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
