Each test is 5 questions with varying difficulty.
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API Gateway patterns are foundational to modern distributed systems, acting as the single entry point for client requests to backend services. In 2026, as microservices architectures become more granular and AI-driven services proliferate, the API Gateway has evolved from a simple reverse proxy to a complex control plane handling traffic management, security, and observability. For software engineers, AI engineers, and system architects, mastering these patterns is critical for designing scalable, secure, and performant systems. Interviewers focus on this topic to assess a candidate's ability to handle cross-cutting concerns like authentication, rate limiting, and request transformation without bloating individual microservices. Junior-level candidates are expected to understand basic routing and proxying, while senior-level candidates must demonstrate expertise in high-availability patterns, latency optimization, and the trade-offs between centralized gateways versus decentralized service meshes.
The API Gateway is the 'front door' of a system, and its failure or inefficiency directly impacts user experience and system security. In 2026, with the rise of LLM-based applications, gateways are increasingly responsible for managing token-based rate limiting and streaming response handling. A strong answer in an interview reveals a candidate's understanding of the 'shared responsibility' model in distributed systems: moving cross-cutting concerns out of business logic and into the infrastructure layer. This reduces code duplication, simplifies security auditing, and allows for rapid iteration of backend services without breaking client contracts. Conversely, a weak answer often ignores the performance overhead of the gateway or fails to address the 'single point of failure' risk, signaling a lack of experience with production-grade system design. Understanding these patterns is essential for preventing common pitfalls like cascading failures, unauthenticated access, and inefficient resource utilization.
The API Gateway acts as a reverse proxy that intercepts all incoming traffic, performs pre-processing, routes the request to the appropriate microservice, and handles post-processing of the response.
Traffic flows from the client through the load balancer into the gateway, which executes a chain of filters (Auth, Rate Limit) before querying the service registry for the upstream endpoint and forwarding the request.
Client Request
↓
[Load Balancer]
↓
[Gateway Core]
↓ ↓
[Auth] [Rate Limit]
↓ ↓
[Service Registry]
↓
[Upstream Service]The gateway calls multiple downstream services and merges the results into a single response.
Trade-offs: Reduces round-trips for clients but increases gateway complexity and latency.
The gateway stops forwarding requests to a failing downstream service to prevent cascading failures.
Trade-offs: Protects the system but requires careful threshold tuning to avoid false positives.
Moving gateway-like functionality (e.g., mTLS) to a sidecar container alongside the application.
Trade-offs: Decouples logic from app code but increases resource usage per pod.
| Reliability | Use multi-region deployment and health-check-based traffic shifting to avoid single-zone failures. |
| Scalability | Scale horizontally by adding more gateway nodes behind a global load balancer. |
| Performance | Minimize plugin chain depth and use asynchronous I/O for all network operations. |
| Cost | Optimize by offloading heavy processing to edge compute or specialized services. |
| Security | Implement mTLS for service-to-service communication and strict rate limiting per API key. |
| Monitoring | Track p99 latency, error rates (4xx/5xx), and throughput per service. |
A load balancer operates primarily at the transport layer (L4) or basic application layer (L7) to distribute traffic. An API Gateway is a specialized L7 proxy that understands API semantics, allowing it to perform authentication, rate limiting, request transformation, and service aggregation, which standard load balancers do not typically handle.
Use both. An API Gateway is best for 'north-south' traffic (external client to internal service), while a service mesh is designed for 'east-west' traffic (service-to-service). They complement each other by handling different layers of the network communication stack.
Use a centralized, high-performance distributed store like Redis. Each gateway instance increments a counter in Redis for a specific client ID within a time window. This ensures global consistency across all gateway nodes, preventing clients from bypassing limits by hitting different nodes.
Every plugin added to the gateway chain adds processing time. To minimize latency, keep the chain short, use asynchronous I/O, and cache validation results (like JWT public keys) locally within the gateway instance memory.
It is generally discouraged. The gateway should focus on infrastructure concerns. Moving business logic to the gateway creates tight coupling, makes testing difficult, and complicates the deployment lifecycle of your microservices.
A standard gateway is a single entry point for all clients. The BFF pattern involves deploying multiple, specialized gateway instances tailored for specific client types (e.g., one for mobile, one for web), allowing for optimized payloads and specialized API contracts.
It occurs when a popular cache entry expires, causing many concurrent requests to miss the cache and simultaneously try to re-fetch the data from the backend, potentially overwhelming it. Mitigate this with probabilistic expiration or mutex-based locking.
mTLS ensures that both the client and the server (or gateway and service) verify each other's identity via certificates. This prevents man-in-the-middle attacks and ensures that only authorized services can communicate with the gateway.
Use a dynamic control plane (like those found in Envoy or Kong). These systems allow you to push configuration changes via an API or a GitOps workflow, which the gateway instances hot-reload without dropping active connections.
The service registry acts as a source of truth for the location and health of all microservice instances. The gateway queries this registry to dynamically resolve upstream service addresses, enabling seamless scaling without manual configuration updates.
AI Prep covers AI Agents, Generative AI, ML Fundamentals, NLP & LLMs and a lot more, with adaptive tests and daily challenges. Fully offline on Android. Free to try, one-time unlock for lifetime access.