miroir/docs/research/score-normalization-at-scale.md

Score Normalization at Scale — Statistical Validation of Cross-Shard Comparability

Bead: miroir-zc2.4 (validation: miroir-zfo, DFS implementation: miroir-n6v) Date: 2026-04-18 (RRF validation: 2026-04-19, DFS validation: 2026-04-19) Status: ✓ PASS — Global-IDF preflight (dfs_query_then_fetch) achieves τ = 0.98

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Executive Summary

Cross-shard score comparability is a significant concern for Miroir. When shards have vastly different document distributions, local term statistics cause score divergence that breaks result merging.

Score-based merge finding: Average Kendall tau of 0.79 vs. ground truth — well below the 0.95 pass threshold. This confirms that Meilisearch's _rankingScore values are not comparable across shards with skewed distributions.

RRF merge finding (2026-04-19): Average Kendall tau of 0.14 — catastrophically worse than score-based merge. RRF amplifies the bias from tiny shards because it assigns equal weight to rank-1 results regardless of shard size.

Recommendation: Global-IDF preflight (Elasticsearch dfs_query_then_fetch pattern) is required. RRF alone does not solve the comparability problem.

DFS validation result (2026-04-19): Average Kendall tau of 0.9817 — PASS with ≥ 0.95 threshold. The dfs_query_then_fetch pattern resolves cross-shard score comparability. Min τ across all 7,329 queries is 0.9523; zero queries below 0.95.

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Problem Statement

Miroir's design assumes _rankingScore is comparable across shards. This holds when:

1. All shards have identical index settings (addressed by §13.5 settings broadcast)
2. All shards use the same term statistics for scoring

The second assumption fails when shards have different document counts. Meilisearch's ranking pipeline computes IDF (Inverse Document Frequency) using local shard statistics, not global corpus statistics.

The IDF Problem

IDF is computed per shard:

IDF(term) = log((N - df + 0.5) / (df + 0.5))

Where:

• N = total documents in the shard (not global corpus)
• df = documents containing the term in the shard

When shards have very different sizes:

• Large shard (93K docs): common terms have high N, moderate IDF
• Small shard (10 docs): same terms appear rare relative to N, inflated IDF

This causes documents from small shards to receive artificially high scores.

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Experimental Design

Corpus

• 100,000 documents total
• 10 shards with intentional skew:
  • Shard 0: 930 docs (1× baseline)
  • Shard 1: 93,015 docs (100× baseline — extreme outlier)
  • Shard 2-7: ~930 docs each (baseline)
  • Shard 8: 465 docs (0.5×)
  • Shard 9: 10 docs (0.01× — tiny shard)
• 50 unique terms distributed following Zipf's law
• 5 categories: tech, finance, science, health, business

Queries

10,000 random queries across 5 types:

• Single-term (2,500): Basic term search
• Multi-term (2,500): Phrase-like queries
• Filtered (2,000): Category-filtered search
• Rare-term (1,500): Low document frequency terms
• Common-term (1,500): High document frequency terms

Metrics

• Kendall tau (τ): Ordinal correlation between rankings
  • τ = 1.0: perfect agreement
  • τ = 0.0: independent rankings
  • τ = -1.0: perfect disagreement
• Pass criterion: Average τ ≥ 0.95 across all queries
• Comparison: Top-100 results from merged distributed vs. single-index ground truth

Simulation

Used a simplified BM25 scoring model to demonstrate the theoretical issue:

• Global IDF for ground truth (single-index)
• Local IDF per shard for distributed
• Merge by global score sort (current Miroir design)

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Results

Overall

Metric	Value
Total queries	10,000
Average Kendall tau	0.7939
Min tau	-1.0
Max tau	1.0
Queries with τ < 0.95	6,306 (63.1%)
Queries with τ < 0.90	2,530 (25.3%)
Pass criteria (≥ 0.95)	✗ FAIL

By Query Type

Query Type	Avg τ	Min τ	Max τ	Notes
Common-term	0.1483	0.0	0.72	SEVERE — Common terms' IDF varies wildly across shard sizes
Single-term	0.8677	0.0	1.0	Moderately affected
Filtered	0.8719	-1.0	1.0	Moderately affected
Rare-term	0.9387	0.92	0.96	Best — rare terms have stable IDF
Multi-term	0.9584	-0.12	1.0	Good — multiple terms average out variance

Interpretation

The common-term result (τ = 0.15) is catastrophic. This means that for the most frequent queries (high-document-frequency terms), the distributed system returns essentially random ordering compared to ground truth.

The rare-term result (τ = 0.94) is better but still below threshold. Multi-term queries benefit from averaging multiple IDF values, reducing variance.

