Senior Backend Developer Interview Questions

A scalable observability system for microservices requires centralized logging, metrics collection, and distributed tracing. Use agents like Prometheus for metrics, Fluentd for logs, and Jaeger for traces. Aggregate data via a stream processor (e.g., Kafka) to handle high throughput. Store time-series metrics in a scalable DB (e.g., InfluxDB), logs in Elasticsearch, and traces in a distributed DB. Employ a service mesh (e.g., Istio) for automatic instrumentation. Balance real-time analytics with batch processing for cost efficiency. Use cloud-native storage solutions for scalability, but consider latency trade-offs. Implement alerting with tools like Grafana for visualization. Prioritize horizontal scaling and decoupling components to ensure resilience and adaptability to growth.

A scalable real-time notification system requires an event-driven architecture with decoupled components. Use a message broker (e.g., Kafka or RabbitMQ) to handle event streaming, a push server (e.g., WebSockets or Firebase Cloud Messaging) for client communication, and a distributed database (e.g., Redis) for caching. Implement load balancing and horizontal scaling for high concurrency. Trade-offs include latency vs. consistency, memory usage vs. throughput, and complexity vs. fault tolerance. Prioritize asynchronous processing and backpressure handling to manage spikes in traffic while ensuring reliability through idempotency and retries.