Robert Jonczy rjonczy

Render Alertmanager templates locally

Normally to test Alertmanager templates you need to restart running Alertmanager and wait for alerts to arrive to Slack or email. To speed up this process parts of templates could be rendered locally using predefined alerts data without the need of the actual Alertmanager.

What you still need in this case is amtool which is a part of Alertmanager delivery which could be downloaded here https://github.com/prometheus/alertmanager/releases

Some examples of templates rendering:

# navigate to the templates dir

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

Certbot

Generate certs

Manually generate cert (without installing) (prefare: dns challenge)

$ certbot --manual -d my.domain.com --preferred-challenges dns certonly

GCP Service Accounts - management

export SA_NAME="my-sa-name"

Create

$ gcloud iam service-accounts create $SA_NAME

	#!/bin/bash

	IMAGE="gcr.io/google-containers/ubuntu-slim:0.14"
	COMMAND="/bin/bash"
	SUFFIX=$(date +%s \| shasum \| base64 \| fold -w 10 \| head -1 \| tr '[:upper:]' '[:lower:]')

	usage_exit() {
	echo "Usage: $0 [-c command] [-i image] PVC ..." 1>&2
	exit 1
	}

	kind: Pod
	apiVersion: v1
	metadata:
	name: apple-app
	labels:
	app: apple
	spec:
	containers:
	- name: apple-app
	image: hashicorp/http-echo

	kind: Pod
	apiVersion: v1
	metadata:
	name: apple-app
	labels:
	app: apple
	spec:
	containers:
	- name: apple-app
	image: hashicorp/http-echo

	kind: Pod
	apiVersion: v1
	metadata:
	name: apple-app
	labels:
	app: apple
	spec:
	containers:
	- name: apple-app
	image: hashicorp/http-echo

	##FILE SPACING:

	# double space a file
	sed G

	# double space a file which already has blank lines in it. Output file
	# should contain no more than one blank line between lines of text.
	sed '/^$/d;G'

	# triple space a file

	# List unique values in a DataFrame column
	pd.unique(df.column_name.ravel())

	# Convert Series datatype to numeric, getting rid of any non-numeric values
	df['col'] = df['col'].astype(str).convert_objects(convert_numeric=True)

	# Grab DataFrame rows where column has certain values
	valuelist = ['value1', 'value2', 'value3']
	df = df[df.column.isin(valuelist)]