Hi, after running a few experiments, I can confirm that putting the combiner after the flatMap is indeed more efficient.
I ran SSSP and Connected Components with Spargel, GSA, and the Pregel model and the results are the following: - for SSSP, Spargel is always the slowest, GSA is a ~1.2x faster and Pregel is ~1.1x faster without combiner, ~1.3x faster with combiner. - for Connected Components, Spargel and GSA perform similarly, while Pregel is 1.4-1.6x slower. To start with, this is much better than I expected :) However, there is a main shortcoming in my current implementation that negatively impacts performance: Since the compute function coGroup needs to output both new vertex values and new messages, I emit a wrapping tuple that contains both vertex state and messages and then filter them out based on a boolean field. The problem is that since I cannot emit null fields, I emit a dummy message for each new vertex state and a dummy vertex state for each new message. That essentially means that the intermediate messages result is double in size, if say the vertex values are of the same type as the messages (can be worse if the vertex values are more complex). So my question is, is there a way to avoid this redundancy, by either emitting null fields or by creating an operator that could emit 2 different types of tuples? Thanks! -Vasia. On 9 November 2015 at 15:20, Fabian Hueske <fhue...@gmail.com> wrote: > Hi Vasia, > > sorry for the late reply. > I don't think there is a big difference. In both cases, the partitioning > and sorting happens at the end of the iteration. > If the groupReduce is applied before the workset is returned, the sorting > happens on the filtered result (after the flatMap) which might be a little > bit more efficient (depending on the ratio of messages and solution set > updates). Also it does not require that the initial workset is sorted for > the first groupReduce. > > I would put it at the end. > > Cheers, Fabian > > 2015-11-05 17:19 GMT+01:00 Vasiliki Kalavri <vasilikikala...@gmail.com>: > > > @Fabian > > > > Is there any advantage in putting the reducer-combiner before updating > the > > workset vs. after (i.e. right before the join with the solution set)? > > > > If it helps, here are the plans of these 2 alternatives: > > > > > > > https://drive.google.com/file/d/0BzQJrI2eGlyYcFV2RFo5dUFNXzg/view?usp=sharing > > > > > https://drive.google.com/file/d/0BzQJrI2eGlyYN014NXp6OEZUdGs/view?usp=sharing > > > > Thanks a lot for the help! > > > > -Vasia. > > > > On 30 October 2015 at 21:28, Fabian Hueske <fhue...@gmail.com> wrote: > > > > > We can of course inject an optional ReduceFunction (or GroupReduce, or > > > combinable GroupReduce) to reduce the size of the work set. > > > I suggested to remove the GroupReduce function, because it did only > > collect > > > all messages into a single record by emitting the input iterator which > is > > > quite dangerous. Applying a combinable reduce function is could improve > > the > > > performance considerably. > > > > > > The good news is that it would come "for free" because the necessary > > > partitioning and sorting can be reused (given the forwardField > > annotations > > > are correctly set): > > > - The partitioning of the reduce can be reused for the join with the > > > solution set > > > - The sort of the reduce is preserved by the join with the in-memory > > > hash-table of the solution set and can be reused for the coGroup. > > > > > > Best, > > > Fabian > > > > > > 2015-10-30 18:38 GMT+01:00 Vasiliki Kalavri <vasilikikala...@gmail.com > >: > > > > > > > Hi Fabian, > > > > > > > > thanks so much for looking into this so quickly :-) > > > > > > > > One update I have to make is that I tried running a few experiments > > with > > > > this on a 6-node cluster. The current implementation gets stuck at > > > > "Rebuilding Workset Properties" and never finishes a single > iteration. > > > > Running the plan of one superstep without a delta iteration > terminates > > > > fine. I didn't have access to the cluster today, so I couldn't debug > > this > > > > further, but I will do as soon as I have access again. > > > > > > > > The rest of my comments are inline: > > > > > > > > On 30 October 2015 at 17:53, Fabian Hueske <fhue...