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https://issues.apache.org/jira/browse/FLINK-2548?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14703337#comment-14703337
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Stephan Ewen commented on FLINK-2548:
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Actually, the second co-group is not a real co-group. It only queries the
solution set for the vertices that have a message.
The first CoGroup is basically like a merge join - it skips over sequences with
no match. This is efficient if you have many updates (generating messages), but
looses efficiency as you have fewer updates that should generate messages.
We used coGroup initially, because it fits the "pregel" model where you have an
iterator over your neighbors. This could be realized by a join as well,
although it is hard to realize that in a memory-safe fashion.
> VertexCentricIteration should avoid doing a coGroup with the edges and the
> solution set
> ---------------------------------------------------------------------------------------
>
> Key: FLINK-2548
> URL: https://issues.apache.org/jira/browse/FLINK-2548
> Project: Flink
> Issue Type: Improvement
> Components: Gelly
> Affects Versions: 0.9, 0.10
> Reporter: Gabor Gevay
> Assignee: Gabor Gevay
>
> Currently, the performance of vertex centric iteration is suboptimal in those
> iterations where the workset is small, because the complexity of one
> iteration contains the number of edges and vertices of the graph because of
> coGroups:
> VertexCentricIteration.buildMessagingFunction does a coGroup between the
> edges and the workset, to get the neighbors to the messaging UDF. This is
> problematic from a performance point of view, because the coGroup UDF gets
> called on all the edge groups, including those that are not getting any
> messages.
> An analogous problem is present in
> VertexCentricIteration.createResultSimpleVertex at the creation of the
> updates: a coGroup happens between the messages and the solution set, which
> has the number of vertices of the graph included in its complexity.
> Both of these coGroups could be avoided by doing a join instead (with the
> same keys that the coGroup uses), and then a groupBy. The complexity of these
> operations would be dominated by the size of the workset, as opposed to the
> number of edges or vertices of the graph. The joins should have the edges and
> the solution set at the build side to achieve this complexity. (They will not
> be rebuilt at every iteration.)
> I made some experiments with this, and the initial results seem promising. On
> some workloads, this achieves a 2 times speedup, because later iterations
> often have quite small worksets, and these get a huge speedup from this.
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