Hi All!
I have made a prototype that simply adds a getPipeline() method to the
JobClient interface. Then I could easily implement the Atlas hook using the
JobListener interface. I simply check if Pipeline is instanceof StreamGraph
and do the logic there.
I think this is so far the cleanest approach and I much prefer this
compared to working on the JobGraph directly which would expose even more
messy internals.
Unfortunately this change alone is not enough for the integration as we
need to make sure that all Sources/Sinks that we want to integrate to atlas
publicly expose some of their properties:
- Kafka source/sink:
- Kafka props
- Topic(s) - this is tricky for sinks
- FS source /sink:
- Hadoop props
- Base path for StreamingFileSink
- Path for ContinuousMonitoringSource
Most of these are straightforward changes, the only question is what we
want to register in Atlas from the available connectors. Ideally users
could also somehow register their own Atlas metadata for custom sources and
sinks, we could probably introduce an interface for that in Atlas.
Cheers,
Gyula
On Fri, Feb 7, 2020 at 10:37 AM Gyula Fóra <gyula.f...@gmail.com> wrote:
> Maybe we could improve the Pipeline interface in the long run, but as a
> temporary solution the JobClient could expose a getPipeline() method.
>
> That way the implementation of the JobListener could check if its a
> StreamGraph or a Plan.
>
> How bad does that sound?
>
> Gyula
>
> On Fri, Feb 7, 2020 at 10:19 AM Gyula Fóra <gyula.f...@gmail.com> wrote:
>
>> Hi Aljoscha!
>>
>> That's a valid concert but we should try to figure something out, many
>> users need this before they can use Flink.
>>
>> I think the closest thing we have right now is the StreamGraph. In
>> contrast with the JobGraph the StreamGraph is pretty nice from a metadata
>> perspective :D
>> The big downside of exposing the StreamGraph is that we don't have it in
>> batch. On the other hand we could expose the JobGraph but then the
>> integration component would still have to do the heavy lifting for batch
>> and stream specific operators and UDFs.
>>
>> Instead of exposing either StreamGraph/JobGraph, we could come up with a
>> metadata like representation for the users but that would be like
>> implementing Atlas integration itself without Atlas dependencies :D
>>
>> As a comparison point, this is how it works in Storm:
>> Every operator (spout/bolt), stores a config map (string->string) with
>> all the metadata such as operator class, and the operator specific configs.
>> The Atlas hook works on this map.
>> This is very fragile and depends on a lot of internals. Kind of like
>> exposing the JobGraph but much worse. I think we can do better.
>>
>> Gyula
>>
>> On Fri, Feb 7, 2020 at 9:55 AM Aljoscha Krettek <aljos...@apache.org>
>> wrote:
>>
>>> If we need it, we can probably beef up the JobListener to allow
>>> accessing some information about the whole graph or sources and sinks.
>>> My only concern right now is that we don't have a stable interface for
>>> our job graphs/pipelines right now.
>>>
>>> Best,
>>> Aljoscha
>>>
>>> On 06.02.20 23:00, Gyula Fóra wrote:
>>> > Hi Jeff & Till!
>>> >
>>> > Thanks for the feedback, this is exactly the discussion I was looking
>>> for.
>>> > The JobListener looks very promising if we can expose the JobGraph
>>> somehow
>>> > (correct me if I am wrong but it is not accessible at the moment).
>>> >
>>> > I did not know about this feature that's why I added my JobSubmission
>>> hook
>>> > which was pretty similar but only exposing the JobGraph. In general I
>>> like
>>> > the listener better and I would not like to add anything extra if we
>>> can
>>> > avoid it.
>>> >
>>> > Actually the bigger part of the integration work that will need more
>>> > changes in Flink will be regarding the accessibility of sources/sinks
>>> from
>>> > the JobGraph and their specific properties. For instance at the moment
>>> the
>>> > Kafka sources and sinks do not expose anything publicly such as topics,
>>> > kafka configs, etc. Same goes for other data connectors that we need to
>>> > integrate in the long run. I guess there will be a separate thread on
>>> this
>>> > once we iron out the initial integration points :)
>>> >
>>> > I will try to play around with the JobListener interface tomorrow and
>>> see
>>> > if I can extend it to meet our needs.
>>> >
>>> > Cheers,
>>> > Gyula
>>> >
>>> > On Thu, Feb 6, 2020 at 4:08 PM Jeff Zhang <zjf...@gmail.com> wrote:
>>> >
>>> >> Hi Gyula,
>>> >>
>>> >> Flink 1.10 introduced JobListener which is invoked after job
>>> submission and
>>> >> finished. May we can add api on JobClient to get what info you
>>> needed for
>>> >> altas integration.
