Retry Mechanism - Problem

Implement a retry wrapper function that attempts to execute an operation up to N times before giving up. The function should handle failures gracefully and include a delay between retry attempts.

Your function should:

Accept an operation function, maximum retry count, and delay duration
Execute the operation and return its result if successful
If the operation fails, wait for the specified delay and try again
After N failed attempts, throw or return an error indicating failure

The operation function will randomly succeed or fail to simulate real-world scenarios.

Input & Output

Example 1 — Basic Retry with Success

$ Input: maxRetries = 3, delayMs = 500

› Output: Success on attempt 2

💡 Note: First attempt fails, wait 500ms, second attempt succeeds and returns success message

Example 2 — All Attempts Fail

$ Input: maxRetries = 2, delayMs = 1000

› Output: Failed after 2 attempts

💡 Note: Both attempts fail despite waiting 1000ms between them, return failure message

Example 3 — Immediate Success

$ Input: maxRetries = 5, delayMs = 200

› Output: Success on attempt 1

💡 Note: First attempt succeeds immediately, no delay needed

Constraints

1 ≤ maxRetries ≤ 10
10 ≤ delayMs ≤ 5000
Operation has random 60% success rate

Visualization

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Asked in

G Google 35 a Amazon 42 M Microsoft 28 N Netflix 31

The key insight is to implement systematic retry logic with appropriate delays between attempts. The best approach uses exponential backoff to be respectful to failing services while maximizing success chances. Time: O(n), Space: O(1).

Common Approaches

✓ Exponential Backoff Retry

⏱️ Time: O(n) Space: O(1)

Implement exponential backoff where delay increases with each failed attempt. This prevents overwhelming failing services while being more aggressive initially.

Simple Loop Retry

⏱️ Time: O(n) Space: O(1)

Use a simple for loop to attempt the operation multiple times. If it fails, wait for the specified delay and try again until max retries reached.

Exponential Backoff Retry — Algorithm Steps

Start with base delay
Try operation
If failed, double the delay
Retry with new delay
Cap maximum delay to prevent infinite waits

Visualization

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Step-by-Step Walkthrough

First Attempt

Try operation with initial delay ready

Double Delay

After failure, double the wait time

Cap Maximum

Prevent excessive waits with maximum delay limit

Code -

solution.c — C

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <time.h>
#include <unistd.h>

char* solution(int maxRetries, int delayMs) {
    static char result[100];
    srand(time(NULL));
    
    int currentDelay = delayMs;
    int maxDelay = delayMs * 32;
    
    for (int attempt = 1; attempt <= maxRetries; attempt++) {
        double randVal = (double)rand() / RAND_MAX;
        if (randVal < 0.6) {
            sprintf(result, "Success on attempt %d", attempt);
            return result;
        } else {
            if (attempt < maxRetries) {
                usleep(currentDelay * 1000);
                currentDelay = currentDelay * 2;
                if (currentDelay > maxDelay) {
                    currentDelay = maxDelay;
                }
            }
            continue;
        }
    }
    
    sprintf(result, "Failed after %d attempts", maxRetries);
    return result;
}

int main() {
    int maxRetries, delayMs;
    scanf("%d", &maxRetries);
    scanf("%d", &delayMs);
    
    char* result = solution(maxRetries, delayMs);
    printf("%s\n", result);
    
    return 0;
}

Time & Space Complexity

Time Complexity

⏱️

O(n)

Linear time where n is maxRetries, but total wait time grows exponentially

✓ Linear Growth

Space Complexity

O(1)

Only uses variables to track attempts and current delay

✓ Linear Space

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Retry Mechanism - Problem

Input & Output

Constraints

Visualization

Related Problems

Common Approaches

Exponential Backoff Retry — Algorithm Steps

Visualization

Code -

Time & Space Complexity

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