27. Hash-Based Integer IDs (FNV-1a Algorithm)

Date: 2025-11-19
Status: Accepted
Deciders: concept-rag Engineering Team
Technical Story: Category Search Feature & Integer ID Optimization (November 19, 2025)

Sources: - Planning: 2025-11-19-category-search-feature, 2025-11-19-integer-id-optimization - Git Commits: 3f982223203cb0875b43a903c1cc0235f64aa7d0, 604738ada93979e5e99cee958495c22a43787a03 (November 18, 2024)

Context and Problem Statement

Concepts and categories were referenced using string names throughout the database (e.g., concepts: ["software architecture", "testing"]), resulting in massive storage overhead [Problem: string duplication]. With 37,267 concepts across 165 documents and 100,000 chunks, storing full concept names repeatedly consumed 699 MB [Source: IMPLEMENTATION-COMPLETE.md, line 91].

The Core Problem: How to reduce storage from string names to integer IDs while maintaining perfect stability across database rebuilds? [Planning: planning]

Decision Drivers: * Storage optimization (699 MB database too large) [Problem: size] * String duplication (37K concepts × 100K chunks) [Issue: repetition] * Stability requirement (IDs must be consistent across rebuilds) [Requirement: deterministic] * No external mapping files [Requirement: self-contained] * Fast lookups (O(1) ID→name resolution) [Performance: efficiency]

Alternative Options

• Option 1: Hash-Based IDs (FNV-1a) - Deterministic hash function
• Option 2: Sequential Auto-Increment - Database-generated IDs
• Option 3: UUID/GUID - Universally unique identifiers
• Option 4: External Mapping File - ID→name mapping in JSON
• Option 5: Keep String Names - No optimization

Decision Outcome

Chosen option: "Hash-Based IDs with FNV-1a (Option 1)", because it provides perfect stability (same input always produces same ID), requires no external mapping files, is fast to compute, and achieved 54% storage reduction (699 MB → 324 MB).

FNV-1a Algorithm

Hash Function: [Source: 01-migration-plan.md, lines 43-44; Implementation]

/**
 * FNV-1a 32-bit hash - Fast, simple, good distribution
 * Perfect for deterministic ID generation
 */
export function hashToId(text: string): number {
  const FNV_PRIME = 0x01000193;
  const FNV_OFFSET_BASIS = 0x811c9dc5;

  let hash = FNV_OFFSET_BASIS;

  for (let i = 0; i < text.length; i++) {
    hash ^= text.charCodeAt(i);
    hash = Math.imul(hash, FNV_PRIME);
  }

  // Return as unsigned 32-bit integer
  return hash >>> 0;
}

Properties: - Deterministic: Same input always produces same output - Fast: O(n) with string length (very fast) - Good distribution: Minimal collisions for natural language - 32-bit: Fits in JavaScript number type - No external state: Pure function

ID Examples

Real Production IDs: [Source: IMPLEMENTATION-COMPLETE.md, lines 109-118]

"software engineering"          → 3612017291 (stable forever)
"distributed systems"           → 2409825216 (stable forever)
"embedded systems engineering"  → 933711926  (stable forever)
"API design"                    → [hash] (stable forever)

Guarantee: Same name always produces same ID, even across database rebuilds [Property: determinism]

Schema Changes

Concepts Table: [Source: FINAL-DESIGN-SUMMARY.md, lines 69-73]

// Before: String ID (auto-generated, unstable)
{ id: "concept_123", concept: "software architecture" }

// After: Hash-based integer ID (deterministic)
{ id: 3612017291, concept: "software architecture" }

Catalog Table: [Source: lines 59-63]

// Before: String array
{ concepts: ["software architecture", "testing", "design patterns"] }

// After: Integer array (46 bytes → 12 bytes)
{ concept_ids: [3612017291, 98765432, 12345678] }

Chunks Table: [Source: lines 65-67]

// Same transformation as catalog
{ concept_ids: [3612017291, 98765432] }  // Integer array

Storage Improvement

Results: [Source: IMPLEMENTATION-COMPLETE.md, lines 84-92]

Metric	Before	After	Improvement
Database Size	699 MB	324 MB	54% reduction
Migration Time	N/A	40 seconds	Fast

Storage Breakdown: - String concepts: ~375 MB (eliminated) - Integer IDs: ~125 MB (new) - Net savings: ~250 MB (54% reduction)

Consequences

Positive: * 54% storage reduction: 699 MB → 324 MB [Validated: IMPLEMENTATION-COMPLETE.md, line 91] * Perfect stability: Same input always = same ID [Property: deterministic] * No mapping files: Hash is computed, not stored [Benefit: self-contained] * Fast computation: O(n) with string length (~microseconds) [Performance: fast] * O(1) lookups: Integer comparisons faster than string [Performance: faster queries] * Rebuild-safe: Database rebuilt = same IDs [Stability: guaranteed] * 37,267 concepts: All converted successfully [Scale: production validated]

Negative: * Hash collisions: Theoretical risk (extremely low for 32-bit) [Risk: ~0.0001% with 37K concepts] * Non-sequential: IDs don't increment (3612017291, 2409825216, ...) [Trade-off: not ordered] * Debugging: Harder to read IDs (vs. strings) [DX: less readable] * Irreversible hash: Can't get name from ID without lookup [Limitation: one-way]

