Chaos Engineering :D

is the discipline of experimenting on a system in order to build confidence in the system’s capability to withstand turbulent conditions in production.

Monitoring Linux Logs with Kibana and Rsyslog

Monitoring Linux logs is crucial and every DevOps engineer should know how to do it. Here’s why :

• You have real-time visual feedback about your logs : probably one of the key aspects of log monitoring, you can build meaningful visualizations (such as datatables, pies, graphs or aggregated bar charts) to give some meaning to your logs.
• You are able to aggregate information to build advanced and more complex dashboards : sometimes raw information is not enough, you may want to join it with other logs or to compare it with other logs to identify a trend. A visualization platform with expression handling lets you perform that.
• You can quickly filter for a certain term, or given a certain time period : if you are only interested in SSH logs, you can build a targeted dashboard for it.
• Logs are navigable in a quick and elegant way : I know the pain of tailing and greping your logs files endlessly. I’d rather have a platform for it.

Notificaciones de Grafana en Slack

Como ya tengo algo saturada la bandeja de correo, acabé decidiéndome por enviar las notificaciones de Grafana a Slack :D.

En primer lugar tendremos que crearnos una cuenta en Slack si no la tenemos todavía. Crearemos un espacio de trabajo (workspace) y un canal al que en mi caso he llamado #grafana. Todas las notificaciones de mi servidor Grafana irán a parar a ese canal.

System metrics with Docker, Telegraf, Influxdb and Grafana

We’ll use Docker, for the quick deployment our monitoring system, also it’ll gives us a freedom to use any software with dependency free and keep our system clean after. Topical, You can look at Docker from this side too, as a cross-platform package system.

Also, we’ll use part of Tick stack, namely an Influxdb base to store our metrics and Telegraf, like an agent on remote system, for nice and pretty graphs we’ll take Grafana.

Profiling Python in Production - How We Reduced CPU Usage by 80% through Python Profiling with Nylas

We recently reduced CPU usage across our fleet by 80%. One key technique that made this possible was a lightweight profiling strategy that we could run in production. This post is about the ways we approached instrumentation, the tradeoffs involved, and some tools you can use to optimize your own apps (including code!).

Background

Nylas is a developer platform that provides APIs to integrate with email, contacts, and calendar. At the core of this is a technology we call the Sync Engine. It’s a large Python application (~30k LOC) which handles syncing via IMAP, SMTP, ActiveSync, and other protocols. The code is open source on GitHub with a solid community.