Junior Microservices Interview Questions

CQRS separates read operations (queries) and write operations (commands) into different models. It improves scalability, performance, and security. CQRS is often combined with event sourcing for robust distributed architectures.

Event Sourcing stores all changes to an application's state as a sequence of events instead of only storing the latest state. The current state is rebuilt by replaying events, enabling audit trails, temporal queries, and strong consistency.

The Saga pattern breaks a distributed transaction into smaller local transactions with compensating actions for rollback. It ensures eventual consistency and is implemented via choreography (events) or orchestration (coordinator service).

Observability is the ability to understand a system’s internal state from external signals. It includes logging, metrics, and distributed tracing to diagnose issues in distributed systems.

Distributed tracing tracks a single request across multiple microservices using trace IDs and span IDs. It helps identify latency, failures, and bottlenecks. Tools include Jaeger and Zipkin.

A circuit breaker prevents repeated calls to a failing service, avoiding cascading failures. A fallback mechanism provides a default response when a service is unavailable. Tools include Hystrix and Resilience4j.

The bulkhead pattern isolates service resources, such as thread pools or memory, to prevent one failing process from impacting others. It improves resilience and fault isolation.

Rate limiting controls how many requests can be handled over a time period. It protects services from overload and DoS attacks and is usually implemented at the API Gateway using tokens or sliding windows.

Retries reattempt failed operations, while exponential backoff increases the wait time between retries to minimize load. Combined with circuit breakers, they prevent service saturation.

The sidecar pattern deploys helper components alongside the main service in the same pod or host. Used for logging, configuration, monitoring, and proxies, especially in Kubernetes environments.

API versioning avoids breaking existing clients by exposing updated versions. Methods include URL versioning (v1), query parameters, or custom headers. It ensures backward compatibility.

A service mesh is an infrastructure layer that handles service-to-service communication. It manages routing, security, and observability. Examples include Istio, Linkerd, and Consul Connect.

Configuration is externalized using config servers or environment variables. Tools like Spring Cloud Config, Consul, and Vault ensure consistent, secure handling across environments.

Blue-green deployment runs two identical environments. The new version (green) is deployed alongside the old (blue), and traffic switches once validated, minimizing downtime.

Canary deployment releases the new application version to a small group of users first. If stable, the rollout continues. It reduces deployment risk significantly.

Use centralized logging (ELK, Graylog), include correlation IDs, avoid sensitive data in logs, and use structured log formats like JSON for easier ingestion.

Resilience is achieved using retries, timeouts, circuit breakers, bulkheads, autoscaling, and health checks. Stateless services simplify recovery and scaling.

Liveness probes check if the service is running. Readiness probes verify if it is ready to accept traffic. Orchestrators like Kubernetes use these checks to manage service availability.

Idempotency ensures that repeating the same request produces the same result. It is critical for retries, payment processing, and message handling to prevent duplication.

Monitoring includes collecting metrics like latency, error rate, and throughput. Tools such as Prometheus, Grafana, and New Relic provide dashboards and alerts. Distributed tracing detects bottlenecks across services.