Hi all,
After a closer look on our client apis, I can see there are two major
issues to consistency and integration, namely different deployment of
job cluster which couples job graph creation and cluster deployment,
and submission via CliFrontend confusing control flow of job graph
compilation and job submission. I'd like to follow the discuss above,
mainly the process described by Jeff and Stephan, and share my
ideas on these issues.
1) CliFrontend confuses the control flow of job compilation and submission.
Following the process of job submission Stephan and Jeff described,
execution environment knows all configs of the cluster and topos/settings
of the job. Ideally, in the main method of user program, it calls #execute
(or named #submit) and Flink deploys the cluster, compile the job graph
and submit it to the cluster. However, current CliFrontend does all these
things inside its #runProgram method, which introduces a lot of subclasses
of (stream) execution environment.
Actually, it sets up an exec env that hijacks the #execute/executePlan
method, initializes the job graph and abort execution. And then
control flow back to CliFrontend, it deploys the cluster(or retrieve
the client) and submits the job graph. This is quite a specific internal
process inside Flink and none of consistency to anything.
2) Deployment of job cluster couples job graph creation and cluster
deployment. Abstractly, from user job to a concrete submission, it requires
create JobGraph --\
create ClusterClient --> submit JobGraph
such a dependency. ClusterClient was created by deploying or retrieving.
JobGraph submission requires a compiled JobGraph and valid ClusterClient,
but the creation of ClusterClient is abstractly independent of that of
JobGraph. However, in job cluster mode, we deploy job cluster with a job
graph, which means we use another process:
create JobGraph --> deploy cluster with the JobGraph
Here is another inconsistency and downstream projects/client apis are
forced to handle different cases with rare supports from Flink.
Since we likely reached a consensus on
1. all configs gathered by Flink configuration and passed
2. execution environment knows all configs and handles execution(both
deployment and submission)
to the issues above I propose eliminating inconsistencies by following
approach:
1) CliFrontend should exactly be a front end, at least for "run" command.
That means it just gathered and passed all config from command line to
the main method of user program. Execution environment knows all the info
and with an addition to utils for ClusterClient, we gracefully get a
ClusterClient by deploying or retrieving. In this way, we don't need to
hijack #execute/executePlan methods and can remove various hacking
subclasses of exec env, as well as #run methods in ClusterClient(for an
interface-ized ClusterClient). Now the control flow flows from CliFrontend
to the main method and never returns.
2) Job cluster means a cluster for the specific job. From another
perspective, it is an ephemeral session. We may decouple the deployment
with a compiled job graph, but start a session with idle timeout
and submit the job following.
These topics, before we go into more details on design or implementation,
are better to be aware and discussed for a consensus.
Best,
tison.
Zili Chen <wander4...@gmail.com> 于2019年6月20日周四 上午3:21写道：
> Hi Jeff,
>
> Thanks for raising this thread and the design document!
>
> As @Thomas Weise mentioned above, extending config to flink
> requires far more effort than it should be. Another example
> is we achieve detach mode by introduce another execution
> environment which also hijack #execute method.
>
> I agree with your idea that user would configure all things
> and flink "just" respect it. On this topic I think the unusual
> control flow when CliFrontend handle "run" command is the problem.
> It handles several configs, mainly about cluster settings, and
> thus main method of user program is unaware of them. Also it compiles
> app to job graph by run the main method with a hijacked exec env,
> which constrain the main method further.
>
> I'd like to write down a few of notes on configs/args pass and respect,
> as well as decoupling job compilation and submission. Share on this
> thread later.
>
> Best,
> tison.
>
>
> SHI Xiaogang <shixiaoga...@gmail.com> 于2019年6月17日周一 下午7:29写道：
>
>> Hi Jeff and Flavio,
>>
>> Thanks Jeff a lot for proposing the design document.
>>
>> We are also working on refactoring ClusterClient to allow flexible and
>> efficient job management in our real-time platform.
>> We would like to draft a document to share our ideas with you.
