commit 4aef77106258a08986f0c2acecf97e545dd1c59f
Author: J. Fernando Sánchez <balkian@gmail.com>
Date:   Tue Oct 16 15:42:30 2018 +0200

    First commit. Already outdated

diff --git a/Functions.scala b/Functions.scala
new file mode 100644
index 0000000..5180b96
--- /dev/null
+++ b/Functions.scala
@@ -0,0 +1,73 @@
+import java.io._
+import scala.io.Source
+import scala.util.Random
+
+object Functions {
+  def helloworld1: Unit = {
+    println("Hello, functions world!")
+  }
+
+  def splitLine(line: String) = line.split(" ")
+
+  def countOccurrences(target: String)(line: String) = {
+    splitLine(line).map(_ == target).foldLeft(0)((acc, n) => if (n) acc+1 else acc)
+  }
+
+  def time[R](block: => R): R = {
+    time("{}")(block)
+  }
+
+  def time[R](name: String)(block: => R): R = {
+    val formatter = java.text.NumberFormat.getIntegerInstance
+    val t0 = System.nanoTime()
+    val result = block    // call-by-name
+    val t1 = System.nanoTime()
+    println("Elapsed time for " + name +": " + formatter.format((t1 - t0)) + "ns. Result: "+result)
+    result
+  }
+
+  def makeFile(size: Integer, name: String): File = {
+
+    val file = new File(name)
+    if (file.exists && file.length >= size) return file
+    val bw = new BufferedWriter(new FileWriter(file))
+
+    for (
+      i <- Stream.continually(Random.nextInt(100)).takeWhile(x => file.length < size)
+    ) {
+      val r = scala.util.Random
+      bw.write(i.toString())
+      bw.write("\n")
+    }
+    bw.close()
+    return file
+  }
+
+  def makeBigFile() = {
+    val size = 1024*1024*100
+    makeFile(size, "BigFile.txt")
+  }
+
+  def makeSmallFile() = {
+    val size = 1024*1024
+    makeFile(size, "SmallFile.txt")
+  }
+
+  def applyToFile[R](file: File)(block: String=>R)(parallel: Boolean): Iterator[R] = {
+    if (parallel) {
+      Source.fromFile(file).getLines().grouped(1000).flatMap({ y=>
+        val z = if (parallel) y.toIndexedSeq.par else y;
+        z.map(x=>block(x))})
+    } else {
+      Source.fromFile(file).getLines().map(x=>block(x))
+    }
+  }
+
+  def mapRedFile[R](file: File)(mapper: String=>R)(reducer: (R,R)=>R)(parallel: Boolean): R = {
+    if (parallel)
+      (applyToFile(file)(mapper)(parallel)).grouped(1000).map(x=>x.toIndexedSeq.par.reduce(reducer)).reduce(reducer)
+    else
+      (applyToFile(file)(mapper)(parallel)).reduce(reducer)
+  }
+
+}
diff --git a/Makefile b/Makefile
new file mode 100644
index 0000000..71f4f10
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,34 @@
+SOURCES = $(shell ls *.scala)
+S = scala
+SC = scalac
+TARGET = target
+CP = $(TARGET):scalatest.jar
+SPEC = scala.RomanSpec
+TEMP_DIR ?= _LATEX
+
+all: pdf
+
+compile: $(SOURCES)
+	@echo "Compiling $(SOURCES)..."
+	@$(SC) -cp . -d $(TARGET) $(SOURCES)
+
+Parallel.class: $(SOURCES)
+
+run: Parallel.class
+	@scala -cp $(TARGET) Parallel
+
+test: compile
+	@$(S) -cp $(CP) org.scalatest.tools.Runner -p . -o -s $(SPEC)
+
+pdf:
+	@mkdir -p $(TEMP_DIR)
+	xelatex -output-directory ./$(TEMP_DIR) spark1.tex
+	xelatex -output-directory ./$(TEMP_DIR) spark2.tex
+	cp $(TEMP_DIR)/*.pdf .
+
+clean:
+	@$(RM) -rf $(TEMP_DIR)
+	@$(RM) $(TARGET)/*.class
+
+clean-all: clean
+	@rm spark*.pdf
diff --git a/Parallel.scala b/Parallel.scala
new file mode 100644
index 0000000..c9e6e15
--- /dev/null
+++ b/Parallel.scala
@@ -0,0 +1,30 @@
+import Functions._
+
+object Parallel {
+  def main(args: Array[String]): Unit = {
+    time("helloworld"){ helloworld1}
+    val countScala = countOccurrences("scala")(_)
+    time("count"){ countScala("scala is the best language for scalability, says the creator of scala :)")}
+    val bigFile = time("makeFile"){ makeBigFile }
+    val smallFile = time("makeSmall"){ makeSmallFile }
+
+    val example1 = (file: java.io.File, parallel: Boolean) => mapRedFile(file)(x=> (1, Math.sqrt(Math.pow(1.1, x.toInt))))((x1,x2)=>(x1._1+x2._1, x1._2+x2._2))(parallel)
+
+    time("applyToFile Small\tConcurrent"){
+      val (total, sum) = example1(smallFile, false)
+      println("Average: " + sum/total)
+    }
+    time("applyToFile Small\tParallel"){
+      val (total, sum) = example1(smallFile, true)
+      println("Average: " + sum/total)
+    }
+    time("applyToFile Big\tConcurrent"){
+      val (total, sum) = example1(bigFile, false)
+      println("Average: " + sum/total)
+    }
+    time("applyToFile Big\tParallel"){
+      val (total, sum) = example1(bigFile, true)
+      println("Average: " + sum/total)
+    }
+  }
+}
diff --git a/SmallFile.txt b/SmallFile.txt
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diff --git a/lstlinebgcolor.sty b/lstlinebgcolor.sty
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index 0000000..cb03d75
--- /dev/null
+++ b/lstlinebgcolor.sty
@@ -0,0 +1,78 @@
+%% WORKAROUND lstlinebgrd: https://tex.stackexchange.com/questions/451532/recent-issues-with-lstlinebgrd-package-with-listings-after-the-latters-updates
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% \btIfInRange{number}{range list}{TRUE}{FALSE}
+%
+% Test if int number <number> is element of a (comma separated) list of ranges
+% (such as: {1,3-5,7,10-12,14}) and processes <TRUE> or <FALSE> respectively
+
+\newcount\bt@rangea
+\newcount\bt@rangeb
+
+\newcommand\btIfInRange[2]{%
+    \global\let\bt@inrange\@secondoftwo%
+    \edef\bt@rangelist{#2}%
+    \foreach \range in \bt@rangelist {%
+        \afterassignment\bt@getrangeb%
+        \bt@rangea=0\range\relax%