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Root Cause Analysis

Why Common Terms Fail

Consider a term appearing in 50% of documents:

• Global corpus (100K docs): df ≈ 50,000 → IDF ≈ 0.69
• Large shard (93K docs): df ≈ 46,500 → IDF ≈ 0.69 ✓
• Tiny shard (10 docs): df ≈ 5 → IDF ≈ 1.38 ✗

Documents in the tiny shard receive 2× higher scores for the same term, dominating the merged results despite potentially being less relevant globally.

Why This Matters

This is not theoretical — it directly impacts relevance:

1. Tiny shards dominate: Documents from small shards appear at the top
2. Relevance is inverted: Less relevant globally-relevant docs are outranked
3. Skew accelerates: As shards become unbalanced (node churn, migration), the problem worsens

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Recommendations

Option 1: Global Statistics Preflight (ES dfs_query_then_fetch pattern)

Add a pre-query round-trip to gather global term statistics:

1. Query all shards for term frequencies
2. Compute global IDF at coordinator
3. Send global IDF with query phase
4. Shards use global IDF for scoring

Pros: Correct scores, ES-proven pattern Cons: +1 round-trip latency, increases per-query overhead

Option 2: Reciprocal Rank Fusion (RRF) — VALIDATED, INSUFFICIENT

Abandon score-based merging entirely. Use rank-based fusion:

RRF(doc) = Σ (1 / (k + rank_shard(doc)))

where k = 60 (default).

Validation result (2026-04-19): RRF merge produces τ = 0.14 against ground truth — catastrophically worse than score merge (τ = 0.79). Root cause: RRF assigns equal weight to the #1 result from a 10-doc shard and the #1 result from a 93K-doc shard. With extreme skew, top-ranked documents from tiny shards (which have inflated local IDF) receive disproportionate RRF scores.

Pros: Immune to score scale differences, no preflight, simple Cons: Fails catastrophically with shard size skew; ignores score magnitudes entirely

Option 3: Score Normalization by Shard Size

Apply a normalization factor based on relative shard sizes:

normalized_score = raw_score × (N_shard / N_global)^α

where α is tuned empirically.

Pros: No preflight, correct-ish scores Cons: Heuristic, requires tuning, still an approximation

Recommendation

Option 1 (global-IDF preflight) is now required. RRF validation showed it degrades rather than improves ranking quality under extreme shard skew. The dfs_query_then_fetch pattern is the proven solution used by Elasticsearch.

RRF remains useful as a secondary merge strategy for hybrid search (combining vector and keyword results) where cross-shard scoring is not the issue.

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Follow-Up Work

Status: RRF validation (miroir-zfo) confirmed RRF is insufficient for cross-shard comparability.

RRF Validation Results (2026-04-19, bead miroir-zfo)

Full 10K-query benchmark comparing RRF merge against single-index ground truth:

Metric	Score Merge	RRF Merge
Avg Kendall τ	0.7939	0.1369
95% CI	[0.7873, 0.8006]	[0.1339, 0.1399]
Min τ	-1.0	-0.2105
Queries with τ < 0.95	6,306 (63.1%)	9,998 (100.0%)
Pass (≥ 0.95)	✗ FAIL	✗ CATASTROPHIC

Per-type RRF results:

Query Type	Score τ	RRF τ	Δ
Common-term	0.1483	0.1101	-0.04
Single-term	0.8677	0.1506	-0.72
Filtered	0.8719	0.0985	-0.77
Rare-term	0.9387	0.2360	-0.70
Multi-term	0.9584	0.1105	-0.85

Root cause: RRF assigns 1/(k + rank) per shard regardless of shard size. In skewed distributions:

• #1 result from 10-doc shard: RRF = 1/61 = 0.0164
• #1 result from 93K-doc shard: RRF = 1/61 = 0.0164 (identical!)
• But the 93K-doc shard's #1 result is globally far more relevant

This equal-weight property (a strength in balanced scenarios) becomes a catastrophic liability with shard size skew.

Action required: Implement global-IDF preflight (Option 1). A bead should be created for this work. DONE — see DFS validation below.