>>
>> I think it's a good idea to have something like Apache Livy for Flink, and
>> the efforts discussed here will take a great step forward to it.
>>
>> Regards,
>> Xiaogang
>>
>> Flavio Pompermaier <pomperma...@okkam.it> 于2019年6月17日周一 下午7:13写道：
>>
>> > Is there any possibility to have something like Apache Livy [1] also for
>> > Flink in the future?
>> >
>> > [1] https://livy.apache.org/
>> >
>> > On Tue, Jun 11, 2019 at 5:23 PM Jeff Zhang <zjf...@gmail.com> wrote:
>> >
>> > > >>> Any API we expose should not have dependencies on the runtime
>> > > (flink-runtime) package or other implementation details. To me, this
>> > means
>> > > that the current ClusterClient cannot be exposed to users because it
>> > uses
>> > > quite some classes from the optimiser and runtime packages.
>> > >
>> > > We should change ClusterClient from class to interface.
>> > > ExecutionEnvironment only use the interface ClusterClient which
>> should be
>> > > in flink-clients while the concrete implementation class could be in
>> > > flink-runtime.
>> > >
>> > > >>> What happens when a failure/restart in the client happens? There
>> need
>> > > to be a way of re-establishing the connection to the job, set up the
>> > > listeners again, etc.
>> > >
>> > > Good point. First we need to define what does failure/restart in the
>> > > client mean. IIUC, that usually mean network failure which will
>> happen in
>> > > class RestClient. If my understanding is correct, restart/retry
>> mechanism
>> > > should be done in RestClient.
>> > >
>> > >
>> > >
>> > >
>> > >
>> > > Aljoscha Krettek <aljos...@apache.org> 于2019年6月11日周二 下午11:10写道：
>> > >
>> > > > Some points to consider:
>> > > >
>> > > > * Any API we expose should not have dependencies on the runtime
>> > > > (flink-runtime) package or other implementation details. To me, this
>> > > means
>> > > > that the current ClusterClient cannot be exposed to users because it
>> > > uses
>> > > > quite some classes from the optimiser and runtime packages.
>> > > >
>> > > > * What happens when a failure/restart in the client happens? There
>> need
>> > > to
>> > > > be a way of re-establishing the connection to the job, set up the
>> > > listeners
>> > > > again, etc.
>> > > >
>> > > > Aljoscha
>> > > >
>> > > > > On 29. May 2019, at 10:17, Jeff Zhang <zjf...@gmail.com> wrote:
>> > > > >
>> > > > > Sorry folks, the design doc is late as you expected. Here's the
>> > design
>> > > > doc
>> > > > > I drafted, welcome any comments and feedback.
>> > > > >
>> > > > >
>> > > >
>> > >
>> >
>> https://docs.google.com/document/d/1VavBrYn8vJeZs-Mhu5VzKO6xrWCF40aY0nlQ_UVVTRg/edit?usp=sharing
>> > > > >
>> > > > >
>> > > > >
>> > > > > Stephan Ewen <se...@apache.org> 于2019年2月14日周四 下午8:43写道：
>> > > > >
>> > > > >> Nice that this discussion is happening.
>> > > > >>
>> > > > >> In the FLIP, we could also revisit the entire role of the
>> > environments
>> > > > >> again.
>> > > > >>
>> > > > >> Initially, the idea was:
>> > > > >> - the environments take care of the specific setup for
>> standalone
>> > (no
>> > > > >> setup needed), yarn, mesos, etc.
>> > > > >> - the session ones have control over the session. The
>> environment
>> > > holds
>> > > > >> the session client.
>> > > > >> - running a job gives a "control" object for that job. That
>> > behavior
>> > > is
>> > > > >> the same in all environments.
>> > > > >>
>> > > > >> The actual implementation diverged quite a bit from that. Happy
>> to
>> > > see a
>> > > > >> discussion about straitening this out a bit more.
