It had been previously assumed that re-writing this function in the style of retain() would make it faster, but it had the opposite effect.
The reason why retain is so fast due to properties of the data that hold true when intersecting document lists, where long runs of adjacent documents are expected, but not when looking up the data associated with the already intersected documents, where the data is more sparse.
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.
Retire search functions that weren't used, including the native implementations. Drop confusing suffixes on search function names. Search functions no longer encode search misses as negative values.
Replaced binary search function with a branchless version that is much faster.
Cleaned up benchmark code.
Retire search functions that weren't used, including the native implementations. Drop confusing suffixes on search function names. Search functions no longer encode search misses as negative values.
Replaced binary search function with a branchless version that is much faster.
Cleaned up benchmark code.
Roll back to JDK 21 for now, and make Java version configurable in the root build.gradle
The project has run into no less than three distinct show-stopping bugs in JDK22, across multiple vendors, and gradle still doesn't fully support it, meaning you need multiple JDK versions installed.
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.
The sign of the counter is used to indicate whether a term has appeared as title. Until it's seen in the title, it's provisionally saved as a negative count.
The code would always re-initialize the static ngramLexicon and rdrposTagger fields with new instances even if they were already instantiated, leading to a ton of unnecessary RAM allocation.
The modified behavior checks for nullity before creating a new instance.
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.
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.
To avoid having to either hard-code or manually configure service addresses (possibly several dozen), and to reduce the project's dependency on docker to deal with routing and discovery, the option to use [Zookeeper](https://zookeeper.apache.org/) to manage services and discovery has been added.
A service registry interface was added, with a Zookeeper implementation and a basic implementation that only works on docker and hard-codes everything.
The last remaining REST service, the assistant-service, has been migrated to gRPC.
This also proved a good time to clear out primordial technical debt from the root of the codebase. The 'service-client' library has been taken behind the barn and given a last farewell. It's replaced by a small library for managing gRPC channels.
Since it's no longer used by anything, RxJava has been removed as a dependency from the project.
Although the current state seems reasonably stable, this is a work-in-progress commit.
The readme for the array library was extremely out of date. Updating it with accurate information about how the library works, and a demo that should compile.
Also added a system property for disabling the use of sun.misc.Unsafe.
The RandomFileAssembler implementations, introduced in commit 53c575db3f were all acting subtly differently. The RWF implementation wrote BigEndian longs instead of the native endianness used by the other implementations (and expected by the index construction code), further the mmap implementation exposed a bug in LongArray.write() that caused it to create a larger file than necessary.
A test was built to ensure the output of these implementations is equivalent.
To cope with writing large files out of order, the system needs some form of strategy to avoid writing them directly to disk, as this causes insane amounts of disk thrashing. By default, the data is just buffered in RAM. This works well on a large server, but smaller systems struggle.
To help systems with small RAM process large amounts of data, the old RandomWriteFunnel is brought back if the system property 'system.conserve-memory' is set to true. RandomWriteFunnel is buffering the construction by creating a series of small files that pigeonhole the writes into rough neighborhoods, and then it goes over the files one by one to construct one area of the file at a time. This is relatively slow and uses more than twice the disk size.
A new interface RandomFileAssembler is introduced as an abstraction for this operation. A third strategy, direct mmaps, is also introduced if the file is very small (less than 1 GB). In this domain, disk thrashing is unlikely since it will comfortably fit in RAM.
The flag is `system.languageDetectionModelVersion`.
* If negative, no model is used.
* If 0, both models are used.
* If 1, the old crappy model is used.
* If 2, the new fasttext model is used.
The flag is `system.languageDetectionModelVersion`.
* If negative, no model is used.
* If 0, both models are used.
* If 1, the old crappy model is used.
* If 2, the new fasttext model is used.
