At Lumigo, we keep improving the coverage and quality of our distributed tracing instrumentation to give you, through Lumigo’s transactions, the most accurate and intuitive representation of how your distributed system behaves. In this blog, we cover a recent development for the Amazon SQS instrumentation in Lumigo’s OpenTelemetry distro for Python, providing a seamless experience for a scenario that otherwise would result in confusing, broken transactions and lost insights.

Profiling is an important tool in every developer’s toolkit because it provides a granular view into the execution of your program from your production environment. This is an important concept, as performance bottlenecks can often be very hard or even impossible to reproduce locally due to external constraints or loads only seen in a production environment. Python is one of the most popular programming languages available, and it is one of the core technologies we use at Sentry.

Not in a reading mood? You also can watch the talk I gave at DjangoCon 2022. One of Sentries core company values is “for every developer”. We want to support every developer out there with our tools. But not every developer uses the newest or widely adopted tech stack, so we also try to support older versions of libraries and frameworks.

Python is one of the most used languages among developers. There are many reasons why python is very famous among developers, which we will discuss in this blog. Due to the fame of python, it is used in many business applications, hence monitoring a python application is crucial. New Relic is one of the oldest monitoring tools for python monitoring. But New Relic competitors are growing rapidly; hence, if you do not like the New Relic user interface, many New Relic alternatives exist.

Building and deploying highly scalable, distributed applications in the ever-changing landscape of software development is only half the journey. The other half is monitoring your application states and instances while recording accurate metrics. There are moments when you wish to check how many resources are being consumed, how many files are under access by the specialized process, etc. These metrics provide valuable insights into our tech stack execution and management.

We eat lots of our own dog food at MetricFire, monitoring our services with a dedicated cluster running the same software. This has worked out really well for us over the years: as our own customer, we quickly spot issues in our various ingestion, storage, and rendering services. It also drives the service status transparency our customers love. Our customers include large multinational coffee brewers, game companies, and other data science/SaaS companies.

This article was originally published in The New Stack and is reposted here with permission. Working with geo-temporal data can be difficult. In addition to the challenges often associated with time-series analysis, like large volumes of data that you want real-time access to, working with latitude and longitude often involves trigonometry because you have to account for the curvature of the Earth. That’s computationally expensive. It can drive costs up and slow down programs.