Hi,

> why the query is 3x slower on 6 nodes

Did you check the hardware differences? Probably the 6-node cluster has slower network, less memory, slower disk, etc.

Another possibility that I can think of is, the data involved in your query is probably distributed across nodes. Since your 3-node cassandra cluster has 3x replication factor, I would presume all data you have is available on every node. Then there would be fewer round-trips happening within the 3-node cluster.

Generally it makes sense to me that the latency of a small cluster is shorter than that of a large cluster, as long as both clusters are not fully loaded. Of course with larger cluster you can achieve higher throughput.

> the profile step above shows a time taken of >1000ms

This can be a bug in profiling. If you can provide a minimal example to reproduce, that would be very helpful.

Best regards,

Boxuan

On Nov 25, 2020, at 6:04 AM, Varun Ganesh <oper...@...> wrote:

Just an additional note,  you may have noticed that the profile step above shows a time taken of >1000ms. I do not know why this is the case.

When run on the console without profile, it reflects the true time taken:

 gremlin> clockWithResult(10) { graph.tx().rollback(); g.V().hasLabel('label_A').has('some_id', 123).has('data.name', 'value1').repeat(both('sample_edge').simplePath()).until(has('data.name', 'value2')).path().by('data.name').limit(1).next() }

 ==>130.9545608

Thanks!

On Tuesday, November 24, 2020 at 4:35:22 PM UTC-5 Varun Ganesh wrote:

Hello,

I am currently using Janusgraph version 0.5.2. I have a graph with about 18 million vertices and 25 million edges.

I have two versions of this graph, one backed by a 3 node Cassandra cluster and another backed by 6 Cassandra nodes (both with 3x replication factor)

I am running the below query on both of them:

g.V().hasLabel('label_A').has('some_id', 123).has('data.name', 'value1').repeat(both('sample_edge').simplePath()).until(has('data.name', 'value2')).path().by('data.name').next()

The issue is that this query takes ~130ms on the 3 node cluster whereas it takes ~400ms on the 6 node cluster.

I have tried running ".profile()" on both versions and the outputs are almost identical in terms of the steps and time taken.

g.V().hasLabel('label_A').has('some_id', 123).has('data.name', 'value1').repeat(both('sample_edge').simplePath()).until(has('data.name', 'value2')).path().by('data.name').limit(1).profile()

==>Traversal Metrics

Step                                                               Count  Traversers       Time (ms)    % Dur

=============================================================================================================

JanusGraphStep([],[~label.eq(label_A), o...                     1           1           4.582     0.39

    \_condition=(~label = label_A AND some_id = 123 AND data.name = value1)

    \_orders=[]

    \_isFitted=true

    \_isOrdered=true

    \_query=multiKSQ[1]@8000

    \_index=someVertexByNameComposite

  optimization                                                                                 0.028

  optimization                                                                                 0.907

  backend-query                                                        1                       3.012

    \_query=someVertexByNameComposite:multiKSQ[1]@8000

    \_limit=8000

RepeatStep([JanusGraphVertexStep(BOTH,[...                     2           2        1167.493    99.45

  HasStep([data.name.eq(...                                                          803.247

  JanusGraphVertexStep(BOTH,[...                           12934       12934         334.095

    \_condition=type[sample_edge]

    \_orders=[]

    \_isFitted=true

    \_isOrdered=true

    \_query=org.janusgraph.diskstorage.keycolumnvalue.SliceQuery@812d311c

    \_multi=true

    \_vertices=264

    optimization                                                                               0.073

    backend-query                                                    266                       5.640

    \_query=org.janusgraph.diskstorage.keycolumnvalue.SliceQuery@812d311c

    optimization                                                                               0.028

    backend-query                                                  12689                     312.544

    \_query=org.janusgraph.diskstorage.keycolumnvalue.SliceQuery@812d311c

  PathFilterStep(simple)                                           12441       12441          10.980

  JanusGraphMultiQueryStep(RepeatEndStep)                           1187        1187          11.825

  RepeatEndStep                                                        2           2         810.468

RangeGlobalStep(0,1)                                                   1           1           0.419     0.04

PathStep([value(data.name)])                                 1           1           1.474     0.13

                                            >TOTAL                     -           -        1173.969        -

I'd really appreciate some input on figuring out why the query is 3x slower on 6 nodes.

I realise that you may require more context. Happy to provide more information as required!

 Thank you!

 

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