Rate limiting algorithms (token bucket, leaky bucket) in HLD - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main purpose of rate limiting in system design?

Rate limiting controls how many requests a user or system can make in a given time to prevent overload and ensure fair resource use.

beginner

Describe the Token Bucket algorithm in simple terms.

Token Bucket allows requests if there are tokens available. Tokens are added at a steady rate. Each request uses one token. If no tokens, request is denied or delayed.

beginner

How does the Leaky Bucket algorithm control request flow?

Leaky Bucket processes requests at a fixed rate. Excess requests wait in a queue. If the queue is full, new requests are dropped.

intermediate

What is a key difference between Token Bucket and Leaky Bucket algorithms?

Token Bucket allows bursts of requests up to token count, while Leaky Bucket smooths out bursts by processing at a constant rate.

intermediate

Why might a system choose Token Bucket over Leaky Bucket for rate limiting?

Token Bucket is better for systems that allow short bursts of traffic, giving flexibility while still controlling average rate.

Which rate limiting algorithm allows bursts of requests up to a token limit?

A Token Bucket

B Leaky Bucket

C Fixed Window

D Sliding Log

Token Bucket allows bursts by accumulating tokens that can be spent quickly.

In the Leaky Bucket algorithm, what happens when the queue is full and a new request arrives?

A The request is dropped

B The request is processed immediately

C The bucket refills tokens

D The request is delayed indefinitely

New requests are dropped if the queue is full to prevent overload.

Which algorithm processes requests at a fixed, steady rate?

A Sliding Window

B Token Bucket

C Leaky Bucket

D Fixed Window

Leaky Bucket processes requests at a constant rate, smoothing bursts.

What does a token represent in the Token Bucket algorithm?

A A dropped request

B Permission to process one request

C A queued request

D A fixed time interval

Each token allows one request to be processed.

Why is rate limiting important in distributed systems?

A To reduce network latency

B To increase request speed

C To store more data

D To prevent system overload and ensure fair use

Rate limiting protects systems from overload and abuse.

Explain how the Token Bucket algorithm works and when it is useful.

Think of tokens as tickets to make requests.

Describe the Leaky Bucket algorithm and how it differs from Token Bucket.

Imagine water leaking from a bucket at a steady rate.

Practice

1. Which rate limiting algorithm allows short bursts of traffic by accumulating tokens over time?

easy

A. Token Bucket

B. Leaky Bucket

C. Fixed Window Counter

D. Sliding Log

Solution

1. Step 1: Understand token bucket behavior

   The token bucket algorithm accumulates tokens at a fixed rate, allowing bursts when tokens are available.

2. Step 2: Compare with leaky bucket

   The leaky bucket processes requests at a steady rate, smoothing bursts rather than allowing them.

3. Final Answer:

   Token Bucket -> Option A

4. Quick Check:

   Token Bucket = allows bursts [OK]

Hint: Token bucket = bursts allowed, leaky bucket = steady flow [OK]

Common Mistakes:

• Confusing leaky bucket with token bucket
• Thinking fixed window allows bursts
• Mixing sliding log with token bucket

2. Which of the following is the correct description of the leaky bucket algorithm?

easy

A. Requests are counted in fixed time windows

B. Tokens accumulate and requests consume tokens

C. Requests are processed at a fixed rate regardless of burst

D. Requests are logged with timestamps for sliding window

Solution

1. Step 1: Recall leaky bucket mechanism

   Leaky bucket processes requests at a constant rate, smoothing out bursts by queuing excess requests.

2. Step 2: Eliminate other options

   Tokens accumulate and requests consume tokens describes token bucket, C and D describe other rate limiting methods.

3. Final Answer:

   Requests are processed at a fixed rate regardless of burst -> Option C

4. Quick Check:

   Leaky bucket = fixed rate processing [OK]

Hint: Leaky bucket = steady output rate, no bursts allowed [OK]

Common Mistakes:

• Confusing token accumulation with leaky bucket
• Mixing fixed window counting with leaky bucket
• Assuming leaky bucket allows bursts

3. Consider a token bucket with a capacity of 5 tokens and a refill rate of 1 token per second. Initially, the bucket is full. If 7 requests arrive instantly, how many requests will be allowed immediately?

medium

A. 5

B. 7

C. 1

D. 0

Solution

1. Step 1: Understand token bucket capacity and refill

   The bucket starts full with 5 tokens, each request consumes one token.

2. Step 2: Calculate allowed requests

   Since 7 requests arrive instantly but only 5 tokens are available, only 5 requests are allowed immediately.

3. Final Answer:

   5 -> Option A

4. Quick Check:

   Tokens available = 5, requests = 7, allowed = 5 [OK]

Hint: Max allowed = tokens in bucket at request time [OK]

Common Mistakes:

• Assuming all 7 requests allowed instantly
• Ignoring token bucket capacity
• Confusing refill rate with immediate tokens

4. A developer implemented a leaky bucket rate limiter but notices that bursts are still allowed. What is the most likely cause?

medium

A. The bucket capacity is set too low

B. Requests are dequeued faster than the leak rate

C. Tokens are refilled too quickly

D. The leak rate is set to zero

Solution

1. Step 1: Analyze leaky bucket behavior

   Leaky bucket allows steady output by leaking requests at a fixed rate.

2. Step 2: Identify cause of bursts

   If requests are dequeued faster than the leak rate, bursts can pass through, breaking the smoothing effect.

3. Final Answer:

   Requests are dequeued faster than the leak rate -> Option B

4. Quick Check:

   Dequeue rate > leak rate = bursts allowed [OK]

Hint: Leaky bucket bursts mean dequeue faster than leak rate [OK]

Common Mistakes:

• Confusing token refill with leaky bucket
• Assuming bucket capacity controls bursts
• Ignoring dequeue speed vs leak rate

5. You need to design a rate limiter for an API that allows short bursts but enforces an average rate limit over time. Which algorithm should you choose and why?

hard

A. Leaky Bucket, because it smooths out bursts by processing requests steadily

B. Fixed Window Counter, because it counts requests in fixed intervals

C. Sliding Log, because it logs every request timestamp

D. Token Bucket, because it allows bursts by accumulating tokens and enforces average rate

Solution

1. Step 1: Understand requirement for bursts and average rate

   The system must allow short bursts but keep average rate limited over time.

2. Step 2: Match algorithm to requirement

   Token bucket allows bursts by using tokens that refill over time, enforcing average rate while permitting bursts.

3. Final Answer:

   Token Bucket, because it allows bursts by accumulating tokens and enforces average rate -> Option D

4. Quick Check:

   Bursts + average rate = Token Bucket [OK]

Hint: Token bucket = bursts + average rate control [OK]

Common Mistakes:

• Choosing leaky bucket which smooths bursts
• Confusing fixed window with burst allowance
• Ignoring average rate enforcement