Hi, Very thanks for all the points raised !
@Piotr For using another edge to broadcast the event, I think it may not be able to address the iteration case. The primary problem is that with two edges we cannot ensure the order of records. However, In the iteration case, the broadcasted event is used to mark the progress of the iteration and it works like watermark, thus its position relative to the normal records can not change. And @Piotr, @Xiaogang, for the requirements on the state, I think different options seems vary. The first option is to allow Operator<T> to broadcast a separate event and have a separate process method for this event. To be detail, we may add a new type of StreamElement called Event and allow Operator<T> to broadcastEmit Event. Then in the received side, we could add a new `processEvent` method to the (Keyed)ProcessFunction. Similar to the broadcast side of KeyedBroadcastProcessFunction, in this new method users cannot access keyed state with specific key, but can register a state function to touch all the elements in the keyed state. This option needs to modify the runtime to support the new type of StreamElement, but it does not affect the semantics of states and thus it has no requirements on state. The second option is to allow Operator<T> to broadcastEmit T and in the receiver side, user can process the broadcast element with the existing process method. This option is consistent with the OperatorState, but for keyedState we may send a record to tasks that do not containing the corresponding keyed state, thus it should require some changes on the State. The third option is to support the generic Multicast. For keyedState it also meets the problem of inconsistency between network partitioner and keyed state partitioner, and if we want to rely on it to implement the non-key join, it should be also meet the problem of cannot control the partitioning of operator state. Therefore, it should also require some changes on the State. Then for the different scenarios proposed, the iteration case in fact requires exactly the ability to broadcast a different event type. In the iteration the fields of the progress event are in fact different from that of normal records. It does not contain actual value but contains some fields for the downstream operators to align the events and track the progress. Therefore, broadcasting a different event type is able to solve the iteration case without the requirements on the state. Besides, allowing the operator to broadcast a separate event may also facilitate some other user cases, for example, users may notify the downstream operators to change logic if some patterns are matched. The notification might be different from the normal records and users do not need to uniform them with a wrapper type manually if the operators are able to broadcast a separate event. However, it truly cannot address the non-key join scenarios. Since allowing broadcasting a separate event seems to be able to serve as a standalone functionality, and it does not require change on the state, I am thinking that is it possible for us to partition to multiple steps and supports broadcasting events first ? At the same time we could also continue working on other options to support more scenarios like non-key join and they seems to requires more thoughts. Best, Yun ------------------------------------------------------------------ From:Piotr Nowojski <pi...@ververica.com> Send Time:2019 Aug. 26 (Mon.) 18:59 To:dev <dev@flink.apache.org> Cc:Yun Gao <yungao...@aliyun.com> Subject:Re: [DISCUSS] Enhance Support for Multicast Communication Pattern Hi, Xiaogang, those things worry me the most. 1. Regarding the broadcasting, doesn’t the BroadcastState [1] cover our issues? Can not we construct a job graph, where one operator has two outputs, one keyed another broadcasted, which are wired together back to the KeyedBroadcastProcessFunction or BroadcastProcessFunction? 2. Multicast on keyed streams, might be done by iterating over all of the keys. However I have a feeling that might not be the feature which distributed cross/theta joins would want, since they would probably need a guarantee to have only a single key per operator instance. Kurt, by broadcast optimisation do you mean [2]? I’m not sure if we should split the discussion yet. Most of the changes required by either multicast or broadcast will be in the API/state layers. Runtime changes for broadcast would be almost none (just exposing existing features) and for multicast they shouldn't be huge as well. However maybe we should consider those two things together at the API level, so that we do not make wrong decisions when just looking at the simpler/more narrow broadcast support? Piotrek [1] https://ci.apache.org/projects/flink/flink-docs-stable/dev/stream/state/broadcast_state.html [2] https://github.com/apache/flink/pull/7713 On 26 Aug 2019, at 09:35, Kurt Young <ykt...