>> > > > >>
>> > > > >>
>> > > > >> On Tue, Feb 12, 2019 at 4:58 AM Jeff Zhang <zjf...@gmail.com>
>> > wrote:
>> > > > >>
>> > > > >>> Hi folks,
>> > > > >>>
>> > > > >>> Sorry for late response, It seems we reach consensus on this, I
>> > will
>> > > > >> create
>> > > > >>> FLIP for this with more detailed design
>> > > > >>>
>> > > > >>>
>> > > > >>> Thomas Weise <t...@apache.org> 于2018年12月21日周五 上午11:43写道：
>> > > > >>>
>> > > > >>>> Great to see this discussion seeded! The problems you face with
>> > the
>> > > > >>>> Zeppelin integration are also affecting other downstream
>> projects,
>> > > > like
>> > > > >>>> Beam.
>> > > > >>>>
>> > > > >>>> We just enabled the savepoint restore option in
>> > > > RemoteStreamEnvironment
>> > > > >>> [1]
>> > > > >>>> and that was more difficult than it should be. The main issue
>> is
>> > > that
>> > > > >>>> environment and cluster client aren't decoupled. Ideally it
>> should
>> > > be
>> > > > >>>> possible to just get the matching cluster client from the
>> > > environment
>> > > > >> and
>> > > > >>>> then control the job through it (environment as factory for
>> > cluster
>> > > > >>>> client). But note that the environment classes are part of the
>> > > public
>> > > > >>> API,
>> > > > >>>> and it is not straightforward to make larger changes without
>> > > breaking
>> > > > >>>> backward compatibility.
>> > > > >>>>
>> > > > >>>> ClusterClient currently exposes internal classes like JobGraph
>> and
>> > > > >>>> StreamGraph. But it should be possible to wrap this with a new
>> > > public
>> > > > >> API
>> > > > >>>> that brings the required job control capabilities for
>> downstream
>> > > > >>> projects.
>> > > > >>>> Perhaps it is helpful to look at some of the interfaces in Beam
>> > > while
>> > > > >>>> thinking about this: [2] for the portable job API and [3] for
>> the
>> > > old
>> > > > >>>> asynchronous job control from the Beam Java SDK.
>> > > > >>>>
>> > > > >>>> The backward compatibility discussion [4] is also relevant
>> here. A
>> > > new
>> > > > >>> API
>> > > > >>>> should shield downstream projects from internals and allow
>> them to
>> > > > >>>> interoperate with multiple future Flink versions in the same
>> > release
>> > > > >> line
>> > > > >>>> without forced upgrades.
>> > > > >>>>
>> > > > >>>> Thanks,
>> > > > >>>> Thomas
>> > > > >>>>
>> > > > >>>> [1] https://github.com/apache/flink/pull/7249
>> > > > >>>> [2]
>> > > > >>>>
>> > > > >>>>
>> > > > >>>
>> > > > >>
>> > > >
>> > >
>> >
>> https://github.com/apache/beam/blob/master/model/job-management/src/main/proto/beam_job_api.proto
>> > > > >>>> [3]
>> > > > >>>>
>> > > > >>>>
>> > > > >>>
>> > > > >>
>> > > >
>> > >
>> >
>> https://github.com/apache/beam/blob/master/sdks/java/core/src/main/java/org/apache/beam/sdk/PipelineResult.java
>> > > > >>>> [4]
>> > > > >>>>
>> > > > >>>>
>> > > > >>>
>> > > > >>
>> > > >
>> > >
>> >
>> https://lists.apache.org/thread.html/064c75c5d10f0806095b14f6d76942598917a14429c1acbddd151fe2@%3Cdev.flink.apache.org%3E
>> > > > >>>>
>> > > > >>>>
>> > > > >>>> On Thu, Dec 20, 2018 at 6:15 PM Jeff Zhang <zjf...@gmail.com>
>> > > wrote:
>> > > > >>>>
>> > > > >>>>>>>> I'm not so sure whether the user should be able to define
>> > where
>> > > > >> the
>> > > > >>>> job
>> > > > >>>>> runs (in your example Yarn). This is actually independent of
>> the
>> > > job
>> > > > >>>>> development and is something which is decided at deployment
>> time.
