Protocol Guide Database Performance Audit

Date: 2026-02-16 Database: Supabase PostgreSQL with pgvector Scale: 58,000+ protocol chunks, 2,738 agencies Critical Path: Vector search latency (target: <200ms)

Executive Summary

Current Status: GOOD (Minor Optimizations Recommended)

Key Findings:

✅ HNSW vector index properly configured (m=16, ef_construction=64)
✅ Comprehensive B-tree indexes for jurisdiction filters
✅ Connection pooling implemented (20 connections production)
✅ Query optimization with multi-query fusion and re-ranking
⚠️ No query runtime HNSW tuning (ef_search not configured)
⚠️ No explicit VACUUM configuration
⚠️ Limited database monitoring/instrumentation

Performance Target: 2-second end-to-end latency Current Performance: 1.5-2.5 seconds (variable)

1. Vector Search Analysis

1.1 HNSW Index Configuration

Current Setup (Excellent):

-- File: drizzle/migrations/0030_optimize_manus_protocol_chunks.sql
CREATE INDEX idx_manus_chunks_embedding_hnsw
ON manus_protocol_chunks
USING hnsw (embedding vector_cosine_ops)
WITH (m = 16, ef_construction = 64);

Analysis:

✅ Index Type: HNSW (optimal for 58k vectors)
✅ Parameters: m=16 (good graph connectivity), ef_construction=64 (good build quality)
✅ Distance Metric: Cosine similarity (matches Voyage AI embeddings)
✅ Dimensions: 1536 (Voyage voyage-large-2)

Index Size Estimation: ~200MB (as documented in DATABASE-REVIEW-SUMMARY.md)

1.2 Query-Time HNSW Parameters

Issue: No explicit ef_search configuration

Current State:

-- File: supabase/migrations/20260127_fix_state_filter.sql (line 59)
ORDER BY pc.embedding <=> query_embedding
LIMIT match_count;

Problem: PostgreSQL uses default ef_search=40 which may be suboptimal for accuracy/speed tradeoff.

Recommendation:

-- Add runtime parameter tuning based on tier
SET LOCAL hnsw.ef_search = 100; -- For high-accuracy (Pro tier)
SET LOCAL hnsw.ef_search = 64;  -- For standard searches (Free tier)

Expected Impact:

Higher ef_search = better recall (+2-5% accuracy) but slower (+20-40ms)
Lower ef_search = faster (-10-20ms) but lower recall
Tier-based tuning allows quality/speed tradeoff

1.3 Vector Search Query Pattern

RPC Function: search_manus_protocols

Current Implementation:

-- File: supabase/migrations/20260127_fix_state_filter.sql
SELECT
  pc.id,
  pc.agency_id,
  pc.protocol_number,
  pc.protocol_title,
  pc.section,
  pc.content,
  pc.image_urls,
  1 - (pc.embedding <=> query_embedding) AS similarity,
  pc.state_code
FROM manus_protocol_chunks pc
WHERE
  pc.embedding IS NOT NULL
  AND (agency_filter IS NULL OR pc.agency_id = agency_filter)
  AND (state_code_filter IS NULL OR pc.state_code = state_code_filter)
  AND (agency_name_filter IS NULL OR pc.agency_name ILIKE '%' || agency_name_filter || '%')
  AND 1 - (pc.embedding <=> query_embedding) > match_threshold
ORDER BY pc.embedding <=> query_embedding
LIMIT match_count;

Analysis:

✅ Filters applied before vector search (optimal)
✅ Uses HNSW index via <=> operator
✅ Threshold filtering (0.3-0.45 depending on query type)
⚠️ ILIKE filter on agency_name may bypass index (minor)

Performance Characteristics:

Best Case (agency_id filter): ~50-100ms (small result set)
Worst Case (state-wide): ~150-250ms (larger result set)
Average: ~150ms (as documented in RAG_OPTIMIZATION.md)

2. Index Coverage Analysis

2.1 Primary Indexes (Excellent Coverage)

Vector Index:

idx_manus_chunks_embedding_hnsw (HNSW) -- PRIMARY SEARCH INDEX

B-tree Indexes:

idx_manus_chunks_agency_id      -- Agency-scoped search
idx_manus_chunks_state_code     -- State-wide search
idx_manus_chunks_protocol_number -- Protocol lookup

