Comments (2)
The ability to configure Lucene file formats by adding tuning knobs has come up a few times in the past and the answer has been to create a custom codec, to keep the scope of what Lucene needs to maintain backward compatibility for contained.
The downside is that you're then on your own when it comes to maintaining backward compatibility for the data (which is typically ok for use-cases that don't need backward compatibility guarantees, e.g. because they reindex data daily from another source of data). But it sounds fair to require users to maintain backward compatibility themselves when Lucene's out-of-the-box file formats are not good for them.
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If you want slower but better compression, maybe consider using BEST_COMPRESSION
instead of BEST_SPEED
? That is the purpose of the option...
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Related Issues (20)
- TestShapeDocValues.testLatLonPolygonBBox
- Merge on Commit: No merges if new data is flushed (but not committed) HOT 3
- Flaky Test in TestMergeSchedulerExternal#testSubclassConcurrentMergeScheduler
- Could Lucene's default Directory (`FSDirectory.open`) somehow preload `.vec` files? HOT 5
- Test TestIndexWriterWithThreads#testIOExceptionDuringWriteSegmentWithThreadsOnlyOnce Failed HOT 2
- Pruning of estimating the point value count since BooleanScorerSupplier HOT 2
- Add refinement of quantized vector scores with fp distance calculations HOT 4
- Try applying bipartite graph reordering to KNN graph node ids HOT 4
- "gradlew clean check" results in internal gradle error "Unable to make progress running work." HOT 1
- DocumentsWriterDeleteQueue.getNextSequenceNumber assertion failure seqNo=9 vs maxSeqNo=8 HOT 4
- Luke does not support `spanNear` queries
- Investigate possible perf regression with off-heap scoring on JDK 22 HOT 1
- Are we properly accounting for `NeighborArray.rwlock`? HOT 4
- Incomplete Javadoc for DirectoryReader#indexExists HOT 1
- Add support for reading/writing dense vectors to MemoryIndex
- ConcurrentMergeScheduler may spawn more merge threads than specified
- Remove redundant code in PointRangeQuery Weight
- KnnFloatVectorQuery misses highest-ranking results that FloatVectorSimilarityQuery retrieves HOT 5
- int4 doesn't handle `cosine` similarity correctly HOT 2
- Add nightly test that calculates recall for vector similarity spaces HOT 1
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