Search & Recommendation Engine
AI / MLFull-text search with ML ranking, collaborative filtering, and real-time personalization
7 nodes7 connections
Use Case
E-commerce product search, content discovery, personalized feeds, marketplace ranking
Stack Breakdown
ElasticsearchML ModelRedisKafkaReact
Architecture Layers
1UI Layer
2Search API
3Recommendation API
4Index & Cache
5ML Inference
6Behavior Tracking
Components by Category
frontend
Storefront
backend
Search APIReco API
database
ElasticsearchRedis
external
ML Model
async
Kafka
Why This Topology Works
Elasticsearch handles full-text search with faceted filtering. ML models re-rank results for personalization. Kafka captures click behavior for continuous model training.
Scaling Notes
Elasticsearch shards by index. ML inference scales with GPU instances. Redis caches user profiles and recent recommendations. Kafka handles behavioral event throughput.
Observability
Track search latency P95, Elasticsearch indexing lag, model inference time, and recommendation CTR. A/B test ranking algorithms.