+Ufuk, looping him in directly
Hmm, I think this is changed for the 1.1 release. Ufuk could you please
comment?
On Mon, 1 Aug 2016 at 08:07 Josh <jof...@gmail.com> wrote:
> Cool, thanks - I've tried out the approach where we replay data from the
> Kafka compacted log, then take a savepoint and switch to the live stream.
>
> It works but I did have to add in a dummy operator for every operator that
> was removed. Without doing this, I got an exception:
> java.lang.IllegalStateException: Failed to rollback to savepoint
> Checkpoint 1 @ 1470059433553 for cb321c233dfd28f73c565030481657cd. Cannot
> map old state for task 02ea922553bc7522bdea373f52a702d6 to the new program.
> This indicates that the program has been changed in a non-compatible way
> after the savepoint.
>
> I had a Kafka source and a flat mapper chained together when replaying, so
> to make it work I had to add two dummy operators and assign the same UID I
> used when replaying, like this:
> stream.map(x =>
> x).uid("kafka-replay").name("dummy-1").startNewChain().map(x =>
> x).name("dummy-2")
>
> I guess it would be nice if Flink could recover from removed
> tasks/operators without needing to add dummy operators like this.
>
> Josh
>
> On Fri, Jul 29, 2016 at 5:46 PM, Aljoscha Krettek <aljos...@apache.org>
> wrote:
>
>> Hi,
>> I have to try this to verify but I think the approach works if you give
>> the two sources different UIDs. The reason is that Flink will ignore state
>> for which it doesn't have an operator to assign it to. Therefore, the state
>> of the "historical Kafka source" should be silently discarded.
>>
>> Cheers,
>> Aljoscha
>>
>> On Fri, 29 Jul 2016 at 18:12 Josh <jof...@gmail.com> wrote:
>>
>>> @Aljoscha - The N-input operator way sounds very nice, for now I think
>>> I'll try and get something quick running the hacky way, then if we decide
>>> to make this a permanent solution maybe I can work on the proper solution.
>>> I was wondering about your suggestion for "warming up" the state and then
>>> taking a savepoint and switching sources - since the Kafka sources are
>>> stateful and are part of Flink's internal state, wouldn't this break when
>>> trying to restore the job with a different source? Would I need to assign
>>> the replay source a UID, and when switching from replay to live, remove the
>>> replay source and replace it with an dummy operator with the same UID?
>>>
>>> @Jason - I see what you mean now, with the historical and live Flink
>>> jobs. That's an interesting approach - I guess it's solving a slightly
>>> different problem to my 'rebuilding Flink state upon starting job' - as
>>> you're rebuilding state as part of the main job when it comes across events
>>> that require historical data. Actually I think we'll need to do something
>>> very similar in the future but right now I can probably get away with
>>> something simpler!
>>>
>>> Thanks for the replies!
>>>
>>> Josh
>>>
>>> On Fri, Jul 29, 2016 at 2:35 PM, Jason Brelloch <jb.bc....@gmail.com>
>>> wrote:
>>>
>>>> Aljoscha's approach is probably better, but to answer your questions...
>>>>
>>>> >How do you send a request from one Flink job to another?
>>>> All of our different flink jobs communicate over kafka. So the main
>>>> flink job would be listening to both a "live" kafka source, and a
>>>> "historical" kafka source. The historical flink job would listen to a
>>>> "request" kafka source. When the main job gets an event that it does not
>>>> have state for it writes to the "request" topic. The historical job would
>>>> read the request, grab the relevant old events from GCS, and write them to
>>>> the "historical" kafka topic. The "historical" source and the "live"
>>>> source are merged and proceed through the main flink job as one stream.
>>>>
>>>> >How do you handle the switchover between the live stream and the
>>>> historical stream? Do you somehow block the live stream source and detect
>>>> when the historical data source is no longer emitting new elements?
