![]() Therefore, I update the processing logic as follows: import json But let’s assume that I can’t control that system and it’s critically important for the business that I process this new type of messages. In the ideal world, this is an upstream issue that should be fixed in the message producer. The problem is clear, and I decide to update my processing code to handle this case properly. I use Poll for messages and fetch all unconsumed messages from the DLQ.Īnd then I inspect the unconsumed message by selecting it. To figure out what’s wrong in the offending message, I use the new SQS redrive functionality, selecting DLQ redrive in my dead-letter queue. REPORT RequestId: 542ac2ca-1db3-5575-a1fb-98ce9b30f4b3ĝuration: 1.69 msěilled Duration: 2 ms Memory Size: 128 MB Max Memory Used: 39 MB ![]() TypeError: can only concatenate str (not "int") to strįile "/var/task/lambda_function.py", line 8, in lambda_handlerĮND RequestId: 542ac2ca-1db3-5575-a1fb-98ce9b30f4b3 In fact, this is what I find in the CloudWatch logs: The first number is a string and this is quite likely to become a problem in my processor. Now I’m ready to process new messages using Send and receive messages for my source queue in the SQS console. I could achieve the same from the SQS console too. I use the Lambda console to set up the SQS trigger. For the IAM permissions, I use the managed policy named AWSLambdaSQSQueueExecutionRole, which grants permissions to invoke sqs:ReceiveMessage, sqs:DeleteMessage, and sqs:GetQueueAttributes. ![]() As you can imagine, this will lead to trouble later on.īefore processing any messages, you must grant this Lambda function enough permissions to read messages from SQS and configure its trigger. The code above assumes that each message’s body contains two integer values that can be summed, without dealing with any validation or error handling. The Lambda function written in Python will iterate over the batch of incoming messages, fetch two values from the message body, and print the sum of these two values. Let’s implement a simple message consumer using AWS Lambda. Once the DLQ is correctly set up, I need a processor. Keep in mind that you’re charged based on the number of API calls, not the number of queues. But usually it’s considered best practices to setup independent DLQs per source queue to simplify the redrive phase without affecting cost. There are cases where you want to reuse a single DLQ for multiple queues. This configuration is optional: when this Redrive allow policy is disabled, any SQS queue can use this DLQ. I also edit the DLQ to make sure that only my source queue is allowed to use this DLQ. In a real-world environment, you might want to set a higher number depending on your requirements and based on what a failure means with respect to your application. This means that every failed message goes to the DLQ immediately. For this demonstration, I’ve set it to one. Here, I pick the DLQ and configure the Maximum receives, which is the number of times after which a message is reprocessed before being sent to the DLQ. I edit the source queue and configure the Dead-letter queue section. This new experience also takes care of redriving messages in batches, reducing overall costs.ĭLQ and Lambda Processor Setup If you’re already comfortable with the DLQ setup, then skip the setup and jump into the new DLQ redrive experience.įirst, I create two queues: the source queue and the dead-letter queue. With this new development experience, you can easily inspect a sample of the unconsumed messages and move them back to the original queue with a click, and without writing, maintaining, and securing any custom code. ![]() This new functionality is available in the SQS console and helps you focus on the important phase of your error handling workflow, which consists of identifying and resolving processing errors. Today, I’m happy to announce the general availability of a new enhanced DLQ management experience for SQS standard queues that lets you easily redrive unconsumed messages from your DLQ to the source queue. The life cycle of these unconsumed messages is part of your error-handling workflow, which is often manual and time consuming. When a message cannot be successfully processed by the queue consumer, you can configure SQS to store it in a dead-letter queue (DLQ).Īs a software developer or architect, you’d like to examine and review unconsumed messages in your DLQs to figure out why they couldn’t be processed, identify patterns, resolve code errors, and ultimately reprocess these messages in the original queue. Hundreds of thousands of customers use Amazon Simple Queue Service (SQS) to build message-based applications to decouple and scale microservices, distributed systems, and serverless apps.
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