@gmail.com> > wrote: > > > > > > > > > Hi Vasia, > > > > > > > > > > I had a look at your new implementation and have a few ideas for > > > > > improvements. > > > > > 1) Sending out the input iterator as you do in the last GroupReduce > > is > > > > > quite dangerous and does not give a benefit compared to collecting > > all > > > > > elements. Even though it is an iterator, it needs to be completely > > > > > materialized in-memory whenever the record is touched by Flink or > > user > > > > > code. > > > > > I would propose to skip the reduce step completely and handle all > > > > messages > > > > > separates and only collect them in the CoGroup function before > giving > > > > them > > > > > into the VertexComputeFunction. Be careful, to only do that with > > > > > objectReuse disabled or take care to properly copy the messages. If > > you > > > > > collect the messages in the CoGroup, you don't need the > GroupReduce, > > > have > > > > > smaller records and you can remove the MessageIterator class > > > completely. > > > > > > > > > > > > > I see. The idea was to expose to message combiner that user could > > > > implement if the messages are combinable, e.g. min, sum. This is a > > > common > > > > case and reduces the message load significantly. Is there a way I > could > > > do > > > > something similar before the coGroup? > > > > > > > > > > > > > > > > > 2) Add this annotation to the AppendVertexState function: > > > > > @ForwardedFieldsFirst("*->f0"). This indicates that the complete > > > element > > > > of > > > > > the first input becomes the first field of the output. Since the > > input > > > is > > > > > partitioned on "f0" (it comes out of the partitioned solution set) > > the > > > > > result of ApplyVertexState will be partitioned on "f0.f0" which is > > > > > (accidentially :-D) the join key of the following coGroup function > -> > > > no > > > > > partitioning :-) > > > > > > > > > > > > > Great! I totally missed that ;) > > > > > > > > > > > > > > > > > 3) Adding the two flatMap functions behind the CoGroup prevents > > > chaining > > > > > and causes therefore some serialization overhead but shouldn't be > too > > > > bad. > > > > > > > > > > So in total I would make this program as follows: > > > > > > > > > > iVertices<K,VV> > > > > > iMessage<K, Message> = iVertices.map(new InitWorkSet()); > > > > > > > > > > iteration = iVertices.iterateDelta(iMessages, maxIt, 0) > > > > > verticesWithMessage<Vertex, Message> = iteration.getSolutionSet() > > > > > .join(iteration.workSet()) > > > > > .where(0) // solution set is local and build side > > > > > .equalTo(0) // workset is shuffled and probe side of hashjoin > > > > > superstepComp<Vertex,Tuple2<K, Message>,Bool> = > > > > > verticesWithMessage.coGroup(edgessWithValue) > > > > > .where("f0.f0") // vwm is locally forward and sorted > > > > > .equalTo(0) // edges are already partitioned and sorted (if > cached > > > > > correctly) > > > > > .with(...) // The coGroup collects all messages in a collection > and > > > > gives > > > > > it to the ComputeFunction > > > > > delta<Vertex> = superStepComp.flatMap(...) // partitioned when > merged > > > > into > > > > > solution set > > > > > workSet<K, Message> = superStepComp.flatMap(...) // partitioned for > > > join > > > > > iteration.closeWith(delta, workSet) > > > > > > > > > > So, if I am correct, the program will > > > > > - partition the workset > > > > > - sort the vertices with messages > > > > > - partition the delta > > > > > > > > > > One observation I have is that this program requires that all > > messages > > > > fit > > > > > into memory. Was that also the case before? > > > > > > > > > > > > > I believe not. The plan has one coGroup that produces the messages > > and a > > > > following coGroup that groups by the messages "target ID" and > consumes > > > > them in an iterator. That doesn't require them to fit in memory, > > right? > > > > > > > > > > > > I'm also working on a version where the graph is represented as an > > > > adjacency list, instead of two separate datasets of vertices and > edges. > > > The > > > > disadvantage is that the graph has to fit in memory, but I think the > > > > advantages are many. We'll be able to support edge value updates, > edge > > > > mutations and different edge access order guarantees. I'll get back > to > > > this > > > > thread when I have a working prototype. > > > > > > > > > > > > > > > > > > Cheers, > > > > > Fabian > > > > > > > > > > > > > Thanks again! > > > > > > > > Cheers, > > > > -Vasia. > > > > > > > > > > > > > > > > > > > > > > > > > > > 2015-10-27 19:10 GMT+01:00 Vasiliki Kalavri < > > vasilikikala...