Composite Indexes:

idx_manus_chunks_state_agency   -- State + Agency filtering
idx_manus_chunks_agency_protocol -- Agency protocol lookup
idx_manus_chunks_state_protocol  -- State protocol lookup

Full-Text Search:

idx_manus_chunks_search_vector (GIN) -- BM25 fallback

Partial Indexes:

idx_manus_chunks_with_embedding  -- Only chunks WITH embeddings
idx_manus_chunks_with_images     -- Image-enabled chunks

Analysis:

✅ Coverage: Excellent - all common query patterns indexed
✅ Composite Indexes: Cover multi-column WHERE clauses
✅ Partial Indexes: Reduce index size for common filters
✅ Full-Text: GIN index provides fast BM25 fallback

2.2 Query Pattern Coverage

Critical Query Patterns:

Pattern	Index Used	Coverage
Vector search (agency-scoped)	HNSW + agency_id	✅ Perfect
Vector search (state-scoped)	HNSW + state_code	✅ Perfect
Protocol number lookup	protocol_number B-tree	✅ Perfect
State + Agency filter	state_agency composite	✅ Perfect
BM25 keyword fallback	search_vector GIN	✅ Perfect
County → Agency mapping	county_agency_mapping PK	✅ Perfect

No Missing Indexes Identified

2.3 Index Maintenance

Current State:

No explicit VACUUM schedule configured
No explicit ANALYZE schedule configured
Relies on PostgreSQL autovacuum

Recommendation:

-- Add explicit VACUUM schedule for manus_protocol_chunks
-- (Large table with frequent reads, infrequent writes)

-- Nightly VACUUM ANALYZE during off-peak
-- Reduces index bloat and keeps query planner statistics fresh
ALTER TABLE manus_protocol_chunks
SET (autovacuum_vacuum_scale_factor = 0.05); -- More aggressive vacuuming

-- Force ANALYZE after bulk ingestion
ANALYZE manus_protocol_chunks;

Expected Impact:

Prevents index bloat over time
Maintains optimal query planning
Minimal overhead (table is mostly read-only)

3. Connection Pooling

3.1 Current Configuration (Good)

File: server/db/connection.ts

const POOL_CONFIG = {
  development: {
    max: 10,
    idleTimeoutMillis: 30000,
    connectionTimeoutMillis: 10000,
  },
  production: {
    max: 20,
    idleTimeoutMillis: 45000,
    connectionTimeoutMillis: 10000,
  },
};

Analysis:

✅ Environment-specific configuration
✅ Connection validation on pool creation
✅ Idle timeout prevents connection leaks
✅ Event handlers for monitoring

Pool Size Justification:

20 connections for production is reasonable for moderate traffic
Supabase default limit: 60 connections (direct), 200 (pooler)
Current usage: Well within limits

3.2 Supabase Pooler

Configuration: supabase/config.toml

default_pool_size = 20

Analysis:

✅ Uses Supabase's PgBouncer pooler
✅ Transaction-level pooling mode (default)
✅ Appropriate pool size for workload

No Issues Identified

3.3 Connection Pooling Recommendations

Current State: Already optimized

Future Considerations:

Monitor connection utilization under load
Consider increasing pool size if P95 latency spikes during traffic peaks
Add connection pool exhaustion alerts

4. Query Optimization

4.1 RAG Pipeline Performance

Target Breakdown:

Query normalization:    10ms
Embedding generation:   300ms (Voyage AI)
Vector search:          200ms (pgvector HNSW)
Re-ranking:             50ms
LLM inference:          1500ms (Claude Haiku/Sonnet)
─────────────────────────────
Total Target:           2060ms

Current Performance (from RAG_OPTIMIZATION.md):

Total: 1500-2500ms (variable)
- On target for cached queries
- Exceeds target for complex queries with multi-query fusion

4.2 Optimizations Implemented (Excellent)

Query Normalization:

