Uploaded image for project: 'TinkerPop'
  1. TinkerPop
  2. TINKERPOP-2352

Gremlin Python driver default pool size makes Gremlin keep-alive difficult



    • Type: Bug
    • Status: Closed
    • Priority: Major
    • Resolution: Done
    • Affects Version/s: 3.3.5, 3.4.5
    • Fix Version/s: 3.5.0, 3.4.8
    • Component/s: python
    • Labels:
    • Environment:
      AWS Lambda, Python 3.7 runtime, AWS Neptune.
      (AWS Lambda functions can remain in memory and thus hold connections open for many minutes between invocations)


      I'm working with a Gremlin database that (like many) terminates connections if they don't execute any transactions with a timeout period.  When we want to run a traversal we first check our `GraphTraversalSource` by running `g.V().limit(1).count().next()` and if that raises an exception we know we need to reconnect before running the actual traversal.

      We've been very confused that this hasn't worked as expected: we intermittently see traversals fail with `WebSocketClosed` or other connection-related errors immediately after the "connection test" passes. 

      I've (finally) found the cause of this inconsistency is the default pool size in `gremlin_python.driver.client.Client` being 4.  This means there's no visiblity outside the `Client` of which connection in the pool is tested and/or used, and in fact no way for the application (`GraphTraversalSource`) to run keep-alive type traversals reliably.  Anytime an application passes in a pool size of `None` or a number > 1 there'll be no way to make sure that each and every connection in the pool actually sends keep-alive traversals to the remote, except in the case of a single-threaded application where a tight loop could issue `pool_size` of them.  In that latter case as the application is single-threaded then a `pool_size` above 1 won't provide much benefit.

      I've raised this as a bug because I think a default `pool_size` of 1 would give much more predictable behaviour, and in the specific case of the Python driver is probably more appropriate because Python applications tend to run single-threaded by default, with multi-threading carefully added when performance requires it.  Perhaps it's a wish, but as the behaviour from the default option is quite confusing it feels more like a bug, at least.  If it would help I'm happy to raise a PR with some updated function header comments or maybe updated documentation about multi-threaded / multi-async-loop usage of gremlin-python.

      (This is my first issue here, apologies if it has some fields wrong.)




            • Assignee:
              spmallette Stephen Mallette
              markbreal Mark Br...e
            • Votes:
              0 Vote for this issue
              4 Start watching this issue


              • Created: