perf(core): Implement sparse LiveDocs to reduce memory by up to 8x

[salvatorecampagna]

Scope

For segments with sparse deletions (<=1%), this change tracks only the deleted document IDs instead of maintaining a full bitset of all documents. This simple change reduces LiveDocs memory usage by up to 8x and speeds up deleted-document iteration by 3-4x in typical append-heavy workloads.

The Problem

Lucene currently allocates maxDoc/8 bytes for LiveDocs, independent of the number of deletions. For example, a 10M-document segment always allocates ~1.2 MB even if only 100K documents (1%) are deleted, wasting memory on mostly live documents.

This change stores only the deleted document IDs, reducing memory by up to 8x at a 1% deletion rate. The savings scale linearly: for example, a 100M-document segment with 1% deletions drops from ~12 MB to ~800 KB (random pattern).

Common Case

The sparse representation targets the most common real-world scenario: large segments with few deletions. In append-heavy workloads, segments often reach 10M-100M documents with no deletions or only 0.1-1% deletions before merging.

For a 100M-document segment with 0.1% deletions (100K deleted docs):

• Memory savings: ~12 MB -> ~800 KB (15× reduction)
• Deleted docs iteration: O(maxDoc) → O(deletedDocs), enabling use cases like histogram correction (Efficient iteration over deleted doc values #15226)
• Live docs iteration: Same performance, dramatically less memory pressure

This memory efficiency is crucial because LiveDocs are held in memory for every open segment. With dozens of segments open simultaneously, the memory savings compound. Additionally, the reduced memory footprint improves cache locality for live document iteration, as we're only storing the small deleted docs bitset rather than a full maxDoc-sized bitset.

The sparse format never makes things worse: it's only selected when deletions are <=1%, where benchmarks confirm consistent wins across all deletion patterns.

How It Works

The implementation uses an adaptive approach:

• SparseLiveDocs (<=1% deletions): Stores only deleted doc IDs using SparseFixedBitSet
• DenseLiveDocs (>1% deletions): Uses traditional FixedBitSet for live docs

This PR introduces two complementary implementations: SparseLiveDocs for low deletion rates and DenseLiveDocs for high deletion rates, each optimized for their respective cases. These implementations add efficient iteration methods via the LiveDocs interface: deletedDocsIterator() for O(deletedDocs) iteration and liveDocsIterator() for O(liveDocs) iteration.

The LiveDocs interface approach allows the test framework to wrap these implementations with AssertingLiveDocs, which validates correctness during testing by delegating to the underlying LiveDocs methods while adding assertions. This preserved compatibility with existing test infrastructure without requiring changes to test code.

The codec automatically selects the right format when reading .liv files from disk. Benchmarks show the crossover point, where sparse and dense performance equalize, occurs around 5-10% depending on deletion pattern. By choosing 1%, sparse provides clear wins in both memory and iteration speed across all patterns. Even in the worst-case (uniform) distribution, sparse remains faster at <=1%. This conservative threshold guarantees predictable behavior while targeting the most common case where sparse representations excel.

This PR also introduces a LiveDocs interface with a new method LeafReader.getLiveDocsWithDeletedIterator() that enables efficient O(deletedDocs) iteration via deletedDocsIterator(). Consumers can check the iterator's cost() method to determine whether iterating deleted docs would be beneficial for their use case. Rather than replacing the existing getLiveDocs() API (which would require extensive changes across the codebase), this approach lets consumers opt-in to the optimization when they need it. Use cases like PointTreeBulkCollector (histogram correction, #15226) can now efficiently iterate only deleted documents to adjust their counts, while existing code continues to work unchanged.

Benchmark Results

10M document segment:

Random pattern (typical real-world scenario)

Deletion Rate	Dense Memory	Sparse Memory	Reduction	Deleted Docs Iter	Speedup
0.1%	1,250,040 bytes	163,776 bytes	7.6x	2.75 ms -> 0.09 ms	31.7x faster
1%	1,250,040 bytes	804,008 bytes	1.6x	3.25 ms -> 0.88 ms	3.7x faster
5%	1,250,040 bytes	1,318,440 bytes	0.9x*	5.57 ms -> 4.56 ms	1.2x faster

Clustered pattern (best case for sparse)

Deletion Rate	Dense Memory	Sparse Memory	Reduction	Deleted Docs Iter	Speedup
0.1%	1,250,040 bytes	30,760 bytes	40.6x	2.75 ms -> 0.08 ms	36.6x faster
1%	1,250,040 bytes	42,376 bytes	29.5x	2.95 ms -> 0.75 ms	3.9x faster
5%	1,250,040 bytes	93,856 bytes	13.3x	2.98 ms -> 3.75 ms	0.8x slower*

Uniform pattern (worst case for sparse)

Deletion Rate	Dense Memory	Sparse Memory	Reduction	Deleted Docs Iter	Speedup
0.1%	1,250,040 bytes	185,624 bytes	6.7x	2.8 ms -> 0.07 ms	40.1x faster
1%	1,250,040 bytes	1,318,368 bytes	0.9x*	2.75 ms -> 0.84 ms	3.3x faster
5%	1,250,040 bytes	1,318,552 bytes	0.9x*	2.9 ms -> 3.85 ms	0.8x slower*

Why the conservative 1% threshold? These benchmarks show significant pattern-dependent behavior:

• CLUSTERED (best case): Sparse wins on memory even at 5% (13x reduction) but loses on iteration speed
• UNIFORM (worst case): Sparse already uses more memory at 1% and loses iteration speed at 5%
• RANDOM (typical): Middle ground between clustered and uniform

By choosing 1%, sparse is used only when it generally delivers clear memory wins across most deletion patterns and never performs catastrophically worse. At <= 1%, sparse provides up to 1.6-40x memory reduction and 3-40x iteration speedup across typical or best-case deletion distributions.

