This is especially important because a subscribed device might be offline when another device publishes a message that it’s interested in. Much of this PubSub messaging happens asynchronously, because a constrained IoT device does not want to spend its limited resources waiting to ensure that all of the subscribed devices, applications, and systems receive a copy. When a device or application publishes a message, the applications with matching subscriptions receive their own copy of the message. With AWS IoT Core, your devices and applications connect and can subscribe to PubSub message topics. With a durable queue, your request can be re-driven if your function fails the first time. Lambda makes sure that your function is run, even in the face of server failures, so it needs a durable queue in which to store your request. Asynchronously: where the HTTP response returns immediately, and your function is executed and retried behind the scenes.Synchronously: where the output of your function is returned to you in the HTTP response. With AWS Lambda, you upload your function code, and then invoke your functions one of two ways: To illustrate queue-based systems in this article, I touch on how two AWS services work under the hood: AWS Lambda, a service that executes your code in response to events without you having worry about the infrastructure that it runs on and AWS IoT Core, a managed service that lets connected devices easily and securely interact with cloud applications and other devices. In the first half, I describe scenarios that lead to backlogs, and in the second half, I describe many approaches used at Amazon to avoid backlogs or deal with them gracefully. Most importantly, I describe how to prevent queue backlogs from building up in the first place. In this article, I discuss strategies we use at Amazon to deal with queue backlog scenarios – design approaches we take to drain queues quickly and to prioritize workloads. It’s only when the queue is backlogged, when the line to an event goes out the door and around the corner,that people start thinking about throughput and prioritization. After all, when a queue is short, everyone is happy. In queueing theory, the behavior of queues when they are short is relatively uninteresting. Working in parallel with so many other people, I found it beautiful to be a part of what is essentially an amazingly orchestrated physical merge sort. I was guided by an algorithm, picking items off of shelves, moving items from one box into another, moving bins around. Over a decade ago, I spent a day working in an Amazon fulfillment center. I’ve found that being bored while standing in lines provides great opportunities to ponder queueing theory. I especially do this when I’m stuck in a line, like in the grocery store, in traffic, or at the airport. When we think about some of the things that happen in the real world, we can come up with algorithms that mimic those things. Ever since my first computer science course in college, I’ve been interested in how algorithms play out in the real world.
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