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docker run -p 2181:2181 -p 9092:9092 --env ADVERTISED_HOST=localhost --env ADVERTISED_PORT=9092 spotify/kafka

docker run -it --rm spotify/kafka /bin/bash
$KAFKA_HOME/bin/kafka-console-producer.sh --broker-list 172.17.0.2:9092 --topic test

docker run -it --rm spotify/kafka /bin/bash
$KAFKA_HOME/bin/kafka-console-consumer.sh --zookeeper 172.17.0.2:2181 --topic test

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// Includes functions for exporting active sheet or all sheets as JSON object (also Python object syntax compatible).
// Tweak the makePrettyJSON_ function to customize what kind of JSON to export.

var FORMAT_ONELINE   = 'One-line';
var FORMAT_MULTILINE = 'Multi-line';
var FORMAT_PRETTY    = 'Pretty';

var LANGUAGE_JS      = 'JavaScript';
var LANGUAGE_PYTHON  = 'Python';

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class MyWorker
  include Sidekiq::Worker
  sidekiq_options unique_for: 10.minutes

  def perform(...)
  end
end

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#!/bin/bash

# Trap sigterm and sleep before passing signal to child for $SIGNAL_TIMEOUT seconds
# this is to address a shutdown issue with traefik: https://docs.traefik.io/user-guide/marathon/#shutdown

cmd=${*}
# default to 10 seconds
SIGNAL_TIMEOUT=${SIGNAL_TIMEOUT:-10}

log() {

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# Add this script in your SublimeText Packages/User directory
import sublime, sublime_plugin

class DefaultSyntaxCommand(sublime_plugin.EventListener):
   def on_new(self, view):
    view.set_syntax_file('Packages/Markdown/Markdown.tmLanguage')

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Studying for a Tech Interview Sucks, so Here's a Cheat Sheet to Help

This list is meant to be a both a quick guide and reference for further research into these topics. It's basically a summary of that comp sci course you never took or forgot about, so there's no way it can cover everything in depth. It also will be available as a gist on Github for everyone to edit and add to.

Data Structure Basics

###Array ####Definition:

• Stores data elements based on an sequential, most commonly 0 based, index.
• Based on tuples from set theory.

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Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

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Backend Architectures

Twitter

ror, scala, jetty, erlang, thrift, mongrel, comet server, my-sql, memchached, varnish, kestrel(mq), starling, gizzard, cassandra, hadoop, vertica, munin, nagios, awstats

• Introduce Gizzard of Twitter - 트위터의 새로운 분산 관리 라이브러리 Gizzard를 소개합니다.
• The Architecture Twitter Uses to Deal with 150M Active Users, 300K QPS, a 22 MB/S Firehose, and Send Tweets in Under 5 Seconds
• Scaling Twitter: Making Twitter 10000 Percent Faster

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version: '2'
services:
  api:
    volumes:
      - "nfsmount:${CONTAINER_DIR}"

volumes:
  nfsmount:
    driver: local
    driver_opts:

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require 'resque/tasks'

namespace :resque do
  def del(key)
    Resque.redis.keys(key).each { |k| Resque.redis.del(k) }
  end

  desc "Resque setup according to installation guide"
  task :setup => :environment