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From Stephan Ewen <se...@apache.org>
Subject Re: [gelly] Spargel model rework
Date Tue, 03 Nov 2015 00:35:27 GMT
When creating the original version of Spargel I was pretty much thinking in
GSA terms, more than in Pregel terms. There are some fundamental
differences between Spargel and Pregel. Spargel is in between GAS and
Pregel in some way, that is how I have always thought about it.

The main reason for the form is that it fits the dataflow paradigm easier:

  - If one function emits the new state of the vertex and the messages, it
has two different return types, which means you need a union type and
filer/split type of operation on the result, which also adds overhead. In
the current model, each function has one return type, which makes it easy.

 - The workset is also the feedback channel, which is materialized at the
superstep boundaries, so keeping it small at O(vertices), rather than
O(edges) is a win for performance.

There is no reason to not add a Pregel model, but I would not kill Spargel
for it. It will be tough to get the Pregel variant to the same efficiency.
Unless you want to say, for efficiency, go with GSA, for convenience with
Pregel.

There are some nice things about the Spargel model. The fact that messages
are first generated then consumes makes the generation of initial messages
simpler in many cases, I think. It was always a bit weird to me in Pregel
that you had to check whether you are in superstep one, in which case you
would expect no message, and generate initial value messages.



On Fri, Oct 30, 2015 at 1:28 PM, Fabian Hueske <fhueske@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 <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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