Rate limiting in Microservices - System Design Guide

Problem Statement

When too many requests hit a service at once, it can slow down or crash, causing poor user experience and downtime. Without control, abusive or accidental traffic spikes can overwhelm resources and break service availability.

Solution

Rate limiting controls how many requests a user or client can make in a given time window. It rejects or delays excess requests to keep the system stable and fair for all users.

Architecture

Client

→Rate Limiter

↓

Request Count

Request Count

This diagram shows a client sending requests through a rate limiter before reaching the service. The rate limiter tracks request counts in a cache to enforce limits.

Trade-offs

✓ Pros

→

Prevents service overload by controlling request rates.

→

Protects against abuse and denial-of-service attacks.

→

Ensures fair resource usage among users.

→

Improves system stability and availability.

✗ Cons

→

Adds latency due to extra processing before requests reach the service.

→

Complexity in managing distributed rate limits across multiple servers.

→

Risk of blocking legitimate traffic if limits are too strict.

Use when your service faces unpredictable or high traffic spikes, especially above 1000 requests per second, or when protecting critical resources from abuse.

Avoid if your system handles very low traffic (under 100 requests per second) where rate limiting overhead outweighs benefits, or if all clients are trusted and well-behaved.

Real World Examples

Twitter

Twitter applies rate limiting on its API endpoints to prevent abuse and ensure fair access for all developers.

Stripe

Stripe uses rate limiting to protect payment APIs from excessive calls that could cause service disruption or fraud.

Amazon

Amazon API Gateway enforces rate limits to maintain backend service stability during traffic surges.

Code Example

The before code allows unlimited requests, risking overload. The after code implements a token bucket algorithm per client IP, allowing a fixed number of requests per time window and rejecting excess requests with a 429 error.

Microservices

### Before (no rate limiting)
from flask import Flask, request
app = Flask(__name__)

@app.route('/api')
def api():
    return 'Success'


### After (with simple token bucket rate limiting)
import time
from flask import Flask, request, jsonify
app = Flask(__name__)

RATE_LIMIT = 5  # requests
TIME_WINDOW = 10  # seconds

clients = {}

@app.route('/api')
def api():
    client_ip = request.remote_addr
    now = time.time()
    if client_ip not in clients:
        clients[client_ip] = {'tokens': RATE_LIMIT, 'last': now}
    elapsed = now - clients[client_ip]['last']
    clients[client_ip]['tokens'] += elapsed * (RATE_LIMIT / TIME_WINDOW)
    if clients[client_ip]['tokens'] > RATE_LIMIT:
        clients[client_ip]['tokens'] = RATE_LIMIT
    clients[client_ip]['last'] = now

    if clients[client_ip]['tokens'] < 1:
        return jsonify({'error': 'Rate limit exceeded'}), 429
    else:
        clients[client_ip]['tokens'] -= 1
        return 'Success'

Alternatives

Circuit Breaker

Circuit breaker stops requests after failures to prevent cascading errors, while rate limiting controls request volume regardless of failures.

Use when: Use circuit breaker when backend failures are frequent and you want to fail fast, not just limit traffic.

Load Balancing

Load balancing distributes traffic evenly across servers, but does not limit total request volume per client.

Use when: Use load balancing to scale horizontally, combined with rate limiting to control per-client usage.

Summary

Rate limiting prevents system overload by controlling request rates per client.

It protects services from abuse and ensures fair resource usage.

Implementing rate limiting improves stability but adds complexity and potential latency.

Practice

1. What is the main purpose of rate limiting in microservices?

easy

A. To control how many requests a user can make in a given time

B. To increase the speed of the service

C. To store user data securely

D. To balance the load between servers

Solution

1. Step 1: Understand the concept of rate limiting

   Rate limiting is designed to restrict the number of requests a user or client can send to a service within a certain time frame.

2. Step 2: Identify the main goal of rate limiting

   The main goal is to prevent overload and abuse by controlling request frequency, not to speed up services or store data.