+        \pgfmathtruncatemacro\result{ ( #1 >= \bt@rangea) && (#1 <= \bt@rangeb) }%
+        \ifnum\result=1\relax%
+            \breakforeach%
+            \global\let\bt@inrange\@firstoftwo%
+        \fi%
+    }%
+    \bt@inrange%
+}
+\newcommand\bt@getrangeb{%
+    \@ifnextchar\relax%
+        {\bt@rangeb=\bt@rangea}%
+        {\@getrangeb}%
+}
+\def\@getrangeb-#1\relax{%
+    \ifx\relax#1\relax%
+        \bt@rangeb=100000%   \maxdimen is too large for pgfmath
+    \else%
+        \bt@rangeb=#1\relax%
+    \fi%
+}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% \btLstHL<overlay spec>{range list}
+%
+\newcommand<>{\btLstHL}[1]{%
+  % \only#2{\btIfInRange{\value{lstnumber}}{#1}{\color{blue!30}}{}}%
+\only#2{\btIfInRange{\value{lstnumber}}{#1}{\color{orange!30}\def\lst@linebgrdcmd{\color@block}}{\def\lst@linebgrdcmd####1####2####3{}}}%
+}%
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% \btInputEmph<overlay spec>[listing options]{range list}{file name}
+%
+\newcommand<>{\btLstInputEmph}[3][\empty]{%                                                    
+  \only#4{%
+    \lstset{linebackgroundcolor=\btLstHL{#2}}%
+    \lstinputlisting{#3}%
+  }% \only
+}
+
+  \makeatletter
+  \let\old@lstKV@SwitchCases\lstKV@SwitchCases
+  \def\lstKV@SwitchCases#1#2#3{}
+  \makeatother
+  \usepackage{lstlinebgrd}
+  \makeatletter
+  \let\lstKV@SwitchCases\old@lstKV@SwitchCases
+
+  \lst@Key{numbers}{none}{%
+      \def\lst@PlaceNumber{\lst@linebgrd}%
+      \lstKV@SwitchCases{#1}%
+      {none:\\%
+      left:\def\lst@PlaceNumber{\llap{\normalfont
+                  \lst@numberstyle{\thelstnumber}\kern\lst@numbersep}\lst@linebgrd}\\%
+      right:\def\lst@PlaceNumber{\rlap{\normalfont
+                  \kern\linewidth \kern\lst@numbersep
+                  \lst@numberstyle{\thelstnumber}}\lst@linebgrd}%
+      }{\PackageError{Listings}{Numbers #1 unknown}\@ehc}}
\ No newline at end of file
diff --git a/spark1.tex b/spark1.tex
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--- /dev/null
+++ b/spark1.tex
@@ -0,0 +1,1165 @@
+\documentclass{beamer}
+\usepackage[utf8]{inputenc}
+\usepackage{media9}
+\usepackage{lipsum}
+\usepackage{amsmath}
+\usepackage{amsfonts}
+\usepackage{amssymb}
+\usetheme{metropolis}
+\usepackage{tcolorbox}
+\usepackage{listings} 
+\usepackage{lstlinebgcolor}
+\usepackage{MnSymbol}
+\usepackage{wasysym}
+\usepackage{animate}
+
+\lstset{% 
+  basicstyle=\ttfamily\large,
+  columns=fullflexible,
+  escapeinside={(*@}{@*)},
+  numbers=none,
+  breaklines=true,
+  numbersep=5pt,                   % how far the line-numbers are from the code
+  numberstyle=\tiny\color{gray}, % the style that is used for the line-numbers
+  numbersep=5pt,                   % how far the line-numbers are from the code
+  % postbreak={\hbox{\raisebox{0ex}[0ex][0ex]\color{red}{\hookrightarrow}\space}}
+  postbreak=\raisebox{0ex}[0ex][0ex]{\ensuremath{\rcurvearrowse\space}},
+  keywordstyle=\color{blue},       % keyword style
+  stringstyle=\color{red},     % string literal style
+  language=Scala,
+  % belowskip=0pt,
+  % aboveskip=0pt,
+  }
+
+\definecolor{lightyellow}{RGB}{255,255,204}
+
+\title{Spark}
+\subtitle{Cluster computing}
+\author{J. Fernando Sánchez, Joaquín Salvachúa, Gabriel Huecas }
+\institute{Universidad Politécnica de Madrid}
+\date{2016}
+
+
+
+\newcommand{\btVFill}{\vskip0pt plus 1filll}
+
+
+\begin{document}
+
+\begin{frame}
+\titlepage{}
+\end{frame}
+\begin{frame}[allowframebreaks]
+\frametitle{Outline}
+\tableofcontents
+\end{frame}
+
+\section{Background}
+
+\begin{frame}
+  \frametitle{LISP and functional programming}
+  \begin{columns}
+\column{0.5\textwidth}
+  \begin{itemize}
+  \item Higher level programming
+  \item Avoid side effects
+  \item Pattern matching
+  \end{itemize}
+\column{0.5\textwidth}
+  \includegraphics[width=\textwidth]{images/lisplogo.png}
+\end{columns}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Scala}
+  \begin{columns}
+\column{0.5\textwidth}
+  \includegraphics[width=\textwidth]{images/scalalogo.png}
+\column{0.5\textwidth}
+  \begin{itemize}
+    \item A \textit{better} Java
+    \item Functional programming (optional)
+    \item Actors for (coarse) concurrency
+  \end{itemize}
+\end{columns}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Docker}
+  \begin{columns}
+\column{0.5\textwidth}
+  \begin{itemize}
+    \item Easy and repeatable deployment
+    \item Lots of pre-built images @ hub.docker.com
+    \item Building block for other tools (swarm, compose, machine...)
+  \end{itemize}
+\column{0.5\textwidth}
+  \includegraphics[width=\textwidth]{images/docker_logo.png}
+\end{columns}
+\end{frame}
+
+{
+  \pagecolor{black}
+  \usebackgroundtemplate{\vbox to \paperheight{\vfil\hbox to \paperwidth{\hfil\includegraphics[height=\paperheight]{images/knowscala.jpg}\hfil}\vfil}}%
+
+\begin{frame}[plain]
+\end{frame}
+}
+{
+  \pagecolor{black}
+  \usebackgroundtemplate{\vbox to \paperheight{\vfil\hbox to \paperwidth{\hfil\includegraphics[height=\paperheight]{images/knowbigdata.jpg}\hfil}\vfil}}%
+
+\begin{frame}[plain]
+\end{frame}
+}
+% \begin{frame}[plain]
+%   \center
+%   \huge{Show me}
+% \end{frame}
+{
+  \pagecolor{white}
+  \usebackgroundtemplate{\vbox to \paperheight{\vfil\hbox to \paperwidth{\hfil\includegraphics[width=\paperwidth]{images/showme.png}\hfil}\vfil}}%
+\begin{frame}[plain]
+
+\end{frame}
+}
+
+\begin{frame}[fragile]
+\frametitle{Word count in Wikipedia}
+
+Problem: find the frequency each word is used in Wikipedia.
