Kelvin Lawrence created TINKERPOP-3074:
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Summary: The sample() step is largely unusable with large graphs
Key: TINKERPOP-3074
URL: https://issues.apache.org/jira/browse/TINKERPOP-3074
Project: TinkerPop
Issue Type: Improvement
Components: process
Reporter: Kelvin Lawrence
While the `sample` step can be useful with smallish sized amounts of data for
random walks and similar, its current implementation makes it unusable with
large graphs if you are looking to sample, say, one node, from a graph with a
millions or billions of nodes in it.
{code:java}
// This generally works assuming the out() step yields limited numbers of nodes
g.V(1).out().sample(1).out.sample(1) //etc
// This fails for a large graph, usually with an OOM error
g.V().sample(1){code}
The current implementation of sample() is quite naive and assumes it can fetch
everything into memory before computing a result. I have seen many users
wanting to start a walk from a random place, and they always try to do
{color:#0747a6}_g.V().sample(1)_{color} or
_{color:#0747a6}g.E().sample(1){color}_ types of queries.
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