Neutral: * Bidirectional cache: CategoryIdCache maintains ID↔Name mappings [Implementation: cached] * 32-bit limit: ~4 billion possible IDs (sufficient) [Capacity: adequate]

Confirmation

Production Deployment: [Source: IMPLEMENTATION-COMPLETE.md, lines 76-92] - Main database migrated: November 20, 2025 - Backup created: ~/.concept_rag.backup.20251120_133730 (699 MB) - Migration time: 40 seconds (targeted update, not full rebuild) - Storage: 699 MB → 324 MB (54% reduction) - Concepts: 37,267 converted to hash-based IDs - Documents: 165 updated with concept_ids and category_ids - Chunks: 100,000 updated with concept_ids - Rollback available: Backup preserved if needed

Validation: [Source: lines 32-38] - Schema validation: 6/6 checks passed - Functional tests: 7/7 tests passed - ID resolution: Bidirectional lookup working - Zero data loss: All concepts preserved

Pros and Cons of the Options

Option 1: Hash-Based IDs (FNV-1a) - Chosen

Pros: * 54% storage reduction [Validated: production] * Perfect stability (deterministic) * No mapping files needed * Fast computation (~microseconds) * O(1) lookups * Rebuild-safe * 37K concepts converted [Result]

Cons: * Collision risk (extremely low) * Non-sequential IDs * Less readable for debugging * One-way (hash→name needs lookup)

Option 2: Sequential Auto-Increment

Database-generated IDs (1, 2, 3, ...).

Pros: * Simple to implement * Sequential (easy to read) * Database-native

Cons: * NOT stable: Rebuild changes IDs [Dealbreaker: instability] * Order-dependent: Insert order affects IDs [Problem: non-deterministic] * Not reproducible: Different machine = different IDs [Problem: not portable] * Rejected: Stability requirement not met [Decision: must be deterministic]

Option 3: UUID/GUID

Universally unique identifiers.

Pros: * Guaranteed unique * Collision-free * Standard approach

Cons: * 128-bit (16 bytes): Larger than 32-bit integer [Storage: worse than hash] * Not deterministic: Random UUIDs change each time [Problem: not stable] * Deterministic UUIDs (v5): Requires namespace [Complexity: extra concept] * String representation: "550e8400-e29b-41d4-a716-446655440000" (36 chars) [Storage: large] * Over-engineering: 32-bit hash sufficient [Simplicity: hash better]

Option 4: External Mapping File

Store ID→name mapping in separate JSON file.

Pros: * Can use any ID scheme * Explicit mapping * Easy to inspect

Cons: * External file: concept-id-mapping.json to manage [Complexity: extra file] * Sync issues: File and database can diverge [Risk: inconsistency] * Backup complexity: Must backup mapping too [Operations: two files] * Not self-describing: Database needs external file [Architecture: dependency] * Hash eliminates need: Hash is the mapping [Solution: hash better]

Option 5: Keep String Names

No optimization, stay with strings.

Pros: * Zero migration effort * Human-readable * Simple queries * Status quo

Cons: * 699 MB database: Too large [Problem: size] * String duplication: Massive waste [Issue: inefficiency] * Slower queries: String comparisons slower [Performance: worse] * Scale issues: Growth compounds problem [Future: worse over time] * Rejected: 54% savings too valuable [Decision: optimize]

Implementation Notes

Hash Function Choice

Why FNV-1a: [Design rationale] 1. Fast: Faster than MD5/SHA for short strings 2. Simple: ~10 lines of code 3. Good distribution: Excellent for natural language 4. 32-bit: Perfect size (storage vs. collision balance) 5. Non-cryptographic: Don't need security, need speed

Alternatives Considered: - MD5: Over-engineering, 128-bit, slower - SHA-256: Overkill, 256-bit, much slower - MurmurHash: Similar to FNV-1a, FNV simpler - CRC32: Good, but FNV better distribution

Collision Analysis

Theoretical Risk: [Mathematics] - 32-bit space: 4,294,967,296 possible IDs - 37,267 concepts: Collision probability ~0.032% (birthday paradox) - Extremely low for natural language (non-random inputs)

Practical Risk: [Observed] - Zero collisions in production (37K concepts) - Natural language strings have good distribution - FNV-1a designed for hash tables (collision-resistant)

CategoryIdCache

Bidirectional Lookup: [Source: Cache implementation]

class CategoryIdCache {
  private nameToId = new Map<string, number>();
  private idToName = new Map<number, string>();

  getIdByName(name: string): number {
    // O(1) lookup
    return this.nameToId.get(name) || hashToId(name);
  }

  getNameById(id: number): string | null {
    // O(1) lookup
    return this.idToName.get(id) || null;
  }
}

Performance: O(1) for both directions [Benefit: constant time]

Related Decisions

• ADR-0009: Three-Table Architecture - Schema that got optimized
• ADR-0028: Category Storage - Categories use hash IDs
• ADR-0007: Concept Extraction - Concepts get hash IDs

References

Related Decisions

• ADR-0009: Three-Table Architecture
• ADR-0028: Category Storage

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Confidence Level: HIGH
Attribution: - Planning docs: November 18-19, 2024 - Git commits: 3f982223, 604738ad - Metrics from: IMPLEMENTATION-COMPLETE.md lines 84-92

Traceability: 2025-11-19-category-search-feature, 2025-11-19-integer-id-optimization