Re: Proposal for Implementing Keyed Watermarks in Apache Flink

[Tawfek Yasser Tawfek]

Hello Alexander,

Will we continue the discussion?



Thanks & BR,

Tawfik


From: Tawfek Yasser Tawfek 
Sent: 30 October 2023 15:32
To: dev@flink.apache.org 
Subject: Re: Proposal for Implementing Keyed Watermarks in Apache Flink

Hi Alexander,

Thank you for your reply.

Yes. As you showed keyed-watermarks mechanism is mainly required for the case 
when we need a fine-grained calculation for each partition
[Calculation over data produced by each individual sensor], as scalability 
factors require partitioning the calculations,
so, the keyed-watermarks mechanism is designed for this type of problem.

Thanks,
Tawfik

From: Alexander Fedulov 
Sent: 30 October 2023 13:37
To: dev@flink.apache.org 
Subject: Re: Proposal for Implementing Keyed Watermarks in Apache Flink

[You don't often get email from alexander.fedu...@gmail.com. Learn why this is 
important at https://aka.ms/LearnAboutSenderIdentification ]

Hi Tawfek,

> The idea is to generate a watermark for each key (sub-stream), in order
to avoid the fast progress of the global watermark which affects low-rate
sources.

Let's consider the sensors example from the paper. Shouldn't it be about
the delay between the time of taking the measurement and its arrival at
Flink, rather than the rate at which the measurements are produced? If a
particular sensor produces no data during a specific time window, it
doesn't make sense to wait for it—there won't be any corresponding
measurement arriving because none was produced. Thus, I believe we should
be talking about situations where data from certain sensors can arrive with
significant delay compared to most other sensors.

>From the perspective of data aggregation, there are two main scenarios:
1) Calculation over data produced by multiple sensors
2) Calculation over data produced by an individual sensor

In scenario 1), there are two subcategories:
a) Meaningful results cannot be produced without data from those delayed
sensors; hence, you need to wait longer.
  => Time is propagated by the mix of all sources. You just need to set
a bounded watermark with enough lag to accommodate the delayed results.
This is precisely what event time processing and bounded watermarks are for
(no keyed watermarking is required).
b) You need to produce the results as they are and perhaps patch them later
when the delayed data arrives.
 => Time is propagated by the mix of all sources. You produce the
results as they are but utilize allowedLateness to patch the aggregates if
needed (no keyed watermarking is required).

So, is it correct to say that keyed watermarking is applicable only in
scenario 2)?

Best,
Alexander Fedulov

On Sat, 28 Oct 2023 at 14:33, Tawfek Yasser Tawfek 
wrote:

> Thanks, Alexander for your reply.
>
> Our solution initiated from this inquiry on Stack Overflow:
>
> https://stackoverflow.com/questions/52179898/does-flink-support-keyed-watermarks-if-not-is-there-any-plan-of-implementing-i
>
> The idea is to generate a watermark for each key (sub-stream), in order to
> avoid the fast progress of the global watermark which affects low-rate
> sources.
>
> Instead of using only one watermark (vanilla/global watermark), we changed
> the API to allow moving the keyBy() before the
> assignTimestampsAndWatermarks() so the stream will be partitioned then the
> TimestampsAndWatermarkOperator will handle the generation of each watermark
> for each key (source/sub-stream/partition).
>
> *Let's discuss more if you want I have a presentation at a conference, we
> can meet or whatever is suitable.*
>
> Also, I contacted David Anderson one year ago and he followed me step by
> step and helped me a lot.
>
> I attached some messages with David.
>
>
> *Thanks & BR,*
>
>
> <http://www.nu.edu.eg/>
>
>
>
>
>  Tawfik Yasser Tawfik
>
> * Teaching Assistant | AI-ITCS-NU*
>
>  Office: UB1-B, Room 229
>
>  26th of July Corridor, Sheikh Zayed City, Giza, Egypt
> --
> *From:* Alexander Fedulov 
> *Sent:* 27 October 2023 20:09
> *To:* dev@flink.apache.org 
> *Subject:* Re: Proposal for Implementing Keyed Watermarks in Apache Flink
>
> [You don't often get email from alexander.fedu...@gmail.com. Learn why
> this is important at https://aka.ms/LearnAboutSenderIdentification ]
>
> Hi Tawfek,
>
> Thanks for sharing. I am trying to understand what exact real-life problem
> you are tackling with this approach. My understanding from skimming through
> the paper is that you are concerned about some outlier event producers from
> which the events can be delayed beyond what is expected in the overall
> system.
> Do I get it correctly that the keyed watermarking only targets scenarios of
> calculating keyed windows (whic

