Operations | Monitoring | ITSM | DevOps | Cloud

In a previous blog post, we built a small Python application that queries Elasticsearch using a mix of vector search and BM25 to help find the most relevant results in a proprietary data set. The top hit is then passed to OpenAI, which answers the question for us. In this blog, we will instrument a Python application that uses OpenAI and analyze its performance, as well as the cost to run the application.

In the not-too-distant past, the debate was between on-prem and cloud-native. You’re now faced with the choice of choosing between the different cloud infrastructure providers, and inevitably, someone will throw in the phrase “vendor lock-in”. And not having a response for the famed “vendor-lockin” sometimes leads to building things that are much more complex than required basis the stage that the product is in.

As an SRE and DevOps evangelist, I talk to many customers and prospects, most of whom run load and stress testing as part of their application delivery chain, often using JMeter for load testing. Many of them have a misconception: “I have JMeter and I am all set from a performance/ scalability perspective. I don’t need any other tools”.

Choosing an excellent application performance monitoring tool is a challenging task. Nowadays, there are dozens of instruments, and it can be problematic to pick the right one. However, when looking into every given “top ten list”, New Relic vs. Datadog will always be there. At this point, instead of focusing on dozens of log management tools, let’s focus on some key ones. Comparing New Relic vs. Datadog offers a distinct perspective on how infrastructure monitoring should look.

Several years ago, there was little choice among performance monitoring tools. You had to deal with what the market offers. Datadog is one of the oldest solutions available and, thus, well-known. Yet, it is not without flaws, which might make people look for alternative solutions since the market is booming and new tools emerge regularly.

DevOps teams and security engineers use monitoring tools like Prometheus and Datadog to search for bugs and find any issues that might put an app or the entire IT infrastructure at risk. Better monitoring capabilities and aspects like event monitoring mean users can log data more effectively and engage in data collection leading to data visualization. These actions lead to infrastructure metrics, which allow experts to conduct timely analysis and prevent an app from crashing.

Google has shared small but important adjustments to the way LCP is assessed. LCP, or Largest Contentful Paint, measures how quickly a page appears to load from the user’s perspective. More specifically, this is the time for the main content to be painted or the “render time of the largest image or text block visible within the viewport”. You’ll get a “Good” score when the load time of this content is 2.5 seconds or less.

Since Microsoft announced the RDP Shortpath feature was going to be enabled by default on September 6, 2022 for all Azure Virtual Desktop (AVD) customers, monitoring and troubleshooting this feature has become important. RDP Shortpath feature improves the AVD connectivity by establishing a direct UDP protocol between the AVD session hosts and the Remote Desktop Client by reducing the dependency on gateways.