+
+We have the text of all wikipedia in a text file\footnote{By happy coincidence, the first two lines are ``hi world'' and ``hi''}. It begins like this:
+
+\begin{lstlisting}[backgroundcolor=\color{lightyellow},language={},postbreak={}, 
+  breakautoindent=false, breakindent=0pt, breaklines]
+hi world
+hi
+Scala (SKAH-lah)[9] is a general-purpose programming language. Scala has full support for functional programming and a strong static type system. Designed to be concise,[10] many of Scala's design decisions were inspired by criticism of Java's shortcomings.[8] 
+\end{lstlisting}
+
+\end{frame}
+
+\begin{frame}[fragile]
+\frametitle{Algorithm}
+
+\begin{itemize}
+  \item Read every line
+  \item Chunk every line into words
+  \item Count every occurrence
+  \item For every word, sum its occurrences
+\end{itemize}
+\end{frame}
+
+\begin{frame}[fragile]
+\frametitle{Possible results of every step}
+\begin{lstlisting}[language={},numbers=none,linebackgroundcolor={
+      \btLstHL<1>{1}%
+      \btLstHL<2>{2}%
+      \btLstHL<3>{3,4}%
+      \btLstHL<4>{5}%
+    }]
+(("hi world"), ("hi") ...)
+List((hi, 1), (hi, 1), (world, 1) ...)
+Map(hi ->(("hi", 1), (hi, 1)),
+     world -> ((world, 1)) ...)
+Map(hi-> 2, world -> 1 ...)
+\end{lstlisting}
+\end{frame}
+
+\begin{frame}[fragile]
+\frametitle{Running the scala shell}
+
+We will use docker.
+
+\begin{lstlisting}[language=bash,numbers=none]
+docker run -it -v $PWD:Wikipedia.txt:Wiki \
+            --rm williamyeh/scala
+\end{lstlisting}
+\end{frame}
+
+\begin{frame}[fragile]
+  \frametitle{Scala code}
+
+%[linebackgroundcolor={
+      % \btLstHL<1>{1} %
+      % \btLstHL<2>{3} %
+      % \btLstHL<3>{5} %
+      % \btLstHL<4>{7} %
+      % \btLstHL<5>{9} %
+      % }]
+\begin{lstlisting}[language=Scala,linebackgroundcolor={
+      \btLstHL<1>{2}%
+      \btLstHL<2>{3}%
+      \btLstHL<3>{4}%
+      \btLstHL<4>{5}%
+      \btLstHL<5>{6,7}%
+    }]
+import scala.io.Source
+val wiki = Source.fromFile("Wiki").getLines
+wiki.flatMap(line=> line.split(" "))
+      map(x=>(x, 1)).toList
+      groupBy(x => x._1)
+      map({case (k, v) =>
+            (k, v.foldLeft(0)((a, b) => a+b._2)))
+  \end{lstlisting}
+
+\end{frame}
+
+\begin{frame}
+
+  \center
+  {\huge Let's run it in the shell.}
+
+\end{frame}
+
+{
+  \pagecolor{black}
+  \usebackgroundtemplate{\vbox to \paperheight{\vfil\hbox to \paperwidth{\hfil\includegraphics[height=\paperheight]{images/justwaithere.jpg}\hfil}\vfil}}%
+
+\begin{frame}[plain]
+\end{frame}
+}
+
+\begin{frame}
+  \frametitle{Wikipedia is big}
+  \includegraphics[width=\textwidth]{images/outofmemory.jpg}
+\end{frame}
+
+\begin{frame}
+
+  \center
+  {\huge What happened?}
+
+\end{frame}
+
+\begin{frame}
+  \frametitle{Limited resources}
+  \begin{itemize}
+  \item CPU limits our speed
+    \begin{itemize}
+    \item Multi-cores help...
+    \item ...but real parallelism is \textbf{hard}
+    \end{itemize}
+  \item RAM limits how much data you can process at the same time
+    \begin{itemize}
+    \item What if you need more than 128GB?
+    \item You could use more than one computer...
+    \item ... but cluster computing is even harder than ``local'' parallelism
+    \end{itemize}
+  \item Functional programming helps a bit
+  \end{itemize}
+\end{frame}
+\begin{frame}[plain]
+  \center
+  \huge But... this was supposed to be fun, wasn't it?
+\end{frame}
+
+\section{Introduction to Spark}
+
+\subsection{What is Spark?}
+{
+  \pagecolor{white}
+  \usebackgroundtemplate{\vbox to \paperheight{\vfil\hbox to \paperwidth{\hfil\includegraphics[width=\paperwidth]{images/sparkweb.png}\hfil}\vfil}}%
+\begin{frame}[plain]
+
+\end{frame}
+}
+\begin{frame}
+\frametitle{Quick definition}
+{\center {\huge Apache Spark™ is a fast and general engine for large-scale data processing.\footnote{\url{http://spark.apache.org}}}}
+
+On top of that:
+ \begin{itemize}
+   \item Open source (Top-level Apache project)
+   \item Plays well with other tools
+   \end{itemize}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Architecture}
+  \includegraphics[width=\textwidth]{images/cluster-overview.png}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Programs}
+  \center
+  \includegraphics[height=.8\textheight]{images/sparkapp.png}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Ecosystem}
+  \includegraphics[width=\textwidth]{images/sparkecosystem.png}
+\end{frame}
+
+\subsection{vs MapReduce}
+
+{
+  \pagecolor{white}
+  \usebackgroundtemplate{\vbox to \paperheight{\vfil\hbox to \paperwidth{\hfil\includegraphics[width=\paperwidth]{images/hadoop-spark.png}\hfil}\vfil}}%
+
+\begin{frame}[plain]
+% \center
+% \begin{tikzpicture}[remember picture,overlay]
+% \node[at=(current page.center)] {
+%   \includegraphics[height=\paperheight]{images/justwaithere.jpg}
+% };
+% \end{tikzpicture}  
+\end{frame}
+}
+
+\begin{frame}
+\frametitle{Comparison to MapReduce}
+\begin{itemize}
+  \item In-memory data
+    \begin{itemize}
+      \item Less i/o overhead
+      \item Faster operations
+      \item Caching
+    \end{itemize}
+  \item Better for recursive tasks (e.g. machine learning)
+  \item Some libraries are dropping MapReduce support
+\end{itemize}
+\end{frame}
+
+  \begin{frame}
+    \frametitle{Contributors to Spark/Hadoop 2014}
+    \includegraphics[width=\textwidth]{images/hadoopsparkcontribs.png}
+  \end{frame}
+
+\begin{frame}
+\frametitle{Project status}
+\center 
+
+\includegraphics[width=\textwidth]{images/sparkgh.png}
+
+\includegraphics[height=.35\textheight]{images/contributors.png}
+\end{frame}
+
+\subsection{Key concepts}
+
+\begin{frame}
+\frametitle{Overview}
+  % {\huge Two core concepts}
+  % \vfill
+\begin{columns}[t]
+    \column{0.5\textwidth}
+    Data (RDDs/Datasets)
+    
+    \begin{itemize}
+    \item RDD: Resilient Distributed Dataset
+    \item Collections of objects spread across a cluster, stored in RAM or on Disk
+    \item Built through parallel transformations
+    \item Automatically rebuilt on failure
+    \end{itemize}
+    \column{0.5\textwidth}
+    Operations
+    \begin{itemize}
+    \item Transformations (e.g. group, map, groupBy)
+    \item Actions (e.g. count, collect, save)
+    \end{itemize}
+\end{columns}
+\end{frame}
+
+\begin{frame}
+\frametitle{Transformations and actions}
+\includegraphics[width=\textwidth]{images/sparktransformations.png}
+\end{frame}
+
+
+\begin{frame}
+  \frametitle{RDDs vs Datasets}
+    Datasets are the future
+    \begin{itemize}
+      \item More memory efficient
+      \item Libraries dropping support for RDDs
+    \end{itemize}
+
+\end{frame}
+\begin{frame}
+  \frametitle{RDDs vs Datasets}
+
+    \includegraphics[width=\textwidth]{images/performance-wordcount-databricks.png}
+
+    \includegraphics[width=\textwidth]{images/Memory-Usage-when-Caching-Chart-databricks.png}
+  \end{frame}
+
+\begin{frame}[fragile]
+  \frametitle{Language support}
+    \begin{lstlisting}[title=Scala]
+val lines = sc.textFile(...)
