Azure

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. ...

The Claim-Check pattern, or Reference-Based Messaging, is a pattern where we split a large message into a claim check and a payload. The claim check is sent to our message broker and the payload will be stored in an external data store. This pattern enables us to process large messages, while preventing our message bus from being overloaded and prevent our client applications slowing down. We can also reduce costs with this pattern, but utilizing cheap storage alternatives rather than increasing the resource units used by our message broker. ...

In Azure AI Search, semantic ranking improves our searches by using language understanding to rerank search results. Semantic search is a collection of query capabilities that improve the quality of search results using text-based queries. Using semantic search we can: Improve Search Results by adding a ranking over initial search results using advanced algorithms that consider the context and meaning of the query, resulting in more relevant search outcomes. Provides Additional Information by extracting and displaying captions and answers from search results, which can be used to improve the user’s search experience. In this article, we’ll talk a little about Semantic Search is in Azure AI Search, its advantages and limitations, how to set it up in Azure AI search, and how we can perform semantic searches using C#. ...