
USt-ID. DE 246585501

-- neuland  – Büro für Informatik GmbHKonsul-Smidt-Str. 8g, 28217 BremenTelefon [phone] 57Fax [phone]https://www.neuland-bfi.dehttps://twitter.com/neulandhttps://facebook.com/neulandbfihttps://xing.com/company/neulandbfiGeschäftsführer: Thomas Gebauer, Jan ZanderRegistergericht: Amtsgericht Bremen, HRB 23395 HBUSt-ID. DE 246585501

-- 
neuland  – Büro für Informatik GmbH
Konsul-Smidt-Str. 8g, 28217 Bremen

Telefon [phone]
Fax [phone]
https://www.neuland-bfi.de

https://twitter.com/neuland
https://facebook.com/neulandbfi
https://xing.com/company/neulandbfi


Geschäftsführer: Thomas Gebauer, Jan Zander
Registergericht: Amtsgericht Bremen, HRB 23395 HB
USt-ID. DE 246585501

[Jan Brusch]

Hi Roman,

thanks for your reply.

Don't timers remove themselves after firing?

Apart from that, the idea is indeed to have one timer per element, so that we count one up whenever the element comes in and count one down exactly <windowsize> later. So we emulate a sliding window without the "hops" in certain intervals. Instead, we always have a real-time running count of elements in the last <windowsize>. But yes, the price for that is to have one timer per element. Which is manageable for our use case (large windowsize, a LOT of sensors but relatively few elements per sensor). In fact, for our use case this solution is much more efficient than a sliding window.

Best regards

Jan

On 02.03.21 20:40, Roman Khachatryan wrote:

Hi Jan,

Thanks for sharing your solution.

You probably also want to remove previously created timer(s) in processElement; so that you don't end up with a timer per element.

For that, you can store the previous time (in function state).

On Fri, Feb 26, 2021 at 10:29 PM Jan Brusch <[hidden email]> wrote:

Hi everybody,

I just wanted to say thanks again for all your input and share the (surprisingly simple) solution that we came up with in the meantime:

class SensorRecordCounter extends KeyedProcessFunction<String, SensorRecord, SensorCount>{

private ValueState<SensorCount> state;
private long windowSizeMs = 60000L;

 @Override
  public void open(Configuration parameters) throws Exception {
        state = getRuntimeContext().getState(new ValueStateDescriptor<>("sensorCount", SensorCount.class));
  }


@Override
public void processElement(SensorRecord sensorRecord, Context ctx, Collector<SensorCount> out) throws Exception {
        SensorCount count = state.value();
        if (count == null) {
            count = new SensorCount();
            count.setSensorID(sensorRecord.getSensorID());
            count.setCount(0);
        }
        count.increase();
        state.update(count);
        out.collect(count);
       
        ctx.timerService().registerEventTimeTimer(ctx.timestamp() + windowSizeMs);
}

@Override
public void onTimer(long timestamp, OnTimerContext ctx, Collector<SensorCount> out) throws Exception {
        SensorCount count = state.value();
        count.decrease();
        state.update(count);
        out.collect(count);
       
        if (count.getCount() <= 0) {
            state.clear();
        }
}

}

Best regards and a nice weekend

Jan

On 09.02.21 08:28, Arvid Heise wrote:

Hi Jan,

Another solution is to insert Heartbeat-events at the source for each sensor. The solution is very similar to how to advance watermarks when there are no elements in the respective source partition.

However, it's only easy to implement if you have your own source and know all sensors on application start. It might also be possible to implement if you use a new Source interface.

On Tue, Feb 9, 2021 at 7:20 AM Yun Gao <[hidden email]> wrote:

Hi,

I also think there should be different ways to achieve the target. For the first option listed previously, 

the pseudo-code roughly like

class MyFunciton extends KeyedProcessFunction {

    ValueState<Integer> count;

    void open() {

       count = ... // Create the value state

   }  

    ​void processElement(T t, Context context, Collector collector) {

            ​Integer current = count.get();

            if (current == null) {

                      context.timeService().registerTimer(30); // Register timer for the first time

                      current = 0;

            }

            count.update(current + 1); // update the count

    }

    void onTimer(...) {

         collector.collect(new Tuple2<>(getCurrentKey(), count.get());

          context.timeService().registerTimer(30);  // register the following timer

    }

}

1. For flink the state and timer are all bound to a key implicitly, thus I think they should

not need to be bound manually.

2. To clear the outdated state, it could be cleared via count.clear(); if it has been 0 

for a long time. There are different ways to count the interval, like register another timer

and clear the timer when received the elements or update the counter to -1, -2... to mark

how much timer it has passed.

