tsumarios

package main

import (
	"fmt"
	"net"
	"os"
	"os/exec"
)

func main() {

slok

Enable profiling

Default http server

import (
    _ "net/http/pprof"
    "net/http"
)

anson-vandoren

version: '3.7'

services:
  drone-server:
    container_name: drone_server
    image: drone/drone:1
    ports:
      - 8080:80
    volumes:
      - /var/lib/drone:/data

jakubigla

ARG BUILD_IMAGE=maven:3.5-jdk-11
ARG RUNTIME_IMAGE=openjdk:11-jdk-slim

#############################################################################################
###                Stage where Docker is pulling all maven dependencies                   ###
#############################################################################################
FROM ${BUILD_IMAGE} as dependencies

ARG PROXY_SET=false
ARG PROXY_HOST=

nicholasjackson

# Consul connect and Nginx

## Example docker compose
```yaml
  nginx:
    image: nginx:latest
    volumes:
      - ./docker-compose-nginx.conf:/etc/nginx/nginx.conf:ro
    ports:
      - "8181:80"

danihodovic

const http = require('http');
const os = require('os');

let num = 0;

setTimeout(() => {
  console.log(os.hostname(), num);
  process.exit(0);
}, 60000);

merikan

Some Jenkinsfile examples

posener

Be Careful with Table Driven Tests and t.Parallel()

We Gophers, love table-driven-tests, it makes our unittesting structured, and makes it easy to add different test cases with ease.

Let’s create our table driven test, for convenience, I chose to use t.Log as the test function. Notice that we don't have any assertion in this test, it is not needed to for the demonstration.

func TestTLog(t *testing.T) {
	t.Parallel()

pydevops

• 0.1. References
• 0.2. Other cheatsheets
• 0.3. Manage multiple gcloud config configurations
  • 0.3.1. Switch gcloud context with gcloud config
• 0.4. Credentials
• 0.5. info
• 0.6. projects
• 0.7. zones & regions
• 0.8. Organization
• 0.9. billing

fralalonde

Metrics are a contract

An application's metrics names and types make up an implicit contract which consequences of ignoring can be serious.

Humans depend on Metrics

Metric are used by systems admins to configure monitoring systems. Good metric names accurately and consistently convey meaning of the associated metric data. Admins should not have to look at the application code to understand what each metric represents. The name of a metric is often the only documentation available. Critical human decisions may have to be quickly made based on metrics, in these situations, their names should be as helpful and trustable as possible.

Machines depend on Metrics

Dashboards and alerting systems entirely depend on the metrics applications provides them. Changing the identifiers or meaning of the metrics will break these downstream applications, negating the very reason to emit metrics in the first place :

• Alarms that shouldn't have gone off will go off