@gmail.com > > > >: > > > > > > > > > > > @Martin: thanks for your input! If you ran into any other issues > > > that I > > > > > > didn't mention, please let us know. Obviously, even with my > > proposal, > > > > > there > > > > > > are still features we cannot support, e.g. updating edge values > and > > > > graph > > > > > > mutations. We'll need to re-think the underlying iteration and/or > > > graph > > > > > > representation for those. > > > > > > > > > > > > @Fabian: thanks a lot, no rush :) > > > > > > Let me give you some more information that might make it easier > to > > > > reason > > > > > > about performance: > > > > > > > > > > > > Currently, in Spargel the SolutionSet (SS) keeps the vertex state > > and > > > > the > > > > > > workset (WS) keeps the active vertices. The iteration is composed > > of > > > 2 > > > > > > coGroups. The first one takes the WS and the edges and produces > > > > messages. > > > > > > The second one takes the messages and the SS and produced the new > > WS > > > > and > > > > > > the SS-delta. > > > > > > > > > > > > In my proposal, the SS has the vertex state and the WS has > > <vertexId, > > > > > > MessageIterator> pairs, i.e. the inbox of each vertex. The plan > is > > > more > > > > > > complicated because compute() needs to have two iterators: over > the > > > > edges > > > > > > and over the messages. > > > > > > First, I join SS and WS to get the active vertices (have > received a > > > > msg) > > > > > > and their current state. Then I coGroup the result with the edges > > to > > > > > access > > > > > > the neighbors. Now the main problem is that this coGroup needs to > > > have > > > > 2 > > > > > > outputs: the new messages and the new vertex value. I couldn't > > really > > > > > find > > > > > > a nice way to do this, so I'm emitting a Tuple that contains both > > > types > > > > > and > > > > > > I have a flag to separate them later with 2 flatMaps. From the > > vertex > > > > > > flatMap, I crete the SS-delta and from the messaged flatMap I > > apply a > > > > > > reduce to group the messages by vertex and send them to the new > WS. > > > One > > > > > > optimization would be to expose a combiner here to reduce message > > > size. > > > > > > > > > > > > tl;dr: > > > > > > 1. 2 coGroups vs. Join + coGroup + flatMap + reduce > > > > > > 2. how can we efficiently emit 2 different types of records from > a > > > > > coGroup? > > > > > > 3. does it make any difference if we group/combine the messages > > > before > > > > > > updating the workset or after? > > > > > > > > > > > > Cheers, > > > > > > -Vasia. > > > > > > > > > > > > > > > > > > On 27 October 2015 at 18:39, Fabian Hueske <fhue...@gmail.com> > > > wrote: > > > > > > > > > > > > > I'll try to have a look at the proposal from a performance > point > > of > > > > > view > > > > > > in > > > > > > > the next days. > > > > > > > Please ping me, if I don't follow up this thread. > > > > > > > > > > > > > > Cheers, Fabian > > > > > > > > > > > > > > 2015-10-27 18:28 GMT+01:00 Martin Junghanns < > > > m.jungha...@mailbox.org > > > > >: > > > > > > > > > > > > > > > Hi, > > > > > > > > > > > > > > > > At our group, we also moved several algorithms from Giraph to > > > Gelly > > > > > and > > > > > > > > ran into some confusing issues (first in understanding, > second > > > > during > > > > > > > > implementation) caused by the conceptional differences you > > > > described. > > > > > > > > > > > > > > > > If there are no concrete advantages (performance mainly) in > the > > > > > Spargel > > > > > > > > implementation, we would be very happy to see the Gelly API > be > > > > > aligned > > > > > > to > > > > > > > > Pregel-like systems. > > > > > > > > > > > > > > > > Your SSSP example speaks for itself. Straightforward, if the > > > reader > > > > > is > > > > > > > > familiar with Pregel/Giraph/... > > > > > > > > > > > > > > > > Best, > > > > > > > > Martin > > > > > > > > > > > > > > > > > > > > > > > > On 27.10.2015 17:40, Vasiliki Kalavri wrote: > > > > > > > > > > > > > > > >> Hello squirrels, > > > > > > > >> > > > > > > > >> I want to discuss with you a few concerns I have about our > > > current > > > > > > > >> vertex-centric model implementation, Spargel, now fully > > subsumed > > > > by > > > > > > > Gelly. > > > > > > > >> > > > > > > > >> Spargel is our implementation of Pregel [1], but it violates > > > some > > > > > > > >> fundamental properties of the model, as described in the > paper > > > and > > > > > as > > > > > > > >> implemented in e.g. Giraph, GPS, Hama. I often find myself > > > > confused > > > > > > both > > > > > > > >> when trying to explain it to current Giraph users and when > > > porting > > > > > my > > > > > > > >> Giraph algorithms to