@gmail.com> wrote: From SQL's perspective, distributed cross join is a valid feature but not very urgent. Actually this discuss reminds me about another useful feature (sorry for the distraction): when doing broadcast in batch shuffle mode, we can make each producer only write one copy of the output data, but not for every consumer. Broadcast join is much more useful, and this is a very important optimization. Not sure if we have already consider this. Best, Kurt On Mon, Aug 26, 2019 at 12:16 PM Guowei Ma <guowei....@gmail.com> wrote: Thanks Yun for bringing up this discussion and very thanks for all the deep thoughts! For now, I think this discussion contains two scenarios: one if for iteration library support and the other is for SQL join support. I think both of the two scenarios are useful but they seem to have different best suitable solutions. For making the discussion more clear, I would suggest to split the discussion into two threads. And I agree with Piotr that it is very tricky that a keyed stream received a "broadcast element". So we may add some new interfaces, which could broadcast or process some special "broadcast event". In that way "broadcast event" will not be sent with the normal process. Best, Guowei SHI Xiaogang <shixiaoga...@gmail.com> 于2019年8月26日周一 上午9:27写道： Hi all, I also think that multicasting is a necessity in Flink, but more details are needed to be considered. Currently network is tightly coupled with states in Flink to achieve automatic scaling. We can only access keyed states in keyed streams and operator states in all streams. In the concrete example of theta-joins implemented with mutlticasting, the following questions exist: - In which type of states will the data be stored? Do we need another type of states which is coupled with multicasting streams? - How to ensure the consistency between network and states when jobs scale out or scale in? Regards, Xiaogang Xingcan Cui <xingc...@gmail.com> 于2019年8月25日周日 上午10:03写道： Hi all, Sorry for joining this thread late. Basically, I think enabling multicast pattern could be the right direction, but more detailed implementation policies need to be discussed. Two years ago, I filed an issue [1] about the multicast API. However, due to some reasons, it was laid aside. After that, when I tried to cherry-pick the change for experimental use, I found the return type of `selectChannels()` method had changed from `int[]` to `int`, which makes the old implementation not work anymore. From my side, the multicast has always been used for theta-join. As far as I know, it’s an essential requirement for some sophisticated joining algorithms. Until now, the Flink non-equi joins can still only be executed single-threaded. If we'd like to make some improvements on this, we should first take some measures to support multicast pattern. Best, Xingcan [1] https://issues.apache.org/jira/browse/FLINK-6936 On Aug 24, 2019, at 5:54 AM, Zhu Zhu <reed...@gmail.com> wrote: Hi Piotr, Thanks for the explanation. Agreed that the broadcastEmit(record) is a better choice for broadcasting for the iterations. As broadcasting for the iterations is the first motivation, let's support it first. Thanks, Zhu Zhu Yun Gao <yungao...@aliyun.com.invalid> 于2019年8月23日周五 下午11:56写道： Hi Piotr, Very thanks for the suggestions! Totally agree with that we could first focus on the broadcast scenarios and exposing the broadcastEmit method first considering the semantics and performance. For the keyed stream, I also agree with that broadcasting keyed records to all the tasks may be confused considering the semantics of keyed partitioner. However, in the iteration case supporting broadcast over keyed partitioner should be required since users may create any subgraph for the iteration body, including the operators with key. I think a possible solution to this issue is to introduce another data type for 'broadcastEmit'. For example, for an operator Operator<T>, it may broadcast emit another type E instead of T, and the transmitting E will bypass the partitioner and setting keyed context. This should result in the design to introduce customized operator event (option 1 in the document). The cost of this method is that we need to introduce a new type of StreamElement and new interface for this type, but it should be suitable for both keyed or non-keyed partitioner. Best, Yun ------------------------------------------------------------------ From:Piotr Nowojski <pi...@ververica.com> Send Time:2019 Aug. 23 (Fri.) 22:29 To:Zhu Zhu <reed...@gmail.com> Cc:dev <dev@flink.apache.org>; Yun Gao <yungao...@aliyun.com> Subject:Re: [DISCUSS] Enhance Support for Multicast Communication Pattern Hi, If the primary motivation is broadcasting (for the iterations) and we have no immediate need for multicast (cross join), I would prefer to first expose broadcast via the DataStream API and only later, once we finally need it, support multicast. As I wrote, multicast would be more challenging to implement, with more complicated runtime and API. And re-using multicast just to support broadcast doesn’t have much sense: 1. It’s a bit obfuscated. It’s easier to understand collectBroadcast(record) or broadcastEmit(record) compared to some multicast channel selector that just happens to return all of the channels. 