Azure

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. ...

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. ...

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!). ...

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. ...

When developing cloud applications, we’ll be interacting with remote services that can suffer from transient faults. Our applications need to be able to handle transient failures when trying to connect to remote services, and retry a failed operation to improve the stability of your applications. This is where the Retry pattern comes into play. N.B Transient faults are faults that include the momentary loss of network connectivity to components and services, the temporary unavailability of a service, and timeouts that occur when a service is busy. ...