>> > > > >>>>>
>> > > > >>>>> User don't need to specify execution mode programmatically.
>> They
>> > > can
>> > > > >>> also
>> > > > >>>>> pass the execution mode from the arguments in flink run
>> command.
>> > > e.g.
>> > > > >>>>>
>> > > > >>>>> bin/flink run -m yarn-cluster ....
>> > > > >>>>> bin/flink run -m local ...
>> > > > >>>>> bin/flink run -m host:port ...
>> > > > >>>>>
>> > > > >>>>> Does this make sense to you ?
>> > > > >>>>>
>> > > > >>>>>>>> To me it makes sense that the ExecutionEnvironment is not
>> > > > >> directly
>> > > > >>>>> initialized by the user and instead context sensitive how you
>> > want
>> > > to
>> > > > >>>>> execute your job (Flink CLI vs. IDE, for example).
>> > > > >>>>>
>> > > > >>>>> Right, currently I notice Flink would create different
>> > > > >>>>> ContextExecutionEnvironment based on different submission
>> > scenarios
>> > > > >>>> (Flink
>> > > > >>>>> Cli vs IDE). To me this is kind of hack approach, not so
>> > > > >>> straightforward.
>> > > > >>>>> What I suggested above is that is that flink should always
>> create
>> > > the
>> > > > >>>> same
>> > > > >>>>> ExecutionEnvironment but with different configuration, and
>> based
>> > on
>> > > > >> the
>> > > > >>>>> configuration it would create the proper ClusterClient for
>> > > different
>> > > > >>>>> behaviors.
>> > > > >>>>>
>> > > > >>>>>
>> > > > >>>>>
>> > > > >>>>>
>> > > > >>>>>
>> > > > >>>>>
>> > > > >>>>>
>> > > > >>>>> Till Rohrmann <trohrm...@apache.org> 于2018年12月20日周四
>> 下午11:18写道：
>> > > > >>>>>
>> > > > >>>>>> You are probably right that we have code duplication when it
>> > comes
>> > > > >> to
>> > > > >>>> the
>> > > > >>>>>> creation of the ClusterClient. This should be reduced in the
>> > > > >> future.
>> > > > >>>>>>
>> > > > >>>>>> I'm not so sure whether the user should be able to define
>> where
>> > > the
>> > > > >>> job
>> > > > >>>>>> runs (in your example Yarn). This is actually independent of
>> the
>> > > > >> job
>> > > > >>>>>> development and is something which is decided at deployment
>> > time.
>> > > > >> To
>> > > > >>> me
>> > > > >>>>> it
>> > > > >>>>>> makes sense that the ExecutionEnvironment is not directly
>> > > > >> initialized
>> > > > >>>> by
>> > > > >>>>>> the user and instead context sensitive how you want to
>> execute
>> > > your
>> > > > >>> job
>> > > > >>>>>> (Flink CLI vs. IDE, for example). However, I agree that the
>> > > > >>>>>> ExecutionEnvironment should give you access to the
>> ClusterClient
>> > > > >> and
>> > > > >>> to
>> > > > >>>>> the
>> > > > >>>>>> job (maybe in the form of the JobGraph or a job plan).
>> > > > >>>>>>
>> > > > >>>>>> Cheers,
>> > > > >>>>>> Till
>> > > > >>>>>>
>> > > > >>>>>> On Thu, Dec 13, 2018 at 4:36 AM Jeff Zhang <zjf...@gmail.com
>> >
>> > > > >> wrote:
>> > > > >>>>>>
>> > > > >>>>>>> Hi Till,
>> > > > >>>>>>> Thanks for the feedback. You are right that I expect better
>> > > > >>>>> programmatic
>> > > > >>>>>>> job submission/control api which could be used by downstream
>> > > > >>> project.
