Rate limiting algorithms (token bucket, leaky bucket) in HLD - Scalability & System Analysis

Scalability Analysis - Rate limiting algorithms (token bucket, leaky bucket)

Growth Table: Rate Limiting Algorithms

Users/Traffic	Token Bucket	Leaky Bucket	System Impact
100 users	Simple in-memory counters, low memory	Simple queue with fixed leak rate	Works well on single server
10K users	Needs distributed counters or shared cache	Queue size grows, needs distributed state	Single server may struggle with concurrency
1M users	Distributed token buckets with sharding or Redis cluster	Distributed leaky bucket queues with partitioning	State synchronization and latency increase
100M users	Highly scalable distributed caches, approximate counters (e.g. Bloom filters)	Approximate leak rate enforcement, multi-region coordination	Network and storage bottlenecks appear

First Bottleneck

The first bottleneck is the state storage for counters or queues that track tokens or leaks.

At low scale, in-memory counters suffice. As users grow, the system must maintain distributed state with low latency.

This state storage (e.g., Redis or distributed cache) can become overwhelmed by high QPS and concurrent updates.

Scaling Solutions

• Horizontal scaling: Add more rate limiter instances behind a load balancer.
• Distributed caching: Use Redis clusters or similar to store counters with sharding.
• Approximate algorithms: Use probabilistic data structures to reduce storage and update costs.
• Local token buckets: Use local counters with periodic sync to reduce latency.
• Rate limit partitioning: Partition users by key (e.g., user ID hash) to spread load.
• Backpressure and queuing: For leaky bucket, use queues with controlled leak rates and backpressure to smooth bursts.

Back-of-Envelope Cost Analysis

Assuming 1M users, each making 1 request per second:

• Requests per second: 1 million QPS total.
• Each request updates a token bucket counter or leaky bucket queue.
• Redis can handle ~100K ops/sec per instance; need ~10 Redis instances for counters.
• Network bandwidth: 1M QPS * ~200 bytes/request = ~200 MB/s (1.6 Gbps), requires multiple network links.
• Memory: Each token bucket counter ~100 bytes; for 1M users = ~100 MB RAM, feasible in distributed cache.

Interview Tip

Start by explaining the rate limiting goal: controlling request rate per user or client.

Describe token bucket and leaky bucket simply, focusing on how they handle bursts and steady rates.

Discuss state storage challenges and how scaling affects latency and consistency.

Outline scaling strategies: horizontal scaling, distributed caches, sharding, and approximate methods.

Use real numbers to show understanding of system limits and trade-offs.

Self Check

Question: Your database handles 1000 QPS for rate limiting counters. Traffic grows 10x to 10,000 QPS. What do you do first?

Answer: Introduce a distributed cache like Redis with sharding to offload counters from the database, reducing latency and increasing throughput.

Key Result

Rate limiting algorithms scale well initially with in-memory counters but face bottlenecks in distributed state storage as traffic grows; scaling requires distributed caches, sharding, and approximate methods to maintain low latency and high throughput.