Pathological case: maximally scattered deletions

This PR also tested a worst-case scenario with deletions maximally scattered across the bitset (1.5625% deletion rate):

Segment Size	Dense Memory	Sparse Memory	Overhead	Deleted Docs Iter	Speedup
100K	12,544 bytes	13,376 bytes	+6.6%	0.03 ms -> 0.01 ms	4.3x faster
500K	62,544 bytes	66,032 bytes	+5.6%	0.13 ms -> 0.03 ms	4.3x faster
1M	125,040 bytes	131,944 bytes	+5.5%	0.27 ms -> 0.06 ms	4.3x faster
5M	625,040 bytes	659,416 bytes	+5.5%	1.34 ms -> 0.32 ms	4.1x faster
10M	1,250,040 bytes	1,318,552 bytes	+5.5%	2.70 ms -> 0.65 ms	4.2x faster
50M	6,250,040 bytes	6,591,904 bytes	+5.5%	13.62 ms -> 3.26 ms	4.2x faster
100M	12,500,040 bytes	13,183,712 bytes	+5.5%	27.47 ms -> 6.52 ms	4.2x faster

Even in this unfavorable scenario, where sparse uses 5-6% more memory, the iteration speedup remains stable at ~4x across all segment sizes. This shows that the overhead remains bounded and predictable, making it an acceptable trade-off for iteration-heavy workloads.

Backward Compatibility

This change introduces no breaking changes:

• Disk format remains unchanged (.liv files are fully compatible)
• Existing indexes require no reindexing
• Code using the Bits API continues to function as before

Note on disk format: This PR keeps the existing Lucene90 .liv format (dense bitset on disk) to minimize changes and maintain compatibility. When reading, Lucene90LiveDocsFormat converts the on-disk dense representation to the in-memory sparse representation for segments with <=1% deletions. This conversion has negligible overhead since it only happens when segments are loaded, not during queries. A follow-up PR could introduce a new codec version that writes sparse deletions natively to disk, eliminating the conversion step entirely and reducing disk space for .liv files.

---

Fixes #13084

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[jainankitk]

Thanks @salvatorecampagna for iterating on this quickly. Most of the changes look good to me!

[jainankitk]

We currently return Bits instead of LiveDocs, which makes it difficult to consume. But, I guess there's no other way without updating the codec?

[salvatorecampagna]

Yeah, right. The issue is LiveDocsFormat.readLiveDocs() returns Bits and changing it would break all existing codecs, which feels too big for this PR.

That said, I added LeafReader::getLiveDocsWithDeletedIterator() that returns LiveDocs directly. Callers can also just cast if they want the extra methods:

Bits bits = liveDocsFormat.readLiveDocs(...);
if (bits instanceof LiveDocs liveDocs) {
  // Use deletedDocsIterator(), deletedCount(), etc.
}

Using LeafReader::getLiveDocsWithDeletedIterator lets consumers opt-in to the optimizations they can't get from just Bits. To me it feels like a reasonable compromise. We avoid breaking changes to existing code while still giving consumers an efficient way to handle both live and deleted documents.

I'm thinking this is better for BWC and API evolution anyway (also considering this is experimental): we can change the return type in a future codec version. For now, Bits works everywhere and LiveDocs is there when you need it.

Happy to file a follow-up issue for changing the return type in a future codec if that makes sense?

[jainankitk]

I kind of assumed that the DenseLiveDocs should not provide iterator over deleted documents and rather throw an error. Better fail instead of slow iterate using DenseLiveDocs? Maybe you have some use case for this

[salvatorecampagna]

I see it differently: the goal is twofold - keeping live document iteration efficient (which both formats handle well, and is the common use case) while also making deleted document iteration efficient when possible. Callers should check the cost before iterating deleted docs:

DocIdSetIterator iter = liveDocs.deletedDocsIterator();
long cost = iter.cost();

if (cost < threshold) {
  // Sparse: O(deletedDocs) - efficient
} else {
  // Dense: O(maxDoc) - skip if you can
}

Worth noting: we're never worse than before. The old FixedBitSet approach was always O(maxDoc) for iterating deleted docs - we're keeping that same behavior for dense deletions while making it much better (O(deletedDocs)) for sparse deletions.

I prefer providing it (even if slow) over throwing because:

• Both implementations honor the full LiveDocs interface
• Slow but correct beats throwing exceptions for debugging/validation
• The javadocs warn about O(maxDoc) and cost() exposes it
• Merge logic, validation tools, etc. benefit from it just working

If we throw, callers would need format-specific handling:

try {
  iter = liveDocs.deletedDocsIterator();
} catch (UnsupportedOperationException e) {
  // now what?
}

The current approach works everywhere - callers check cost if performance matters. Open to changing it if you feel strongly though.