Thanks Jiangang for bringing this up.
As mentioned in Jiangang's email, `dynamic configuration framework`
provides many useful functions in Kuaishou, because it could update job
behavior without relaunching the job. The functions are very popular in
Kuaishou, we also see similar demands in maillist [1].
I'm big +1 for this feature.
Thanks Xintong and Yun for deep thoughts about the issue. I like the idea
about introducing control mode in Flink.
It takes the original issue a big step closer to essence which also
provides the possibility for more fantastic features as mentioned in
Xintong and Jark's response.
Based on the idea, there are at least two milestones to achieve the goals
which were proposed by Jiangang:
(1) Build a common control flow framework in Flink.
It focuses on control flow propagation. And, how to integrate the
common control flow framework with existing mechanisms.
(2) Builds a dynamic configuration framework which is exposed to users
directly.
We could see dynamic configuration framework is a top application on
the underlying control flow framework.
It focuses on the Public API which receives configuration updating
requests from users. Besides, it is necessary to introduce an API
protection mechanism to avoid job performance degradation caused by too
many control events.
I suggest splitting the whole design into two after we reach a consensus on
whether to introduce this feature because these two sub-topic all need
careful design.
[
http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/Dynamic-configuration-of-Flink-checkpoint-interval-td44059.html
]
Best regards,
JING ZHANG
刘建刚 <liujiangangp...@gmail.com> 于2021年6月8日周二 上午10:01写道：
> Thanks Xintong Song for the detailed supplement. Since flink is
> long-running, it is similar to many services. So interacting with it or
> controlling it is a common desire. This was our initial thought when
> implementing the feature. In our inner flink, many configs used in yaml can
> be adjusted by dynamic to avoid restarting the job, for examples as follow:
>
> 1. Limit the input qps.
> 2. Degrade the job by sampling and so on.
> 3. Reset kafka offset in certain cases.
> 4. Stop checkpoint in certain cases.
> 5. Control the history consuming.
> 6. Change log level for debug.
>
>
> After deep discussion, we realize that a common control flow
> will benefit both users and developers. Dynamic config is just one of the
> use cases. For the concrete design and implementation, it relates with many
> components, like jobmaster, network channel, operators and so on, which
> needs deeper consideration and design.
>
> Xintong Song [via Apache Flink User Mailing List archive.] <
> ml+s2336050n44245...@n4.nabble.com> 于2021年6月7日周一 下午2:52写道：
>
>> Thanks Jiangang for bringing this up, and Steven & Peter for the feedback.
>>
>> I was part of the preliminary offline discussions before this proposal
>> went public. So maybe I can help clarify things a bit.
>>
>> In short, despite the phrase "control mode" might be a bit misleading,
>> what we truly want to do from my side is to make the concept of "control
>> flow" explicit and expose it to users.
>>
>> ## Background
>> Jiangang & his colleagues at Kuaishou maintain an internal version of
>> Flink. One of their custom features is allowing dynamically changing
>> operator behaviors via the REST APIs. He's willing to contribute this
>> feature to the community, and came to Yun Gao and me for suggestions. After
>> discussion, we feel that the underlying question to be answered is how do
>> we model the control flow in Flink. Dynamically controlling jobs via REST
>> API can be one of the features built on top of the control flow, and there
>> could be others.
>>
>> ## Control flow
>> Control flow refers to the communication channels for sending
>> events/signals to/between tasks/operators, that changes Flink's behavior in
>> a way that may or may not affect the computation logic. Typical control
>> events/signals Flink currently has are watermarks and checkpoint barriers.
>>
>> In general, for modeling control flow, the following questions should be
>> considered.
>> 1. Who (which component) is responsible for generating the control
>> messages?
>> 2. Who (which component) is responsible for reacting to the messages.
>> 3. How do the messages propagate?
>> 4. When it comes to affecting the computation logics, how should the
>> control flow work together with the exact-once consistency.
>>
>> 1) & 2) may vary depending on the use cases, while 3) & 4) probably share
>> many things in common. A unified control flow model would help deduplicate
>> the common logics, allowing us to focus on the use case specific parts.
>>
>> E.g.,
>> - Watermarks: generated by source operators, handled by window operators.
>> - Checkpoint barrier: generated by the checkpoint coordinator, handled by
>> all tasks
>> - Dynamic controlling: generated by JobMaster (in reaction to the REST
>> command), handled by specific operators/UDFs
>> - Operator defined events: The following features are still in planning,
>> but may potentially benefit from the control flow model. (Please correct me
>> if I'm wrong, @Yun, @Jark)
>> * Iteration: When a certain condition is met, we might want to signal
>> downstream operators with an event
>> * Mini-batch assembling: Flink currently uses special watermarks for
>> indicating the end of each mini-batch, which makes it tricky to deal with
>> event time related computations.