Re: Proposal for Implementing Keyed Watermarks in Apache Flink

[Tawfek Yasser Tawfek]

Hi Alexander,

Thank you for your reply.

Yes. As you showed keyed-watermarks mechanism is mainly required for the case 
when we need a fine-grained calculation for each partition
[Calculation over data produced by each individual sensor], as scalability 
factors require partitioning the calculations,
so, the keyed-watermarks mechanism is designed for this type of problem.

Thanks,
Tawfik

From: Alexander Fedulov 
Sent: 30 October 2023 13:37
To: dev@flink.apache.org 
Subject: Re: Proposal for Implementing Keyed Watermarks in Apache Flink

[You don't often get email from alexander.fedu...@gmail.com. Learn why this is 
important at https://aka.ms/LearnAboutSenderIdentification ]

Hi Tawfek,

> The idea is to generate a watermark for each key (sub-stream), in order
to avoid the fast progress of the global watermark which affects low-rate
sources.

Let's consider the sensors example from the paper. Shouldn't it be about
the delay between the time of taking the measurement and its arrival at
Flink, rather than the rate at which the measurements are produced? If a
particular sensor produces no data during a specific time window, it
doesn't make sense to wait for it—there won't be any corresponding
measurement arriving because none was produced. Thus, I believe we should
be talking about situations where data from certain sensors can arrive with
significant delay compared to most other sensors.

>From the perspective of data aggregation, there are two main scenarios:
1) Calculation over data produced by multiple sensors
2) Calculation over data produced by an individual sensor

In scenario 1), there are two subcategories:
a) Meaningful results cannot be produced without data from those delayed
sensors; hence, you need to wait longer.
  => Time is propagated by the mix of all sources. You just need to set
a bounded watermark with enough lag to accommodate the delayed results.
This is precisely what event time processing and bounded watermarks are for
(no keyed watermarking is required).
b) You need to produce the results as they are and perhaps patch them later
when the delayed data arrives.
 => Time is propagated by the mix of all sources. You produce the
results as they are but utilize allowedLateness to patch the aggregates if
needed (no keyed watermarking is required).

So, is it correct to say that keyed watermarking is applicable only in
scenario 2)?

Best,
Alexander Fedulov

On Sat, 28 Oct 2023 at 14:33, Tawfek Yasser Tawfek 
wrote:

> Thanks, Alexander for your reply.
>
> Our solution initiated from this inquiry on Stack Overflow:
>
> https://stackoverflow.com/questions/52179898/does-flink-support-keyed-watermarks-if-not-is-there-any-plan-of-implementing-i
>
> The idea is to generate a watermark for each key (sub-stream), in order to
> avoid the fast progress of the global watermark which affects low-rate
> sources.
>
> Instead of using only one watermark (vanilla/global watermark), we changed
> the API to allow moving the keyBy() before the
> assignTimestampsAndWatermarks() so the stream will be partitioned then the
> TimestampsAndWatermarkOperator will handle the generation of each watermark
> for each key (source/sub-stream/partition).
>
> *Let's discuss more if you want I have a presentation at a conference, we
> can meet or whatever is suitable.*
>
> Also, I contacted David Anderson one year ago and he followed me step by
> step and helped me a lot.
>
> I attached some messages with David.
>
>
> *Thanks & BR,*
>
>
> <http://www.nu.edu.eg/>
>
>
>
>
>  Tawfik Yasser Tawfik
>
> * Teaching Assistant | AI-ITCS-NU*
>
>  Office: UB1-B, Room 229
>
>  26th of July Corridor, Sheikh Zayed City, Giza, Egypt
> --
> *From:* Alexander Fedulov 
> *Sent:* 27 October 2023 20:09
> *To:* dev@flink.apache.org 
> *Subject:* Re: Proposal for Implementing Keyed Watermarks in Apache Flink
>
> [You don't often get email from alexander.fedu...@gmail.com. Learn why
> this is important at https://aka.ms/LearnAboutSenderIdentification ]
>
> Hi Tawfek,
>
> Thanks for sharing. I am trying to understand what exact real-life problem
> you are tackling with this approach. My understanding from skimming through
> the paper is that you are concerned about some outlier event producers from
> which the events can be delayed beyond what is expected in the overall
> system.
> Do I get it correctly that the keyed watermarking only targets scenarios of
> calculating keyed windows (which are also keyed by the same producer ids)?
>
> Best,
> Alexander Fedulov
>
> On Fri, 27 Oct 2023 at 19:07, Tawfek Yasser Tawfek 
> wrote:
>
> > Dear Apache Flink Development Team,
> >
> > I hope this email finds you well. I pro

