Operations | Monitoring | ITSM | DevOps | Cloud

OpenTelemetry can be used to trace Go applications that use Kafka to find performance issues and bugs. OpenTelemetry is an open-source project under the Cloud Native Computing Foundation (CNCF) that aims to standardize the generation and collection of telemetry data. Telemetry data includes logs, metrics, and traces. Apache Kafka introduced the ability to add headers to Kafka messages from version 0.11 onwards.

Database monitoring is a critical component in your application performance monitoring. Apart from application code issues, database issues are one of the most common reasons for a bad user experience. MySQL is one of the most popular open-source DBMS that businesses have widely adopted. MySQL monitoring tools can help you identify potential issues with your database, keep a continuous check on your database instances, improve performance and detect and alert you about real-time issues.

Administrators and IT management are increasingly leveraging simple quantifiable KPI indicators such as “Performance Ratings” to gain rapid overviews and track key outcomes. Modern IT architectures are designed and built to scale and be resilient. Systems are now usually built to handle failover and auto-scale up and down to handle varying demand and workloads with very different properties and needs.

High availability and flawless performance of business applications are vital to maintaining a company’s online reputation and keeping its customers satisfied. If a business-critical application crashes, frustrated users may abandon the service, leading to a loss in brand value and revenue. Internal business application performance issues can also cause a drop in employee productivity. To prevent these performance issues, enterprises turn to application performance monitoring solutions.

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.