Error handling and retries in DynamoDB - Step-by-Step Execution

Concept Flow - Error handling and retries

Start Operation

↓

Try Operation

↓

Success?

Yes→Return Result

No↓

Check Error Type

↓

Retryable

↓

Retry with Delay

↓

Max Retries Reached?

No→Try Operation

Yes↓

Return Error

This flow shows trying an operation, checking if it succeeded, retrying if the error is retryable, and stopping after max retries or on non-retryable errors.

Execution Sample

DynamoDB

max_retries = 3
attempt = 0
while attempt < max_retries:
    try:
        result = dynamodb.get_item(...)
        break
    except ProvisionedThroughputExceededException:
        attempt += 1
        sleep(2 ** attempt)

This code tries to get an item from DynamoDB, retries on throughput errors with exponential backoff, and stops after 3 tries.

Execution Table

Attempt	Action	Error Occurred	Error Type	Retry?	Wait Time (seconds)	Result/Outcome
1	Try get_item	Yes	ProvisionedThroughputExceededException	Yes	2	Retrying
2	Try get_item	Yes	ProvisionedThroughputExceededException	Yes	4	Retrying
3	Try get_item	No	-	No	0	Success, return result
4	Exit loop	-	-	-	-	Operation completed successfully

💡 Operation succeeded on attempt 3, so loop exits.

Variable Tracker

Variable	Start	After Attempt 1	After Attempt 2	After Attempt 3	Final
attempt	0	1	2	2	2
result	None	None	None	Item data	Item data

Key Moments - 3 Insights

Why do we increase the attempt counter only on retryable errors?

Because as shown in execution_table rows 1 and 2, we only increment 'attempt' when a retryable error occurs to limit retries. Non-retryable errors would stop retries immediately.

What happens if the error is not retryable?

The flow goes to the 'Non-Retryable' branch in concept_flow and stops retries, returning the error immediately without incrementing 'attempt'.

Why do we use exponential backoff for wait time?

Exponential backoff (2, 4 seconds here) reduces load on the service and increases chances of success on retries, as seen in the 'Wait Time' column in execution_table.

Visual Quiz - 3 Questions

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

A 1

B 2

C 3

D 0

💡 Hint

Check the 'attempt' variable in variable_tracker after Attempt 2.

At which attempt does the operation succeed without error?

A Attempt 1

B Attempt 2

C Attempt 3

D Attempt 4

💡 Hint

See execution_table row where 'Error Occurred' is 'No'.

If the error was non-retryable on the first attempt, what would happen?

A The code retries up to max_retries anyway

B The code stops and returns the error immediately

C The code waits and retries once

D The code ignores the error and returns success

💡 Hint

Refer to concept_flow branch for 'Non-Retryable' errors.

Concept Snapshot

Error handling in DynamoDB involves trying an operation,
checking if it succeeded, and retrying on retryable errors like
ProvisionedThroughputExceededException.
Retries use exponential backoff delays.
Stop retrying after max retries or on non-retryable errors.
Return result on success or error on failure.

Full Transcript

This visual execution trace shows how error handling and retries work in DynamoDB operations. The process starts by attempting the operation. If it succeeds, the result is returned immediately. If an error occurs, the code checks if the error is retryable, such as a throughput exceeded exception. If retryable, it waits using exponential backoff (doubling wait time each retry) and tries again, increasing the attempt count. If the error is non-retryable, it stops retrying and returns the error. The example code tries up to 3 times, and the execution table shows attempts 1 and 2 fail with retryable errors, waiting 2 and 4 seconds respectively, and attempt 3 succeeds. Variables like 'attempt' and 'result' update accordingly. Key moments clarify why attempts increment only on retryable errors, what happens on non-retryable errors, and why exponential backoff is used. The quiz tests understanding of attempt counts, success timing, and error handling branches.