Rate limiting in Microservices - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define the maximum number of requests allowed per user.

Microservices

MAX_REQUESTS_PER_MINUTE = [1]

Drag options to blanks, or click blank then click option'

A1000

B100

C10

D10000

Attempts:

3 left

💡 Hint

Common Mistakes

Setting the limit too high causing server overload.

Setting the limit too low causing poor user experience.

The typical rate limit is set to 100 requests per minute to balance user experience and server load.

2fill in blank

medium

Complete the code to check if the user has exceeded the rate limit.

Microservices

if user_requests > [1]:
    block_request()

Drag options to blanks, or click blank then click option'

AMAX_REQUESTS_PER_HOUR

BMAX_REQUESTS_PER_SECOND

CMAX_REQUESTS_PER_MINUTE

DMAX_REQUESTS_TOTAL

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong time window limit variable.

Comparing against a total or hourly limit instead of per-minute.

We compare the user's requests against the per-minute limit to decide blocking.

3fill in blank

hard

Fix the error in the code that resets the request count after the time window.

Microservices

if current_time - window_start >= [1]:
    reset_request_count()

Drag options to blanks, or click blank then click option'

A1

B3600

C600

D60

Attempts:

3 left

💡 Hint

Common Mistakes

Using 3600 seconds which is 1 hour instead of 1 minute.

Using 600 seconds which is 10 minutes.

The time window for rate limiting is 60 seconds (1 minute), so reset after 60 seconds.

4fill in blank

hard

Fill both blanks to implement a sliding window rate limiter using timestamps.

Microservices

request_times = [t for t in request_times if t > current_time - [1]]
if len(request_times) >= [2]:
    block_request()

Drag options to blanks, or click blank then click option'

A60

B100

C120

D50

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up the time window and max request count values.

Using a time window longer than the defined rate limit period.

The sliding window is 60 seconds and the max allowed requests is 100 in that window.

5fill in blank

hard

Fill all three blanks to implement token bucket rate limiting logic.

Microservices

tokens = min(capacity, tokens + (current_time - last_checked) * [1])
if tokens < [2]:
    block_request()
else:
    tokens -= [3]

Drag options to blanks, or click blank then click option'

Arefill_rate

B1

Ctoken_cost

Dcapacity

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing capacity with refill_rate.

Using token_cost incorrectly in comparison or subtraction.

The refill_rate controls token addition, 1 token is needed per request, and token_cost is subtracted when allowing a request.

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