Tom Kolanko created TINKERPOP-2679:
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Summary: Update JavaScript driver to support processing messages
as a stream
Key: TINKERPOP-2679
URL: https://issues.apache.org/jira/browse/TINKERPOP-2679
Project: TinkerPop
Issue Type: Improvement
Components: javascript
Affects Versions: 3.5.1
Reporter: Tom Kolanko
Fix For: 3.5.2
The JavaScript driver's
[_handleMessage|https://github.com/apache/tinkerpop/blob/d4bd5cc5a228fc22442101ccb6a9751653900d32/gremlin-javascript/src/main/javascript/gremlin-javascript/lib/driver/connection.js#L249]
receives messages from the gremlin server and stores each message in an object
associated with the handler for the specific request. Currently, the driver
waits until all the data is available from the gremlin server before allowing
further processing of it.
The following examples assume that you have 100 vertices in your graph.
{code:javascript}
const result = await client.submit("g.V()")
console.log(result.toArray()) // 100 - all the vertices in your graph
{code}
However, this can lead to cases where a lot of memory is required to hold onto
the results before any processing can take place. If we had the abilty to
process results as they come in from the gremlin server we could reduce memory
in some cases
If you are open to it I would like to submit a PR where {{submit}} can take an
optional callback which is run on each set of data returned from the gremlin
server, rather than waiting for the entire result set:
{code:javascript}
await client.submit("g.V()", {}, { batchSize: 25 }, (data) => {
console.log(data.toArray().length) // 25 - this callback will be called 4
times (100 / 25 = 4)
})
{code}
I have the changes running locally and the overall performance is unchanged,
queries run about the same as they used to, however, for some specific queries
memory usage has dropped considerably.
With the process-on-message strategy the memory usage will be related to how
large the {{batchSize}} is rather than the final result set. Using the default
of 64 and testing some specific cases we have I can get the memory to go from
1.2gb to 10mb.
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