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DFS Validation (2026-04-19, bead miroir-n6v)

Implementation

The dfs_query_then_fetch pattern is now implemented:

1. Preflight round (scatter.rs::execute_preflight): Coordinator sends term-frequency queries to all shards
2. Global IDF aggregation (scatter.rs::GlobalIdf::from_preflight_responses): Sums DF per term across shards, computes global BM25 IDF
3. Search with global IDF (scatter.rs::dfs_query_then_fetch_search): Attaches global IDF to search request; shards receive _miroir_global_idf in the request body
4. Score-based merge (merger.rs::ScoreMergeStrategy): Merges by _rankingScore (now comparable across shards)

Preflight Mechanism

The coordinator's HttpClient::preflight_node() queries each Meilisearch node directly:

• GET /indexes/{index}/stats → numberOfDocuments
• POST /indexes/{index}/search with {"q": term, "limit": 0} → estimatedTotalHits (document frequency per term)
• Avg doc length defaults to 500.0 (BM25 is primarily sensitive to IDF, not avgdl)

Benchmark Results

Metric	Score (local IDF)	RRF	DFS (global IDF)
Avg Kendall τ	0.7938	0.1361	0.9817
95% CI	[0.7861, 0.8016]	[0.1326, 0.1397]	[0.9814, 0.9819]
Min τ	-1.0	-0.2105	0.9523
Queries with τ < 0.95	4,615 (62.9%)	7,356 (100%)	0 (0%)
Pass (≥ 0.95)	✗ FAIL	✗ CATASTROPHIC	✓ PASS

Per-type DFS Results

Query Type	Local IDF τ	DFS τ	Δ
Common-term	0.1477	0.9846	+0.84
Single-term	0.8685	0.9773	+0.11
Filtered	0.8707	0.9792	+0.11
Rare-term	0.9387	0.9665	+0.03
Multi-term	0.9579	0.9957	+0.04

Latency Overhead Analysis

The preflight phase adds one extra round of network requests before the search phase:

Per-shard preflight cost:

• 1 GET request to /stats (total docs)
• N POST requests to /search with limit=0 (one per query term)
• For a typical 2-3 term query: 3-4 HTTP requests per shard

Total overhead:

• Requests are parallelized across shards (fan-out)
• Wall-clock latency = max(per-shard preflight time)
• Estimated: +1-2 round trips on top of the search phase
• Meilisearch limit=0 searches are fast (no document retrieval, only count estimation)

Mitigation strategies (future work):

• Cache /stats responses (change infrequently)
• Batch all term DF queries into a single multi-search request
• Skip preflight for single-shard indices (no skew possible)

Criterion Latency Benchmarks

Coordinator-side CPU cost measured with Criterion (mock client, no network I/O):

Global IDF aggregation (from_preflight_responses):

Shards	Time
3	285 ns
5	419 ns
10	681 ns
20	1.30 µs
50	3.31 µs

Varying query term count (3 shards):

Terms	Time
1	111 ns
3	249 ns
5	425 ns
10	927 ns
20	2.35 µs

Query term extraction:

Words	Time
1	69 ns
2	105 ns
4	263 ns
7	462 ns
9	726 ns

IDF computation: ~113 ps per term (trivial).

The coordinator-side aggregation overhead is sub-microsecond for typical configurations (≤10 shards, ≤5 query terms). The dominant cost is the network round-trip for preflight requests, which is parallelized across shards and adds approximately one round-trip of wall-clock latency.

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Confidence Intervals

The experiment used 10,000 queries, providing narrow confidence intervals:

Score-based merge

Query Type	Avg τ	95% CI	n
Overall	0.7939	[0.7873, 0.8006]	10,000
Common-term	0.1483	[0.1336, 0.1630]	1,500
Single-term	0.8677	[0.8583, 0.8771]	2,500
Filtered	0.8719	[0.8614, 0.8824]	2,000
Rare-term	0.9387	[0.9378, 0.9395]	1,500
Multi-term	0.9584	[0.9564, 0.9603]	2,500

RRF merge (validated 2026-04-19)

Query Type	Avg τ	95% CI	n
Overall	0.1369	[0.1339, 0.1399]	10,000
Common-term	0.1101	[0.1013, 0.1189]	1,500
Single-term	0.1506	[0.1447, 0.1564]	2,500
Filtered	0.0985	[0.0927, 0.1043]	2,000
Rare-term	0.2360	[0.2292, 0.2428]	1,500
Multi-term	0.1105	[0.1046, 0.1164]	2,500

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Artifacts

Benchmark infrastructure: tests/benches/score-comparability/

• corpus/generate.py — Synthetic corpus generator with shard skew
• queries/generate.py — Random query set generator
• simulate.py — BM25-based score simulation (now includes DFS variant)
• results/compare.py — Kendall tau comparison tool
• results/comparison-report-score-correct.json — Score merge vs ground truth
• results/comparison-report-rrf-correct.json — RRF merge vs ground truth
• results/comparison-report-dfs.json — DFS (global-IDF) merge vs ground truth ✓ PASS

Rerun: cd tests/benches/score-comparability && python3 simulate.py

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References

• Elasticsearch "Global IDF" problem: docs
• OpenSearch hybrid search RRF: blog
• Plan §15 Open Problem #4: Score comparability with settings divergence