stonegao

diff --git a/Makefile b/Makefile
index 33bfb0a..af625c8 100644
--- a/Makefile
+++ b/Makefile
@@ -29,10 +29,10 @@ OBJS = \
        vm.o\
 
 # Cross-compiling (e.g., on Mac OS X)
-#TOOLPREFIX = i386-jos-elf-
+TOOLPREFIX = /opt/gnu/bin/i386-jos-elf-

stonegao

require 'rubygems'
require 'keen'
require 'json'
require 'date'
require 'active_support/all' #for datetime calculation e.g. weeks.ago.at_beginning_of_week
require 'simple_xlsx' #for outputting excel files
require 'cgi' #for URL encoding


#================================oOo===================================

stonegao

#!/usr/bin/env python2

# Quick and dirty demonstration of CVE-2014-0160 by Jared Stafford (jspenguin@jspenguin.org)
# The author disclaims copyright to this source code.

import sys
import struct
import socket
import time
import select

stonegao

Producer

Setup
bin/kafka-topics.sh --zookeeper esv4-hcl197.grid.linkedin.com:2181 --create --topic test-rep-one --partitions 6 --replication-factor 1
bin/kafka-topics.sh --zookeeper esv4-hcl197.grid.linkedin.com:2181 --create --topic test --partitions 6 --replication-factor 3

Single thread, no replication

bin/kafka-run-class.sh org.apache.kafka.clients.tools.ProducerPerformance test7 50000000 100 -1 acks=1 bootstrap.servers=esv4-hcl198.grid.linkedin.com:9092 buffer.memory=67108864 batch.size=8196

stonegao

1. 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

stonegao

從 SocialCalc 到 EtherCalc

先前在《開源應用程式架構》 一書中，我介紹了 SocialCalc 這個在瀏覽器中運行的試算表編輯器，以取代伺服器為中心的 WikiCalc 架構。SocialCalc 在瀏覽器中執行所有的運算，只有在載入和儲存試算表時才會使用伺服器。

追求效能是 Socialtext 團隊在 2006 年時設計 SocialCalc 的主要目的。重點在於：在 JavaScript 環境下執行客戶端運算，儘管在當年的速度僅有伺服器端 Perl 運算的十分之一，但仍然勝過 AJAX 來回傳輸資料造成的網路延遲：

---

******

stonegao

#!/usr/bin/env bash
#
# File:      kafka-move-leadership.sh
#
# Description
# ===========
#
# Generates a Kafka partition reassignment JSON snippet to STDOUT to move the leadership
# of any replicas away from the provided "source" broker to different, randomly selected
# "target"brokers.  Run this script with `-h` to show detailed usage instructions.

stonegao

require 'rubygems'
require 'sinatra'

get '/' do
  "Hello from Sinatra running on Java!"
end

stonegao

require 'rbconfig'

$stdout.sync = true

def hello(source, expect_input)
  puts "[child] Hello from #{source}"
  if expect_input
    puts "[child] Standard input contains: \"#{$stdin.readline.chomp}\""
  else
    puts "[child] No stdin, or stdin is same as parent's"

stonegao

# Demonstration of a handful of the metaprogramming features of Ruby.
# 
# This code uses the same class/module configuration as Rails' standard
# acts_as plugins. I was looking for a way to extend the behavior of an 
# ActiveRecord model in practice, and this came out of that exploration.
#
# Each "method" of extending the target class is tested and evaluated 
# for functionality and a report is printed. 
#
# Each method is tested as a class method and an instance method,