Backend Engineer Interview Crash Course

Explain the Global Interpreter Lock (GIL) and its practical implications for concurrency in Python web servers.

Compare threading, multiprocessing, and asyncio, and select the appropriate concurrency model for I/O-bound vs. CPU-bound backend tasks.

Describe Python's memory management and garbage collection, and identify common memory leak patterns in long-running services.

Analyze the trade-offs between monolith and microservices architectures regarding development velocity, operational complexity, and fault isolation.

Compare RESTful and event-driven architectures, articulating the trade-offs in coupling, scalability, and data consistency.

Evaluate API versioning strategies (path, header, query parameter) and justify a choice based on client impact and maintainability.

Explain idempotency in API design and describe techniques to implement it, such as using idempotency keys.

Design a rate-limiting system by comparing token bucket vs. leaky bucket algorithms for protecting services from overload.

Implement a retry strategy with exponential backoff and jitter, explaining how it handles transient failures without causing a thundering herd.

Describe the circuit breaker pattern and its states (closed, open, half-open) as a mechanism to prevent cascading failures.

Formulate an indexing strategy for a PostgreSQL database using B-tree, composite, and partial indexes to optimize specific query patterns.

Explain transaction isolation levels (e.g., Read Committed, Repeatable Read) and their trade-offs between data consistency and concurrency.

Analyze the trade-offs between at-least-once, at-most-once, and exactly-once message delivery guarantees.

Design a failure handling mechanism for message processing using a Dead-Letter Queue (DLQ).

Apply the CAP theorem to analyze real-world distributed systems (e.g., a database, a cache) and explain their consistency/availability trade-offs.

Describe eventual consistency and identify scenarios where it is an acceptable design choice for high-availability systems.

Apply a structured 5-step approach to system design interviews: requirements clarification, scale estimation, API design, high-level architecture, and component deep-dive.

Explain the trade-offs between horizontal and vertical scaling in terms of cost, complexity, and performance limits.

Compare load balancing strategies (e.g., Round Robin, Least Connections, IP Hash) and select one based on application requirements.

Evaluate caching strategies (e.g., write-through, write-around, write-back) and associated cache invalidation techniques.

Architect the asynchronous workflow for the notification system using a message queue to handle fan-out, retries, and ensure reliable delivery.

Sketch the complete high-level architecture, integrating all components, and discuss potential bottlenecks and scaling strategies.

Implement a thread-safe token bucket rate limiter in Python that can be applied as a decorator to API endpoints.

Implement an async task executor in Python using asyncio that processes a queue of I/O-bound tasks with a configurable concurrency limit.

Implement a generic retry decorator in Python with exponential backoff, configurable max retries, and specific exception filtering.

Walk through a systematic process for diagnosing a sudden API latency spike, from initial alert to root cause analysis.