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From Fabian Hueske <fhue...@gmail.com>
Subject Re: [gelly] Spargel model rework
Date Fri, 30 Oct 2015 20:28:20 GMT
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 <vasilikikalavri@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 <fhueske@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 <vasilikikalavri@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 <fhueske@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.junghanns@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
> > > > >>
> > > > >>
> > > >
> > >
> >
>

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