A significant concept relating to fault tolerance in application architecture is to inculcate the redirection of failed attempts, also known as a retry pattern. However, continuously retrying after a failed attempt can lead to increased network congestion and cascading failures in case of high request frequency.
To combat this, we implement a strategy called Exponential Backoff where the time between retry attempts is progressively increased. While it alleviates the problem to some degree, when multiple instances fail and they all implement the same tactic, they all could end up retrying at the same time causing ‘retry storms’.
Recognizing this, we incorporate ‘Jitter’, a random deviation in the retry interval. Utilizing a mix of Exponential Backoff and Full Jitter can balance out retry attempts to prevent simultaneous overloads and equally distribute the load.
Here’s a simple implementation in Python:
import time, random
def retry_with_backoff_and_jitter(base, cap):
attempt = 0
while True:
wait_time = min(cap, base * 2 attempt)
jitter = random.uniform(0.0, wait_time)
time.sleep(wait_time + jitter)
attempt += 1
# Continue with the logic for retrying the operation
In the above code, `base` determines the initial wait time, `cap` secures the maximum wait time, and each `attempt` exponentially increments the wait time. The jitter is randomly chosen between 0 and the wait time.
Dead-Letter Queues
Another important strategy in error handling is the use of Dead-Letter Queues (DLQ). In AWS, when a message in SQS or an event in SNS fails to be processed after a certain number of attempts, it gets redirected to a DLQ. The DLQ holds these unprocessed events for further analysis, it provides the capability to isolate the failed events and handle them separately ensuring the smooth running of the primary queue.
Here’s an AWS CLI command showing how to set up a DLQ in SQS:
aws sqs set-queue-attributes –queue-url https://SQS_REGION.amazonaws.com/ACCOUNT_ID/QUEUE_NAME –attributes ‘{“RedrivePolicy”:”{\”deadLetterTargetArn\”:\”arn:aws:sqs:SQS_REGION:ACCOUNT_ID:DLQ_NAME\”,\”maxReceiveCount\”:\”5\”}”}’
The attached `RedrivePolicy` JSON object indicates the DLQ where unprocessed messages should be sent (`deadLetterTargetArn`) and the maximum receive count (`maxReceiveCount`) after which the message is considered unprocessed.
Fault-tolerant design patterns like these heighten the durability of an application and should be flawlessly understood and utilized when dealing with applications on the AWS Cloud. These practices are essential not only to ensure an application’s robustness but also a key component for the AWS Certified Developer – Associate (DVA-C02) exam preparation.
Practice Test
True or False? Fault-tolerant Design Patterns are not necessary for developing robust systems in the AWS environment.
- Answer: False
Explanation: Fault-tolerance capability is a key factor for any application in a distributed environment like AWS. Leveraging Fault-tolerant design patterns helps in developing robust systems in AWS.
In the context of AWS, what is the purpose of dead-letter queues?
- A) To find out who killed the server
- B) To manage messages that couldn’t be processed
- C) To log out users who are inactive
- D) To communicate with deceased AWS developers
Answer: B) To manage messages that couldn’t be processed
Explanation: Dead-letter queues are used by AWS services like SES and SQS to handle messages that are not able to be processed successfully for any reason.
What is exponential backoff and jitter in AWS?
- A) A secure password generator
- B) A throttling policy
- C) An Instance type
- D) A formula to calculate the AWS bill
Answer: B) A throttling policy
Explanation: Exponential backoff and jitter is a method used in AWS to gradually and randomly reduce the retry rate for API calls, helping to distribute demand evenly and avoid throttling.
What is the main benefit of using retries with exponential backoff and jitter in AWS?
- A) Reduced cost
- B) Improved security
- C) Increased fault tolerance
- D) Easier debugging
Answer: C) Increased fault tolerance
Explanation: Retries with exponential backoff and jitter help to increase fault tolerance by adjusting the frequency and randomness of retries in response to API errors or rate limits.
True or False? In AWS, unnecessary retries without using exponential backoff and jitter can lead to retries happening at the same time, even escalating the issue.
- Answer: True
Explanation: This pattern imposes a delay for retry attempts that is a random variable to prevent a thundering herd problem wherein all retry attempts happen at the same moment, overwhelming the system.
Multiple select: What are some of the key benefits of using design patterns like exponential backoff and jitter, and dead-letter queues?
- A) Reduced costs
- B) Increased fault tolerance
- C) Reduced bugs
- D) Improved performance
- E) Figure out bugs quickly
Answer: A) Reduced costs, B) Increased fault tolerance, D) Improved performance
Explanation: These patterns reduce costs by avoiding unnecessary usage, improve fault tolerance by handling exceptions more efficiently and improve performance by preventing overload.
Single select: Which AWS messaging service supports the use of dead-letter queues?