>> * Hive dimension table join: For periodically reloaded hive tables, it
>> would be helpful to have specific events signaling that a reloading is
>> finished.
>> * Bootstrap dimension table join: This is similar to the previous one.
>> In cases where we want to fully load the dimension table before starting
>> joining the mainstream, it would be helpful to have an event signaling the
>> finishing of the bootstrap.
>>
>> ## Dynamic REST controlling
>> Back to the specific feature that Jiangang proposed, I personally think
>> it's quite convenient. Currently, to dynamically change the behavior of an
>> operator, we need to set up a separate source for the control events and
>> leverage broadcast state. Being able to send the events via REST APIs
>> definitely improves the usability.
>>
>> Leveraging dynamic configuration frameworks is for sure one possible
>> approach. The reason we are in favor of introducing the control flow is
>> that:
>> - It benefits not only this specific dynamic controlling feature, but
>> potentially other future features as well.
>> - AFAICS, it's non-trivial to make a 3rd-party dynamic configuration
>> framework work together with Flink's consistency mechanism.
>>
>> Thank you~
>>
>> Xintong Song
>>
>>
>>
>> On Mon, Jun 7, 2021 at 11:05 AM 刘建刚 <[hidden email]
>> <http:///user/SendEmail.jtp?type=node&node=44245&i=0>> wrote:
>>
>>> Thank you for the reply. I have checked the post you mentioned. The
>>> dynamic config may be useful sometimes. But it is hard to keep data
>>> consistent in flink, for example, what if the dynamic config will take
>>> effect when failover. Since dynamic config is a desire for users, maybe
>>> flink can support it in some way.
>>>
>>> For the control mode, dynamic config is just one of the control modes.
>>> In the google doc, I have list some other cases. For example, control
>>> events are generated in operators or external services. Besides user's
>>> dynamic config, flink system can support some common dynamic configuration,
>>> like qps limit, checkpoint control and so on.
>>>
>>> It needs good design to handle the control mode structure. Based on
>>> that, other control features can be added easily later, like changing log
>>> level when job is running. In the end, flink will not just process data,
>>> but also interact with users to receive control events like a service.
>>>
>>> Steven Wu <[hidden email]
>>> <http:///user/SendEmail.jtp?type=node&node=44245&i=1>> 于2021年6月4日周五
>>> 下午11:11写道：
>>>
>>>> I am not sure if we should solve this problem in Flink. This is more
>>>> like a dynamic config problem that probably should be solved by some
>>>> configuration framework. Here is one post from google search:
>>>> https://medium.com/twodigits/dynamic-app-configuration-inject-configuration-at-run-time-using-spring-boot-and-docker-ffb42631852a
>>>>
>>>> On Fri, Jun 4, 2021 at 7:09 AM 刘建刚 <[hidden email]
>>>> <http:///user/SendEmail.jtp?type=node&node=44245&i=2>> wrote:
>>>>
>>>>> Hi everyone,
>>>>>
>>>>> Flink jobs are always long-running. When the job is running,
>>>>> users may want to control the job but not stop it. The control reasons can
>>>>> be different as following:
>>>>>
>>>>> 1.
>>>>>
>>>>> Change data processing’ logic, such as filter condition.
>>>>> 2.
>>>>>
>>>>> Send trigger events to make the progress forward.
>>>>> 3.
>>>>>
>>>>> Define some tools to degrade the job, such as limit input qps,
>>>>> sampling data.
>>>>> 4.
>>>>>
>>>>> Change log level to debug current problem.
>>>>>
>>>>> The common way to do this is to stop the job, do modifications
>>>>> and start the job. It may take a long time to recover. In some situations,
>>>>> stopping jobs is intolerable, for example, the job is related to money or
>>>>> important activities.So we need some technologies to control the
>>>>> running job without stopping the job.
>>>>>
>>>>>
>>>>> We propose to add control mode for flink. A control mode based on the
>>>>> restful interface is first introduced. It works by these steps:
>>>>>
>>>>>
>>>>> 1. The user can predefine some logic which supports config
>>>>> control, such as filter condition.
>>>>> 2. Run the job.
>>>>> 3. If the user wants to change the job's running logic, just send
>>>>> a restful request with the responding config.
>>>>>
>>>>> Other control modes will also be considered in the future. More
>>>>> introduction can refer to the doc
>>>>> https://docs.google.com/document/d/1WSU3Tw-pSOcblm3vhKFYApzVkb-UQ3kxso8c8jEzIuA/edit?usp=sharing
>>>>> . If the community likes the proposal, more discussion is needed and a
>>>>> more
>>>>> detailed design will be given later. Any suggestions and ideas are
>>>>> welcome.
>>>>>
>>>>>
>>
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