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.

Docker in Action

With Docker you can easily deploy a web application along with it’s dependencies, environment variables, and configuration settings - everything you need to recreate your environment quickly and efficiently.

We’ll start by creating a Docker container for running a Python Flask application. From there, we’ll look at a nice development workflow to manage the local development of an app as well as continuous integration and delivery, step by step …

Offline Python Deployments With Docker

In cases when a production server does not have access to the Internet or to the internal network, you will need to bundle up the Python dependencies (as wheel files) and interpreter along with the source code.

Objectives

By the end of this post, you will be able to…

1. Describe the difference between a Python wheel and egg
2. Explain why you may want to build Python wheel files within a Docker container
3. Spin up a custom environment for building Python wheels using Docker
4. Bundle and deploy a Python project to an environment without access to the Internet
5. Explain how this deployment setup can be considered immutable

What is Continuous Delivery?

Continuous Delivery is the ability to get changes of all types—including new features, configuration changes, bug fixes and experiments—into production, or into the hands of users, safely and quickly in a sustainable way.

Our goal is to make deployments—whether of a large-scale distributed system, a complex production environment, an embedded system, or an app—predictable, routine affairs that can be performed on demand.

We achieve all this by ensuring our code is always in a deployable state, even in the face of teams of thousands of developers making changes on a daily basis. We thus completely eliminate the integration, testing and hardening phases that traditionally followed “dev complete”, as well as code freezes.

Continuous Integration With Python: An Introduction

What Is Continuous Integration?

Continuous integration (CI) is the practice of frequently building and testing each change done to your code automatically and as early as possible. Prolific developer and author Martin Fowler defines CI as follows:

“Continuous Integration is a software development practice where members of a team integrate their work frequently, usually each person integrates at least daily - leading to multiple integrations per day. Each integration is verified by an automated build (including test) to detect integration errors as quickly as possible.” (Source)

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.

Comandos frecuentes de Docker ;)

Los contenedores son para Maquinas Virtuales como los threads son para los procesos. O puedes pensar en ellos como chroots en esteroides.

Lifecycle

• docker create crea un contenedor pero no lo comienza.
• docker rename permite renombrar al contenedor.
• docker run crea y comienza un contenedor en una operación.
• docker rm borra un contenedor.
• docker update actualiza los recursos limitados de un contenedor.