Best,

 Yun

------------------Original Mail ------------------

Sender:Khachatryan Roman <[hidden email]>

Send Date:Tue Feb 9 02:35:20 2021

Recipients:Jan Brusch <[hidden email]>

CC:Yun Gao <[hidden email]>, user <[hidden email]>

Subject:Re: Sliding Window Count: Tricky Edge Case / Count Zero Problem

Hi,

Probably another solution would be to register a timer (using KeyedProcessFunction) once we see an element after keyBy. The timer will fire in windowIntervalMs. Upon firing, it will emit a dummy element which will be ignored (or subtracted) in the end.

Upon receiving each new element, the function will shift the timer accordingly.

Regards,
Roman

On Mon, Feb 8, 2021 at 10:50 AM Jan Brusch <[hidden email]> wrote:

Hi Yun,

thanks for your reply.

I do agree with your point about standard windows being for high level operations and the lower-level apis offering a rich toolset for most advanced use cases.

I have tried to solve my problem with keyedProcessFunctions also but was not able to get it to work for two reasons:

1) I was not able to set up a combination of ValueState, Timers and Triggers that emulated a sliding window with a rising and falling count (including 0) good enough.

2) Memory Leak: States / Windows should be cleared after a certain time of being at count 0 in order to prevent an infinitely rising of ValueStates (that are not needed anymore)

Can you maybe please elaborate in pseudocode how you would envision your solution?

Best regards

Jan

On 08.02.21 05:31, Yun Gao wrote:

Hi Jan,

From my view, I think in Flink Window should be as a "high-level" operation for some kind

of aggregation operation and if it could not satisfy the requirements, we could at least turn to

using the "low-level" api by using KeyedProcessFunction[1].

In this case, we could use a ValueState to store the current value for each key, and increment

the value on each element. Then we could also register time for each key on receiving the first 

element for this key,  and in the onTimer callback, we could send the current state value, update

the value to 0 and register another timer for this key after 30s.

Best,

 Yun

[1] https://ci.apache.org/projects/flink/flink-docs-stable/dev/stream/operators/process_function.html#the-keyedprocessfunction

------------------Original Mail ------------------

Sender:Jan Brusch [hidden email]

Send Date:Sat Feb 6 23:44:00 2021

Recipients:user [hidden email]

Subject:Sliding Window Count: Tricky Edge Case / Count Zero Problem

Hi,
I was recently working on a problem where we wanted to implement a 
simple count on a sliding window, e.g. "how many messages of a certain 
type were emitted by a certain type of sensor in the last n minutes". 
Which sounds simple enough in theory:

messageStream
     .keyBy(//EmitterType + MessageType)
     .assignWindow(SlidingProcessingTimeWindows.of(Time.minutes(n), 
Time.seconds(30)))
     .map(_ => 1)
     .reduce((x,y) => x + y)
     .addSink(...)

But there is a tricky edge case: The downstream systems will never know 
when the count for a certain key goes back to 0, which is important for 
our use case. The technical reason being that flink doesn't open a 
window if there are no entries, i.e. a window with count 0 doesn't exist 
in flink.

We came up with the following solution for the time being:

messageStream
     .keyBy(//EmitterType + MessageType)
     .window(GlobalWindows.create())
     .trigger(ContinuousEventTimeTrigger.of(Time.seconds(30)))
     .evictor(// CustomEvictor: Evict all messages older than n minutes 
BEFORE processing the window)
     .process(// CustomCounter: Count all Messages in Window State);
     .addSink(...)

In the case of zero messages in the last n minutes, all messages will be 
evicted from the window and the process-function will get triggered one 
last time on the now empty window, so we can produce a count of 0.

I have two problems, though, with this solution:
1) It is computationally inefficient for a simple count, as custom 
process functions will always keep all messages in state. And, on every 
trigger all elements will have to be touched twice: To compare the 
timestamp and to count.
2) It does seem like a very roundabout solution to a simple problem.

So, I was wondering if there was a more efficient or "flink-like" 
approach to this. Sorry for the long writeup, but I would love to hear 
your takes.


Best regards
Jan

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-- neuland  – Büro für Informatik GmbHKonsul-Smidt-Str. 8g, 28217 BremenTelefon [phone] 57Fax [phone]https://www.neuland-bfi.dehttps://twitter.com/neulandhttps://facebook.com/neulandbfihttps://xing.com/company/neulandbfiGeschäftsführer: Thomas Gebauer, Jan ZanderRegistergericht: Amtsgericht Bremen, HRB 23395 HBUSt-ID. DE 246585501

-- 
neuland  – Büro für Informatik GmbH
Konsul-Smidt-Str. 8g, 28217 Bremen

Telefon [phone]
Fax [phone]
https://www.neuland-bfi.de

https://twitter.com/neuland
https://facebook.com/neulandbfi
https://xing.com/company/neulandbfi


Geschäftsführer: Thomas Gebauer, Jan Zander
Registergericht: Amtsgericht Bremen, HRB 23395 HB
USt-ID. DE 246585501