This functionality fell into disrepair some while ago. It's supposed to allow non-mandatory search terms that boost the ranking if they are present in the document.
The change set cleans up the data model for the term-level data. This used to contain a bunch of fields with document-level metadata. This data-duplication means a larger memory footprint and worse memory locality.
The ranking code is also modified to not accept SearchResultKeywordScores, but rather CompiledQueryLong and CqDataInts containing only the term metadata and the frequency information needed for ranking. This is again an effort to improve memory locality.
We no longer break the query into "sets" of search terms and need to adapt the code to not use this assumption.
For the API service, we'll simulate the old behavior to keep the API stable.
For the search service, we'll introduce a new way of calculating positions through tree aggregation.
Seems to work, tests are green and initial testing finds no errors. Still a bit untested, committing WIP as-is because it would suck to lose weeks of work due to a drive failure or something.
This addresses the relatively common case where the graph consists of two segments, such as x y, z w; in this case we want an output like (x_y) (z w | z_w) | x y (z_w). The generated output does somewhat pessimize a few other cases, but this one is arguably more important.
Netty and GRPC by default spawns an incredible number of threads on high-core CPUs, which amount to a fair bit of RAM usage.
Add custom executors that throttle this behavior.
Look, this will make the git history look funny, but trimming unnecessary depth from the source tree is a very necessary sanity-preserving measure when dealing with a super-modularized codebase like this one.
While it makes the project configuration a bit less conventional, it will save you several clicks every time you jump between modules. Which you'll do a lot, because it's *modul*ar. The src/main/java convention makes a lot of sense for a non-modular project though. This ain't that.
Cleaning out a lot of old junk from the code, and one thing lead to another...
* Build is improved, now constructing docker images with 'jib'. Clean build went from 3 minutes to 50 seconds.
* The ProcessService's spawning is smarter. Will now just spawn a java process instead of relying on the application plugin's generated outputs.
* Project is migrated to GraalVM
* gRPC clients are re-written with a neat fluent/functional style. e.g.
```channelPool.call(grpcStub::method)
.async(executor) // <-- optional
.run(argument);
```
This change is primarily to allow handling ManagedChannel errors, but it turned out to be a pretty clean API overall.
* For now the project is all in on zookeeper
* Service discovery is now based on APIs and not services. Theoretically means we could ship the same code either a monolith or a service mesh.
* To this end, began modularizing a few of the APIs so that they aren't strongly "living" in a service. WIP!
Missing is documentation and testing, and some more breaking apart of code.
