Jonatas Pedraza jonatasnona

Learn Go in ~5mins

This is inspired by A half-hour to learn Rust and Zig in 30 minutes.

Basics

Your first Go program as a classical "Hello World" is pretty simple:

First we create a workspace for our project:

Elasticsearch Cheat-sheet

Links

Cluster
Nodes
Indices
Docs
Search
Tasks
Snapshots

Cluster

Elasticsearch settings for single-node cluster

1 shard, 0 replica.

For future indices

Update default template:

curl -X PUT http://localhost:9200/_template/default -H 'Content-Type: application/json' -d '{"index_patterns": ["*"],"order": -1,"settings": {"number_of_shards": "1","number_of_replicas": "0"}}'

An Ansible summary

Jon Warbrick, July 2014, V3.2 (for Ansible 1.7)

Configuration file

intro_configuration.html

First one found from of

My Elasticsearch cheatsheet with example usage via rest api (still a work-in-progress)

Shortlinks:

Cluster Health
- Index Level
- Shard Level
Nodes Overview
Indices Overview
Cluster Maintenance

AWS Golang SDK examples

Docker - How to cleanup (unused) resources

Once in a while, you may need to cleanup resources (containers, volumes, images, networks) ...

delete volumes

// see: https://github.com/chadoe/docker-cleanup-volumes

$ docker volume rm $(docker volume ls -qf dangling=true)

$ docker volume ls -qf dangling=true | xargs -r docker volume rm

from streams session

end-of-stream - specify a callback to be called when a stream ends (which is surpsingly hard to get right)
duplexify - compose a Duplex stream from a Readable and a Writable stream
pump - pipe streams together and close all of them if one of them closes
pumpify - combine an array of streams into a single duplex stream using pump and duplexify
through2 - tools for making Transform streams
from2 - tools for making Readable streams

from "participatory modules" session

General Background and Overview

Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
Models and Issues in Data Stream Systems
Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
[Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t


	#user nobody;
	#Defines which Linux system user will own and run the Nginx server

	worker_processes 1;
	#Referes to single threaded process. Generally set to be equal to the number of CPUs or cores.

	#error_log logs/error.log; #error_log logs/error.log notice;
	#Specifies the file where server logs.