![]() However, this article approaches the problem from a more generic perspective. You can also use this policy to avoid loops, rather than managing your own counters. SQS also offers Redrive Policy which employs a “Maximum Receives” attribute representing the maximum number of receives before the message is sent to a dead letter queue. Luckily, SQS maintains just such a managed attribute at the message level (ApproximateReceiveCount) and we will use this in our sample retry setup. If we are dealing with a consumer fleet, we need to move this Map to a central repository such as Redis or an RDBMS. delivery_count in the consumer application. For these kinds of brokers, we can maintain a Map of message_id vs. JMS 2.0 defines a managed header attribute for broker re-deliveries, but this is not available on all other non-JMS broker implementations. This will allow for the replaying of the failed messages in the future. If the consumer realizes that the message has reached its maximum retries, the consumer will move that message to another queue (dead letter queue). On every consumption, this counter is increased by 1. The message header or an external system should keep track of the re-delivery count of the same message. To tackle this problem, we need to introduce a “maximum retries” feature. There is a possibility of endless loops in here, which we should also avoid. The same cycle starts again which will lead to a retry of the same message. The leftover message becomes visible again to this consumer and other consumers after the visibility timeout is reached.The consumer will move to the next message in the queue, without deleting the failed one.The consumer will fail to process the message (due to external web service failure, DB failure, etc.).The consumer will pick it up and start processing it. ![]() A producer will put a message on the queue.The scenario will include the following steps: We can leverage this mechanism in a sample retry scenario. If the consumer has not deleted the message from the queue by the end of the visibility timeout period, the message becomes visible again for the other consumers (or for the same consumer). As soon as a consumer fetches a message from the queue, the visibility timer starts ticking. In short, visibility timeout refers to the time period that a message is “invisible” to other consumers of the queue. TTLs, Dead Letter Exchanges or Queues, Routing, and others allow us to cover some non-functional requirements without writing any code.ĪWS Simple Queue Service (SQS) also has its own additional features, and the one we will use here is called "Visibility Timeout" (you can refer to the AWS documentation for a detailed explanation of AWS SQS and its features). However, modern brokers enabling queues (RabbitMQ, ActiveMQ, Some JMS Providers such as JBoss, AWS SQS, etc.) also put advanced features on the top of this queue concept. Publishers publish a message to a queue consumers consume from the queue. The one I will concentrate on in this article utilizes the Message Queues. Asynchronous retries can be implemented in multiple ways. If the remote API's nature is asynchronous and the order of API operations are not that important, we should leverage asynchronous retry mechanisms for better scalability. For example, if the second operation after the failed one requires an input from the first, you need to block your flow and follow synchronous retries. ![]() Synchronous retries are expensive but sometimes inevitable. These libraries are also pre-packed with advanced policies which enable exponential backoffs (send, retry after 3 seconds, next retry after 6 seconds, etc.). You can write your own algorithm or rely on libraries such as Spring Retry to implement these kinds of retries. Synchronous retries are straightforward: you block your thread and retry on the spot, while "sleeping" in between attempts. We can implement two kinds of retry-mechanisms against these failures: synchronous and asynchronous. ![]() Most non-functional errors, (like temporary network outages, infrastructure problems, slow resources) are retriable. Two types of errors/failures can occur during API communication: functional and non-functional. While designing our integration points against the remote APIs, we also need to consider error scenarios.
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