>>>> When the main job sends a request to the historical job, the main job
>>>> starts storing any events that are come in for that key. As the historical
>>>> events come in they are processed immediately. The historical flink job
>>>> flags the last event it sends. When the main flink job sees the flagged
>>>> event it knows it is caught up to where it was when it sent the request.
>>>> You can then process the events that the main job stored, and when that is
>>>> done you are caught up to the live stream, and can stop storing events for
>>>> that key and just process them as normal.
>>>>
>>>> Keep in mind that this is the dangerous part that I was talking about,
>>>> where memory in the main job would continue to build until the "historical"
>>>> events are all processed.
>>>>
>>>> >In my case I would want the Flink state to always contain the latest
>>>> state of every item (except when the job first starts and there's a period
>>>> of time where it's rebuilding its state from the Kafka log).
>>>> You could absolutely do it by reading from the beginning of a kafka
>>>> topic. The reason we do it with GCS is it is really cheap storage, and we
>>>> are not planning on storing forever on the kafka topic.
>>>>
>>>> >Since I would have everything needed to rebuild the state persisted
>>>> in a Kafka topic, I don't think I would need a second Flink job for this?
>>>> The reason for the second flink job in our case is that we didn't
>>>> really want to block the flink task slot while a single key gets caught
>>>> up. We have a much larger key domain then we have number of task slots, so
>>>> there would be multiple keys on single task slot. If you go with the
>>>> single job approach (which might be the right approach for you guys) any
>>>> other keys on that task slot will be blocked until the one key is getting
>>>> it's state built up.
>>>>
>>>> Hope that helps,
>>>>
>>>> On Fri, Jul 29, 2016 at 5:27 AM, Josh <jof...@gmail.com> wrote:
>>>>
>>>>> Hi Jason,
>>>>>
>>>>> Thanks for the reply - I didn't quite understand all of it though!
>>>>>
>>>>> > it sends a request to the historical flink job for the old data
>>>>> How do you send a request from one Flink job to another?
>>>>>
>>>>> > It continues storing the live events until all the events form the
>>>>> historical job have been processed, then it processes the stored events,
>>>>> and finally starts processing the live stream again.
>>>>> How do you handle the switchover between the live stream and the
>>>>> historical stream? Do you somehow block the live stream source and detect
>>>>> when the historical data source is no longer emitting new elements?
>>>>>
>>>>> > So in you case it looks like what you could do is send a request to
>>>>> the "historical" job whenever you get a item that you don't yet have the
>>>>> current state of.
>>>>> In my case I would want the Flink state to always contain the latest
>>>>> state of every item (except when the job first starts and there's a period
>>>>> of time where it's rebuilding its state from the Kafka log). Since I would
>>>>> have everything needed to rebuild the state persisted in a Kafka topic, I
>>>>> don't think I would need a second Flink job for this?
>>>>>
>>>>> Thanks,
>>>>> Josh
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> On Thu, Jul 28, 2016 at 6:57 PM, Jason Brelloch <jb.bc....@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Hey Josh,
>>>>>>
>>>>>> The way we replay historical data is we have a second Flink job that
>>>>>> listens to the same live stream, and stores every single event in Google
>>>>>> Cloud Storage.
>>>>>>
>>>>>> When the main Flink job that is processing the live stream gets a
>>>>>> request for a specific data set that it has not been processing yet, it
>>>>>> sends a request to the historical flink job for the old data. The live
>>>>>> job
>>>>>> then starts storing relevant events from the live stream in state. It
>>>>>> continues storing the live events until all the events form the
>>>>>> historical
>>>>>> job have been processed, then it processes the stored events, and finally
>>>>>> starts processing the live stream again.
>>>>>>
>>>>>> As long as it's properly keyed (we key on the specific data set) then
>>>>>> it doesn't block anything, keeps everything ordered, and eventually
>>>>>> catches
>>>>>> up. It also allows us to completely blow away state and rebuild it from
>>>>>> scratch.