+lines.filter(x => x.contains("ERROR")).count()
+\end{lstlisting}
+    \begin{lstlisting}[title=Python,language=Python]
+lines = sc.textFile(...)
+lines.filter(lambda s: "ERROR" in s).count()
+\end{lstlisting}
+    \begin{lstlisting}[title=Java,language=Java]
+      Removed, to keep the slides clean :)
+\end{lstlisting}
+
+\center
+sc is the Spark Context (more or this later)
+\end{frame}
+
+\begin{frame}
+  \frametitle{Different flavors}
+  \begin{center}
+    \begin{tabular}{ | l | c | c | c |}
+      \hline
+      Language & App & REPL & Performance \\ \hline
+      Scala & Yes & Yes & \huge{\color{green}\smiley} \\
+      Java & Yes & No & \huge{\color{green}\smiley} \\
+      Python & Yes & Yes & \huge{\color{yellow}\smiley}\\ \hline
+    \end{tabular}
+  \end{center}
+The Read-eval-print-loop (REPL) is the easiest way to get started and explore datasets. It is just a special Spark application that accepts user input (scala code).
+  
+\end{frame}
+
+\section{Working with RDDs}
+
+\begin{frame}[fragile]
+\frametitle{Creation}
+
+\begin{lstlisting}[title=From normal data structures,numbers=none]
+val nums = sc.parallelize(List(1, 2, 3))
+val cont = sc.parallelize(List(("a", 1),
+                                   ("a", 1),
+                                   ("b", 3)))
+\end{lstlisting}
+
+\begin{lstlisting}[title=From distributed/local sources,numbers=none]
+sc.textFile('myfile')
+\end{lstlisting}
+
+\vfill
+\vfill
+Note: sc is the spark context in the Spark interpreter
+
+\end{frame}
+
+%  #################################
+%  Starts Collect
+%  #################################
+
+\begin{frame}[fragile]
+\frametitle{Operations: collect}
+\only<1>{
+  \btVFill
+{\huge\ttfamily collect()}
+}
+
+\begin{onlyenv}<2->
+
+\btVFill
+
+\begin{lstlisting}[numbers=none,basicstyle=\Large\ttfamily,title={Runs any pending transformation and returns the real values},linebackgroundcolor={\btLstHL{1,3}}]
+nums.collect()
+> List(1, 2, 3) 
+cont.collect()
+> List((a, 1), (a, 1), (b, 3))
+\end{lstlisting}
+
+\end{onlyenv}
+
+\btVFill
+
+Reminder:
+\begin{lstlisting}[numbers=none,linebackgroundcolor={\color{lightyellow}}]
+nums: List(1, 2, 3)
+cont: List(("a", 1), ("a", 1), ("b", 3))
+\end{lstlisting}
+
+\end{frame}
+
+%  #################################
+%  Ends Collect
+%  #################################
+
+%  #################################
+%  Starts Take
+%  #################################
+
+\begin{frame}[fragile]
+\frametitle{Operations: take}
+\only<1>{
+  \btVFill
+{\huge\ttfamily take(N)}
+}
+
+\begin{onlyenv}<2>
+
+\btVFill
+
+\begin{lstlisting}[title=Returns the N first elements,basicstyle=\Large\ttfamily,linebackgroundcolor={\btLstHL<2>{1,3}}]
+nums.take(2) 
+>  List(1, 2)
+cont.take(1) 
+>  List((a, 1))
+\end{lstlisting}
+
+\end{onlyenv}
+
+\btVFill
+
+Reminder:
+\begin{lstlisting}[numbers=none,linebackgroundcolor={\color{lightyellow}}]
+nums: List(1, 2, 3)
+cont: List(("a", 1), ("a", 1), ("b", 3))
+\end{lstlisting}
+
+\end{frame}
+
+%  #################################
+%  Ends Take
+%  #################################
+
+%  #################################
+%  Starts Count
+%  #################################
+
+\begin{frame}[fragile]
+\frametitle{Operations: count}
+\only<1>{
+  \btVFill
+{\huge\ttfamily count()}
+}
+
+\begin{onlyenv}<2>
+
+\btVFill
+
+\begin{lstlisting}[title=Returns the number of elements in a collection,basicstyle=\Large\ttfamily,linebackgroundcolor={\btLstHL<2>{1,3}}]
+nums.count() 
+>  3
+cont.count() 
+>  3
+\end{lstlisting}
+
+\end{onlyenv}
+
+\btVFill
+
+Reminder:
+\begin{lstlisting}[numbers=none,linebackgroundcolor={\color{lightyellow}}]
+nums: List(1, 2, 3)
+cont: List(("a", 1), ("a", 1), ("b", 3))
+\end{lstlisting}
+
+\end{frame}
+
+%  #################################
+%  Ends Count
+%  #################################
+%  #################################
+%  Starts filter
+%  #################################
+
+\begin{frame}[fragile]
+\frametitle{Operations: filter}
+  \btVFill
+\only<1,2>{
+{\huge\ttfamily filter(fn)}
+  \btVFill
+}
+
+\begin{onlyenv}<2>
+This time, we need to define a function.
+
+Filter applies that function to every element, and returns those where the function returns true.
+
+For example:
+
+\end{onlyenv}
+\begin{onlyenv}<2,3>
+\begin{lstlisting}[basicstyle=\ttfamily\Large]
+val fn = (x:Int)) => x > 1
+\end{lstlisting}
+\end{onlyenv}
+\begin{onlyenv}<3>
+
+\btVFill
+
+\begin{lstlisting}[title=Return a list containing the values where the function returns true,linebackgroundcolor={\btLstHL<3>{1}},basicstyle=\ttfamily\Large]
+nums.filter(fn) 
+>  List(2, 3)
+\end{lstlisting}
+\end{onlyenv}
+
+\btVFill
+
+Reminder:
+\begin{lstlisting}[numbers=none,linebackgroundcolor={\color{lightyellow}}]
+nums: List(1, 2, 3)
+cont: List(("a", 1), ("a", 1), ("b", 3))
+\end{lstlisting}
+
+\end{frame}
+
+%  #################################
+%  Ends filter
+%  #################################
+
+\begin{frame}[fragile]
+  \frametitle{Quick aside: anonymous functions and underscores}
+
+% Defining a function when it is only going to be used once is tedious and makes reading code harder.