✅ 150+ EMS abbreviation expansion
✅ Typo correction
✅ Intent classification
✅ Complexity detection

Search Execution:

✅ Multi-query fusion (for complex queries)
✅ Reciprocal Rank Fusion (RRF)
✅ Adaptive similarity thresholds (0.25-0.45)
✅ Keyword + semantic hybrid search

Re-ranking:

✅ Advanced scoring (term frequency, position, exact matches)
✅ Context boosting (+15 same agency, +5 same state)
✅ Intent-specific signals (dosage for meds, steps for procedures)

Caching:

✅ Query result cache (1-hour TTL)
✅ Embedding cache (24-hour TTL)
✅ Redis-backed rate limiting

4.3 Database-Level Query Optimization

Timeout Protection:

// File: server/_core/rag/timeout.ts
const TIMEOUT_CONFIG = {
  vectorSearch: 3000,        // 3 seconds
  totalPipeline: 2000,       // 2 seconds
  multiQueryVariation: 800,  // 800ms per variation
};

Analysis:

✅ Prevents hanging queries
✅ Graceful degradation on timeout
✅ Budget tracking for pipeline stages

EXPLAIN ANALYZE Recommendation:

Add periodic query plan analysis:

-- Run this monthly to verify HNSW index is being used
EXPLAIN (ANALYZE, BUFFERS)
SELECT
  id,
  1 - (embedding <=> $1::vector) AS similarity
FROM manus_protocol_chunks
WHERE
  agency_id = 123
  AND embedding IS NOT NULL
ORDER BY embedding <=> $1::vector
LIMIT 10;

-- Expected plan:
-- Index Scan using idx_manus_chunks_embedding_hnsw
-- Buffers: shared hit=X

5. Performance Bottlenecks & Priorities

5.1 Critical Path Analysis

Latency Breakdown (Average Query):

Stage	Current	Target	Status
Query normalization	<10ms	10ms	✅ On target
Embedding generation	~300ms	300ms	✅ On target
Vector search (HNSW)	~150ms	200ms	✅ On target
Re-ranking	~50ms	50ms	✅ On target
LLM inference (Haiku)	800-1500ms	1500ms	✅ Variable
Total	1310-2010ms	2060ms	✅ Mostly on target

Outliers:

Complex queries with multi-query fusion: +200-400ms (3 parallel searches)
State-wide searches (no agency filter): +50-100ms (larger result set)
Cold cache / first query: +100-200ms (no cached results)

5.2 Priority Recommendations

Priority 1: HNSW Query-Time Tuning

Impact: Medium (±30ms, +2-5% accuracy) Effort: Low (1 hour)

-- Add to search_manus_protocols function
CREATE OR REPLACE FUNCTION search_manus_protocols_tuned(
  query_embedding vector(1536),
  agency_filter integer DEFAULT NULL,
  state_code_filter char(2) DEFAULT NULL,
  match_count integer DEFAULT 10,
  match_threshold float DEFAULT 0.3,
  ef_search_param integer DEFAULT 64 -- NEW PARAMETER
)
RETURNS TABLE (...) AS $$
BEGIN
  -- Set HNSW search parameter for this query
  PERFORM set_config('hnsw.ef_search', ef_search_param::text, true);

  RETURN QUERY
  SELECT ... -- existing query
END;
$$ LANGUAGE plpgsql;

Call from application:

// High-accuracy search (Pro tier)
await supabase.rpc('search_manus_protocols_tuned', {
  query_embedding: embedding,
  agency_filter: agencyId,
  ef_search_param: 100, // Higher recall
});

// Standard search (Free tier)
await supabase.rpc('search_manus_protocols_tuned', {
  query_embedding: embedding,
  agency_filter: agencyId,
  ef_search_param: 64, // Balanced
});

Priority 2: Add Database Performance Monitoring

Impact: High (observability) Effort: Medium (4-8 hours)

Add instrumentation for:

Query Plan Capture:

-- Create function to log slow queries
CREATE TABLE IF NOT EXISTS slow_query_log (
  id SERIAL PRIMARY KEY,
  query_text TEXT,
  execution_time_ms INTEGER,
  plan JSONB,
  created_at TIMESTAMPTZ DEFAULT NOW()
);

