Karim Essawi karimEssawi

## Time-based-auto-scaling-on-fargate.md

      
              1 file
            
          
              11 forks
            
          
              0 comments
            
          
              22 stars
            
          
                toricls
                / Time-based-auto-scaling-on-fargate.md
            
            
              Created
              August 5, 2019 01:49
            
              
                Example: Time-based Auto Scaling on Amazon ECS + AWS Fargate
              
          
    Set parameters

$ export ECS_CLUSTER_NAME={YOUR_ECS_CLUSTER_NAME}
$ export ECS_SERVICE_NAME={YOUR_ECS_SERVICE_NAME}
RegisterScalableTarget


## register_schema.py
#!/usr/bin/python

import os
import sys

import requests

schema_registry_url = sys.argv[1]
topic = sys.argv[2]
schema_file = sys.argv[3]

## docker_aliases.sh
# These should go in ~/.bashrc or an equivalent area that is sourced into your shell environmemnt

# Remove all docker containers running and exited
alias docker-rma='__drma() { docker ps -aq "$@" | xargs -r docker rm -f; }; __drma'
# Remove all docker images
alias docker-rmia='__drmia() { docker images -q "$@" | xargs -r docker rmi -f; }; __drmia'
# Remove all custom docker networks
alias docker-rmnet='__drmnet() { docker network ls -q -f type=custom "$@" | xargs -r docker network rm; }; __drmnet'
# Remove all unused volumes
alias docker-rmvol='__drmvol() { docker volume ls -q "$@" | xargs -r docker volume rm; }; __drmvol'

## Oh my zsh with autosuggestions & syntax-highlighting.md

      
              1 file
            
          
              229 forks
            
          
              112 comments
            
          
              1037 stars
            
          
                dogrocker
                / Oh my zsh with autosuggestions & syntax-highlighting.md
            
            
              Last active
              May 1, 2024 21:18
            
          
    Oh my zsh.

Install with curl

sh -c "$(curl -fsSL https://raw.githubusercontent.com/robbyrussell/oh-my-zsh/master/tools/install.sh)"

Enabling Plugins (zsh-autosuggestions & zsh-syntax-highlighting)


Download zsh-autosuggestions by


## SudokuSolver.scala
val n = 9
val s = Math.sqrt(n).toInt
type Board = IndexedSeq[IndexedSeq[Int]]

def solve(board: Board, cell: Int = 0): Option[Board] = (cell%n, cell/n) match {
  case (r, `n`) => Some(board)
  case (r, c) if board(r)(c) > 0 => solve(board, cell + 1)
  case (r, c) =>
    def guess(x: Int) = solve(board.updated(r, board(r).updated(c, x)), cell + 1)
    val used = board.indices.flatMap(i => Seq(board(r)(i), board(i)(c), board(s*(r/s) + i/s)(s*(c/s) + i%s)))

## TwitterTopics.r
# Twitter Topic Modeling Using R
# Author: Bryan Goodrich
# Date Created: February 13, 2015
# Last Modified: April 3, 2015
#
# Use twitteR API to query Twitter, parse the search result, and
# perform a series of topic models for identifying potentially
# useful topics from your query content. This has applications for
# social media, research, or general curiosity
#
	#!/usr/bin/python

	import os
	import sys

	import requests

	schema_registry_url = sys.argv[1]
	topic = sys.argv[2]
	schema_file = sys.argv[3]
	# These should go in ~/.bashrc or an equivalent area that is sourced into your shell environmemnt

	# Remove all docker containers running and exited
	alias docker-rma='__drma() { docker ps -aq "$@" \| xargs -r docker rm -f; }; __drma'
	# Remove all docker images
	alias docker-rmia='__drmia() { docker images -q "$@" \| xargs -r docker rmi -f; }; __drmia'
	# Remove all custom docker networks
	alias docker-rmnet='__drmnet() { docker network ls -q -f type=custom "$@" \| xargs -r docker network rm; }; __drmnet'
	# Remove all unused volumes
	alias docker-rmvol='__drmvol() { docker volume ls -q "$@" \| xargs -r docker volume rm; }; __drmvol'
	val n = 9
	val s = Math.sqrt(n).toInt
	type Board = IndexedSeq[IndexedSeq[Int]]

	def solve(board: Board, cell: Int = 0): Option[Board] = (cell%n, cell/n) match {
	case (r, `n`) => Some(board)
	case (r, c) if board(r)(c) > 0 => solve(board, cell + 1)
	case (r, c) =>
	def guess(x: Int) = solve(board.updated(r, board(r).updated(c, x)), cell + 1)
	val used = board.indices.flatMap(i => Seq(board(r)(i), board(i)(c), board(s(r/s) + i/s)(s(c/s) + i%s)))
	# Twitter Topic Modeling Using R
	# Author: Bryan Goodrich
	# Date Created: February 13, 2015
	# Last Modified: April 3, 2015
	#
	# Use twitteR API to query Twitter, parse the search result, and
	# perform a series of topic models for identifying potentially
	# useful topics from your query content. This has applications for
	# social media, research, or general curiosity
	#