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import java.util.Arrays;

public class ArrayPrintingExamples {

    public static void main(String[] args) {

        int[][] array2D = new int[][]{
                    {1,2,3},
                    {4,5,6},
                    {7,8,9}

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find . -name target -type d -exec rm -rf {} \;

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public class AdministrationEmployee extends Employee {


    public AdministrationEmployee(String name) {
        super(name, Employee.TEMPORARY, 0, "Administration");
    }

    public double getSalary() {
        return  18000;
    }

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##Reactive System Design Links

#Articles and Papers

• The Reactive Manifesto: http://www.reactivemanifesto.org/
• https://en.wikipedia.org/wiki/Reactive_programming
• https://en.wikipedia.org/wiki/Functional_reactive_programming
• Design Methods for Reactive Systems book slides: http://booksite.elsevier.com/9781558607552/slides/slides.pdf
• Programming without a callstack Event Driven Architectures By G Hohpe: http://www.eaipatterns.com/docs/EDA.pdf
• On Distributed Memory Systems: http://blog.paralleluniverse.co/2012/07/10/on-distributed-memory/
• Disruptor source code, papers and articles: https://lmax-exchange.github.io/disruptor/

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import java.security.MessageDigest;
import java.util.Arrays;

import javax.crypto.Cipher;
import javax.crypto.SecretKey;
import javax.crypto.spec.IvParameterSpec;
import javax.crypto.spec.SecretKeySpec;
import javax.swing.JOptionPane;
import javax.xml.bind.DatatypeConverter;
import javax.crypto.IllegalBlockSizeException;

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  val n = 5
  val ss: Seq[String] = 0 to math.pow(2, n).toInt - 1 map((x: Int) => x.toBinaryString)
  ss.map(String.format(s"%${n}s", _).replace(' ', '0')).foreach(println)

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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

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Latency Comparison Numbers
--------------------------
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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----- Esc -----
Quick change directory: Esc + c
Quick change directory history: Esc + c and then Esc + h
Quick change directory previous entry: Esc + c and then Esc + p
Command line history: Esc + h
Command line previous command: Esc + p
View change: Esc + t (each time you do this shortcut a new directory view will appear)
Print current working directory in command line: Esc + a
Switch between background command line and MC: Ctrl + o
Search/Go to directory in active panel: Esc + s / Ctrl + s then start typing directory name

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Advanced Functional Programming with Scala - Notes

Copyright © 2017 Fantasyland Institute of Learning. All rights reserved.

1. Mastering Functions

A function is a mapping from one set, called a domain, to another set, called the codomain. A function associates every element in the domain with exactly one element in the codomain. In Scala, both domain and codomain are types.

val square : Int => Int = x => x * x