API Gateway throttling in AWS - Time & Space Complexity

Time Complexity: API Gateway throttling

O(n)

Understanding Time Complexity

When using API Gateway throttling, we want to understand how the number of requests affects the system's behavior.

Specifically, how does the throttling process handle increasing request volumes?

Scenario Under Consideration

Analyze the time complexity of throttling requests in API Gateway.

// Pseudocode for API Gateway throttling
for each incoming request {
  if (currentRequestCount < throttleLimit) {
    allow request
    increment currentRequestCount
  } else {
    reject request with 429 error
  }
}

This sequence checks each request against a limit and either allows or rejects it.

Identify Repeating Operations

Identify the API calls, resource provisioning, data transfers that repeat.

• Primary operation: Checking and updating the current request count for each incoming request.
• How many times: Once per request received by the API Gateway.

How Execution Grows With Input

Each new request triggers one check and possibly an update. So, as requests increase, operations increase at the same rate.

Input Size (n)	Approx. Api Calls/Operations
10	10 checks and updates
100	100 checks and updates
1000	1000 checks and updates

Pattern observation: The number of operations grows directly with the number of requests.

Final Time Complexity

Time Complexity: O(n)

This means the work done grows linearly as more requests come in.

Common Mistake

[X] Wrong: "Throttling happens instantly without checking each request."

[OK] Correct: Each request must be checked against the limit, so the system does work proportional to the number of requests.

Interview Connect

Understanding how throttling scales helps you design systems that handle traffic smoothly and avoid overload.

Self-Check

"What if the throttle limit was dynamic and changed based on time of day? How would the time complexity change?"

Practice

1. What is the primary purpose of API Gateway throttling in AWS?

easy

A. To store API request logs permanently

B. To limit the number of requests to protect backend services from overload

C. To increase the speed of API responses

D. To encrypt API requests automatically

Solution

1. Step 1: Understand API Gateway throttling purpose

   API Gateway throttling is designed to control the rate of incoming requests to prevent backend overload.

2. Step 2: Evaluate options against throttling purpose

   Only To limit the number of requests to protect backend services from overload correctly describes throttling as limiting requests to protect backend services.

3. Final Answer:

   To limit the number of requests to protect backend services from overload -> Option B

4. Quick Check:

   Throttling = Limit requests [OK]

Hint: Throttling means limiting requests to avoid overload [OK]

Common Mistakes:

• Confusing throttling with encryption
• Thinking throttling speeds up responses
• Assuming throttling stores logs

2. Which of the following correctly represents the syntax to set a rate limit of 100 requests per second in AWS API Gateway throttling settings?

easy

A. throttle(100, 200)

B. rate_limit = 100, burst_limit = 200

C. rateLimit: 100, burstLimit: 200

D. setRate(100), setBurst(200)

Solution

1. Step 1: Identify correct AWS API Gateway throttling syntax

   The correct syntax uses camelCase keys like rateLimit and burstLimit with numeric values.

2. Step 2: Compare options to AWS documentation style

   rateLimit: 100, burstLimit: 200 matches the correct syntax format; others use invalid or pseudo-code styles.

3. Final Answer:

   rateLimit: 100, burstLimit: 200 -> Option C

4. Quick Check:

   Correct syntax uses camelCase keys [OK]

Hint: AWS uses camelCase keys like rateLimit, burstLimit [OK]

Common Mistakes:

• Using underscores instead of camelCase
• Using function calls instead of key-value pairs
• Confusing method names with settings

3. Given the following API Gateway throttling settings: rateLimit: 50 and burstLimit: 100, what happens when 120 requests arrive simultaneously?

medium

A. Up to 100 requests are processed immediately; the rest are throttled

B. Only 50 requests are processed; the rest are throttled

C. All 120 requests are processed immediately

D. Requests are queued and processed one by one without throttling

Solution

1. Step 1: Understand burstLimit and rateLimit roles

   BurstLimit allows short spikes up to 100 requests at once; rateLimit controls steady rate at 50 requests per second.

2. Step 2: Analyze 120 simultaneous requests

   Up to 100 requests are allowed immediately (burst), the remaining 20 exceed burst and get throttled.

3. Final Answer:

   Up to 100 requests are processed immediately; the rest are throttled -> Option A

4. Quick Check:

   BurstLimit = immediate spike limit [OK]

Hint: BurstLimit allows short spikes, rateLimit steady rate [OK]

Common Mistakes:

• Confusing rateLimit with burstLimit
• Assuming all requests pass immediately
• Thinking requests queue without throttling

4. You configured API Gateway throttling with rateLimit: 200 and burstLimit: 300. However, clients report receiving HTTP 429 errors even when sending 150 requests per second. What is the most likely cause?

medium

A. The usage plan or API key limits are lower than throttling settings

B. The API Gateway cache is full, causing throttling

C. The backend service is down, causing 429 errors

D. The rateLimit and burstLimit values are too high

Solution

1. Step 1: Understand causes of HTTP 429 in API Gateway

   429 errors mean throttling triggered due to exceeding limits.

2. Step 2: Check usage plan and API key limits

   Usage plans or API keys can have lower throttling limits overriding global settings, causing 429 errors at lower rates.

3. Final Answer:

   The usage plan or API key limits are lower than throttling settings -> Option A

4. Quick Check:

   Usage plan limits override throttling [OK]

Hint: Check usage plan limits if throttling occurs unexpectedly [OK]

Common Mistakes:

• Blaming backend service for 429 errors
• Assuming cache affects throttling
• Thinking high limits cause throttling

5. You design an API for a popular mobile app expecting sudden traffic spikes up to 1000 requests per second for short bursts, but steady traffic around 300 requests per second. How should you configure API Gateway throttling to handle this efficiently?

hard

A. Set rateLimit to 1000 and burstLimit to 300

B. Set both rateLimit and burstLimit to 1000

C. Set both rateLimit and burstLimit to 300

D. Set rateLimit to 300 and burstLimit to 1000

Solution

1. Step 1: Match throttling settings to traffic patterns

   RateLimit controls steady request rate; BurstLimit allows short spikes above steady rate.

2. Step 2: Apply to given traffic

   Set rateLimit to steady 300 requests/sec and burstLimit to 1000 for short spikes.

3. Final Answer:

   Set rateLimit to 300 and burstLimit to 1000 -> Option D

4. Quick Check:

   RateLimit steady, BurstLimit spike [OK]

Hint: RateLimit steady rate, BurstLimit handles spikes [OK]

Common Mistakes:

• Swapping rateLimit and burstLimit values
• Setting both limits too low
• Ignoring burstLimit for spikes