That would add a synchronization point that forces extra latency especially
in streaming mode.
Wouldn't it be possible for the runner to assign the token when starting
the bundle and for the SDK to pass it along the state requests? That way,
there would be no need to batch and wait for a flush.
On Mon, Aug 5, 2019 at 2:49 PM Lukasz Cwik <lc...@google.com> wrote:
> I believe the intent is to add a new state API call telling the runner
> that it is blocked waiting for a response (BEAM-7000).
>
> This should allow the runner to wait till it sees one of these I'm blocked
> requests and then merge + batch any state calls it may have at that point
> in time allowing it to convert clear + appends into set calls and do any
> other optimizations as well. By default, the runner would have a time and
> space based limit on how many outstanding state calls there are before
> choosing to resolve them.
>
> On Mon, Aug 5, 2019 at 5:43 PM Lukasz Cwik <lc...@google.com> wrote:
>
>> Now I see what you mean.
>>
>> On Mon, Aug 5, 2019 at 5:42 PM Thomas Weise <t...@apache.org> wrote:
>>
>>> Hi Luke,
>>>
>>> I guess the answer is that it depends on the state backend. If a set
>>> operation in the state backend is available that is more efficient than
>>> clear+append, then it would be beneficial to have a dedicated fn api
>>> operation to allow for such optimization. That's something that needs to be
>>> determined with a profiler :)
>>>
>>> But the low hanging fruit is cross-bundle caching.
>>>
>>> Thomas
>>>
>>> On Mon, Aug 5, 2019 at 2:06 PM Lukasz Cwik <lc...@google.com> wrote:
>>>
>>>> Thomas, why do you think a single round trip is needed?
>>>>
>>>> clear + append can be done blindly from the SDK side and it has total
>>>> knowledge of the state at that point in time till the end of the bundle at
>>>> which point you want to wait to get the cache token back from the runner
>>>> for the append call so that for the next bundle you can reuse the state if
>>>> the key wasn't processed elsewhere.
>>>>
>>>> Also, all state calls are "streamed" over gRPC so you don't need to
>>>> wait for clear to complete before being able to send append.
>>>>
>>>> On Tue, Jul 30, 2019 at 12:58 AM jincheng sun <sunjincheng...@gmail.com>
>>>> wrote:
>>>>
>>>>> Hi Rakesh,
>>>>>
>>>>> Glad to see you pointer this problem out!
>>>>> +1 for add this implementation. Manage State by write-through-cache is
>>>>> pretty important for Streaming job!
>>>>>
>>>>> Best, Jincheng
>>>>>
>>>>> Thomas Weise <t...@apache.org> 于2019年7月29日周一 下午8:54写道：
>>>>>
>>>>>> FYI a basic test appears to confirm the importance of the
>>>>>> cross-bundle caching: I found that the throughput can be increased by
>>>>>> playing with the bundle size in the Flink runner. Default caps at 1000
>>>>>> elements (or 1 second). So on a high throughput stream the bundles would
>>>>>> be
>>>>>> capped by the count limit. Bumping the count limit increases the
>>>>>> throughput
>>>>>> by reducing the chatter over the state plane (more cache hits due to
>>>>>> larger
>>>>>> bundle).
>>>>>>
>>>>>> The next level of investigation would involve profiling. But just by
>>>>>> looking at metrics, the CPU utilization on the Python worker side dropped
>>>>>> significantly while on the Flink side it remains nearly same. There are
>>>>>> no
>>>>>> metrics for state operations on either side, I think it would be very
>>>>>> helpful to get these in place also.
>>>>>>
>>>>>> Below the stateful processing code for reference.