>>> >>
>>> >>
>>> >>
>>> https://github.com/apache/flink/blob/master/flink-core/src/main/java/org/apache/flink/core/execution/JobListener.java#L46
>>> >>
>>> >>
>>> >> Gyula Fóra <gyf...@apache.org> 于2020年2月5日周三 下午7:48写道：
>>> >>
>>> >>> Hi all!
>>> >>>
>>> >>> We have started some preliminary work on the Flink - Atlas
>>> integration at
>>> >>> Cloudera. It seems that the integration will require some new hook
>>> >>> interfaces at the jobgraph generation and submission phases, so I
>>> >> figured I
>>> >>> will open a discussion thread with my initial ideas to get some early
>>> >>> feedback.
>>> >>>
>>> >>> *Minimal background*
>>> >>> Very simply put Apache Atlas is a data governance framework that
>>> stores
>>> >>> metadata for our data and processing logic to track ownership,
>>> lineage
>>> >> etc.
>>> >>> It is already integrated with systems like HDFS, Kafka, Hive and many
>>> >>> others.
>>> >>>
>>> >>> Adding Flink integration would mean that we can track the input
>>> output
>>> >> data
>>> >>> of our Flink jobs, their owners and how different Flink jobs are
>>> >> connected
>>> >>> to each other through the data they produce (lineage). This seems to
>>> be a
>>> >>> very big deal for a lot of companies :)
>>> >>>
>>> >>> *Flink - Atlas integration in a nutshell*
>>> >>> In order to integrate with Atlas we basically need 2 things.
>>> >>> - Flink entity definitions
>>> >>> - Flink Atlas hook
>>> >>>
>>> >>> The entity definition is the easy part. It is a json that contains
>>> the
>>> >>> objects (entities) that we want to store for any give Flink job. As a
>>> >>> starter we could have a single FlinkApplication entity that has a
>>> set of
>>> >>> inputs and outputs. These inputs/outputs are other Atlas entities
>>> that
>>> >> are
>>> >>> already defines such as Kafka topic or Hbase table.
>>> >>>
>>> >>> The Flink atlas hook will be the logic that creates the entity
>>> instance
>>> >> and
>>> >>> uploads it to Atlas when we start a new Flink job. This is the part
>>> where
>>> >>> we implement the core logic.
>>> >>>
>>> >>> *Job submission hook*
>>> >>> In order to implement the Atlas hook we need a place where we can
>>> inspect
>>> >>> the pipeline, create and send the metadata when the job starts. When
>>> we
>>> >>> create the FlinkApplication entity we need to be able to easily
>>> determine
>>> >>> the sources and sinks (and their properties) of the pipeline.
>>> >>>
>>> >>> Unfortunately there is no JobSubmission hook in Flink that could
>>> execute
>>> >>> this logic and even if there was one there is a mismatch of
>>> abstraction
>>> >>> levels needed to implement the integration.
>>> >>> We could imagine a JobSubmission hook executed in the JobManager
>>> runner
>>> >> as
>>> >>> this:
>>> >>>
>>> >>> void onSuccessfulSubmission(JobGraph jobGraph, Configuration
>>> >>> configuration);
>>> >>>
>>> >>> This is nice but the JobGraph makes it super difficult to extract
>>> sources
>>> >>> and UDFs to create the metadata entity. The atlas entity however
>>> could be
>>> >>> easily created from the StreamGraph object (used to represent the
>>> logical
>>> >>> flow) before the JobGraph is generated. To go around this limitation
>>> we
>>> >>> could add a JobGraphGeneratorHook interface:
>>> >>>
>>> >>> void preProcess(StreamGraph streamGraph); void postProcess(JobGraph
>>> >>> jobGraph);
>>> >>>
>>> >>> We could then generate the atlas entity in the preprocess step and
>>> add a
>>> >>> jobmission hook in the postprocess step that will simply send the
>>> already
>>> >>> baked in entity.
>>> >>>
>>> >>> *This kinda works but...*
>>> >>> The approach outlined above seems to work and we have built a POC
>>> using
>>> >> it.
>>> >>> Unfortunately it is far from nice as it exposes non-public APIs such
>>> as
>>> >> the
>>> >>> StreamGraph. Also it feels a bit weird to have 2 hooks instead of
>>> one.
>>> >>>
>>> >>> It would be much nicer if we could somehow go back from JobGraph to
>>> >>> StreamGraph or at least have an easy way to access source/sink UDFS.
>>> >>>
>>> >>> What do you think?
>>> >>>
>>> >>> Cheers,
>>> >>> Gyula
>>> >>>
>>> >>
>>> >>
>>> >> --
>>> >> Best Regards
>>> >>
>>> >> Jeff Zhang
>>> >>
>>> >
>>>
>>
