The purpose of the new state API call in BEAM-7000 is to tell the runner that the SDK is now blocked waiting for the result of a specific state request and it should be used for fetches (not updates) and is there to allow for SDKs to differentiate readLater (I will need this data at some point in time in the future) from read (I need this data now). This comes up commonly where the user prefetches multiple state cells and then looks at their content allowing the runner to batch up those calls on its end.
The way it can be used for clear+append is that the runner can store requests in memory up until some time/memory limit or until it gets its first "blocked" call and then issue all the requests together. On Thu, Aug 8, 2019 at 9:42 AM Robert Bradshaw <rober...@google.com> wrote: > On Tue, Aug 6, 2019 at 12:07 AM Thomas Weise <t...@apache.org> wrote: > > > > 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. > > I think it makes sense to let the runner pre-assign these state update > tokens rather than forcing a synchronization point. > > Here's some pointers for the Python implementation: > > Currently, when a DoFn needs UserState, a StateContext object is used > that converts from a StateSpec to the actual value. When running > portably, this is FnApiUserStateContext [1]. The state handles > themselves are cached at [2] but this context only lives for the > lifetime of a single bundle. Logic could be added here to use the > token to share these across bundles. > > Each of these handles in turn invokes state_handler.get* methods when > its read is called. (Here state_handler is a thin wrapper around the > service itself) and constructs the appropriate result from the > StateResponse. We would need to implement caching at this level as > well, including the deserialization. This will probably require some > restructoring of how _StateBackedIterable is implemented (or, > possibly, making that class itself cache aware). Hopefully that's > enough to get started. > > [1] > https://github.com/apache/beam/blob/release-2.14.0/sdks/python/apache_beam/runners/worker/bundle_processor.py#L402 > [2] > https://github.com/apache/beam/blob/release-2.14.0/sdks/python/apache_beam/runners/worker/bundle_processor.py#L436 > . > > > 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? > >>>>>>>> >> >>>> >> >