>>>>>>
>>>>>> So in you case it looks like what you could do is send a request to
>>>>>> the "historical" job whenever you get a item that you don't yet have the
>>>>>> current state of.
>>>>>>
>>>>>> The potential problems you may have are that it may not be possible
>>>>>> to store every single historical event, and that you need to make sure
>>>>>> there is enough memory to handle the ever increasing state size while the
>>>>>> historical events are being replayed (and make sure to clear the state
>>>>>> when
>>>>>> it is done).
>>>>>>
>>>>>> It's a little complicated, and pretty expensive, but it works. Let
>>>>>> me know if something doesn't make sense.
>>>>>>
>>>>>>
>>>>>> On Thu, Jul 28, 2016 at 1:14 PM, Josh <jof...@gmail.com> wrote:
>>>>>>
>>>>>>> Hi all,
>>>>>>>
>>>>>>> I was wondering what approaches people usually take with
>>>>>>> reprocessing data with Flink - specifically the case where you want to
>>>>>>> upgrade a Flink job, and make it reprocess historical data before
>>>>>>> continuing to process a live stream.
>>>>>>>
>>>>>>> I'm wondering if we can do something similar to the 'simple rewind'
>>>>>>> or 'parallel rewind' which Samza uses to solve this problem, discussed
>>>>>>> here:
>>>>>>> https://samza.apache.org/learn/documentation/0.10/jobs/reprocessing.html
>>>>>>>
>>>>>>> Having used Flink over the past couple of months, the main issue
>>>>>>> I've had involves Flink's internal state - from my experience it seems
>>>>>>> it
>>>>>>> is easy to break the state when upgrading a job, or when changing the
>>>>>>> parallelism of operators, plus there's no easy way to view/access an
>>>>>>> internal key-value state from outside Flink.
>>>>>>>
>>>>>>> For an example of what I mean, consider a Flink job which consumes a
>>>>>>> stream of 'updates' to items, and maintains a key-value store of items
>>>>>>> within Flink's internal state (e.g. in RocksDB). The job also writes the
>>>>>>> updated items to a Kafka topic:
>>>>>>>
>>>>>>> http://oi64.tinypic.com/34q5opf.jpg
>>>>>>>
>>>>>>> My worry with this is that the state in RocksDB could be lost or
>>>>>>> become incompatible with an updated version of the job. If this
>>>>>>> happens, we
>>>>>>> need to be able to rebuild Flink's internal key-value store in RocksDB.
>>>>>>> So
>>>>>>> I'd like to be able to do something like this (which I believe is the
>>>>>>> Samza
>>>>>>> solution):
>>>>>>>
>>>>>>> http://oi67.tinypic.com/219ri95.jpg
>>>>>>>
>>>>>>> Has anyone done something like this already with Flink? If so are
>>>>>>> there any examples of how to do this replay & switchover (rebuild state
>>>>>>> by
>>>>>>> consuming from a historical log, then switch over to processing the live
>>>>>>> stream)?
>>>>>>>
>>>>>>> Thanks for any insights,
>>>>>>> Josh
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>> --
>>>>>> *Jason Brelloch* | Product Developer
>>>>>> 3405 Piedmont Rd. NE, Suite 325, Atlanta, GA 30305
>>>>>> <http://www.bettercloud.com/>
>>>>>> Subscribe to the BetterCloud Monitor
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>>>>>> -
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>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>> --
>>>> *Jason Brelloch* | Product Developer
>>>> 3405 Piedmont Rd. NE, Suite 325, Atlanta, GA 30305
>>>> <http://www.bettercloud.com/>
>>>> Subscribe to the BetterCloud Monitor
>>>> <https://www.bettercloud.com/monitor?utm_source=bettercloud_email&utm_medium=email_signature&utm_campaign=monitor_launch>
>>>> -
>>>> Get IT delivered to your inbox
>>>>
>>>
>>>
>
