Operations | Monitoring | ITSM | DevOps | Cloud



What's in an instrumentation? An SQS and Python study

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.


Solve code-level bottlenecks with Profiling for Python

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.


How we run our Python tests in hundreds of environments really fast

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.


New Relic Alternative for Python

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.


TL;DR Python, Pandas Dataframes, and InfluxDB

InfluxDB has over a dozen client libraries so developers can get started more easily and program in the language they’re most comfortable with. One of our most popular options is the Python client library. InfluxDB supports not just Python but pandas, a tool popular with data scientists for analyzing and manipulating data. You can use the client library to output data from InfluxDB into a DataFrame format pandas can ingest, and you can write pandas DataFrames directly to InfluxDB.


Develop and Deploy a Python API with Kubernetes and Docker

Docker is one of the most popular containerization technologies. It is a simple-to-use, developer-friendly tool, and has advantages over other similar technologies that make using it smooth and easy. Since its first open-source release in March 2013, Docker has gained attention from developers and ops engineers. According to Docker Inc., Docker users have downloaded over 105 billion containers and 'dockerized' 5.8 million containers on Docker Hub. The project has over 32K stars on Github.

Develop and Deploy a Python API with Kubernetes and Docker - part II

In part I of this tutorial, we developed a Python API then we used Docker and Docker Compose to containerize the application and create a development environment. In part II, we are going to discover some other details about Docker and Docker Compose as well as how to deploy the same app to a GKE cluster.


Configuring Python StatsD Client

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.


Pandora's Flask: Monitoring a Python web app with Prometheus

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.


Getting Started with Python and Geo-Temporal Analysis

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.