How to create indexes by Label?
In search of performance improvements, the AWS Neptune experts suggested that I create some indexes. To better contextualize, I have 3 operations in a single POST endpoint with the database. A query of previous data bringing the relationships of a specific ID, a deletion of edges if there is a registration in the database and a registration/update of vertices and edges. Today I am trying to attack two problems. Improve the performance of the creation that takes approximately 150ms and improve the performance of the query that is currently bogging down between 1.2-17 seconds.
Is it possible to create an index for vertexes and edges by specifying them by label since I have vertices and edges with different labels that have different properties? Does anyone know what this implementation would look like?
In my current implementation I do it in a simple way as follows:
client_write = client.Client(neptune_url, "g", message_serializer=serializer.GraphSONMessageSerializer())
queries = [
"graph.createIndex('journey_id', Vertex.class)",
"graph.createIndex('person_type', Vertex.class)",
"graph.createIndex('relationship_type', Edge.class)"
]
for query in queries:
client_write.submit(query).all().result()
Is it possible to create an index for vertexes and edges by specifying them by label since I have vertices and edges with different labels that have different properties? Does anyone know what this implementation would look like?
In my current implementation I do it in a simple way as follows:
client_write = client.Client(neptune_url, "g", message_serializer=serializer.GraphSONMessageSerializer())
queries = [
"graph.createIndex('journey_id', Vertex.class)",
"graph.createIndex('person_type', Vertex.class)",
"graph.createIndex('relationship_type', Edge.class)"
]
for query in queries:
client_write.submit(query).all().result()
Solution
just by calling g.V().limit(1) with concurrent calls on an r6g.2xlarge machine, the average time is 250ms
How may concurrent calls? An
r6g.2xlarge/gremlin/status%gremlin_statusBut in the query mentioned, the bottleneck starts at the stage where it calls the last otherV() before path().
g.V().has(T.id, "client-id-uuid").bothE("has_profile", "has_affiliated", "has_controlling").has(T.id, containing("tenant-id-uuid")).otherV().path().unfold().dedup().elementMap().toList()
Makes sense as you're using a text predicate here (
containing()containing()startingWith()endingWith()
Apache TinkerPop is an open source graph computing framework and the home of the Gremlin graph query language.
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