I use the data here, This data is in CSV format, and it has 41.7 million nodes and 1.47 billion relationships.
I’m sharing the converted RDF file now, and my schema:
id: int @index(int) .
followers: [uid] @reverse .
type twitter_user {
id: int
followers: [twitter_user]
}
I did three tests
The results show that the performance of the type function is poor.
And there’s another problem, when I run a query on the k8s cluster, the start_ts returned is always the same.
The second query should definitely be fast here.
{
x(func: eq(id, 12)) @filter(type(twitter_user)) {
dgraph.type
}
}
I suppose we are fetching all the uids associated with the type before doing the intersection. We could be smarter here. Accepting this as an enhancement.
Can you share a way to replicate this? You should get different start_ts unless you are reusing the same transaction every time.
@pawan Thank you for your reply.
I don’t know how to replicate this problem right now, it won’t happen with a single alpha, but three of my four clusters have this problem.