+
+\only<1,2>{
+{\Large In scala, we can define ``anonymous functions'', also known as lambda functions.}
+}
+
+\pause
+
+\begin{lstlisting}[basicstyle=\ttfamily\Large]
+val fn = (x:Int)) => x > 1
+cont.filter(fn)
+\end{lstlisting}
+
+\only<2>{
+{\Large is equivalent to:}
+}
+
+\begin{lstlisting}[basicstyle=\ttfamily\Large]
+cont.filter((x:Int) => x>1)
+\end{lstlisting}
+
+\pause
+
+\only<3>{
+{\Large Additionally, the scala compiler is smart enough to infer types in this example. Hence, we could simply write:}
+}
+
+\begin{lstlisting}[basicstyle=\ttfamily\Large]
+cont.filter(x => x>1)
+\end{lstlisting}
+
+\pause
+
+{\Large Furthermore, we could use underscores to replace arguments:}
+
+\begin{lstlisting}[basicstyle=\ttfamily\huge]
+cont.filter(_>1)
+\end{lstlisting}
+
+\end{frame}
+
+\begin{frame}[fragile]
+  \frametitle{Quick aside: anonymous functions and underscores}
+  
+{\large Every new argument in the lambda function represents a parameter
+  
+Hence, these two expresions are equivalent
+}
+  
+\begin{lstlisting}[basicstyle=\ttfamily\huge]
+_ + _
+(x,y) => x+y
+\end{lstlisting}
+
+
+\end{frame}
+
+\begin{frame}[fragile]
+  \frametitle{Operations: filter}
+  
+\Large Our last example could be written more concisely as:
+
+\vfill
+
+\begin{lstlisting}[linebackgroundcolor={\btLstHL<1>{1}},basicstyle=\ttfamily\Large]
+nums.filter(_>1) 
+>  List(2, 3)
+\end{lstlisting}
+
+\end{frame}
+
+\begin{frame}[fragile]
+  \frametitle{Operations: filter}
+  
+\Large What would this filter do?
+
+\vfill
+
+\begin{lstlisting}[basicstyle=\ttfamily\Large,linebackgroundcolor={\btLstHL<1>{1}}]
+nums.filter(_._1 == "a" && _._1 == 1) 
+>  ???
+\end{lstlisting}
+
+\end{frame}
+
+\begin{frame}[fragile]
+  \frametitle{Operations: filter}
+  
+
+\begin{lstlisting}[basicstyle=\ttfamily\normalsize,language={}]
+<console>:9: error: missing parameter type
+for expanded function ((x$1, x$2) =>
+x$1._1.$eq$eq(a).$amp$amp(x$2._1.$eq$eq(1)))
+Note: The expected type requires a
+one-argument function accepting a 2-Tuple.
+\end{lstlisting}
+\end{frame}
+
+{
+  \pagecolor{black}
+  \usebackgroundtemplate{\vbox to \paperheight{\vfil\hbox to \paperwidth{\hfil\includegraphics[height=\paperheight]{images/whatsparrow.jpg}\hfil}\vfil}}%
+
+\begin{frame}[plain]
+\end{frame}
+}
+
+\begin{frame}[fragile]
+  \frametitle{Operations: filter}
+  
+Remember, each new underscore represents a new argument. So that expression expands to:
+
+\begin{lstlisting}[language=TeX,basicstyle=\ttfamily\Large]
+nums.filter((x, y) => x._1 == "a" &&
+                   y._2 == 1) 
+\end{lstlisting}
+\end{frame}
+
+\begin{frame}[fragile]
+  \frametitle{Operations: filter}
+
+The right expression is:
+
+\begin{lstlisting}[basicstyle=\ttfamily\Large,linebackgroundcolor={\btLstHL<1>{1,2}}]
+nums.filter(x => x._1 == "a" &&
+                   x._2 == 1) 
+> List((a, 1), (a, 1))
+\end{lstlisting}
+\end{frame}
+
+
+%  #################################
+%  Starts map
+%  #################################
+
+\begin{frame}[fragile]
+\frametitle{Operations: map}
+  \btVFill
+\only<1>{
+{\huge\ttfamily map(fn)}
+  \btVFill
+}
+
+\begin{onlyenv}<2>
+\begin{lstlisting}[title=Apply a function to every item in the list,basicstyle=\Large\ttfamily,linebackgroundcolor={\btLstHL<2>{1,3}},basicstyle=\ttfamily\Large]
+cont.map(x._2) 
+>  List(1, 1, 3)
+nums.map(_*3) 
+>  List(3, 6, 9)
+\end{lstlisting}
+\end{onlyenv}
+
+Reminder:
+\begin{lstlisting}[numbers=none,linebackgroundcolor={\color{lightyellow}}]
+nums: List(1, 2, 3)
+cont: List(("a", 1), ("a", 1), ("b", 3))
+\end{lstlisting}
+
+\end{frame}
+
+
+%  #################################
+%  Ends map
+%  #################################
+%  #################################
+%  Starts reduce
+%  #################################
+
+\begin{frame}[fragile]
+\frametitle{Operations: reduce}
+  \btVFill
+\only<1>{
+{\huge\ttfamily reduce(fn)}
+  \btVFill
+}
+
+\begin{onlyenv}<2>
+
+\btVFill
+
+\begin{lstlisting}[title={Merge elements with an associative function (concisely)},basicstyle=\Large\ttfamily,linebackgroundcolor={\btLstHL<2>{1,3,4}}]
+nums.reduce(_+_) 
+>  6
+cont.reduce((x, y) => (x._1+y._1,
+                          x._2*y._2) 
+>  (aab, 3)
+\end{lstlisting}
+\end{onlyenv}
+
+\btVFill
+
+Reminder:
+\begin{lstlisting}[numbers=none,linebackgroundcolor={\color{lightyellow}}]
+nums: List(1, 2, 3)
+cont: List(("a", 1), ("a", 1), ("b", 3))
+\end{lstlisting}
+
+\end{frame}
+
+%  #################################
+%  Ends reduce
+%  #################################
+
+\begin{frame}[fragile]
+\frametitle{Operations}
+\begin{onlyenv}<1>
+  \btVFill
+{\huge\ttfamily groupByKey()}
+  \btVFill
+\end{onlyenv}
+
+\btVFill
+\begin{onlyenv}<2>
+  \begin{lstlisting}[title={Group elements of a list by the first item in the tuple}, numbers=none,basicstyle=\Large\ttfamily,linebackgroundcolor={\btLstHL<2>{1}}]
+cont.groupByKey()
+>  [(b, [3]), (a, [1,1])]
+    \end{lstlisting}
+\end{onlyenv}
+
+\btVFill
+
+Reminder:
+\begin{lstlisting}[numbers=none,linebackgroundcolor={\color{lightyellow}}]
+nums: List(1, 2, 3)
+cont: List(("a", 1), ("a", 1), ("b", 3))
+\end{lstlisting}
+
+\end{frame}
+\begin{frame}[fragile]
+\frametitle{Operations}
+\btVFill
+\begin{onlyenv}<1>
+  \btVFill
+{\huge \ttfamily reduceByKey(fn)}
+  \btVFill
+\btVFill
+\end{onlyenv}
+\begin{onlyenv}<2>
+  \begin{lstlisting}[title={Group by key and reduce each value},basicstyle=\huge\ttfamily,basicstyle=\Large\ttfamily,linebackgroundcolor={\btLstHL<2>{1}}]
+cont.reduceByKey((x,y)=>x+y) 
+>  [(b,3), (a,2)]
+\end{lstlisting}
+  
+reduceByKey is more efficient than applying group, map and reduce separately. The reduce function can be given to each worker, which avoids passing unnecessary data.