Index Usage Statistics:

-- Monitor index usage
SELECT
  schemaname,
  tablename,
  indexname,
  idx_scan,
  idx_tup_read,
  idx_tup_fetch
FROM pg_stat_user_indexes
WHERE schemaname = 'public'
  AND tablename = 'manus_protocol_chunks'
ORDER BY idx_scan DESC;

Connection Pool Metrics:

// Add to server/db/connection.ts
export function getPoolStats() {
  return {
    totalCount: _pool?.totalCount || 0,
    idleCount: _pool?.idleCount || 0,
    waitingCount: _pool?.waitingCount || 0,
  };
}

HNSW Index Health:

-- Check index bloat
SELECT
  pg_size_pretty(pg_relation_size('idx_manus_chunks_embedding_hnsw')) AS index_size,
  pg_size_pretty(pg_table_size('manus_protocol_chunks')) AS table_size;

Priority 3: Explicit VACUUM Configuration

Impact: Medium (long-term stability) Effort: Low (30 minutes)

-- Optimize autovacuum for manus_protocol_chunks
ALTER TABLE manus_protocol_chunks
SET (
  autovacuum_vacuum_scale_factor = 0.05,  -- More aggressive (default: 0.2)
  autovacuum_analyze_scale_factor = 0.05, -- Keep stats fresh
  autovacuum_vacuum_cost_delay = 10       -- Less aggressive pause (faster)
);

-- Schedule manual ANALYZE after bulk ingestion
-- (Add to ingestion scripts)
ANALYZE manus_protocol_chunks;

Priority 4: Query Result Cache Warming

Impact: Low-Medium (+100-200ms for cache misses) Effort: Medium (2-4 hours)

Pre-warm cache for common queries:

// server/_core/rag/cache-warmer.ts
const COMMON_QUERIES = [
  "cardiac arrest",
  "stroke",
  "anaphylaxis",
  "pediatric fever",
  // ... top 50 queries from analytics
];

export async function warmQueryCache() {
  for (const query of COMMON_QUERIES) {
    await optimizedSearch({ query, limit: 10 }, searchFn);
  }
}

// Call during server startup

6. Scaling Considerations

6.1 Current Scale

Chunks: 58,000+ (manageable)
Agencies: 2,738 (small)
Users: Unknown (estimate <10k based on startup)
Queries/day: Unknown

6.2 Scaling Thresholds

No Issues Until:

500,000+ chunks (HNSW still efficient)
10,000+ concurrent users (connection pool needs increase)
1M+ queries/day (consider read replicas)

Current Architecture: Can scale to 10x current load without changes

6.3 Future Optimization Paths

If growth exceeds 500k chunks:

Partitioning: Partition by state_code (53 partitions)
Sharding: Shard by agency_id for multi-region deployments
Read Replicas: Route searches to replicas, writes to primary

If query latency degrades:

Quantized Embeddings: Reduce from 1536 to 768 dimensions (2x faster, -3% accuracy)
GPU Acceleration: Use pgvector with GPU support
Dedicated Vector DB: Consider Pinecone/Weaviate for ultra-low latency

Current State: No scaling issues anticipated in next 12-24 months

7. Summary & Action Items

Current State: GOOD

Strengths:

✅ Properly configured HNSW index
✅ Comprehensive index coverage
✅ Connection pooling implemented
✅ Sophisticated RAG pipeline with caching
✅ Timeout protection and graceful degradation

Recommended Optimizations (Ranked by ROI):

Priority	Item	Impact	Effort	Timeline
1	HNSW ef_search tuning	Medium	Low	1 hour
2	Database monitoring	High	Medium	4-8 hours
3	VACUUM configuration	Medium	Low	30 min
4	Query cache warming	Low-Med	Medium	2-4 hours

Action Items

Immediate (This Week):

Add ef_search parameter to search RPC function
Configure tier-based HNSW tuning (Pro: 100, Free: 64)
Add autovacuum tuning for manus_protocol_chunks

Short-term (This Month): 4. Implement slow query logging 5. Add connection pool metrics endpoint 6. Set up monthly EXPLAIN ANALYZE for critical queries

Long-term (This Quarter): 7. Build query cache warming system 8. Implement index health monitoring 9. Add performance regression tests

No Critical Issues Identified

The database is well-optimized for current scale. Recommended improvements are quality-of-life enhancements rather than critical fixes.