2. There are performance benefits of explicitly calling `RecordWriter#broadcastEmit`. On a different note, what would be the semantic of such broadcast emit on KeyedStream? Would it be supported? Or would we limit support only to the non-keyed streams? Piotrek On 23 Aug 2019, at 12:48, Zhu Zhu <reed...@gmail.com> wrote: Thanks Piotr, Users asked for this feature sometimes ago when they migrating batch jobs to Flink(Blink). It's not very urgent as they have taken some workarounds to solve it.(like partitioning data set to different job vertices) So it's fine to not make it top priority. Anyway, as a commonly known scenario, I think users can benefit from cross join sooner or later. Thanks, Zhu Zhu Piotr Nowojski <pi...@ververica.com <mailto:pi...@ververica.com>> 于2019年8月23日周五 下午6:19写道： Hi, Thanks for the answers :) Ok I understand the full picture now. +1 from my side on solving this issue somehow. But before we start discussing how to solve it one last control question: I guess this multicast is intended to be used in blink planner, right? Assuming that we implement the multicast support now, when would it be used by the blink? I would like to avoid a scenario, where we implement an unused feature and we keep maintaining it for a long period of time. Piotrek PS, try to include motivating examples, including concrete ones in the proposals/design docs, for example in the very first paragraph. Especially if it’s a commonly known feature like cross join :) On 23 Aug 2019, at 11:38, Yun Gao <yungao...@aliyun.com.INVALID> wrote: Hi Piotr, Thanks a lot for sharing the thoughts! For the iteration, agree with that multicasting is not necessary. Exploring the broadcast interface to Output of the operators in some way should also solve this issue, and I think it should be even more convenient to have the broadcast method for the iteration. Also thanks Zhu Zhu for the cross join case! Best, Yun ------------------------------------------------------------------ From:Zhu Zhu <reed...@gmail.com <mailto:reed...@gmail.com>> Send Time:2019 Aug. 23 (Fri.) 17:25 To:dev <dev@flink.apache.org <mailto:dev@flink.apache.org>> Cc:Yun Gao <yungao...@aliyun.com <mailto:yungao...@aliyun.com>> Subject:Re: [DISCUSS] Enhance Support for Multicast Communication Pattern Hi Piotr, Yes you are right it's a distributed cross join requirement. Broadcast join can help with cross join cases. But users cannot use it if the data set to join is too large to fit into one subtask. Sorry for left some details behind. Thanks, Zhu Zhu Piotr Nowojski <pi...@ververica.com <mailto:pi...@ververica.com>> 于2019年8月23日周五 下午4:57写道： Hi Yun and Zhu Zhu, Thanks for the more detailed example Zhu Zhu. As far as I understand for the iterations example we do not need multicasting. Regarding the Join example, I don’t fully understand it. The example that Zhu Zhu presented has a drawback of sending both tables to multiple nodes. What’s the benefit of using broadcast join over a hash join in such case? As far as I know, the biggest benefit of using broadcast join instead of hash join is that we can avoid sending the larger table over the network, because we can perform the join locally. In this example we are sending both of the tables to multiple nodes, which should defeat the purpose. Is it about implementing cross join or near cross joins in a distributed fashion? if we introduce a new MulticastRecordWriter That’s one of the solutions. It might have a drawback of 3 class virtualisation problem (We have RecordWriter and BroadcastRecordWriter already). With up to two implementations, JVM is able to devirtualise the calls. Previously I was also thinking about just providing two different ChannelSelector interfaces. One with `int[]` and `SingleChannelSelector` with plain `int` and based on that, RecordWriter could perform some magic (worst case scenario `instaceof` checks). Another solution might be to change `ChannelSelector` interface into an iterator. But let's discuss the details after we agree on implementing this. Piotrek On 23 Aug 2019, at 10:20, Yun Gao <yungao...@aliyun.com <mailto: yungao...@aliyun.com>> wrote: Hi Piotr, Thanks a lot for the suggestions! The core motivation of this discussion is to implement a new iteration library on the DataStream, and it requires to insert special records in the stream to notify the progress of the iteration. The mechanism of such records is very similar to the current Watermark, and we meet the problem of sending normal records according to the partition (Rebalance, etc..) and also be able to broadcast the inserted progress records to all the connected records. I have read the notes in the google doc and I totally agree with that exploring the broadcast interface in RecordWriter in some way is able to solve this issue. Regarding to `int[] ChannelSelector#selectChannels()`, I'm wondering if we introduce a new MulticastRecordWriter and left the current RecordWriter untouched, could we avoid the performance degradation ? Since with such a modification the normal RecordWriter does not need to iterate the return array by ChannelSelector, and the only difference will be returning an array instead of an integer, and accessing the first element of the returned array instead of reading the integer directly. Best, Yun ------------------------------------------------------------------ From:Piotr Nowojski <pi...