+\footnote{\href{https://databricks.gitbooks.io/databricks-spark-knowledge-base/content/best_practices/prefer_reducebykey_over_groupbykey.html}{Databricks' post on avoiding groupByKey }}
+\end{onlyenv}
+
+\btVFill
+
+Reminder:
+\begin{lstlisting}[numbers=none,linebackgroundcolor={\color{lightyellow}}]
+nums: List(1, 2, 3)
+cont: List(("a", 1), ("a", 1), ("b", 3))
+\end{lstlisting}
+
+\end{frame}
+\begin{frame}[fragile]
+\frametitle{Operations}
+
+And once you are done, save your results to a file.
+
+  \begin{lstlisting}[basicstyle=\Large\ttfamily]
+nums.saveAsTextFile("hdfs://file.txt")
+  \end{lstlisting}
+
+\end{frame}
+
+\begin{frame}[fragile]
+\frametitle{Example: Search in logs}
+\begin{lstlisting}
+val lines = sc.textFile("hdfs://...")
+val errors = lines.filter(s =>
+                        s.startswith("ERROR"))
+val messages = errors.map(s => s.split("\t")._2)
+messages.cache()
+messages.filter(s => s.contains("mysq")).count()
+messages.filter(s => s.contains("php").count()
+\end{lstlisting}
+\end{frame}
+
+% \begin{frame}
+% \frametitle{Dependencies}
+% \includegraphics[width=\textwidth]{images/RDDdependencies.png}
+% \end{frame}
+
+% \begin{frame}[fragile]
+% \frametitle{Working with Key-value pairs}
+% \begin{lstlisting}{title=Key-value pairs in different languages}
+% pair = (a, b)
+%     pair[0] # => a 
+%     pair[1] # => b
+% val pair = (a, b)
+%     pair._1 // => a
+%     pair._2 // => b
+% Tuple2 pair = new Tuple2(a, b); 
+%     pair._1 // => a
+%     pair._2 // => b
+% \end{lstlisting}
+% \end{frame}
+
+
+\section{Using Spark}
+
+\begin{frame}[fragile]
+  \frametitle{Local deployment using docker-compose}
+  
+  \center
+{  \Large
+\only<1>{
+  Get the repo
+}
+  
+\only<2>{
+  Move to the repo
+}
+\only<3>{
+  Run all the containers
+}
+\only<4>{
+  Launch spark-shell inside the master container
+}
+}
+\vfill
+
+  \begin{lstlisting}[basicstyle=\ttfamily,linebackgroundcolor={
+      \btLstHL<1>{1}%
+      \btLstHL<2>{2}%
+      \btLstHL<3>{3}%
+      \btLstHL<4>{4}%
+    }]
+git clone http://github.com/gettyimages/docker-spark
+cd docker-spark
+docker-compose up
+docker exec -it dockerspark_master_1 bin/spark-shell 
+\end{lstlisting}
+
+\end{frame}
+
+
+\begin{frame}[fragile]
+  \frametitle{Demo}
+  \centering
+\href{https://asciinema.org/a/92977}{\includegraphics[width=\textwidth]{gif/sparkrepl-thumb.png}}
+
+\end{frame}
+\begin{frame}[fragile]{Compose.yml Master}
+\begin{lstlisting}[basicstyle=\ttfamily,language={},linebackgroundcolor={
+    \btLstHL<1>{2}%
+    \btLstHL<2>{3}%
+    \btLstHL<3>{11}%
+    \btLstHL<4>{12}%
+    }]
+master:
+  image: gettyimages/spark
+  command: bin/spark-class org.apache.spark.deploy.master.Master -h master
+  hostname: master
+  environment:
+    MASTER: spark://master:7077
+    SPARK_CONF_DIR: /conf
+    SPARK_PUBLIC_DNS: localhost
+    ... bunch of ports ...
+  volumes:
+    - ./conf/master:/conf
+    - ./data:/tmp/data
+  \end{lstlisting}
+\end{frame}
+\begin{frame}[fragile]{Compose.yml Worker}
+\begin{lstlisting}[language={},basicstyle=\ttfamily,linebackgroundcolor={
+    \btLstHL<1>{2}%
+    \btLstHL<2>{3}%
+    \btLstHL<3>{12,13}%
+    \btLstHL<4>{6-8}%
+    }]
+worker:
+  image: gettyimages/spark
+  command: bin/spark-class org.apache.spark.deploy.worker.Worker spark://master:7077
+  hostname: worker
+  environment:
+    SPARK_CONF_DIR: /conf
+    SPARK_WORKER_CORES: 2
+    SPARK_WORKER_MEMORY: 1g
+  links:
+    - master
+  volumes:
+    - ./conf/worker:/conf
+    - ./data:/tmp/data
+    \end{lstlisting}
+\end{frame}
+\begin{frame}[fragile]
+  \frametitle{Useful info}
+  \begin{itemize}
+  \item ./data folder is mounted as /tmp/data
+    \begin{itemize}
+    \item Copy your datasets there
+    \item Load them in the shell: \texttt{sc.textFile("/tmp/data/...")}
+    \end{itemize}
+  \item Master Web UI (localhost:8080)
+  \item Worker Web UI (localhost:8081)
+  \item REPL Web UI (localhost:4040 when launched)
+    \end{itemize}
+\textbf{A word of caution}: as any other app, the shell reserves resources on startup, whether you are using them or not.
+\end{frame}
+
+\begin{frame}[fragile]
+  \frametitle{Applications}
+  Steps:
+  \begin{itemize}
+    \item Write the code
+    \item Compile the jar
+    \item Make your data available to every node in the cluster
+    \item Submit it to your cluster 
+  \end{itemize}
+\end{frame}
+\begin{frame}[fragile]
+  \frametitle{Writing applications}
+    \begin{lstlisting}[title=Example application,basicstyle=\ttfamily]
+import org.apache.spark.SparkContext
+import org.apache.spark.SparkContext._
+import org.apache.spark.SparkConf
+
+object SparkWordCount {
+  def main(args: Array[String]) {
+    // create Spark context with Spark configuration
+    val sc = new SparkContext(new SparkConf().setAppName("Spark Example"))
+  ... Your program ...