- A) Simple Notification Service (SNS)
- B) Simple Email Service (SES)
- C) Kinesis Data Streams (KDS)
- D) All of the above
Answer: D) All of the above
Explanation: All three AWS services – Simple Notification Service (SNS), Simple Email Service (SES), and Kinesis Data Streams (KDS) support the use of dead-letter queues.
In AWS, where do unprocessed messages in a dead-letter queue usually end up?
- A) They get deleted
- B) They get archived
- C) They get resent
- D) They get logged
Answer: B) They get archived
Explanation: Unprocessed messages in a dead-letter queue are generally moved to an archive for further analysis or reprocessing.
True or False? Retries with exponential backoff and jitter help to maintain the high availability of AWS services.
- Answer: True
Explanation: Yes, this strategy is used in AWS to increase the availability of services by smartly handling retries in case of issues.
Multiple select: What could be the reasons for the unprocessed messages in dead-letter queues?
- A) Network connectivity issues
- B) Invalid message format
- C) Hardware failures
- D) Financial issues
Answer: A) Network connectivity issues, B) Invalid message format, C) Hardware failures
Explanation: Network problems, invalid message formats, and hardware failures are some of the many reasons some messages may fail to be processed and end in dead-letter queues.
Interview Questions
What is the concept of a dead-letter queue in AWS?
A Dead-Letter Queue (DLQ) is a queue to which other (source) queues can send messages that could not be successfully processed. SQS messages that contain errors or have remained in the queue for an extended period can be moved to a DLQ.
What is the primary purpose of using retries with exponential backoff and jitter when designing AWS applications?
Retries with exponential backoff and jitter are used to improve the fault tolerance of AWS applications. They are used to prevent a system from becoming unresponsive or failing when under heavy load or during temporary network or service issues.
How does the exponential backoff algorithm work?
The exponential backoff algorithm increases the waiting time between retries exponentially up to a maximum backoff time. It introduces a delay that grows exponentially with each successive retry attempt, effectively spreading retries over a larger period and helping to minimize the impact of cascading failures in a system.
How does adding jitter to the exponential backoff strategy improve system reliability?
Jitter introduces a random element to the backoff delay, thus preventing scenarios where resources become available all at once and systems simultaneously try to regain access, causing another failure wave. It helps to randomize retry attempts from different clients.
Which AWS service can be used to implement dead-letter queues?
Amazon Simple Queue Service (SQS) can be used to implement dead-letter queues. It allows setting up DLQs to receive unprocessable messages from other queues.
How does a retry policy in AWS SDK distinguish between different types of errors?
AWS SDK defines two types of errors: client errors and service errors. Client errors are caused by the client, for example, invalid input, and AWS SDK won’t retry these errors. Service errors are caused by issues on AWS end, and these are subject to retry policy.
How can you handle poison-pill messages in AWS SQS?
Poison pill messages can be handled by using a dead-letter queue (DLQ) in AWS SQS. Any message that cannot be processed successfully is moved to the DLQ after a certain number of processing attempts.
In what situations is it advisable to disable retries in the AWS SDK?
It’s advisable to disable retries for time-sensitive operations because retries introduce additional latency. It can also be considered to prevent unnecessary charges for requests, for large payloads, or in use cases where client operations need fail-fast behavior.
What is a circuit breaker pattern in AWS?
The circuit breaker pattern is a design pattern used in AWS to detect failures and prevent an application from trying to perform an operation that’s likely to fail. Once the failures reach a certain threshold, the circuit breaker trips and all further calls return an error immediately, without making network calls.
What AWS service can help in implementing the circuit breaker pattern?
AWS Step Functions can be used to implement the circuit breaker pattern. It can manage state, checkpoints, and restarts in a multi-step serverless workflow.
What’s the default number of maximum retries in AWS SDK before a service error is considered non-retriable?
In AWS SDKs, the default number of maximum retries for service errors is 3.
What does “Maximum Receives” mean in the context of an AWS SQS Dead Letter Queue?
Maximum Receives is the count of the number of times a message is delivered to the source queue. If a message is delivered this many times and is still not processed successfully, it is sent to the Dead Letter Queue.
Can Amazon SNS use Dead Letter Queues?
Yes, Amazon SNS supports delivering failed messages to a Dead Letter Queue (DLQ) which needs to be an Amazon SQS queue.
What determines the maximum delay in the exponential backoff with jitter strategy?
In the exponential backoff with jitter strategy, the maximum delay is determined by both the maximum backoff time configured and the specific jitter value calculated for each retry attempt.
How long does Amazon SQS retain messages in a dead-letter queue?
Amazon SQS retains messages in a dead-letter queue for a maximum of 14 days from the time they are received. After this period, the message is automatically deleted.
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