@ververica.com <mailto: pi...@ververica.com Send Time:2019 Aug. 23 (Fri.) 15:20 To:dev <dev@flink.apache.org <mailto:dev@flink.apache.org>> Cc:Yun Gao <yungao...@aliyun.com <mailto:yungao...@aliyun.com>> Subject:Re: [DISCUSS] Enhance Support for Multicast Communication Pattern Hi, Yun: Thanks for proposing the idea. I have checked the document and left couple of questions there, but it might be better to answer them here. What is the exact motivation and what problems do you want to solve? We have dropped multicast support from the network stack [1] for two reasons: 1. Performance 2. Code simplicity The proposal to re introduce `int[] ChannelSelector#selectChannels()` would revert those changes. At that time we were thinking about a way how to keep the multicast support on the network level, while keeping the performance and simplicity for non multicast cases and there are ways to achieve that. However they would add extra complexity to Flink, which it would be better to avoid. On the other hand, supporting dual pattern: standard partitioning or broadcasting is easy to do, as LatencyMarkers are doing exactly that. It would be just a matter of exposing this to the user in some way. So before we go any further, can you describe your use cases/motivation? Isn’t mix of standard partitioning and broadcasting enough? Do we need multicasting? Zhu: Could you rephrase your example? I didn’t quite understand it. Piotrek [1] https://issues.apache.org/jira/browse/FLINK-10662 < https://issues.apache.org/jira/browse/FLINK-10662> < https://issues.apache.org/jira/browse/FLINK-10662 < https://issues.apache.org/jira/browse/FLINK-10662>> On 23 Aug 2019, at 09:17, Zhu Zhu <reed...@gmail.com <mailto: reed...@gmail.com> <mailto:reed...@gmail.com <mailto: reed...@gmail.com wrote: Thanks Yun for starting this discussion. I think the multicasting can be very helpful in certain cases. I have received requirements from users that they want to do broadcast join, while the data set to broadcast is too large to fit in one task. Thus the requirement turned out to be to support cartesian product of 2 data set(one of which can be infinite stream). For example, A(parallelism=2) broadcast join B(parallelism=2) in JobVertex C. The idea to is have 4 C subtasks to deal with different combinations of A/B partitions, like C1(A1,B1), C2(A1,B2), C3(A2,B1), C4(A2,B2). This requires one record to be sent to multiple downstream subtasks, but not to all subtasks. With current interface this is not supported, as one record can only be sent to one subtask, or to all subtasks of a JobVertex. And the user had to split the broadcast data set manually to several different JobVertices, which is hard to maintain and extend. Thanks, Zhu Zhu Yun Gao <yungao...@aliyun.com.invalid <mailto: yungao...@aliyun.com.invalid <mailto:yungao...@aliyun.com.invalid 于2019年8月22日周四 下午8:42写道： Hi everyone, In some scenarios we met a requirement that some operators want to send records to theirs downstream operators with an multicast communication pattern. In detail, for some records, the operators want to send them according to the partitioner (for example, Rebalance), and for some other records, the operators want to send them to all the connected operators and tasks. Such a communication pattern could be viewed as a kind of multicast: it does not broadcast every record, but some record will indeed be sent to multiple downstream operators. However, we found that this kind of communication pattern seems could not be implemented rightly if the operators have multiple consumers with different parallelism, using the customized partitioner. To solve the above problem, we propose to enhance the support for such kind of irregular communication pattern. We think there may be two options: 1. Support a kind of customized operator events, which share much similarity with Watermark, and these events can be broadcasted to the downstream operators separately. 2. Let the channel selector supports multicast, and also add the separate RecordWriter implementation to avoid impacting the performance of the channel selector that does not need multicast. The problem and options are detailed in https://docs.google.com/document/d/1npi5c_SeP68KuT2lNdKd8G7toGR_lxQCGOnZm_hVMks/edit?usp=sharing < https://docs.google.com/document/d/1npi5c_SeP68KuT2lNdKd8G7toGR_lxQCGOnZm_hVMks/edit?usp=sharing < https://docs.google.com/document/d/1npi5c_SeP68KuT2lNdKd8G7toGR_lxQCGOnZm_hVMks/edit?usp=sharing < https://docs.google.com/document/d/1npi5c_SeP68KuT2lNdKd8G7toGR_lxQCGOnZm_hVMks/edit?usp=sharing We are also wondering if there are other methods to implement this requirement with or without changing Runtime. Very thanks for any feedbacks ! Best, Yun
