A rate limiting system implemented from scratch to control API request throughput and protect backend services from abuse, overload, and unfair resource usage.
This project focuses on algorithmic design, time-based logic, and system behavior under concurrent load, rather than framework usage.
- Overview
- Motivation
- What Is Rate Limiting
- Algorithms Implemented
- System Design
- Data Structures Used
- Time & Space Complexity
- Concurrency Considerations
- Edge Cases & Failure Scenarios
- Tech Stack
- Project Structure
- How to Run
- Design Decisions & Trade-offs
- Limitations & Future Improvements
This project implements a rate limiter that restricts how frequently a client can make requests to a service.
It simulates a real-world backend component commonly used in:
- API gateways
- authentication services
- distributed systems
- microservice architectures
The implementation emphasizes correctness, fairness, and efficiency, rather than external tooling.
Without rate limiting:
- A single client can overwhelm a server
- Backend resources can be exhausted
- Fairness between users is lost
- Systems become vulnerable to abuse
Rate limiting is a foundational backend concept, combining:
- data structures
- time-based logic
- concurrency reasoning
This project was built to understand how rate limiting actually works, not just how to enable it in a framework.
Rate limiting restricts the number of requests a client can make within a given time window.
Typical use cases:
- Prevent brute-force attacks
- Protect expensive endpoints
- Enforce API usage policies
- Ensure fair resource sharing
This project demonstrates algorithmic approaches to solving this problem.
Concept:
- Tokens are added to a bucket at a fixed rate
- Each request consumes one token
- Requests are allowed only if a token is available
Why Token Bucket:
- Allows short bursts of traffic
- Smooths average request rate
- Commonly used in real systems
Concept:
- Tracks request timestamps within a moving time window
- Counts only requests within the current window
- Rejects requests once the limit is exceeded
Why Sliding Window:
- More accurate than fixed windows
- Prevents boundary burst issues
- Strong fairness guarantees
The rate limiter is designed as a middleware-style component that can be integrated into a backend service.
Core responsibilities:
- Identify client (IP / user / API key)
- Track request history
- Decide allow / reject per request
- Enforce limits efficiently
The design mirrors how real API gateways work at a conceptual level.
Depending on the algorithm:
- Hash Maps – map clients to state
- Queues / Deques – store timestamps for sliding windows
- Counters – track available tokens
The choice of data structures ensures:
- fast lookups
- predictable performance
- low overhead per request
- Time Complexity: O(1) per request
- Space Complexity: O(N) where N = number of clients
- Time Complexity: O(1) amortized per request
- Space Complexity: O(N × R) where R = requests per window
These trade-offs are explicitly considered in the implementation.
- Multiple requests may arrive simultaneously
- Shared state must be updated safely
- Race conditions can cause incorrect request counts
The project discusses:
- why naive implementations fail
- how concurrency affects correctness
- what would be required for a thread-safe or distributed version
Concurrency is treated as a first-class concern, not an afterthought.
Handled cases include:
- Burst traffic
- Window boundary effects
- New client initialization
- Idle client cleanup
- Clock drift considerations
These cases highlight why rate limiting is deceptively complex.
- Language: Java / C++ / Python / JavaScript (use what you implemented)
- Core Concepts: Data Structures, Algorithms, Time-based Logic
- Environment: Standalone backend component
No external rate-limiting libraries are used.
src/
├── limiter/ # Rate limiting logic
├── algorithms/ # Token Bucket and Sliding Window implementations
├── utils/ # Time and helper utilities
└── main # Entry point / simulation
The structure emphasizes clarity and testability.
git clone https://github.com/yourusername/rate-limiter
cd rate-limiter
# run command based on languageYou can simulate requests to observe:
- accepted vs rejected requests
- behavior under burst traffic
- differences between algorithms
- Implemented multiple algorithms to compare fairness vs simplicity
- Prioritized correctness over micro-optimizations
- Avoided distributed complexity to keep reasoning clear
- Explicitly analyzed algorithmic complexity
Each decision was made for learning value and interview explainability.
- No distributed state sharing (e.g., Redis)
- No persistence across restarts
- No production-grade synchronization
- No adaptive rate limits
These would be natural extensions in a real system.
This project demonstrates that backend engineering is applied algorithms.
Rate limiting sits at the intersection of:
- theory (DSA)
- systems (concurrency)
- real-world constraints (fairness, performance)
Understanding this deeply is essential for building reliable backend systems.