>>>>>>
>>>>>> Thomas
>>>>>>
>>>>>>
>>>>>> class StatefulFn(beam.DoFn):
>>>>>> count_state_spec = userstate.CombiningValueStateSpec(
>>>>>> 'count',
>>>>>> beam.coders.IterableCoder(beam.coders.VarIntCoder()), sum)
>>>>>> timer_spec = userstate.TimerSpec('timer',
>>>>>> userstate.TimeDomain.WATERMARK)
>>>>>>
>>>>>> def process(self, kv,
>>>>>> count=beam.DoFn.StateParam(count_state_spec),
>>>>>> timer=beam.DoFn.TimerParam(timer_spec), window=beam.DoFn.WindowParam):
>>>>>> count.add(1)
>>>>>> timer_seconds = (window.end.micros // 1000000) - 1
>>>>>> timer.set(timer_seconds)
>>>>>>
>>>>>> @userstate.on_timer(timer_spec)
>>>>>> def process_timer(self,
>>>>>> count=beam.DoFn.StateParam(count_state_spec),
>>>>>> window=beam.DoFn.WindowParam):
>>>>>> if count.read() == 0:
>>>>>> logging.warning("###timer fired with count %d, window %s"
>>>>>> % (count.read(), window))
>>>>>>
>>>>>>
>>>>>>
>>>>>> On Thu, Jul 25, 2019 at 5:09 AM Robert Bradshaw <rober...@google.com>
>>>>>> wrote:
>>>>>>
>>>>>>> On Wed, Jul 24, 2019 at 6:21 AM Rakesh Kumar <rakeshku...@lyft.com>
>>>>>>> wrote:
>>>>>>> >
>>>>>>> > Thanks Robert,
>>>>>>> >
>>>>>>> > I stumble on the jira that you have created some time ago
>>>>>>> > https://jira.apache.org/jira/browse/BEAM-5428
>>>>>>> >
>>>>>>> > You also marked code where code changes are required:
>>>>>>> >
>>>>>>> https://github.com/apache/beam/blob/7688bcfc8ebb4bedf26c5c3b3fe0e13c0ec2aa6d/sdks/python/apache_beam/runners/worker/bundle_processor.py#L291
>>>>>>> >
>>>>>>> https://github.com/apache/beam/blob/7688bcfc8ebb4bedf26c5c3b3fe0e13c0ec2aa6d/sdks/python/apache_beam/runners/worker/bundle_processor.py#L349
>>>>>>> >
>>>>>>> https://github.com/apache/beam/blob/7688bcfc8ebb4bedf26c5c3b3fe0e13c0ec2aa6d/sdks/python/apache_beam/runners/worker/bundle_processor.py#L465
>>>>>>> >
>>>>>>> > I am willing to provide help to implement this. Let me know how I
>>>>>>> can help.
>>>>>>>
>>>>>>> As far as I'm aware, no one is actively working on it right now.
>>>>>>> Please feel free to assign yourself the JIRA entry and I'll be happy
>>>>>>> to answer any questions you might have if (well probably when) these
>>>>>>> pointers are insufficient.
>>>>>>>
>>>>>>> > On Tue, Jul 23, 2019 at 3:47 AM Robert Bradshaw <
>>>>>>> rober...@google.com> wrote:
>>>>>>> >>
>>>>>>> >> This is documented at
>>>>>>> >>
>>>>>>> https://docs.google.com/document/d/1BOozW0bzBuz4oHJEuZNDOHdzaV5Y56ix58Ozrqm2jFg/edit#heading=h.7ghoih5aig5m
>>>>>>> >> . Note that it requires participation of both the runner and the
>>>>>>> SDK
>>>>>>> >> (though there are no correctness issues if one or the other side
>>>>>>> does
>>>>>>> >> not understand the protocol, caching just won't be used).
>>>>>>> >>
>>>>>>> >> I don't think it's been implemented anywhere, but could be very
>>>>>>> >> beneficial for performance.
>>>>>>> >>
>>>>>>> >> On Wed, Jul 17, 2019 at 6:00 PM Rakesh Kumar <
>>>>>>> rakeshku...@lyft.com> wrote:
>>>>>>> >> >
>>>>>>> >> > I checked the python sdk[1] and it has similar implementation
>>>>>>> as Java SDK.
>>>>>>> >> >
>>>>>>> >> > I would agree with Thomas. In case of high volume event stream
>>>>>>> and bigger cluster size, network call can potentially cause a
>>>>>>> bottleneck.
>>>>>>> >> >
>>>>>>> >> > @Robert
>>>>>>> >> > I am interested to see the proposal. Can you provide me the
>>>>>>> link of the proposal?
