API monitoring and alerting in Rest API - Time & Space Complexity

Time Complexity: API monitoring and alerting

O(n)

Understanding Time Complexity

When monitoring APIs, we want to know how the time to check and alert changes as the number of API calls grows.

We ask: How does the work increase when more API requests happen?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

// Pseudocode for API monitoring
function monitorAPIs(apiCalls) {
  for (let call of apiCalls) {
    if (call.status === 'error') {
      sendAlert(call);
    }
  }
}

function sendAlert(call) {
  // send alert for the failed API call
}
    

This code checks each API call for errors and sends an alert if needed.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Looping through each API call in the list.
• How many times: Once for every API call received.

How Execution Grows With Input

As the number of API calls grows, the time to check each one grows too.

Input Size (n)	Approx. Operations
10	10 checks
100	100 checks
1000	1000 checks

Pattern observation: The work grows directly with the number of API calls.

Final Time Complexity

Time Complexity: O(n)

This means the time to monitor grows in a straight line with the number of API calls.

Common Mistake

[X] Wrong: "Checking all API calls happens instantly no matter how many there are."

[OK] Correct: Each API call needs to be checked one by one, so more calls mean more work and more time.

Interview Connect

Understanding how monitoring scales helps you design systems that stay reliable as traffic grows. This skill shows you can think about real-world system behavior.

Self-Check

"What if we only checked API calls that failed instead of all calls? How would the time complexity change?"