Retry and failure handling in Rest API - Step-by-Step Execution

Concept Flow - Retry and failure handling

Start API Request

↓

Send Request

↓

Response Received?

No→Retry Count < Max?

↓

Process Response

v +---> Back to Send Request↓

Success: End

↩v +---> Back to Send Request

This flow shows how an API request is sent, checked for success, retried if it fails, and stops after max retries.

Execution Sample

Rest API

max_retries = 3
retry_count = 0
while retry_count < max_retries:
    response = send_api_request()
    if response == 'success':
        break
    retry_count += 1

This code tries to send an API request up to 3 times until it succeeds.

Execution Table

Attempt	retry_count	send_api_request() result	Condition (success?)	Action
1	0	'fail'	No	retry_count = 1, retry again
2	1	'fail'	No	retry_count = 2, retry again
3	2	'success'	Yes	break loop, success

💡 Loop ends because response was 'success' on attempt 3

Variable Tracker

Variable	Start	After 1	After 2	After 3	Final
retry_count	0	1	2	2	2
response	N/A	'fail'	'fail'	'success'	'success'

Key Moments - 3 Insights

Why does retry_count increase even when the response is 'fail'?

Because each failed attempt increments retry_count to track how many tries have been made, as shown in rows 1 and 2 of the execution_table.

What stops the retry loop from running forever?

The loop stops when retry_count reaches max_retries or when a 'success' response is received, as shown by the break on attempt 3 in the execution_table.

Why do we break the loop when response is 'success'?

Because the goal is to stop retrying once the API call succeeds, which is shown in row 3 where the condition is 'Yes' and the loop breaks.

Visual Quiz - 3 Questions

Look at the execution_table, what is the value of retry_count after the second attempt?

A 2

B 1

C 0

D 3

💡 Hint

Check the 'retry_count' column in the execution_table row for Attempt 2.

At which attempt does the API request succeed according to the execution_table?

A Attempt 1

B Attempt 3

C Attempt 2

D Attempt 4

💡 Hint

Look at the 'send_api_request() result' and 'Condition (success?)' columns.

If the API never returns 'success', what will happen to retry_count?

A It will decrease

B It will stay at 0

C It will increase until max_retries

D It will become negative

💡 Hint

Refer to the variable_tracker and the loop condition in the code sample.

Concept Snapshot

Retry and failure handling:
- Send API request
- Check if response is success
- If fail, increase retry count
- Retry until max retries or success
- Stop retrying on success or max attempts

Full Transcript

This visual execution shows how retry and failure handling works in API calls. The program sends a request and checks if it succeeded. If it fails, it increases a retry counter and tries again. This repeats until the request succeeds or the maximum number of retries is reached. The execution table tracks each attempt, the retry count, the response, and the action taken. Key moments clarify why retry_count increases on failure, what stops the loop, and why the loop breaks on success. The quiz tests understanding of retry counts, success attempt, and behavior when success never happens.

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