Re: Proposal for Implementing Keyed Watermarks in Apache Flink

[Tawfek Yasser Tawfek]

Thanks, Alexander for your reply.

Our solution initiated from this inquiry on Stack Overflow:
https://stackoverflow.com/questions/52179898/does-flink-support-keyed-watermarks-if-not-is-there-any-plan-of-implementing-i

The idea is to generate a watermark for each key (sub-stream), in order to 
avoid the fast progress of the global watermark which affects low-rate sources.

Instead of using only one watermark (vanilla/global watermark), we changed the 
API to allow moving the keyBy() before the assignTimestampsAndWatermarks() so 
the stream will be partitioned then the TimestampsAndWatermarkOperator will 
handle the generation of each watermark for each key 
(source/sub-stream/partition).

Let's discuss more if you want I have a presentation at a conference, we can 
meet or whatever is suitable.

Also, I contacted David Anderson one year ago and he followed me step by step 
and helped me a lot.

I attached some messages with David.


Thanks & BR,


[https://lh5.googleusercontent.com/MiACj615x33GxCbMNBzNppSALcuxUYChqrKMR1YZdR4zYSu1HFg3rbI7HUhmzrZhlHwHHF3QAhx2M4wlB_OQ1mmq1C6gQHVjhrfeGzmSvZdfszJeZHB8ll_WKwuxN6b6BRih4lkw5P-LuBtMKZweJD0]<http://www.nu.edu.eg/>



[https://lh4.googleusercontent.com/YakyNaMyo42CikAlRHeAN0s-s5bwjaJsThDsWKKWN0Hc7BXS4Fg57ZGWnvCGm3tZxfifJWvPrj9fyDBvtg61xDQ0ZF6eGM8kCFU-qa3gPXHTSxWlYugBFDGH2hGw-gfsz546mPGLIQSI__MruVabQCs]


 Tawfik Yasser Tawfik

 Teaching Assistant | AI-ITCS-NU

 Office: UB1-B, Room 229

 26th of July Corridor, Sheikh Zayed City, Giza, Egypt


From: Alexander Fedulov 
Sent: 27 October 2023 20:09
To: dev@flink.apache.org 
Subject: Re: Proposal for Implementing Keyed Watermarks in Apache Flink

[You don't often get email from alexander.fedu...@gmail.com. Learn why this is 
important at https://aka.ms/LearnAboutSenderIdentification ]

Hi Tawfek,

Thanks for sharing. I am trying to understand what exact real-life problem
you are tackling with this approach. My understanding from skimming through
the paper is that you are concerned about some outlier event producers from
which the events can be delayed beyond what is expected in the overall
system.
Do I get it correctly that the keyed watermarking only targets scenarios of
calculating keyed windows (which are also keyed by the same producer ids)?