+}
+\end{lstlisting}
+\end{frame}
+
+\begin{frame}[fragile]
+  \frametitle{Running an aplication}
+    \begin{lstlisting}[language={},numbers=none]
+./bin/spark-submit --class <main-class> \
+                     --master <master-url> \
+                     --deploy-mode <deploy-mode> \
+                     --conf <key>=<value> \
+                     ... # other options
+                     <application-jar> \
+                     [application-arguments]
+\end{lstlisting}
+\end{frame}
+
+\section{Full examples}
+
+\begin{frame}[fragile]
+  \frametitle{Word frequency in wikipedia, revisited}
+  \begin{lstlisting}[title=Spark,basicstyle=\ttfamily,linebackgroundcolor={\color{orange!30}}]
+val wiki = sc.textFile("Wikipedia.txt")
+val counts = wiki.flatMap(line=> line.split(" ")
+                map(word => (word, 1)))
+                reduceByKey(_ + _)
+\end{lstlisting}
+
+\begin{lstlisting}[title=Pure scala,basicstyle=\ttfamily]
+val wiki = scala.io.Source.fromFile("Wiki").getLines
+wiki.flatMap(line=> line.split(" "))
+      map(x=>(x, 1)).toList
+      groupBy(x => x._1)
+      map({case (k, v) =>
+            (k, v.foldLeft(0)((a, b) => a+b._2)))
+  \end{lstlisting}
+\end{frame}
+
+\begin{frame}[plain]
+  \center
+  \huge{Shall we try it in the shell?}
+\end{frame}
+
+{
+  \pagecolor{black}
+  \usebackgroundtemplate{\vbox to \paperheight{\vfil\hbox to \paperwidth{\hfil\includegraphics[height=\paperheight]{images/justwaithere.jpg}\hfil}\vfil}}%
+
+\begin{frame}[plain]
+\end{frame}
+}
+
+\begin{frame}
+  \frametitle{Spark is not magic}
+  \begin{itemize}
+    \item We still have to add more resources
+    \item Caching may cause the spark version to use \textbf{more memory} (this can be configured)
+  \end{itemize}
+  \center
+  \pause
+  \huge{However, it allows us to scale our application}
+
+\end{frame}
+
+
+% \begin{frame}
+%   \frametitle{Word count}
+% \includegraphics[width=\textwidth]{images/wordcount.png}
+% \end{frame}
+
+\begin{frame}[fragile]
+  \frametitle{Page rank}
+
+  \begin{columns}
+    \column{0.5\textwidth}
+    \includegraphics[width=\textwidth]{images/pagerank.png}
+    \column{0.5\textwidth}
+    \begin{itemize}
+    \item Created by Google
+    \item Rank given by links and their importance
+    \item Iterative (Perfect for Spark!)
+    \end{itemize}
+  \end{columns}
+  \begin{centering}
+
+$
+ \text{PageRank of site} = \sum \frac{\text{Page rank of inbound link}}{\text{Number of links on that page}}
+$
+
+OR
+
+$
+ PR(u) = (1-d)+d\times \sum \frac{PR(v)}{N(V)}
+$
+
+\end{centering}
+\end{frame}
+
+{
+  \pagecolor{white}
+  \usebackgroundtemplate{\vbox to \paperheight{\vfil\hbox to \paperwidth{\hfil\includegraphics[height=\paperheight]{images/pageranktoy.jpg}\hfil}\vfil}}%
+
+\begin{frame}[plain]
+% \includegraphics[width=\textwidth]{images/pageranktoy.jpg}
+\footnote{\url{http://www.slideshare.net/sscdotopen/large-scale}}
+
+\end{frame}
+}
+
+\begin{frame}
+\frametitle{Page rank (code)}
+\includegraphics[width=\textwidth]{images/example.png}
+\end{frame}
+
+\section*{Next week on SIBD}
+
+\begin{frame}{Next week on SIBD}
+  \begin{itemize}
+  \item Advanced Spark configuration
+  \item Multiple hosts
+  \item Spark ecosystem
+  \item More examples in IBM BlueMix
+  \end{itemize}
+\end{frame}
+
+\section{Acknowledgements and useful links}
+
+\begin{frame}
+  \begin{itemize}
+\item \href{http://spark.apache.org/docs/latest/programming-guide.html}{Spark programming guide}
+\item \href{https://databricks.com/blog/2016/01/04/introducing-apache-spark-datasets.html}{Databricks introducing apache spark datasets}
+\item \href{https://www.safaribooksonline.com/library/view/data-analytics-with/9781491913734/ch04.html}{Data Analytics with Hadoop: In-Memory Computing with Spark}
+  \end{itemize}
+\end{frame}
+
+\end{document}
diff --git a/spark2.tex b/spark2.tex
new file mode 100644
index 0000000..d8a65ec
--- /dev/null
+++ b/spark2.tex
@@ -0,0 +1,307 @@
+\documentclass{beamer}
+\usepackage[utf8]{inputenc}
+\usepackage{media9}
+\usepackage{lipsum}
+\usepackage{amsmath}
+\usepackage{amsfonts}
+\usepackage{amssymb}
+\usetheme{metropolis}
+\usepackage{tcolorbox}
+\usepackage{listings} 
+% \usepackage{lstlinebgrd} % see http://www.ctan.org/pkg/lstaddons
+\usepackage{MnSymbol}
+\usepackage{wasysym}
+\usepackage{animate}
+\usepackage{xcolor,pgffor}
+
+\lstset{% 
+  basicstyle=\ttfamily\large,
+  columns=fullflexible,
+  escapeinside={(*@}{@*)},
+  numbers=none,
+  breaklines=true,
+  numbersep=5pt,                   % how far the line-numbers are from the code
+  numberstyle=\tiny\color{gray}, % the style that is used for the line-numbers
+  numbersep=5pt,                   % how far the line-numbers are from the code
+  % postbreak={\hbox{\raisebox{0ex}[0ex][0ex]\color{red}{\hookrightarrow}\space}}
+  postbreak=\raisebox{0ex}[0ex][0ex]{\ensuremath{\rcurvearrowse\space}},
+  keywordstyle=\color{blue},       % keyword style
+  stringstyle=\color{red},     % string literal style
+  % belowskip=0pt,
+  % aboveskip=0pt,
+  }
+
+\definecolor{lightyellow}{RGB}{255,255,204}
+
+\title{Spark}
+\subtitle{Cluster computing (take 2)}
+\author{J. Fernando Sánchez, Joaquín Salvachúa, Gabriel Huecas }
+\institute{Universidad Politécnica de Madrid}
+\date{2016}
+
+
+\newcommand{\btVFill}{\vskip0pt plus 1filll}
+
+
+
+\begin{document}
+\begin{frame}
+\titlepage{}
+\end{frame}
+
+\begin{frame}{Schedule for today}
+  \tableofcontents
+\end{frame}
+
+\section{Recap}
+
+{
+  \pagecolor{black}
+  \usebackgroundtemplate{\vbox to \paperheight{\vfil\hbox to \paperwidth{\hfil\includegraphics[height=\paperheight]{images/collectionsmethods.png}\hfil}\vfil}}%
+
+\begin{frame}[plain]
+\end{frame}
+}
+
+\begin{frame}[fragile]
+  \frametitle{Spark specific methods}
+
+  Some examples:
+
+  \begin{itemize}
+    \item collect: to apply all transformations and get their results
+    \item cache: to save results for later use
+    \item groupByKey: to group items by key
+    \item reduceByKey\footnote{Prefer this to groupbykey+map+reduce. See the slides from our previous class for more information}: group and apply reduce in the same step
+    \item<2-> keyBy: to creates [K,V] tuples by applying a function to each value
+    \item<3-> ...
