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@bradtraversy
bradtraversy / mongodb_cheat_sheet.md
Last active July 13, 2024 07:53
MongoDB Cheat Sheet

MongoDB Cheat Sheet

Show All Databases

show dbs

Show Current Database

@narkq
narkq / hostsfile.py
Last active February 13, 2019 15:52 — forked from jtyr/hostsfile.py
Ansible dynamic inventory script that reads any Ansible hosts file and transforms it into the JSON data structure.
#!/usr/bin/python2
#####
#
# Description
# -----------
#
# This is an Ansible dynamic inventory script that reads any Ansible hosts file
# and transforms it into the JSON data structure.
#
@michaeltreat
michaeltreat / mongodb_shell_commands.md
Last active July 9, 2024 02:54
Quick Cheat Sheet for Mongo DB Shell commands.

MongoDB Shell Commands Cheat Sheet.

This is a Cheat Sheet for interacting with the Mongo Shell ( mongo on your command line). This is for MongoDB Community Edition.

Preface:

Mongo Manual can help you with getting started using the Shell.

FAQ for MongoDB Fundamentals and other FAQs can be found in the side-bar after visiting that link.

@andreicristianpetcu
andreicristianpetcu / ansible-summary.md
Created May 30, 2016 19:25
This is an ANSIBLE Cheat Sheet from Jon Warbrick

An Ansible summary

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

Configuration file

intro_configuration.html

First one found from of

@shashir
shashir / ArgMinMax.scala
Created March 4, 2016 23:43
argmin and argmax
object ArgMinMax {
import scala.reflect.ClassTag
def argmin[X: ClassTag, Y <% Ordered[Y] : ClassTag](n: Iterable[X], f: X => Y): X = {
val (argmin: X, min: Y) = n.tail.fold((n.head, f(n.head))) { case ((a: X, m: Y), x: X) =>
val fOfx: Y = f(x)
if (fOfx < m) (x, fOfx) else (a, m)
}
return argmin
}
@hofmannsven
hofmannsven / README.md
Last active July 4, 2024 09:14
MySQL CLI Cheatsheet
@debasishg
debasishg / gist:8172796
Last active July 5, 2024 11:53
A collection of links for streaming algorithms and data structures

General Background and Overview

  1. 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.
  2. Models and Issues in Data Stream Systems
  3. 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
  4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
  5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&amp;rep=rep1&amp;t