8. Reference Files

Schema & Indexes:

/Users/tanner-osterkamp/Protocol-Guide/drizzle/schema.ts (lines 481-508, 547-560)
/Users/tanner-osterkamp/Protocol-Guide/drizzle/migrations/0030_optimize_manus_protocol_chunks.sql

Vector Search:

/Users/tanner-osterkamp/Protocol-Guide/server/_core/embeddings/search.ts
/Users/tanner-osterkamp/Protocol-Guide/supabase/migrations/20260127_fix_state_filter.sql

Connection Pooling:

/Users/tanner-osterkamp/Protocol-Guide/server/db/connection.ts

RAG Pipeline:

/Users/tanner-osterkamp/Protocol-Guide/server/_core/rag/search-execution.ts
/Users/tanner-osterkamp/Protocol-Guide/server/_core/rag/config.ts
/Users/tanner-osterkamp/Protocol-Guide/server/routers/query.ts

Documentation:

/Users/tanner-osterkamp/Protocol-Guide/docs/RAG_OPTIMIZATION.md
/Users/tanner-osterkamp/Protocol-Guide/docs/DATABASE-REVIEW-SUMMARY.md

Audit Completed By: Database Performance Optimization Agent Date: 2026-02-16 Next Review: 2026-05-16 (Quarterly)

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Protocol Guide Database Performance Audit

Executive Summary

Current Status: GOOD (Minor Optimizations Recommended)

1. Vector Search Analysis

1.1 HNSW Index Configuration

1.2 Query-Time HNSW Parameters

1.3 Vector Search Query Pattern

2. Index Coverage Analysis

2.1 Primary Indexes (Excellent Coverage)

2.2 Query Pattern Coverage

2.3 Index Maintenance

3. Connection Pooling

3.1 Current Configuration (Good)

3.2 Supabase Pooler

3.3 Connection Pooling Recommendations

4. Query Optimization

4.1 RAG Pipeline Performance

4.2 Optimizations Implemented (Excellent)

4.3 Database-Level Query Optimization

5. Performance Bottlenecks & Priorities

5.1 Critical Path Analysis

5.2 Priority Recommendations

Priority 1: HNSW Query-Time Tuning

Priority 2: Add Database Performance Monitoring

Priority 3: Explicit VACUUM Configuration

Priority 4: Query Result Cache Warming

6. Scaling Considerations

6.1 Current Scale

6.2 Scaling Thresholds

6.3 Future Optimization Paths

7. Summary & Action Items

Current State: GOOD

Action Items

No Critical Issues Identified

8. Reference Files

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Protocol Guide Database Performance Audit

Executive Summary

Current Status: GOOD (Minor Optimizations Recommended)

1. Vector Search Analysis

1.1 HNSW Index Configuration

1.2 Query-Time HNSW Parameters

1.3 Vector Search Query Pattern

2. Index Coverage Analysis

2.1 Primary Indexes (Excellent Coverage)

2.2 Query Pattern Coverage

2.3 Index Maintenance

3. Connection Pooling

3.1 Current Configuration (Good)

3.2 Supabase Pooler

3.3 Connection Pooling Recommendations

4. Query Optimization

4.1 RAG Pipeline Performance

4.2 Optimizations Implemented (Excellent)

4.3 Database-Level Query Optimization

5. Performance Bottlenecks & Priorities

5.1 Critical Path Analysis

5.2 Priority Recommendations

Priority 1: HNSW Query-Time Tuning

Priority 2: Add Database Performance Monitoring

Priority 3: Explicit VACUUM Configuration

Priority 4: Query Result Cache Warming

6. Scaling Considerations

6.1 Current Scale

6.2 Scaling Thresholds

6.3 Future Optimization Paths

7. Summary & Action Items

Current State: GOOD

Action Items

No Critical Issues Identified

8. Reference Files