Refactoring keyword extraction to extract spans information.
Modifying the intermediate storage of converted data to use the new slop library, which is allows for easier storage of ad-hoc binary data like spans and positions.
This is a bit of a katamari damacy commit that ended up dragging along a bunch of other fairly tangentially related changes that are hard to break out into separate commits after the fact. Will push as-is to get back to being able to do more isolated work.
The change also restructures the internal API a bit, moving resultsFromDomain from RpcRawResultItem into RpcDecoratedResultItem, as the previous order was driving complexity in the code that generates these objects, and the consumer side of things puts all this data in the same object regardless.
Adding new ranking parameters to the API and routing them through the system, in order to permit integration of the new position data with the ranking algorithm.
The change also cleans out several parameters that no longer filled any function.
IntArray gets the YAGNI axe. The array library had two implementations, one for longs which was used, and one for ints, which only ever saw bit rot. Removing the latter, as all it ever did was clutter up the codebase and add technical debt. If we need int arrays, we fork LongArray again (or add int capabilities to it)
Also cleaning up the interfaces, removing layers of redundant abstractions and adding javadocs.
Finally adding sz=2 specializations to the quick- and insertion sort algorithms. It seems the JIT isn't optimizing these particularly well, this is an attempt to help it out a bit.
Since the performance fix in 3359f72239 had a huge positive impact without reducing result quality, it's possible to remove the QueryBranchWalker and associated code.
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.
