# Advanced Search & Filtering System - Technical Specification
## Architecture Overview
### Components
1. **Search Service** (Node.js + Express)
2. **Elasticsearch Cluster** (3-node minimum)
3. **Redis Cache Layer**
4. **Query Parser & Builder**
5. **Analytics Tracker**
## Implementation Details
### Phase 1: Elasticsearch Setup (Week 1-2)
**Task 1.1**: Provision and configure Elasticsearch cluster
- Set up 3-node cluster on AWS OpenSearch
- Configure index templates for products
- Set up index lifecycle management
- Implement backup strategy
- Configure security and access controls
**Technical Requirements**:
```json
{
"cluster_size": "3 nodes",
"node_specs": "r6g.xlarge (4 vCPU, 32GB RAM)",
"storage": "500GB SSD per node",
"replication_factor": 2
}
```
**Task 1.2**: Product data indexing pipeline
- Create bulk indexing script
- Implement incremental update mechanism
- Add real-time sync with PostgreSQL via CDC
- Set up mapping for product attributes
- Optimize for search performance
**Mapping Strategy**:
- Analyzed fields: title, description, features
- Keyword fields: SKU, brand, category
- Numeric fields: price, rating, stock_count
- Date fields: created_at, updated_at
### Phase 2: Query Engine (Week 3-4)
**Task 2.1**: Build query parser
- Parse natural language queries
- Extract filters and sort criteria
- Handle complex boolean logic
- Implement query validation
- Add query correction/suggestions
**Example Queries**:
- "red nike shoes under $100"
- "electronics with 4+ stars"
- "laptops with 16GB RAM and SSD"
**Task 2.2**: Multi-faceted filtering
- Dynamic facet generation
- Range filters (price, date)
- Multi-select category filtering
- Brand and vendor filtering
- Rating and review filtering
- Custom attribute filtering
**Performance Target**: < 50ms for filter generation
### Phase 3: Ranking Algorithm (Week 5)
**Task 3.1**: Implement scoring algorithm
```python
def calculate_score(query, product):
score = (
text_relevance_score(query, product) * 0.4 +
popularity_score(product.sales) * 0.2 +
rating_score(product.rating) * 0.2 +
freshness_score(product.created_at) * 0.1 +
availability_score(product.stock) * 0.1
)
return score
```
**Task 3.2**: A/B testing framework for ranking
- Multiple ranking strategies
- User cohort assignment
- Performance metrics collection
- Statistical significance testing
### Phase 4: Auto-complete & Suggestions (Week 6)
**Task 4.1**: Implement auto-complete
- Prefix-based suggestions
- Fuzzy matching for typos
- Popular searches boosting
- Personalized suggestions
- Category-aware completions
**Task 4.2**: "Did you mean?" corrections
- Phonetic matching
- Edit distance algorithms
- Common misspelling database
- Context-aware corrections
### Phase 5: Caching & Optimization (Week 7-8)
**Task 5.1**: Redis caching layer
- Cache popular queries (TTL: 5 min)
- Cache filter options (TTL: 15 min)
- Cache search results for common queries
- Implement cache warming
- Monitor cache hit rates
**Task 5.2**: Performance optimization
- Query response time < 200ms (p95)
- Support 1000 QPS
- Index optimization
- Shard allocation strategy
- Connection pooling
**Monitoring Metrics**:
- Query latency (p50, p95, p99)
- Search success rate
- Zero-result queries
- Filter usage statistics
- Cache hit ratio
### Phase 6: Analytics & Monitoring (Week 8)
**Task 6.1**: Search analytics
- Track all search queries
- User session analysis
- Conversion tracking
- Zero-result analysis
- Popular searches dashboard
**Task 6.2**: Alerting setup
- Elasticsearch cluster health
- Query latency spikes
- Error rate monitoring
- Index size monitoring
- Cache performance
## Testing Strategy
### Unit Tests
- Query parser logic
- Scoring algorithm
- Filter builders
- Cache invalidation
### Integration Tests
- End-to-end search flows
- Multi-filter combinations
- Pagination and sorting
- Auto-complete accuracy
### Performance Tests
- Load testing (10k concurrent users)
- Stress testing (sustained load)
- Spike testing (sudden traffic)
- Soak testing (24h sustained)
## Dependencies
- Elasticsearch 8.x
- Redis 7.x
- PostgreSQL 15.x (product database)
- Node.js 18.x
- AWS OpenSearch Service
## Success Criteria
✅ Search results in < 200ms (p95)
✅ 95%+ search relevance accuracy
✅ Support 10+ concurrent filters
✅ Handle 1000+ QPS
✅ < 5% zero-result queries
✅ 80%+ cache hit rate