>> > > > >>>>> And
>> > > > >>>>>>> it would benefit for the flink ecosystem. When I look at the
>> > code
>> > > > >>> of
>> > > > >>>>>> flink
>> > > > >>>>>>> scala-shell and sql-client (I believe they are not the core
>> of
>> > > > >>> flink,
>> > > > >>>>> but
>> > > > >>>>>>> belong to the ecosystem of flink), I find many duplicated
>> code
>> > > > >> for
>> > > > >>>>>> creating
>> > > > >>>>>>> ClusterClient from user provided configuration
>> (configuration
>> > > > >>> format
>> > > > >>>>> may
>> > > > >>>>>> be
>> > > > >>>>>>> different from scala-shell and sql-client) and then use that
>> > > > >>>>>> ClusterClient
>> > > > >>>>>>> to manipulate jobs. I don't think this is convenient for
>> > > > >> downstream
>> > > > >>>>>>> projects. What I expect is that downstream project only
>> needs
>> > to
>> > > > >>>>> provide
>> > > > >>>>>>> necessary configuration info (maybe introducing class
>> > FlinkConf),
>> > > > >>> and
>> > > > >>>>>> then
>> > > > >>>>>>> build ExecutionEnvironment based on this FlinkConf, and
>> > > > >>>>>>> ExecutionEnvironment will create the proper ClusterClient.
>> It
>> > not
>> > > > >>>> only
>> > > > >>>>>>> benefit for the downstream project development but also be
>> > > > >> helpful
>> > > > >>>> for
>> > > > >>>>>>> their integration test with flink. Here's one sample code
>> > snippet
>> > > > >>>> that
>> > > > >>>>> I
>> > > > >>>>>>> expect.
>> > > > >>>>>>>
>> > > > >>>>>>> val conf = new FlinkConf().mode("yarn")
>> > > > >>>>>>> val env = new ExecutionEnvironment(conf)
>> > > > >>>>>>> val jobId = env.submit(...)
>> > > > >>>>>>> val jobStatus = env.getClusterClient().queryJobStatus(jobId)
>> > > > >>>>>>> env.getClusterClient().cancelJob(jobId)
>> > > > >>>>>>>
>> > > > >>>>>>> What do you think ?
>> > > > >>>>>>>
>> > > > >>>>>>>
>> > > > >>>>>>>
>> > > > >>>>>>>
>> > > > >>>>>>> Till Rohrmann <trohrm...@apache.org> 于2018年12月11日周二
>> 下午6:28写道：
>> > > > >>>>>>>
>> > > > >>>>>>>> Hi Jeff,
>> > > > >>>>>>>>
>> > > > >>>>>>>> what you are proposing is to provide the user with better
>> > > > >>>>> programmatic
>> > > > >>>>>>> job
>> > > > >>>>>>>> control. There was actually an effort to achieve this but
>> it
>> > > > >> has
>> > > > >>>>> never
>> > > > >>>>>>> been
>> > > > >>>>>>>> completed [1]. However, there are some improvement in the
>> code
>> > > > >>> base
>> > > > >>>>>> now.
>> > > > >>>>>>>> Look for example at the NewClusterClient interface which
>> > > > >> offers a
>> > > > >>>>>>>> non-blocking job submission. But I agree that we need to
>> > > > >> improve
>> > > > >>>>> Flink
>> > > > >>>>>> in
>> > > > >>>>>>>> this regard.
>> > > > >>>>>>>>
>> > > > >>>>>>>> I would not be in favour if exposing all ClusterClient
>> calls
>> > > > >> via
>> > > > >>>> the
>> > > > >>>>>>>> ExecutionEnvironment because it would clutter the class and
>> > > > >> would
>> > > > >>>> not
>> > > > >>>>>> be
>> > > > >>>>>>> a
>> > > > >>>>>>>> good separation of concerns. Instead one idea could be to
>> > > > >>> retrieve
>> > > > >>>>> the
>> > > > >>>>>>>> current ClusterClient from the ExecutionEnvironment which
>> can
>> > > > >>> then
>> > > > >>>> be
>> > > > >>>>>>> used
>> > > > >>>>>>>> for cluster and job control. But before we start an effort
>> > > > >> here,
>> > > > >>> we
>> > > > >>>>>> need
>> > > > >>>>>>> to
>> > > > >>>>>>>> agree and capture what functionality we want to provide.
