Add AnytimeRankingSearcher for SLA-Aware Early Termination with Bin-Based Score Boosting

[atris]

Add AnytimeRankingSearcher for SLA-aware early termination with bin-based score boosting

This patch adds AnytimeRankingSearcher, a new low-latency search implementation that supports early termination under SLA constraints, combined with bin-aware score boosting.

Architecture

Index-time binning uses a configurable post-indexing pass to assign each document to one of bin.count bins. This pass is activated via field attributes (doBinning=true, bin.count=N, etc.) and is triggered after all standard postings are written. Binning uses a segment-local sparse similarity graph where each node is a document and edges represent cosine similarity between term frequency vectors.

The bin distribution is computed via recursive graph bisection. The graph is recursively split into halves using a seeded heuristic that assigns each document to the closer of two seed nodes based on edge weights. This ensures intra-bin similarity and minimizes cross-bin connectivity. A fixed number of bins is produced, and the assignment is saved to a .binmap file.

In approximate mode (graph.builder=approx), we avoid building explicit term vectors. Instead, token co-occurrence is tracked using per-term BitSets, and documents are grouped using lightweight overlap heuristics. This trades off precision for speed and scales better on large segments.

At search time, BinMapReader loads the bin assignments, and BinScoreReader makes them accessible to search collectors. BinBoostCalculator assigns a boost score to each bin based on estimated bin quality (e.g. average term frequency or rank share in a warmup run). This boost is applied additively during ranking, allowing the collector to prioritize high-quality bins earlier and exit faster under SLA pressure.

Binning Modes (Index Time)

This patch supports two modes of document binning during indexing:
• Absolute mode: computes exact bin assignments using full document similarity graphs.
• Approximate mode: enabled when document count exceeds a threshold; skips graph construction and uses faster heuristics to assign bins.

Bin assignment is handled by DocBinningGraphBuilder and switches to ApproximateDocGraphBuilder automatically when needed.

To enable binning, field attributes must be set:

fieldType.putAttribute("postingsFormat", "Lucene103");
fieldType.putAttribute("doBinning", "true");
fieldType.putAttribute("bin.count", "4"); // total number of bins
fieldType.putAttribute("bin.builder", "exact" | "approx" | "auto"); // binning strategy

Search-Time Integration

At search time, bin boosts are loaded using BinScoreReader. To enable anytime ranking:

AnytimeRankingSearcher searcher = new AnytimeRankingSearcher(reader, topK, slaMs, fieldName);
TopDocs results = searcher.search(query);

Internally:
• Bin scores are applied per segment at query time.
• The collector monitors elapsed time and stops scoring once SLA is exhausted.

Test Coverage

Includes a full test (TestAnytimeRankingSearchQuality) that:

• Indexes 10k docs with periodic relevant content
• Runs baseline and anytime search
• Computes NDCG, precision, recall
• Asserts average and max position delta across result sets
• Verifies minimal degradation under SLA constraints

Performance

• AnytimeRankingSearcher provides ~2–3x speedup at low SLA targets
• Recall, precision, and NDCG remain within 95%+ of baseline
• Position delta of relevant docs remains bounded

Notes

• Readers are wrapped using BinScoreUtil.wrap(reader) to enable bin-aware scoring
• Compound readers are tracked and closed explicitly
• BinFilter skipping is not implemented yet — will be added in a follow-up patch
• Fallback to approximate binning ensures indexing remains scalable for large segments

Benchmarks

[atris]

@jpountz This PR is now ready for review. I will post luceneutil benchmarks tomorrow. Please let me know if anything else is needed from me.