>>>>>>> >> >
>>>>>>> >> > [1]:
>>>>>>> https://github.com/apache/beam/blob/db59a3df665e094f0af17fe4d9df05fe420f3c16/sdks/python/apache_beam/transforms/userstate.py#L295
>>>>>>> >> >
>>>>>>> >> >
>>>>>>> >> > On Tue, Jul 16, 2019 at 9:43 AM Thomas Weise <t...@apache.org>
>>>>>>> wrote:
>>>>>>> >> >>
>>>>>>> >> >> Thanks for the pointer. For streaming, it will be important to
>>>>>>> support caching across bundles. It appears that even the Java SDK
>>>>>>> doesn't
>>>>>>> support that yet?
>>>>>>> >> >>
>>>>>>> >> >>
>>>>>>> https://github.com/apache/beam/blob/77b295b1c2b0a206099b8f50c4d3180c248e252c/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/FnApiDoFnRunner.java#L221
>>>>>>> >> >>
>>>>>>> >> >> Regarding clear/append: It would be nice if both could occur
>>>>>>> within a single Fn Api roundtrip when the state is persisted.
>>>>>>> >> >>
>>>>>>> >> >> Thanks,
>>>>>>> >> >> Thomas
>>>>>>> >> >>
>>>>>>> >> >>
>>>>>>> >> >>
>>>>>>> >> >> On Tue, Jul 16, 2019 at 6:58 AM Lukasz Cwik <lc...@google.com>
>>>>>>> wrote:
>>>>>>> >> >>>
>>>>>>> >> >>> User state is built on top of read, append and clear and not
>>>>>>> off a read and write paradigm to allow for blind appends.
>>>>>>> >> >>>
>>>>>>> >> >>> The optimization you speak of can be done completely inside
>>>>>>> the SDK without any additional protocol being required as long as you
>>>>>>> clear
>>>>>>> the state first and then append all your new data. The Beam Java SDK
>>>>>>> does
>>>>>>> this for all runners when executed portably[1]. You could port the same
>>>>>>> logic to the Beam Python SDK as well.
>>>>>>> >> >>>
>>>>>>> >> >>> 1:
>>>>>>> https://github.com/apache/beam/blob/41478d00d34598e56471d99d0845ac16efa5b8ef/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/state/BagUserState.java#L84
>>>>>>> >> >>>
>>>>>>> >> >>> On Tue, Jul 16, 2019 at 5:54 AM Robert Bradshaw <
>>>>>>> rober...@google.com> wrote:
>>>>>>> >> >>>>
>>>>>>> >> >>>> Python workers also have a per-bundle SDK-side cache. A
>>>>>>> protocol has
>>>>>>> >> >>>> been proposed, but hasn't yet been implemented in any SDKs
>>>>>>> or runners.
>>>>>>> >> >>>>
>>>>>>> >> >>>> On Tue, Jul 16, 2019 at 6:02 AM Reuven Lax <re...@google.com>
>>>>>>> wrote:
>>>>>>> >> >>>> >
>>>>>>> >> >>>> > It's runner dependent. Some runners (e.g. the Dataflow
>>>>>>> runner) do have such a cache, though I think it's currently has a cap
>>>>>>> for
>>>>>>> large bags.
>>>>>>> >> >>>> >
>>>>>>> >> >>>> > Reuven
>>>>>>> >> >>>> >
>>>>>>> >> >>>> > On Mon, Jul 15, 2019 at 8:48 PM Rakesh Kumar <
>>>>>>> rakeshku...@lyft.com> wrote:
>>>>>>> >> >>>> >>
>>>>>>> >> >>>> >> Hi,
>>>>>>> >> >>>> >>
>>>>>>> >> >>>> >> I have been using python sdk for the application and also
>>>>>>> using BagState in production. I was wondering whether state logic has
>>>>>>> any
>>>>>>> write-through-cache implemented or not. If we are sending every read and
>>>>>>> write request through network then it comes with a performance cost. We
>>>>>>> can
>>>>>>> avoid network call for a read operation if we have write-through-cache.
>>>>>>> >> >>>> >> I have superficially looked into the implementation and I
>>>>>>> didn't see any cache implementation.
>>>>>>> >> >>>> >>
>>>>>>> >> >>>> >> is it possible to have this cache? would it cause any
>>>>>>> issue if we have the caching layer?
>>>>>>> >> >>>> >>
>>>>>>>
>>>>>>