>> > > > >>>>>>>>
>> > > > >>>>>>>> Initially, the idea was that we have the ClusterDescriptor
>> > > > >>>> describing
>> > > > >>>>>> how
>> > > > >>>>>>>> to talk to cluster manager like Yarn or Mesos. The
>> > > > >>>> ClusterDescriptor
>> > > > >>>>>> can
>> > > > >>>>>>> be
>> > > > >>>>>>>> used for deploying Flink clusters (job and session) and
>> gives
>> > > > >>> you a
>> > > > >>>>>>>> ClusterClient. The ClusterClient controls the cluster (e.g.
>> > > > >>>>> submitting
>> > > > >>>>>>>> jobs, listing all running jobs). And then there was the
>> idea
>> > to
>> > > > >>>>>>> introduce a
>> > > > >>>>>>>> JobClient which you obtain from the ClusterClient to
>> trigger
>> > > > >> job
>> > > > >>>>>> specific
>> > > > >>>>>>>> operations (e.g. taking a savepoint, cancelling the job).
>> > > > >>>>>>>>
>> > > > >>>>>>>> [1] https://issues.apache.org/jira/browse/FLINK-4272
>> > > > >>>>>>>>
>> > > > >>>>>>>> Cheers,
>> > > > >>>>>>>> Till
>> > > > >>>>>>>>
>> > > > >>>>>>>> On Tue, Dec 11, 2018 at 10:13 AM Jeff Zhang <
>> zjf...@gmail.com
>> > >
>> > > > >>>>> wrote:
>> > > > >>>>>>>>
>> > > > >>>>>>>>> Hi Folks,
>> > > > >>>>>>>>>
>> > > > >>>>>>>>> I am trying to integrate flink into apache zeppelin which
>> is
>> > > > >> an
>> > > > >>>>>>>> interactive
>> > > > >>>>>>>>> notebook. And I hit several issues that is caused by flink
>> > > > >>> client
>> > > > >>>>>> api.
>> > > > >>>>>>> So
>> > > > >>>>>>>>> I'd like to proposal the following changes for flink
>> client
>> > > > >>> api.
>> > > > >>>>>>>>>
>> > > > >>>>>>>>> 1. Support nonblocking execution. Currently,
>> > > > >>>>>>> ExecutionEnvironment#execute
>> > > > >>>>>>>>> is a blocking method which would do 2 things, first submit
>> > > > >> job
>> > > > >>>> and
>> > > > >>>>>> then
>> > > > >>>>>>>>> wait for job until it is finished. I'd like introduce a
>> > > > >>>> nonblocking
>> > > > >>>>>>>>> execution method like ExecutionEnvironment#submit which
>> only
>> > > > >>>> submit
>> > > > >>>>>> job
>> > > > >>>>>>>> and
>> > > > >>>>>>>>> then return jobId to client. And allow user to query the
>> job
>> > > > >>>> status
>> > > > >>>>>> via
>> > > > >>>>>>>> the
>> > > > >>>>>>>>> jobId.
>> > > > >>>>>>>>>
>> > > > >>>>>>>>> 2. Add cancel api in
>> > > > >>>>> ExecutionEnvironment/StreamExecutionEnvironment,
>> > > > >>>>>>>>> currently the only way to cancel job is via cli
>> (bin/flink),
>> > > > >>> this
>> > > > >>>>> is
>> > > > >>>>>>> not
>> > > > >>>>>>>>> convenient for downstream project to use this feature. So
>> I'd
>> > > > >>>> like
>> > > > >>>>> to
>> > > > >>>>>>> add
>> > > > >>>>>>>>> cancel api in ExecutionEnvironment
>> > > > >>>>>>>>>
>> > > > >>>>>>>>> 3. Add savepoint api in
>> > > > >>>>>>> ExecutionEnvironment/StreamExecutionEnvironment.