it. > > > > > > > >> > > > > > > > >> More specifically: > > > > > > > >> - in the Pregel model, messages produced in superstep n, are > > > > > received > > > > > > in > > > > > > > >> superstep n+1. In Spargel, they are produced and consumed in > > the > > > > > same > > > > > > > >> iteration. > > > > > > > >> - in Pregel, vertices are active during a superstep, if they > > > have > > > > > > > received > > > > > > > >> a message in the previous superstep. In Spargel, a vertex is > > > > active > > > > > > > during > > > > > > > >> a superstep if it has changed its value. > > > > > > > >> > > > > > > > >> These two differences require a lot of rethinking when > porting > > > > > > > >> applications > > > > > > > >> and can easily cause bugs. > > > > > > > >> > > > > > > > >> The most important problem however is that we require the > user > > > to > > > > > > split > > > > > > > >> the > > > > > > > >> computation in 2 phases (2 UDFs): > > > > > > > >> - messaging: has access to the vertex state and can produce > > > > messages > > > > > > > >> - update: has access to incoming messages and can update the > > > > vertex > > > > > > > value > > > > > > > >> > > > > > > > >> Pregel/Giraph only expose one UDF to the user: > > > > > > > >> - compute: has access to both the vertex state and the > > incoming > > > > > > > messages, > > > > > > > >> can produce messages and update the vertex value. > > > > > > > >> > > > > > > > >> This might not seem like a big deal, but except from forcing > > the > > > > > user > > > > > > to > > > > > > > >> split their program logic into 2 phases, Spargel also makes > > some > > > > > > common > > > > > > > >> computation patterns non-intuitive or impossible to write. A > > > very > > > > > > simple > > > > > > > >> example is propagating a message based on its value or > sender > > > ID. > > > > To > > > > > > do > > > > > > > >> this with Spargel, one has to store all the incoming > messages > > in > > > > the > > > > > > > >> vertex > > > > > > > >> value (might be of different type btw) during the messaging > > > phase, > > > > > so > > > > > > > that > > > > > > > >> they can be accessed during the update phase. > > > > > > > >> > > > > > > > >> So, my first question is, when implementing Spargel, were > > other > > > > > > > >> alternatives considered and maybe rejected in favor of > > > performance > > > > > or > > > > > > > >> because of some other reason? If someone knows, I would love > > to > > > > hear > > > > > > > about > > > > > > > >> them! > > > > > > > >> > > > > > > > >> Second, I wrote a prototype implementation [2] that only > > exposes > > > > one > > > > > > > UDF, > > > > > > > >> compute(), by keeping the vertex state in the solution set > and > > > the > > > > > > > >> messages > > > > > > > >> in the workset. This way all previously mentioned > limitations > > go > > > > > away > > > > > > > and > > > > > > > >> the API (see "SSSPComputeFunction" in the example [3]) > looks a > > > lot > > > > > > more > > > > > > > >> like Giraph (see [4]). > > > > > > > >> > > > > > > > >> I have not run any experiments yet and the prototype has > some > > > ugly > > > > > > > hacks, > > > > > > > >> but if you think any of this makes sense, then I'd be > willing > > to > > > > > > follow > > > > > > > up > > > > > > > >> and try to optimize it. If we see that it performs well, we > > can > > > > > > consider > > > > > > > >> either replacing Spargel or adding it as an alternative. > > > > > > > >> > > > > > > > >> Thanks for reading this long e-mail and looking forward to > > your > > > > > input! > > > > > > > >> > > > > > > > >> Cheers, > > > > > > > >> -Vasia. > > > > > > > >> > > > > > > > >> [1]: https://kowshik.github.io/JPregel/pregel_paper.pdf > > > > > > > >> [2]: > > > > > > > >> > > > > > > > >> > > > > > > > > > > > > > > > > > > > > > > > > > > > > https://github.com/vasia/flink/tree/spargel-2/flink-libraries/flink-gelly/src/main/java/org/apache/flink/graph/spargelnew > > > > > > > >> [3]: > > > > > > > >> > > > > > > > >> > > > > > > > > > > > > > > > > > > > > > > > > > > > > https://github.com/vasia/flink/blob/spargel-2/flink-libraries/flink-gelly/src/main/java/org/apache/flink/graph/spargelnew/example/SSSPCompute.java > > > > > > > >> [4]: > > > > > > > >> > > > > > > > >> > > > > > > > > > > > > > > > > > > > > > > > > > > > > https://github.com/grafos-ml/okapi/blob/master/src/main/java/ml/grafos/okapi/graphs/SingleSourceShortestPaths.java > > > > > > > >> > > > > > > > >> > > > > > > > > > > > > > > > > > > > > > > > > > > > >