+  \end{itemize}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Closer look at our demo}
+  
+  \center
+{\huge  Now, I'll start using the terminal.}
+\pause
+{\huge Don't freak out}
+\end{frame}
+
+\begin{frame}[plain]
+  \begin{tikzpicture}[remember picture,overlay]
+    \node[at=(current page.center)] {
+\href{https://www.youtube.com/watch?v=SOyZd3D65Z4}{\includegraphics[width=\paperwidth]{images/enrique.jpg}}
+            };
+\end{tikzpicture}
+
+\btVFill
+
+\center
+\Large Apologies in advance.
+\vfill
+
+\end{frame}
+
+\section{Advanced Spark configuration}
+
+\begin{frame}[fragile]
+  \frametitle{Spark conf}
+  \centering
+\href{http://spark.apache.org/docs/latest/configuration.html}{\includegraphics[width=\textwidth]{images/spark-conf.png}}
+
+\end{frame}
+
+\begin{frame}
+  \begin{itemize}
+    \item Application, Runtime, UI and RDD settings
+    \item Highlights
+      \begin{itemize}
+        \item Master URL and Port
+        \item CPU and memory per worker
+        \item Parallelism: default partition size
+      \end{itemize}
+    
+  \end{itemize}
+\end{frame}
+
+
+\section{Deploying a cluster with 20+ nodes}
+\begin{frame}
+  \frametitle{Our big data cluster}
+
+  We will illustrate what we've already covered by deploying a cluster of more than 20 nodes.
+  \pause
+  
+  Top of the line servers in Azure and Amazon.
+  \pause
+
+  Backed up by BlueMix's Spark infrastructure.
+  
+\end{frame}
+\begin{frame}[plain]
+  \begin{tikzpicture}[remember picture,overlay]
+    \node[at=(current page.center)] {
+\includegraphics[width=\paperwidth]{images/recortes.png}
+            };
+\end{tikzpicture}
+
+\end{frame}
+\begin{frame}
+  \frametitle{Our brain-powered cluster}
+
+  \center
+  {\huge This is embarrassing...}
+
+  \pause
+  
+  {\huge We'll have to make do with our brains and paper.}
+
+  \pause
+  
+  {\huge That's probably how Amazon Mechanical Turk was born.}
+
+\end{frame}
+
+\begin{frame}[fragile]
+  \frametitle{Input}
+  
+We have a log of users that know or ask about a topic.
+It looks like this:
+
+\begin{verbatim}
+  Alice knows Scala
+  Bob asks about Scala
+  Caroline asks about Java
+  Don knows about Scala
+   ...
+\end{verbatim}
+
+In other words, lines have this format:
+
+\begin{verbatim}
+  {student} {action} {topic}
+\end{verbatim}
+
+\end{frame}
+
+\begin{frame}
+  \frametitle{Your task}
+
+  Answer the questions:
+\begin{itemize}
+  \item How many questions were asked about each topic?
+    \pause
+  \item How many times did each student ask about each topic?
+    \pause
+  \item (Harder) Is there any topic with questions that no other student knows about?
+    \pause
+  \item (Even harder) Pair students that know about a topic with students that don't
+
+\end{itemize}
+  
+\end{frame}
+
+\section{Different orchestrators}
+
+\begin{frame}
+  \frametitle{Spark architecture}
+  \includegraphics[width=\textwidth]{images/cluster-overview.png}
+  \end{frame}
+
+\begin{frame}
+  \frametitle{Spark architecture}
+  \includegraphics[width=\textwidth]{images/cluster-overview-question.png}
+\end{frame}
+
+
+\begin{frame}
+  \frametitle{Cluster managers}
+  \large Cluster managers (either Spark’s own standalone cluster manager, Mesos or YARN), which allocate resources across applications. Once connected, Spark acquires executors on nodes in the cluster, which are processes that run computations and store data for your application.
+\end{frame}
+
+\begin{frame}
+  \frametitle{Mesos}
+  \begin{tikzpicture}[remember picture,overlay]
+    \node[at=(current page.center)] {
+\href{https://github.com/apache/spark/tree/master/examples/src/main/scala/org/apache/spark/examples/}{\includegraphics[width=\paperwidth]{images/mesosweb.png}}
+            };
+\end{tikzpicture}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Cluster managers}
+  \includegraphics[width=\textwidth]{images/mesosyarn.png}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Spark architecture}
+  \includegraphics[height=.9\textheight]{images/mesos-spark.png}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Spark on mesos}
+  \btVFill
+  \center
+  {\Large Once again, Spark has astoundingly good documentation}
+  \btVFill
+
+{\large  \href{http://spark.apache.org/docs/latest/running-on-mesos.html}{http://spark.apache.org/docs/latest/running-on-mesos.html}}
+  \btVFill
+\end{frame}
+
+
+  
+\section{Spark Ecosystem}
+
+\begin{frame}
+  \frametitle{Ecosystem}
+  \includegraphics[width=\textwidth]{images/sparkecosystem.png}
+\end{frame}
+
+
+\begin{frame}[plain]
+  \begin{tikzpicture}[remember picture,overlay]
+    \node[at=(current page.center)] {
+\href{https://github.com/apache/spark/tree/master/examples/src/main/scala/org/apache/spark/examples/}{\includegraphics[width=\paperwidth]{images/examples.png}}
+            };
+\end{tikzpicture}
+
+\end{frame}
+
+\begin{frame}[fragile,allowframebreaks]
+  \frametitle{Spark Streaming}
+  \includegraphics[width=\textwidth]{images/streaming-arch.png}
+
+  \framebreak
+
+  \includegraphics[width=\textwidth]{images/streaming-flow.png}
+  
+\end{frame}
+
+\begin{frame}[fragile]
+  \frametitle{Demo!}
+  
+We'll show a demo of a modified Spark Streaming task.
+
+Here is a quick ``video'' of the demo: 
+  {\Large \href{https://asciinema.org/a/6kp49z5m3hq9vja9r7x4rjq26}{https://asciinema.org/a/6kp49z5m3hq9vja9r7x4rjq26} }
+  
+All the code and instructions are available in our repository (in several branches):
+
+{\Large \href{https://github.com/balkian/docker-spark}{https://github.com/balkian/docker-spark}}
+
+\end{frame}
+
+\section{Acknowledgements and useful links}
+
+\begin{frame}
+  \begin{itemize}
+\item \href{http://spark.apache.org/docs/latest/programming-guide.html}{Spark programming guide}
+\item \href{https://databricks.com/blog/2016/01/04/introducing-apache-spark-datasets.html}{Databricks introducing apache spark datasets}
+\item \href{https://www.safaribooksonline.com/library/view/data-analytics-with/9781491913734/ch04.html}{Data Analytics with Hadoop: In-Memory Computing with Spark}
+\item \href{https://trongkhoanguyenblog.wordpress.com/2014/11/27/understand-rdd-operations-transformations-and-actions/}{Understanding RDD operations, transformations and actions}
+\item \href{http://spark.apache.org/docs/latest/streaming-programming-guide.html}{Spark Streaming programming guide}
+  \end{itemize}
+\end{frame}
+
+\end{document}
diff --git a/sparkdemo.mp4 b/sparkdemo.mp4
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index 0000000..a5a65f2
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