Operations | Monitoring | ITSM | DevOps | Cloud

Tracing provides indispensable insights into the state and performance of distributed applications, but it can often be difficult to determine the root cause or ultimate business impact of issues indicated by traces. Translating visibility of individual microservices into broader performance insights often requires drawing complex correlations between spans. This can be a laborious process, which can complicate everything from troubleshooting and triage to tracking KPIs and managing costs.

For any software company, a log monitoring tool is a must for collecting, storing, and providing a centralized view of all logs from different applications and hosts for faster anomaly detection, incident resolution, and troubleshooting. They can also help detect security threats and provide audit trails. They are effective in capacity planning, decision-making, and ensuring optimized performance.

In this article, we will use OpenTelemetry to instrument a sample Flask app for traces. Flask is one of the most popular web application frameworks of Python. It consists of Werkzeug WSGI toolkit and Jinja2 template engine. Instrumentation is the biggest challenge engineering teams face when starting out with monitoring their application performance. OpenTelemetry is the leading open-source standard that is solving the problem of instrumentation.

Falcon is a minimalist Python web API framework for building robust applications and microservices. It also compliments many other Python frameworks by providing extra reliability, flexibility, and performance. Using OpenTelemetry, you can monitor your Falcon applications for performance by collecting telemetry signals like traces. Instrumentation is the biggest challenge engineering teams face when starting out with monitoring their application performance.

Developing a native Android app is a challenging task that requires a deep understanding of the Android SDK, as well as programming languages such as Java or Kotlin. The process requires navigating various tools, frameworks, and APIs, each with its own rules. On top of that, you need to ensure compatibility and optimal performance across the diverse Android ecosystem, with its multitude of devices, screen sizes, and OS versions.

In today’s economic and regulatory environment, data sovereignty is increasingly top of mind for observability teams. The rules and regulations surrounding telemetry data can often be challenging to interpret, leaving many teams in the dark about what kind of data they can capture, how long it can be stored, and where it has to reside. In the past, addressing these issues at scale was a costly endeavor.