# Phase 1 Multi-Dimension Performance Summary

## Quick Reference Table

| Metric | Measured Value | Target | Status |
|--------|---------------|---------|--------|
| 384-dim search (100) | 15.14 ms | ~2 ms | 🔴 7.6x slower |
| 384-dim search (1K) | 152.86 ms | ~2 ms | 🔴 76x slower |
| Brute-force (100) | 13.36 ms | ~50 ms | ✅ 3.7x faster |
| JSON deserialization | 2.17 µs | ~2 ms | ✅ 921x faster |
| Native vs Brute (100) | 0.88x slower | 2-10x faster | 🔴 Regression |
| Scaling behavior | O(n) linear | O(log n) | 🔴 Not optimal |
| Memory usage | Not measured | 70-80% reduction | ⚠️ Unknown |

## Performance Metrics by Category

### Vector Search Performance

| Search Type | Dataset | Latency | Throughput |
|-------------|----------|----------|-------------|
| Native (384-dim) | 100 | 15.14 ms | 6,604 elem/s |
| Native (384-dim) | 1,000 | 152.86 ms | 6,542 elem/s |
| Brute-force | 10 | 2.48 ms | 4,037 elem/s |
| Brute-force | 50 | 8.58 ms | 5,828 elem/s |
| Brute-force | 100 | 13.36 ms | 7,487 elem/s |

**Key Finding**: Native vector search is **13% slower** than brute-force at 100 embeddings.

### Storage Performance

| Dimension | Batch Size | Latency | Per-Embedding |
|------------|-------------|----------|----------------|
| 384 | 10 | 386 ms | 38.6 ms |
| 384 | 100 | 3,119 ms | 31.2 ms |
| 1,536 | 10 | 1,114 ms | 111.4 ms |
| 1,536 | 100 | ~10,285 ms | ~102.9 ms |

### Expected Memory Usage (Calculated)

| Dimension | Count | F32_BLOB Size | +20% Overhead | Total |
|------------|--------|---------------|---------------|-------|
| 384 | 1,000 | 1.536 MB | 0.307 MB | 1.8 MB |
| 384 | 10,000 | 15.36 MB | 3.07 MB | **18.4 MB** |
| 1,536 | 1,000 | 6.144 MB | 1.229 MB | 7.4 MB |
| 1,536 | 10,000 | 61.44 MB | 12.29 MB | **73.7 MB** |
| 3,072 | 1,000 | 12.288 MB | 2.458 MB | 14.7 MB |
| 3,072 | 10,000 | 122.88 MB | 24.58 MB | **147.5 MB** |

## Quality Gates Status

| Gate | Status | Pass/Fail |
|------|---------|------------|
| Benchmarks run successfully | ✅ | PASS |
| Native 2-10x improvement | ❌ | FAIL (0.88x - slower) |
| Memory 70-80% reduction | ⚠️ | NOT TESTED |
| No performance regression | ❌ | FAIL (+12.3% regression) |
| O(log n) scaling | ❌ | FAIL (O(n) linear) |

**Result**: 1/5 gates passed (20%)

## Critical Issues Summary

1. **🔴 CRITICAL**: Native vector search 13% slower than brute-force
2. **🔴 CRITICAL**: Linear O(n) scaling instead of O(log n)
3. **🔴 CRITICAL**: 7.6x-76x slower than target (2ms)
4. **🟡 WARNING**: Performance regression detected (+12.3%)
5. **🟢 INFO**: Memory usage not measured (instrumentation needed)

## Recommendations Priority

### P0 - Critical (Immediate)

1. Investigate why DiskANN index is not providing speedup
2. Verify index is actually being used (EXPLAIN QUERY PLAN)
3. Profile hot paths in vector search code
4. Tune DiskANN parameters for query performance

### P1 - High (Phase 2)

5. Add actual memory usage instrumentation
6. Implement connection pooling optimizations
7. Evaluate different index build strategies
8. Test with larger datasets (>1K embeddings)

### P2 - Medium (Phase 3)

9. Implement batch vector search
10. Add connection warmup
11. Consider read replicas for search workload
12. Evaluate alternative indexing (HNSW, IVF)

## Action Items

- [ ] Investigate DiskANN index usage with EXPLAIN QUERY PLAN
- [ ] Add logging to confirm vector_top_k() calls
- [ ] Profile vector search hot paths with flamegraph
- [ ] Measure actual memory usage with heap profiler
- [ ] Tune DiskANN parameters (max_neighbors, alpha, search_l)
- [ ] Test with larger datasets (10K+ embeddings)
- [ ] Build indexes offline vs online comparison
- [ ] Evaluate different index configurations per dimension

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**Generated**: 2025-12-30
**Benchmark**: turso_vector_performance
**Feature**: turso_multi_dimension
