Optimizing Static File Performance: Implementing Caching and Compression with Azure Front Door

Azure Front Door is a global CDN service that enables you to securely expose your web artifacts to the external world. In this short article, I will demonstrate the process of caching and compressing responses by leveraging the caching and compression features provided by Azure Front Door.

It is advisable to apply caching and compression to static files such as CSS, images, JSON files, CSV, etc., as opposed to dynamic content. Therefore, careful route planning is imperative before embarking on the implementation of caching and compression strategies.

Following is an example.

• route 1 - /api/*
• route 2 -/assets/*

Following the example mentioned above, we'll designate the /api route for dynamic API content and the /assets route for static content. Let's proceed with the implementation. Let's focus on /assets route.

Let's start by navigating to the Front Door manager and selecting the desired endpoint.

Click on "Add a route" to begin configuring the routes for your Front Door setup.

Next, specify the path of the route to match

Let's explore how to define cache and compression settings.

I prefer selecting the Use Query String option, as it allows Front Door to independently cache responses with query strings. However, there are other options available for you to choose from.

This completes the necessary steps to optimize your responses for static content.

Investigate the root cause for latency with Azure Application Insights

In this article, I will demonstrate how to pinpoint the root cause when end users experience general latency. Azure Monitor - Application Insights will be instrumental in this process.

Firstly, we need to navigate to the Performance blade of the Application Insights instance.

Following that, it's better apply filters to refine the dataset.

As we aim to identify the lowest performance, it is advisable to conduct the investigation using the 99th percentile.

There is a distinct outlier present. Let's delve deeper into the investigation by narrowing down the time range to examine specific instances of the failure.

The DELTA indicates the extent to which the selected data points differ from the rest of the transactions within the chosen timeframe.

The Insights tile lets you identify the specific method, representing the best possible cause, that contributes to the majority of the latency, as illustrated below.

Utilize the Distribution of Durations tile to narrow down and select the incidents with the least performance. Check samples for further analysis.

This analysis is very important in pinpointing the root cause of latency and facilitating the implementation of necessary corrective actions.

Analyzing Worst-Case latency experience with Azure Monitor - Application Insights

This article discusses how we can identify and analyze the worst case response of an application/API with Azure Monitor - Application Insights.

We can start the analysis with the Performance blade of the Application Insights workspace.

By default it provides a glimpse of average response.

However, our focus lies in identifying the worst-performing results. Let's transition to the 99th percentile.

We've identified a culprit. There's a conspicuous outlier. Now, let's delve deeper. Our goal is to pinpoint the root cause. Is it within our codebase, or is it linked to a dependency?

There are several avenues to explore this, but I find the Transaction Search feature to be the most effective. Let's navigate to that.

For now, we won't filter by API name, but it's advisable to choose the 99th percentile.

We can observe the peak request there. Let's refine our view by selecting only "Request & Dependency" as filters.

We've already got the answer. It appears to be due to a dependency, as indicated by the yellow line on the graph.

We can even delve deeper to the leaf level to identify the specific request and dependency. Let's narrow down the time period and revisit the results.

Here are the results I obtained in the results section.

This discovery is the starting point for my optimization. We can proceed to take the necessary actions regarding the dependencies.

How to use Azure Monitor - Application Insights to identify performance bottlenecks with dependent resources

When implementing services, we frequently rely on other services or resources. To ensure the efficiency of our solution, it's crucial to pinpoint performance bottlenecks within our system. These bottlenecks can originate from our own codebase or may stem from dependent services, such as external APIs.

You are in safe hands if you have configured Azure Monitor - Application Insights with your solution.

One valuable starting point is to explore Application Insights - Application Map. In the diagram below, you'll notice that calls to External API 2 are slower, as highlighted by the red arrow.

We can further drill down by clicking on the red arrow (the link from our API to External API 2) to identify exactly what API endpoints are slower.

When you click on it, another blade will open, revealing a sample of the slowest calls and offering the option to delve deeper into performance issues. Let's click on the 'Investigate Performance' button.

This action will unveil an informative dashboard that provides a snapshot of performance with regards to the execution of External API 2.

As you can observe, there are certain slow API calls. If you wish to examine samples, simply click on a slow API call and select the 'Samples' button. Then, choose a specific sample to access more detailed information.

In most cases, this information should be adequate to identify performance issues and make improvements.