Retry and failure handling in Rest API - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is retry handling in REST APIs?

Retry handling means trying a failed request again automatically to recover from temporary problems like network issues or server overload.

beginner

Why is failure handling important in REST APIs?

Failure handling helps your app respond gracefully when something goes wrong, like showing a message or trying again, so users have a better experience.

intermediate

What is exponential backoff in retry strategies?

Exponential backoff means waiting longer and longer between retries, like 1 second, then 2, then 4, to avoid overwhelming the server.

intermediate

Name two common HTTP status codes that should trigger a retry.

Common retry codes are 429 (Too Many Requests) and 503 (Service Unavailable), which mean the server is busy or overloaded.

advanced

What is a circuit breaker in failure handling?

A circuit breaker stops retrying after many failures to prevent wasting resources and lets the system recover before trying again.

Which HTTP status code usually means you should retry the request?

A 503 Service Unavailable

B 404 Not Found

C 200 OK

D 400 Bad Request

503 means the server is temporarily unavailable, so retrying later might succeed.

What does exponential backoff do in retry handling?

A Retries immediately without delay

B Retries only once

C Stops retrying after the first failure

D Waits longer between each retry attempt

Exponential backoff increases wait time between retries to reduce server load.

What is the main goal of failure handling in REST APIs?

A To handle errors gracefully and improve user experience

B To ignore errors silently

C To crash the app on errors

D To always retry forever

Failure handling helps apps respond well to errors, keeping users informed and safe.

Which of these is NOT a good reason to retry a REST API request?

A Network timeout

B Server overload (503)

C Client sent bad data (400)

D Too many requests (429)

400 means the client made a bad request, so retrying won't help without fixing the data.

What does a circuit breaker do in failure handling?

A Keeps retrying endlessly

B Stops retries after many failures to protect the system

C Ignores all errors

D Sends error logs only

Circuit breakers prevent wasting resources by stopping retries after repeated failures.

Explain how retry and failure handling improve REST API reliability.

Think about what happens when a request fails and how retry helps.

Describe what exponential backoff is and why it is used in retry strategies.

Imagine waiting longer each time you try again.

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