Best,
Alexander Fedulov

On Fri, 27 Oct 2023 at 19:07, Tawfek Yasser Tawfek 
wrote:

> Dear Apache Flink Development Team,
>
> I hope this email finds you well. I propose an exciting new feature for
> Apache Flink that has the potential to significantly enhance its
> capabilities in handling unbounded streams of events, particularly in the
> context of event-time windowing.
>
> As you may be aware, Apache Flink has been at the forefront of Big Data
> Stream processing engines, leveraging windowing techniques to manage
> unbounded event streams effectively. The accuracy of the results obtained
> from these streams relies heavily on the ability to gather all relevant
> input within a window. At the core of this process are watermarks, which
> serve as unique timestamps marking the progression of events in time.
>
> However, our analysis has revealed a critical issue with the current
> watermark generation method in Apache Flink. This method, which operates at
> the input stream level, exhibits a bias towards faster sub-streams,
> resulting in the unfortunate consequence of dropped events from slower
> sub-streams. Our investigations showed that Apache Flink's conventional
> watermark generation approach led to an alarming data loss of approximately
> 33% when 50% of the keys around the median experienced delays. This loss
> further escalated to over 37% when 50% of random keys were delayed.
>
> In response to this issue, we have authored a research paper outlining a
> novel strategy named "keyed watermarks" to address data loss and
> substantially enhance data processing accuracy, achieving at least 99%
> accuracy in most scenarios.
>
> Moreover, we have conducted comprehensive comparative studies to evaluate
> the effectiveness of our strategy against the conventional watermark
> generation method, specifically in terms of event-time tracking accuracy.
>
> We believe that implementing keyed watermarks in Apache Flink can greatly
> enhance its performance and reliability, making it an even more valuable
> tool for organizations dealing with complex, high-throughput data
> processing tasks.
>
> We kindly request your consideration of this proposal. We would be eager
> to discuss further details, provide the full research paper, or collaborate
> closely to facilitate the integration of this feature into Apache Flink.
>
> Please check this preprint on Research Square:
> https://www.researchsquare.com/article/rs-3395909/<
> https://www.researchsquare.com/article/rs-3395909/v1>
>

Proposal for Implementing Keyed Watermarks in Apache Flink

[Tawfek Yasser Tawfek]

Dear Apache Flink Development Team,

I hope this email finds you well. I propose an exciting new feature for Apache 
Flink that has the potential to significantly enhance its capabilities in 
handling unbounded streams of events, particularly in the context of event-time 
windowing.

As you may be aware, Apache Flink has been at the forefront of Big Data Stream 
processing engines, leveraging windowing techniques to manage unbounded event 
streams effectively. The accuracy of the results obtained from these streams 
relies heavily on the ability to gather all relevant input within a window. At 
the core of this process are watermarks, which serve as unique timestamps 
marking the progression of events in time.

However, our analysis has revealed a critical issue with the current watermark 
generation method in Apache Flink. This method, which operates at the input 
stream level, exhibits a bias towards faster sub-streams, resulting in the 
unfortunate consequence of dropped events from slower sub-streams. Our 
investigations showed that Apache Flink's conventional watermark generation 
approach led to an alarming data loss of approximately 33% when 50% of the keys 
around the median experienced delays. This loss further escalated to over 37% 
when 50% of random keys were delayed.

In response to this issue, we have authored a research paper outlining a novel 
strategy named "keyed watermarks" to address data loss and substantially 
enhance data processing accuracy, achieving at least 99% accuracy in most 
scenarios.

Moreover, we have conducted comprehensive comparative studies to evaluate the 
effectiveness of our strategy against the conventional watermark generation 
method, specifically in terms of event-time tracking accuracy.

We believe that implementing keyed watermarks in Apache Flink can greatly 
enhance its performance and reliability, making it an even more valuable tool 
for organizations dealing with complex, high-throughput data processing tasks.

We kindly request your consideration of this proposal. We would be eager to 
discuss further details, provide the full research paper, or collaborate 
closely to facilitate the integration of this feature into Apache Flink.

Please check this preprint on Research Square: 
https://www.researchsquare.com/article/rs-3395909/

Thank you for your time and attention to this proposal. We look forward to the 
opportunity to contribute to the continued success and evolution of Apache 
Flink.

