We use Gradle for managing dependencies in our project, but it will work as well with Maven. Then, we will write a listener method, that will implement the processing logic. First, we will take a look at some of the beans we can define to configure our SQS consumer. Spring way is to separate configuration and logic. If Spring Boot is your framework of choice and you plan to implement the consumer, you are in luck, Spring Cloud AWS project implements a wrapper on top of the AWS Java SDK to make it natural for the Spring project by providing Bean-based configuration and set of annotations to easily create SQS consumers. For consumers, it might get a little more complex. It is more than enough if you are writing SQS producer, as it is just one API call to produce a message. Amazon provides low-level SDK for calling various SQS APIs. If you plan to work with Amazon SQS from your JVM application, you have several technologies to choose from. One might manually investigate reasons for failure or just set up alerts on the rate of messages produced to the DLQ. What to do with messages in DLQ is up to the developer and is very domain-specific. One can set up DLQ for an SQS queue with the “maxReceiveCount” parameter, which defines, how many receives indicate that something is wrong with the message and it should be put aside, to DLQ. To avoid this, Amazon SQS introduces the concept of the DLQ - Dead Letter Queues. If a message cannot be processed successfully - it will be retried forever. Dead Letter QueueĮvery time the same message is consumed, the special counter called ReceiveCount is increased. For more details, see the official documentation. The consumer can freely change the visibility timeout of the message it acquired, as long as the total time it is being invisible is less than a maximum of 12 hours. After the visibility timeout elapses, the message becomes available for any free consumer to get again. The visibility timeout value is set on the queue level (default is 30 seconds) but can be overridden for each message by the consumer. To make sure that if the current consumer fails in the middle of processing, the message will not be lost and will become visible after the visibility timeout passes.So no other consumer will be able to get the message that is being processed.Only one consumer can take a specific message and when it does, the message is not deleted from the queue but marked as invisible. When a message is produced to the SQS queue, it becomes visible to all consumers that are subscribed to the queue. One of the most important concepts is message visibility. Let’s review some concepts of the Amazon SQS. Before getting to the consumer implementation. Amazon SQSĪmazon SQS stands for Simple Queue Service and it is a general-purpose messaging service with client libraries for various languages and frameworks. In this article, we will set up a consumer of messages from an SQS queue using Spring Boot and Spring Cloud AWS. One of the few native options is to automatically deliver such notifications to SQS. For example, when you need to trigger some processing on uploads to the Amazon S3 bucket. In case your system is deeply integrated with AWS services, sometimes you are limited to Amazon SQS as a message queuing service. Schedule and batch-based approaches to data processing are imposing a number of limitations. Event-driven architecture is one of the hottest topics in the software development community nowadays.
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