Azure

The image shows a complex 3D rendering of a Sequential Convoy pattern, which is designed to represent a data processing system. Multiple conveyor belts are intricately laid out in parallel and perpendicular formations, each carrying a series of cubes and rectangular blocks in various colors such as blue, green, orange, and pink. These blocks symbolize data packets or messages. The conveyor belts are interlinked but operate independently, showcasing the non-blocking processing of different sequences of data. Each belt runs without interference from the others, indicating the system's ability to handle multiple data streams simultaneously. The overall scene conveys a bustling, efficient data processing facility, with each color-coded or labeled block moving in a defined, orderly sequence, illustrating the concept of processing related messages in order without delaying other processes.

The Sequential Convoy Pattern

In most cases, we will our applications to process a sequence of messages in the order that they arrive. This is especially the case if and when we need to scale out our message processors to handle any increased load in messages. This isn’t always an easy task, because as our application scales out, instances can often pull messages from the queue independently, similar to how this happens when implementing the Competing Consumers pattern. ...

The Asynchronous Request-Reply pattern

Client-side applications often rely on APIs to provide data and functionality, which are either in your control, or provided by a 3rd party. We usually communicate with these APIs using HTTP(S) protocols and REST. Usually, these APIs are designed to respond quickly to requests. However, there can be a variety of things that affect the latency response from these APIs. These include network infrastructure, how many clients are attempting to call the API, the difference in location between the caller and API itself etc. ...

The Queue-Based Load Leveling Pattern

Applications in the cloud can be subjected to heavy peaks of traffic in intermittent phases. If our applications can’t handle these peaks, this can lead to performance, availability and reliability issues. For example, imagine we have an application that stores state temporarily in a cache. We could have a single task within our application that performs this for us, and we could have a good idea of how many times a single instance of our application would perform this task. ...

A dynamic 3D illustration of a Priority Queue system in computing. At the bottom, a vast array of futuristic computers forms a grid, with a central towering structure that represents the sending application. From this, digital envelopes adorned with glowing edges, indicative of their priority status, burst forth in an array. High-priority envelopes emit brighter light and are surrounded by visible energy fields, while lower-priority ones have a softer glow. They ascend towards an elaborate, radiant queue structure above, which sorts them before they continue towards their destinations. The background resembles a star-filled night sky, signifying the vastness and complexity of the data processing universe. The overall image conveys a sense of grandeur and high-stakes data management.

The Priority Queue Pattern

The Priority Queue pattern is a pattern that allows us to prioritize requests sent to services so that higher priority requests are received and processed faster than lower priority requests. This is useful for when we are building applications where we need to prioritize some clients over others, or if some requests need to be processed faster than usual for business/compliance reasons. In this article, I’ll talk about what the Priority Queue pattern is, what advantages priority queues can provide, what we need to consider when implementing the Priority Queue pattern and how can implement it priority queues in Azure (Spoiler alert, it’s a little hacky!). ...

A conceptual 3D rendering depicting the 'Competing Consumers' model in message processing. Multiple futuristic robotic arms, representing consumers, extend towards a central, glowing digital messaging channel. This channel is overflowing with luminous data messages, illustrating a high volume of information. The robotic arms are engaged in grabbing and processing these messages, symbolizing the concepts of optimizing throughput, enhancing scalability, and distributing workload. The scene is set against a sleek, high-tech backdrop, emphasizing advanced technology and digital processing.

The Competing Consumers Pattern

Applications running in the cloud should expect to handle a large number of requests. One method of dealing with large amounts of requests is to pass them through a message broker to a consumer service that handles these requests asynchronously. This helps ensure that requests are being processed without throttling or degrading the performance of our applications. The volume of these requests can vary over a period of time. Imagine that you’re a developer for an e-commerce website. The traffic you’ll receive during events like Black Friday or Boxing Day sales will be significantly than usual, with requests coming from multiple users that is difficult to predict. During peak hours, you may have to process hundred or thousands of requests per second, while other times demand may be significantly smaller. ...