Best Regards,

Tawfik Yasser
Senior Teaching Assistant @ Nile University, Egypt
Email: tyas...@nu.edu.eg
LinkedIn: https://www.linkedin.com/in/tawfikyasser/

Proposal for Implementing Keyed Watermarks in Apache Flink

[Tawfek Yasser Tawfek]

Dear Apache Flink Development Team,

I hope this email finds you well. I am writing to propose an exciting new 
feature for Apache Flink that has the potential to significantly enhance its 
capabilities in handling unbounded streams of events, particularly in the 
context of event-time windowing.

As you may be aware, Apache Flink has been at the forefront of Big Data Stream 
processing engines, leveraging windowing techniques to manage unbounded event 
streams effectively. The accuracy of the results obtained from these streams 
relies heavily on the ability to gather all relevant input within a window. At 
the core of this process are watermarks, which serve as unique timestamps 
marking the progression of events in time.

However, our analysis has revealed a critical issue with the current watermark 
generation method in Apache Flink. This method, which operates at the input 
stream level, exhibits a bias towards faster sub-streams, resulting in the 
unfortunate consequence of dropped events from slower sub-streams. Our 
investigations showed that Apache Flink's conventional watermark generation 
approach led to an alarming data loss of approximately 33% when 50% of the keys 
around the median experienced delays. This loss further escalated to over 37% 
when 50% of random keys were delayed.

In response to this issue, we have authored a research paper outlining a novel 
strategy named "keyed watermarks" to address data loss and substantially 
enhance data processing accuracy, achieving at least 99% accuracy in most 
scenarios.

Moreover, we have conducted comprehensive comparative studies to evaluate the 
effectiveness of our strategy against the conventional watermark generation 
method, specifically in terms of event-time tracking accuracy.

We believe that implementing keyed watermarks in Apache Flink can greatly 
enhance its performance and reliability, making it an even more valuable tool 
for organizations dealing with complex, high-throughput data processing tasks.

We kindly request your consideration of this proposal. We would be eager to 
discuss further details, provide the full research paper, or collaborate 
closely to facilitate the integration of this feature into Apache Flink.

Thank you for your time and attention to this proposal. We look forward to the 
opportunity to contribute to the continued success and evolution of Apache 
Flink.

Best Regards,

Tawfik Yasser
Senior Teaching Assistant @ Nile University, Egypt
Email: tyas...@nu.edu.eg
LinkedIn: https://www.linkedin.com/in/tawfikyasser/

Understanding the Internals of Watermark Generation | Actual Implementation

[Tawfek Yasser Tawfek]

Dear Flink Developers,

Hope all is well,

I'm trying to understand how flink in runtime generates and emits watermarks,
I think that the TimestampsAndWatermarksOperator class is responsible for 
assigning timestamps and generating watermarks actually,
based on the specified watermark strategy in the DataStream API.

If this is correct, please how could I understand how this operator works, else 
can you please guide me on where should I work to understand the watermark 
generation process?

Thank you so much.


Best Regards.


[https://lh6.googleusercontent.com/gwaS1SR2iejRdgnwu7EZRmqp6XnYEdkf8nLoDs1YqEyXL9tgDMhRXCUfIidQwgSw5RpwcIl1i9r-A2ks6tQTTxZVens5XUI9lULU21NL1pZqJ19BcHtIdV3ACHtNZnVzgNNn4prfiaohFmfDmvlZy1fXwfHhycJDietmyqjJ3JbmQ6GCGw2xEYifvg]

  
[https://lh4.googleusercontent.com/-Alfe_t6TtKrNqUPaVDM0mPuwlKVUeZmK5n-jTDJKo70dgD8OXsNSmPv3PbkEMH-1iHoxiIuz_czeq1CkpSPdxmFbKZkRCt5YZktDRrfvUS5s9QnfqByTfVPZc8NqMqeV3sbdnyTIhuBqRPRWxOnCh9ZYncxZWSOXFLy04JXhT8jfaoVrRO4AUk3Yg]
 

 Tawfik Yasser

 Teaching Assistant, Nile University | M.Sc. INF

 Artificial Intelligence Program, ITCS

 26th of July Corridor, Sheikh Zayed City, Giza, Egypt

 www.nu.edu.eg