Hi Marc,
I think it will be as same slow as the initial one if not even slower. If I recall correctly, JanusGraph always fetches the whole edge (column + value) even if you only need the count (in which case neither column nor value is really needed), or you only need edge id (in which case only column is needed). I created https://github.com/JanusGraph/janusgraph/discussions/2315 to discuss about this potential optimization. Btw, even if we assume this optimization is in-place, I don’t expect significant performance boost for Zach’s usecase.
Best regards, Boxuan
On Dec 30, 2020, at 4:44 PM, HadoopMarc < bi...@...> wrote:
Hi Zach, Boxuan,
There is one thing I do not understand. According to the JanusGraph datamodel, the outE relatationIdentifiers are stored in the vertex. So, retrieving all outE() relationIdentifiers with the vertex for counting them should not take long, even if there are 100 thousands of them. What happens if you do: g.V() .has("someProperty",eq("someValue")) .where(outE().id().count().is(gt(10)));
If this does not work, it should be possible to configure/modify JanusGraph such, that it does not start fetching edge properties that are not needed for the count.
Best wishes, Marc
Op woensdag 30 december 2020 om 04:15:46 UTC+1 schreef libo...@ connect.hku.hk: Hi Zach,
I have some concerns over concurrency and consistency issues, but this might still be a nice feature to have. I think you could open a new discussion on https://github.com/JanusGraph/janusgraph/discussions. That would be a better place for brainstorming. It would be awesome if you can share more context on why you think this is a very common business requirement.
Cheers, Thank you Boxuan, Was using the term “job” pretty loosely. Your inference about doing these things within ingest/deletion process makes sense. I know there is a lot on the community’s plate now, but if my above solution is truly optimal for current state, I wonder if a JG feature addition may help tackle this problem more consistently. Something like an additional, 3rd , index type (in addition to “graph” and “vertex-centric” indices) . i.e. “edge-connection” or “degree-centrality” index. The feature would require a mixed indexing backend, and minimally a mechanism to choose vertex and edge label combinations to count IN, OUT, and/or BOTH degree centrality. Not sure what the level of effort or implementation details would be, but this is a very common business requirement for graph-based search. If JanusGraph has native/tested support for it, it would make JanusGraph even easier to champion. 😊 Best, Zach On Tuesday, December 29, 2020 at 3:19:46 AM UTC-5 libo...@ connect.hku.hk wrote: Hi Zach,
Personally I think your workaround is the most optimal one. JanusGraph does not store number of edges as metadata in the vertex (there are both Pros & Cons for doing / not doing this).
Btw do you have to have another job doing centrality calculation separately? If your application is built on top of JanusGraph, then probably you can maintain the “outDegree” property when inserting/deleting edges.
Best regards, Hello all, Curious about best approaches/practices for scalable
degree-centrality search filters on large (millions to billions of nodes) JanusGraphs. i.e. something like : g.V() .has("someProperty",eq("someValue")) .where(outE().count().is(gt(10))); Suppose the has-step narrows down to a large number
of vertices (hundreds of thousands), then performing that form of count on that
many vertices will result in timeouts and inefficiencies (at least in my experience). My workaround for this has been pre-calculating
centrality in another job and writing to a Vertex Property that can subsequently be included
in a mixed index. So we can do: g.V() .has("someProperty",eq("someValue")) .has(“outDegree”,gt(10)) This works, but it is yet another calculation we must maintain
in our pipeline and while sufficing, it seems like more of a workaround then a
great solution. I was hoping there was a more optimal approach/strategy. Please let me know. Thank you, Zach
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