Search & Recommendation Engine
AI / MLProductionTeam to orgSearch and recommendation system with Elasticsearch full-text retrieval, ML-powered ranking and embedding, collaborative filtering, and real-time click event feedback loop.
Recommended for: E-commerce product search
Use Case
E-commerce product search, content discovery, personalized feeds, marketplace ranking
Best Fit Scenarios
- E-commerce product search
- Content discovery
- Personalized feeds
Stack Breakdown
Architecture Layers
Components by Category
frontend
backend
database
external
async
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.
Typical Bottlenecks
- Frontend rendering and bundle delivery under peak traffic
- Service latency and timeout behavior on critical routes
- Write amplification and query contention on primary stores
Async Flow and Reliability
User-facing operations remain synchronous while long-running work moves through queues or streams. Workers consume jobs independently with retry and failure isolation, improving resilience under burst load.
Upgrade Path
Split high-churn domains into dedicated services, then introduce stronger queue policies and SLO-driven monitoring.
Operating Envelope
Complexity is marked as Production with an intended scope of Team to org. Use this as a planning baseline before adapting the template to your reliability and team constraints.