>> > > > >>>>>>>> It
>> > > > >>>>>>>>> is similar as cancel api, we should use
>> ExecutionEnvironment
>> > > > >> as
>> > > > >>>> the
>> > > > >>>>>>>> unified
>> > > > >>>>>>>>> api for third party to integrate with flink.
>> > > > >>>>>>>>>
>> > > > >>>>>>>>> 4. Add listener for job execution lifecycle. Something
>> like
>> > > > >>>>>> following,
>> > > > >>>>>>> so
>> > > > >>>>>>>>> that downstream project can do custom logic in the
>> lifecycle
>> > > > >> of
>> > > > >>>>> job.
>> > > > >>>>>>> e.g.
>> > > > >>>>>>>>> Zeppelin would capture the jobId after job is submitted
>> and
>> > > > >>> then
>> > > > >>>>> use
>> > > > >>>>>>> this
>> > > > >>>>>>>>> jobId to cancel it later when necessary.
>> > > > >>>>>>>>>
>> > > > >>>>>>>>> public interface JobListener {
>> > > > >>>>>>>>>
>> > > > >>>>>>>>> void onJobSubmitted(JobID jobId);
>> > > > >>>>>>>>>
>> > > > >>>>>>>>> void onJobExecuted(JobExecutionResult jobResult);
>> > > > >>>>>>>>>
>> > > > >>>>>>>>> void onJobCanceled(JobID jobId);
>> > > > >>>>>>>>> }
>> > > > >>>>>>>>>
>> > > > >>>>>>>>> 5. Enable session in ExecutionEnvironment. Currently it is
>> > > > >>>>> disabled,
>> > > > >>>>>>> but
>> > > > >>>>>>>>> session is very convenient for third party to submitting
>> jobs
>> > > > >>>>>>>> continually.
>> > > > >>>>>>>>> I hope flink can enable it again.
>> > > > >>>>>>>>>
>> > > > >>>>>>>>> 6. Unify all flink client api into
>> > > > >>>>>>>>> ExecutionEnvironment/StreamExecutionEnvironment.
>> > > > >>>>>>>>>
>> > > > >>>>>>>>> This is a long term issue which needs more careful
>> thinking
>> > > > >> and
>> > > > >>>>>> design.
>> > > > >>>>>>>>> Currently some of features of flink is exposed in
>> > > > >>>>>>>>> ExecutionEnvironment/StreamExecutionEnvironment, but some
>> are
>> > > > >>>>> exposed
>> > > > >>>>>>> in
>> > > > >>>>>>>>> cli instead of api, like the cancel and savepoint I
>> mentioned
>> > > > >>>>> above.
>> > > > >>>>>> I
>> > > > >>>>>>>>> think the root cause is due to that flink didn't unify the
>> > > > >>>>>> interaction
>> > > > >>>>>>>> with
>> > > > >>>>>>>>> flink. Here I list 3 scenarios of flink operation
>> > > > >>>>>>>>>
>> > > > >>>>>>>>> - Local job execution. Flink will create
>> LocalEnvironment
>> > > > >>> and
>> > > > >>>>>> then
>> > > > >>>>>>>> use
>> > > > >>>>>>>>> this LocalEnvironment to create LocalExecutor for job
>> > > > >>>> execution.
>> > > > >>>>>>>>> - Remote job execution. Flink will create ClusterClient
>> > > > >>> first
>> > > > >>>>> and
>> > > > >>>>>>> then
>> > > > >>>>>>>>> create ContextEnvironment based on the ClusterClient and
>> > > > >>> then
>> > > > >>>>> run
>> > > > >>>>>>> the
>> > > > >>>>>>>>> job.
>> > > > >>>>>>>>> - Job cancelation. Flink will create ClusterClient first
>> > > > >> and
>> > > > >>>>> then
>> > > > >>>>>>>> cancel
>> > > > >>>>>>>>> this job via this ClusterClient.
