Retry and failure handling in Rest API

Introduction

Sometimes, when you ask a server for information, it might not respond right away or may have a problem. Retry and failure handling helps your program try again or handle errors smoothly so users don't get stuck or confused.

When your app calls a web service that might be temporarily down.

When network connections are unstable and requests can fail.

When you want to make sure important data is sent even if the first try fails.

When you want to show a friendly message if the server cannot be reached after several tries.

Syntax

Rest API

def retry_request(url, max_retries=3, delay=2):
    for attempt in range(max_retries):
        response = send_request(url)
        if response.is_success():
            return response.data
        wait(delay)
    handle_failure()

retry_request is a function that tries to get data multiple times.

max_retries is how many times it tries before giving up.

Examples

Try up to 5 times, waiting 1 second between tries.

Rest API

retry_request('https://api.example.com/data', max_retries=5, delay=1)

Try 3 times by default, waiting 2 seconds between tries.

Rest API

retry_request('https://api.example.com/data')

Sample Program

This program tries to get data from a fake URL. It tries up to 3 times, waiting 2 seconds between tries. It prints messages to show what is happening. If all tries fail, it prints a failure message and returns None.

Rest API

import time
import random

def send_request(url):
    # Simulate a request that fails randomly
    if random.random() < 0.7:
        return type('Response', (), {'is_success': lambda self: False, 'data': None})()
    else:
        return type('Response', (), {'is_success': lambda self: True, 'data': 'Success!'})()

def retry_request(url, max_retries=3, delay=2):
    for attempt in range(1, max_retries + 1):
        print(f'Attempt {attempt} to fetch data...')
        response = send_request(url)
        if response.is_success():
            print('Request succeeded!')
            return response.data
        else:
            print('Request failed, retrying...')
            time.sleep(delay)
    print('All retries failed. Handling failure.')
    return None

result = retry_request('https://api.example.com/data')
print('Final result:', result)

Important Notes

Always set a limit on retries to avoid endless loops.

Use delays between retries to give the server time to recover.

Handle failure gracefully to keep users informed and avoid crashes.

Summary

Retry and failure handling helps your app try again when requests fail.

Set a maximum number of retries and wait time between tries.

Always handle the case when all retries fail to keep your app stable.

Practice

1.

What is the main purpose of retry and failure handling in REST API calls?

easy

A. To speed up the API response time

B. To ignore errors and continue without notification

C. To automatically try the request again if it fails

D. To permanently stop the app on first failure

Solution

1. Step 1: Understand retry purpose

   Retry means trying the same request again if it fails, to handle temporary issues.

2. Step 2: Compare options

   Only To automatically try the request again if it fails describes retry correctly. Others describe wrong or harmful behaviors.

3. Final Answer:

   To automatically try the request again if it fails -> Option C

4. Quick Check:

   Retry = try again [OK]

Hint: Retry means trying again after failure [OK]

Common Mistakes:

• Thinking retry speeds up response
• Ignoring errors without handling
• Stopping app immediately on failure

2.

Which of the following is the correct way to set a maximum retry count in a REST API client configuration?

retry_config = {
    'max_retries': ?
}

easy

A. 'max_retry': 5

B. 'max_retries': 5

C. 'maxRetries': 5

D. 'max-retries': 5

Solution

1. Step 1: Identify correct key name

   The standard key for retry count is 'max_retries' with underscore.

2. Step 2: Check option values

   'max_retries': 5 uses correct key and value format. Others use wrong keys or casing.

3. Final Answer:

   'max_retries': 5 -> Option B

4. Quick Check:

   Correct key is max_retries [OK]

Hint: Use underscores for config keys like max_retries [OK]

Common Mistakes:

• Using camelCase instead of snake_case
• Misspelling the key as max_retry
• Using hyphens in keys

3.

Consider this Python code snippet for retrying a REST API call:

import time

def call_api():
    for attempt in range(3):
        response = make_request()
        if response.status_code == 200:
            return 'Success'
        time.sleep(1)
    return 'Failed'

What will be the output if all three requests fail with status code 500?

medium

A. "Success"

B. An exception is raised

C. The function runs forever

D. "Failed"

Solution

1. Step 1: Analyze retry loop behavior

   The loop tries 3 times. If status_code is 200, returns 'Success'. Otherwise, waits 1 second and retries.

2. Step 2: Check failure case

   All attempts return 500, so loop finishes without returning 'Success'. Then returns 'Failed'.

3. Final Answer:

   "Failed" -> Option D

4. Quick Check:

   All fail -> return 'Failed' [OK]

Hint: If no success in retries, function returns failure [OK]

Common Mistakes:

• Assuming success on failure status
• Expecting infinite loop
• Thinking exception is raised automatically

4.

Find the error in this retry logic snippet:

def fetch_data():
    retries = 3
    while retries > 0:
        response = api_call()
        if response.status_code == 200:
            return response.data
        retries -= 1
    return None
    retries -= 1

medium

A. The last line decreases retries after return, unreachable code

B. Missing sleep between retries

C. No check for network exceptions

D. Should use for loop instead of while

Solution

1. Step 1: Review code flow

   The last line retries -= 1 is after return None, so it never runs.

2. Step 2: Identify unreachable code problem

   Code after return is unreachable and unnecessary, indicating a logic error.

3. Final Answer:

   The last line decreases retries after return, unreachable code -> Option A

4. Quick Check:

   Code after return is unreachable [OK]

Hint: Code after return is never executed [OK]

Common Mistakes:

• Ignoring unreachable code warnings
• Forgetting to add wait between retries
• Not handling exceptions

5.

You want to implement retry logic that tries up to 4 times with increasing wait times: 1s, 2s, 4s, then stops. Which code snippet correctly implements this?

Option A:
for i in range(3):
    response = call_api()
    if response.ok:
        break
    time.sleep(2 ** i)

Option B:
for i in range(1, 5):
    response = call_api()
    if response.ok:
        break
    time.sleep(i)

Option C:
for i in range(4):
    response = call_api()
    if response.ok:
        break
    time.sleep(i)

Option D:
for i in range(4):
    response = call_api()
    if response.ok:
        break
    time.sleep(2 * i)

hard

A. Option A

B. Option B

C. Option C

D. Option D

Solution

1. Step 1: Understand wait time pattern

   Wait times should double each retry: 1s, 2s, 4s, then stop after 4 tries.

2. Step 2: Check the options' sleep times

   The code with time.sleep(2 ** i) and range(3) gives waits of 1s (i=0), 2s (i=1), 4s (i=2). This matches exponential backoff for up to 4 tries (3 retries after initial call).

3. Step 3: Compare distractors

   The range(1,5) with time.sleep(i) uses linear 1,2,3,4s. range(4) with time.sleep(i) uses 0,1,2,3s. time.sleep(2 * i) uses 0,2,4,6s. None match doubling from 1s.

4. Final Answer:

   Option A -> Option A

5. Quick Check:

   Exponential backoff uses 2 ** i [OK]

Hint: Use 2 ** i for doubling wait times [OK]

Common Mistakes:

• Starting wait time at 0 seconds
• Using linear instead of exponential waits
• Not stopping after max retries