Rate Limiting & Token Buckets
Protect downstream APIs and services from Denial-of-Service spikes using Token Bucket and Sliding Window Log algorithms.
💡 Why It Matters
Uncontrolled API traffic can crash databases, exhaust connection pools, and burn cloud infrastructure budgets.
🌐 Real-World Production Usages & Examples
GitHub API Tier Limiting
Scenario: Enforcing 5,000 requests/hour for authenticated users.
✓ When to Use
- •Public API endpoints exposed to third-party developers.
- •Authentication / login routes.
- •Costly AI LLM inference endpoints.
✕ When NOT to Use
- •Internal microservice IPC where mesh circuit breaking is preferred.
🔑 Core Architecture & Rules
- 1Token Bucket fills tokens at a constant rate; permits short bursts.
- 2Sliding Window Counter combines window counters with weighted overlap percentages.
⚖️ Architectural Trade-Off Analysis
Algorithm Selection
⚠️ Failure Modes & Mitigations
Race Condition in Counter
Non-atomic counter updates across gateway nodes.
🛠️ Example Usages
Reviewed practical implementations and concrete code walkthroughs for Rate Limiting & Token Buckets.
Atomic Redis Lua Token Bucket Rate Limiter
Applies the Token Bucket design pattern atomically across distributed gateway instances using Redis Lua scripts.
⬡Explore Related Concepts & Guides
Curated technical reading and direct concept relationships.
📖Core Implementation Guides & Patterns(3 resources)
Trace how Redis Event Reactor multiplexes thousands of concurrent TCP client connections through a single main thread loop, avoiding thread synchronization overhead.
Analyze how high-volume API gateways protect downstream microservices using distributed Token Bucket and Sliding Window Log algorithms in Redis.
🔗 Related Concepts
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