>> > > > >>>>>>>>>
>> > > > >>>>>>>>> As you can see in the above 3 scenarios. Flink didn't use
>> the
>> > > > >>>> same
>> > > > >>>>>>>>> approach(code path) to interact with flink
>> > > > >>>>>>>>> What I propose is following:
>> > > > >>>>>>>>> Create the proper LocalEnvironment/RemoteEnvironment
>> (based
>> > > > >> on
>> > > > >>>> user
>> > > > >>>>>>>>> configuration) --> Use this Environment to create proper
>> > > > >>>>>> ClusterClient
>> > > > >>>>>>>>> (LocalClusterClient or RestClusterClient) to interactive
>> with
>> > > > >>>>> Flink (
>> > > > >>>>>>> job
>> > > > >>>>>>>>> execution or cancelation)
>> > > > >>>>>>>>>
>> > > > >>>>>>>>> This way we can unify the process of local execution and
>> > > > >> remote
>> > > > >>>>>>>> execution.
>> > > > >>>>>>>>> And it is much easier for third party to integrate with
>> > > > >> flink,
>> > > > >>>>>> because
>> > > > >>>>>>>>> ExecutionEnvironment is the unified entry point for flink.
>> > > > >> What
>> > > > >>>>> third
>> > > > >>>>>>>> party
>> > > > >>>>>>>>> needs to do is just pass configuration to
>> > > > >> ExecutionEnvironment
>> > > > >>>> and
>> > > > >>>>>>>>> ExecutionEnvironment will do the right thing based on the
>> > > > >>>>>>> configuration.
>> > > > >>>>>>>>> Flink cli can also be considered as flink api consumer. it
>> > > > >> just
>> > > > >>>>> pass
>> > > > >>>>>>> the
>> > > > >>>>>>>>> configuration to ExecutionEnvironment and let
>> > > > >>>> ExecutionEnvironment
>> > > > >>>>> to
>> > > > >>>>>>>>> create the proper ClusterClient instead of letting cli to
>> > > > >>> create
>> > > > >>>>>>>>> ClusterClient directly.
>> > > > >>>>>>>>>
>> > > > >>>>>>>>>
>> > > > >>>>>>>>> 6 would involve large code refactoring, so I think we can
>> > > > >> defer
>> > > > >>>> it
>> > > > >>>>>> for
>> > > > >>>>>>>>> future release, 1,2,3,4,5 could be done at once I believe.
>> > > > >> Let
>> > > > >>> me
>> > > > >>>>>> know
>> > > > >>>>>>>> your
>> > > > >>>>>>>>> comments and feedback, thanks
>> > > > >>>>>>>>>
>> > > > >>>>>>>>>
>> > > > >>>>>>>>>
>> > > > >>>>>>>>> --
>> > > > >>>>>>>>> Best Regards
>> > > > >>>>>>>>>
>> > > > >>>>>>>>> Jeff Zhang
>> > > > >>>>>>>>>
>> > > > >>>>>>>>
>> > > > >>>>>>>
>> > > > >>>>>>>
>> > > > >>>>>>> --
>> > > > >>>>>>> Best Regards
>> > > > >>>>>>>
>> > > > >>>>>>> Jeff Zhang
>> > > > >>>>>>>
>> > > > >>>>>>
>> > > > >>>>>
>> > > > >>>>>
>> > > > >>>>> --
>> > > > >>>>> Best Regards
>> > > > >>>>>
>> > > > >>>>> Jeff Zhang
>> > > > >>>>>
>> > > > >>>>
>> > > > >>>
>> > > > >>>
>> > > > >>> --
>> > > > >>> Best Regards
>> > > > >>>
>> > > > >>> Jeff Zhang
>> > > > >>>
>> > > > >>
>> > > > >
>> > > > >
>> > > > > --
>> > > > > Best Regards
>> > > > >
>> > > > > Jeff Zhang
>> > > >
>> > > >
>> > >
>> > > --
>> > > Best Regards
>> > >
>> > > Jeff Zhang
>> > >
>> >
>>
>