3. Final Answer:

   To control how many requests a user can make in a given time -> Option A

4. Quick Check:

   Rate limiting = Control request count [OK]

Hint: Rate limiting limits request count per time [OK]

Common Mistakes:

• Confusing rate limiting with load balancing
• Thinking rate limiting speeds up the service
• Mixing rate limiting with data storage

2. Which of the following is the correct way to represent a fixed window rate limiter allowing 100 requests per minute in pseudocode?

easy

A. if requests_in_last_minute < 100 then block else allow

B. if requests_in_last_hour > 100 then block else allow

C. if requests_in_last_minute > 100 then block else allow

D. if requests_in_last_second > 100 then allow else block

Solution

1. Step 1: Understand fixed window rate limiting logic

   Fixed window rate limiting counts requests in a fixed time window (e.g., 1 minute) and blocks if the count exceeds the limit.

2. Step 2: Match the correct condition for allowing or blocking

   If requests exceed 100 in the last minute, block; otherwise, allow. if requests_in_last_minute > 100 then block else allow matches this logic exactly.

3. Final Answer:

   if requests_in_last_minute > 100 then block else allow -> Option C

4. Quick Check:

   Fixed window limit = block if over limit [OK]

Hint: Block when requests exceed limit in fixed window [OK]

Common Mistakes:

• Using wrong time window (hour instead of minute)
• Reversing the condition (blocking when under limit)
• Allowing requests when they should be blocked

3. Given this pseudocode for a token bucket rate limiter:

bucket_capacity = 5
refill_rate = 1 token per second
current_tokens = 3
request_tokens = 2
if current_tokens >= request_tokens:
    current_tokens -= request_tokens
    allow request
else:
    block request

What happens if a request for 4 tokens arrives immediately?

medium

A. Request is allowed and tokens reduce to -1

B. Request is blocked because refill rate is too low

C. Request is allowed and tokens reduce to 1

D. Request is blocked because not enough tokens

Solution

1. Step 1: Check current tokens against requested tokens

   Current tokens are 3, request needs 4 tokens, which is more than available.

2. Step 2: Determine if request is allowed or blocked

   Since current tokens (3) < request tokens (4), the request is blocked.

3. Final Answer:

   Request is blocked because not enough tokens -> Option D

4. Quick Check:

   Tokens < request = block [OK]

Hint: Allow only if tokens ≥ requested tokens [OK]

Common Mistakes:

• Allowing request when tokens are insufficient
• Ignoring token count and refill rate
• Assuming tokens can go negative

4. A microservice uses a sliding window rate limiter but users report some requests are blocked even when they seem under the limit. Which is the most likely cause?

medium

A. The sliding window is not updating timestamps correctly

B. The service has too many servers without shared state

C. The rate limit is set too high

D. The users are sending requests too slowly

Solution

1. Step 1: Understand sliding window rate limiter behavior

   Sliding window requires accurate tracking of request timestamps across all servers to count requests correctly.

2. Step 2: Identify issue with multiple servers and no shared state

   If servers do not share state, each counts requests independently, causing incorrect blocking even if total requests are under limit.

3. Final Answer:

   The service has too many servers without shared state -> Option B

4. Quick Check:

   Multiple servers need shared state for sliding window [OK]

Hint: Sliding window needs shared state across servers [OK]

Common Mistakes:

• Blaming slow user requests
• Assuming rate limit is too high causes blocking
• Ignoring distributed state issues

5. You design a rate limiter for a microservice that must handle 10 million users, each allowed 100 requests per hour. Which approach best balances accuracy and scalability?

hard

A. Use distributed token buckets with local caches and periodic sync

B. Use a centralized fixed window counter stored in a single database

C. Use client-side rate limiting without server checks

D. Use a sliding window log storing every request timestamp centrally

Solution

1. Step 1: Analyze scalability needs for 10 million users

   A centralized database (Use a centralized fixed window counter stored in a single database) or storing every timestamp centrally (Use a sliding window log storing every request timestamp centrally) will cause bottlenecks and high latency.

2. Step 2: Evaluate distributed token bucket with local caches

   Distributed token buckets with local caches reduce central load and sync periodically, balancing accuracy and scalability well.

3. Step 3: Consider client-side rate limiting

   Client-side (Use client-side rate limiting without server checks) is unreliable as clients can bypass limits.

4. Final Answer:

   Use distributed token buckets with local caches and periodic sync -> Option A

5. Quick Check:

   Distributed token bucket = scalable + accurate [OK]

Hint: Distributed token buckets scale best for millions [OK]

Common Mistakes:

• Choosing centralized storage causing bottlenecks
• Relying only on client-side limits
• Storing all request logs centrally causing overload