[GitHub] [druid] paul-rogers commented on pull request #13196: Async reads for JDBC

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paul-rogers commented on PR #13196:
URL: https://github.com/apache/druid/pull/13196#issuecomment-1272466167

   @FrankChen021, you get the Fastest Review Ever award! Thanks!
   
   > I always thought the HTTP timeout is a client behavior, and didn't know that it can be controlled by the server.
   And I'm curious that is such internal feature implemented by Avatica JDBC driver only? Could you show me some materials to learn more about it?
   
   You are right: the client (and proxies and Router) all determine timeout: the shortest timeout wins. The challenge is when that HTTP timeout is shorter than the amount of time Druid needs to compute the next batch of results. In a regular REST API via the `/sql` endpoint, we have to compute and return all the results within this timeout. This is usually not a problem because Druid is intended for simple queries: those that return in 100ms with a few hundred or thousand rows, and that exploit Druid's time partitioning, filters, etc.
   
   The challenge is with the occasional "BI" style query that, for whatever reason, returns many rows, or takes a long time to compute. Maybe you've got billions of rows and need to find the one event, anywhere in that set, that represents access to some resource. There is nothing for it but to scan all the data, and that might take a while depending on the filter used. Or, maybe someone wants to grab several million rows of data to feed into an ML model. And so on.
   
   Normally you would not want to run such a query on Druid: that;'s not what Druid is designed for. But, sometimes you just gotta do what you gotta do. In this case, if your query takes a minute to run, and one of the network elements in the chain has a 30 second timeout, you can't successfully run the query with request response.
   
   But, JDBC is different. It works by sending multiple requests for each query, each returning some number of rows, generally in the thousands. The client keeps asking for more batches until the server returns EOF. So, if we had a long running query, but each batch of records could be computed in less time than the HTTP timeout, the query would succeed.
   
   Most of the time in a big query, however, is the work before we get the first row. So, we want to generalize the above idea. JDBC allows us to return a batch of 0 rows. If so, the client just turns around and requests another batch until it gets rows or is told that an EOF occurred. Just to be clear, this polling is a feature of the Avatica client. The application just iterates though a `ResultSet`, blissfully ignorant of the goings-on under the covers.
   
   That's what the async thing in this PR does for us. We tell Druid to go get a batch of rows. If this takes longer than the *fetch timeout*, we return an empty batch, and remember that Druid is busily working away to get us a batch. Maybe it will be ready next time. If, so, we return it. If not, we again return an empty batch and the whole thing repeats.
   
   For this to work, the fetch timeout has to be less than the HTTP timeout. Given jitter, we might use the Nyquist rule and set the fetch timeout to half of the HTTP timeout. It doesn't have to be exact, any value less than 1/2 the network timeout should be fine.
   
   Voila! We've decoupled query run time from network timeouts by running the query (or at least each batch fetch) asynchronously with the Avatica REST HTTP requests. So, you see, we don't set the HTTP timeout, we just work around it by *always* returning within the timeout, knowing that the client will do the right thing if we return 0 rows to stay within the timeout.


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