Recently, I was asked to Design a Rate Limiter (LLD) in a Product-Based Company interview.

At first glance, it sounds simple.

But then the interviewer added constraints:

• Rate limit users based on userId + tier (Free / Premium)
• Support multiple algorithms (extensible design)
• Must be thread-safe
• Efficient under high concurrency

Now it’s no longer about just writing Token Bucket logic.

It becomes a design discussion.

Here’s how I structured my approach 👇

🔹 1. Clarified Requirements

Functional:

• Per-user rate limiting
• Different limits for Free vs Premium

Non-Functional:

• Thread safety
• Low latency
• Extensibility
• Clean abstraction

🔹 2. Designed Using Strategy Pattern

Created a common RateLimiter interface:

• TokenBucketRateLimiter
• FixedWindowRateLimiter
• SlidingWindowRateLimiter
• LeakyBucketRateLimiter

This allowed plugging new algorithms without changing existing code.

🔹 3. Tier-Based Policy Mapping

Used a configuration layer to map:
Free → 10 req/sec
Premium → 100 req/sec

Clean separation of policy from implementation.

🔹 4. Concurrency Handling

Used:

• ConcurrentHashMap for user storage
• Atomic variables / synchronized blocks for state mutation
• Avoided global locking

Discussed trade-offs between:

• Lock-based vs Lock-free approaches
• Memory vs Accuracy (Sliding Window Log vs Counter)

🔹 5. Extensibility via Factory Pattern

Algorithm selection driven by configuration.

This made the system open for extension, closed for modification (OCP).

But the real challenge started after the initial design.

The interviewer asked cross-questions like:

• What happens if 1 million users hit the system simultaneously?
• How would you scale this in a distributed environment?
• How would you implement rate limiting in multiple instances?
• Would in-memory rate limiting work behind a load balancer?
• How would you prevent race conditions under heavy traffic?
• How do you handle clock drift in Sliding Window?
• What if Redis goes down (if using distributed store)?
• How would you make it production-ready?

That’s when the discussion shifted from LLD to system design thinking.

💡 What I Realized:

In product-based interviews, they evaluate:

• How you break down the problem
• How you think about scale
• How you handle concurrency
• How clean your abstractions are
• Whether you discuss trade-offs
• How you handle edge cases

It’s not about memorizing an algorithm.
It’s about designing a system that can evolve and scale.

If you’re preparing for backend interviews, master these LLD topics:

• Rate Limiter
• Circuit Breaker
• Distributed Locking
• Idempotency
• Retry mechanisms

Design thinking > Code length.

Happy to discuss approaches with fellow backend engineers 🚀

#SystemDesign #LowLevelDesign #Java #Backend #InterviewPreparation #ProductBased #Concurrency