Rate limiting in Microservices - Architecture Diagram

System Overview - Rate limiting

This system controls how many requests a user or client can make to an API within a certain time. It protects the service from overload and abuse by limiting request rates. The system must be scalable and handle many users simultaneously.

Architecture Diagram

User
  |
  v
Load Balancer
  |
  v
API Gateway -- Rate Limiter -- Redis Cache
  |                      |
  v                      v
Microservices ---------> Database

Components

User

client

Sends requests to the system

Load Balancer

load_balancer

Distributes incoming requests evenly to API Gateway instances

API Gateway

api_gateway

Entry point for requests; enforces rate limiting before forwarding

Rate Limiter

service

Checks and enforces request limits per user using counters

Redis Cache

cache

Stores counters for requests per user with fast access

Microservices

service

Handles business logic after passing rate limiting

Database

database

Stores persistent data for the microservices

Request Flow - 10 Hops

User → Load Balancer

Load Balancer → API Gateway

API Gateway → Rate Limiter

Rate Limiter → Redis Cache

Redis Cache → Rate Limiter

Rate Limiter → API Gateway

API Gateway → Microservices

Microservices → Database

Microservices → API Gateway

API Gateway → User

Failure Scenario

Component Fails:Redis Cache

Impact:Rate limiter cannot check or update counters, so rate limiting may fail or become inaccurate, risking overload or blocking all requests.

Mitigation:Fallback to in-memory counters with limited accuracy or temporarily disable rate limiting with alerts; restore Redis quickly with replication and monitoring.

Architecture Quiz - 3 Questions

Which component is responsible for evenly distributing incoming user requests?

A Load Balancer

B API Gateway

C Rate Limiter

D Redis Cache

Design Principle

This architecture uses a centralized rate limiter with a fast cache to efficiently track request counts per user. It protects backend services from overload by rejecting excessive requests early. The load balancer and API gateway ensure scalability and control entry points. Using Redis for counters provides low latency and atomic increments, essential for accurate rate limiting.

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