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<!DOCTYPE html>
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<meta name="description" content="Course homepage for CS 431/631 451/651 Data-Intensive Distributed Computing (Winter 2019) at the University of Waterloo">
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<li class="active"><a href="syllabus.html">Syllabus</a></li>
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<div style="float: right"><img width="250" src="images/waterloo_logo.png" alt="University of Waterloo logo"/></div>
<h1>Syllabus <br/><small>Data-Intensive Distributed Computing (Winter 2019)</small></h1>
</div>
<section id="schedule">
<div>
<h3>Schedule</h3>
<table class="table table-striped table-condensed">
<thead>
<tr>
<td><b>Part</b></td>
<td><b>Description</b></td>
<td><b>Dates</b></td>
<td><b>CS 451/651 Assignments</b></td>
<td><b>CS 431/631 Assignments</b></td>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td><a href="#part01">MapReduce Algorithm Design</a></td>
<td>Jan 8, 10, 15, 17</td>
<td><a href="assignment0-451.html" class="btn btn-success btn-xs">A0: Jan 17</a></td>
<td></td>
</tr>
<tr>
<td>2</td>
<td><a href="#part02">From MapReduce to Spark</a></td>
<td>Jan 22, 24</td>
<td><a href="assignment1-451.html" class="btn btn-success btn-xs">A1: Jan 24</a></td>
<td><a href="assignment0-431.html" class="btn btn-info btn-xs">A0: Jan 22</a></td>
</tr>
<tr>
<td>3</td>
<td><a href="#part03">Analyzing Text</a></td>
<td>Jan 29, 31</td>
<td><a href="assignment2-451.html" class="btn btn-success btn-xs">A2: Jan 31</a></td>
<td><a href="assignment1-431.html" class="btn btn-info btn-xs">A1: Jan 29</a></td>
</tr>
<tr>
<td>4</td>
<td><a href="#part04">Analyzing Graphs</a></td>
<td>Feb 5, 7</td>
<td><a href="assignment3-451.html" class="btn btn-success btn-xs">A3: Feb 7</a></td>
<td><a href="assignment2-431.html" class="btn btn-info btn-xs">A2: Feb 12</a></td>
</tr>
<tr>
<td>5</td>
<td><a href="#part05">Analyzing Relational Data</a></td>
<td>Feb 12, 14, 26</td>
<td></td>
<td><a href="assignment3-431.html" class="btn btn-info btn-xs">A3: Feb 23</a></td>
</tr>
<tr>
<td>6</td>
<td><a href="#part06">Data Mining and Machine Learning</a></td>
<td>Feb 28, Mar 5, 7, 12</td>
<td><a href="assignment4-451.html" class="btn btn-success btn-xs">A4: Feb 28</a></td>
<td></td>
</tr>
<tr>
<td>7</td><td><a href="#part07">Mutable State</a></td>
<td>Mar 14, 19</td>
<td><a href="assignment5-451.html" class="btn btn-success btn-xs">A5: Mar 14</a></td>
<td><a href="assignment4-431.html" class="btn btn-info btn-xs">A4: Mar 14</a></td>
</tr>
<tr>
<td>8</td>
<td><a href="#part08">Analyzing Graphs, Redux</a></td>
<td>Mar 21, 26</td>
<td></td>
<td></td>
</tr>
<tr>
<td>9</td>
<td><a href="#part09">Real-Time Analytics</a></td>
<td>Mar 28, Apr 2</td>
<td><a href="assignment6-451.html" class="btn btn-success btn-xs">A6: Mar 28</a></td>
<td><a href="assignment5-431.html" class="btn btn-info btn-xs">A5: Apr 2</a></td>
</tr>
<tr>
<td>10</td>
<td><a href="#part10">Looking Ahead</a></td>
<td>Apr 4</td>
<td><a href="assignment7-451.html" class="btn btn-success btn-xs">A7: Apr 4</a></td>
<td></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="part01" style="padding-top:35px"><div>
<h3>Part 1: MapReduce Algorithm Design <small>Jan 8, 10, 15, 17</small></h3>
<h4>Topics</h4>
<ul>
<li>What's this course about?</li>
<li>Why big data?</li>
<li>The datacenter is the computer and other "big ideas"</li>
<li>MapReduce programming model</li>
<li>Cloud computing and datacenters</li>
<li>Hadoop API</li>
<li>Hadoop physical execution</li>
<li>MapReduce design patterns</li>
<li>Intermediate aggregation and combiners</li>
<li>Partitioning, grouping, sorting, and monoids</li>
</ul>
<div style="padding-top: 5px"></div>
<h4>Readings</h4>
<ul>
<li>Data-Intensive Text Processing with MapReduce
<ul>
<li><a href="content/MapReduce-algorithms-ch1-20171225.pdf">Chapter 1: Introduction</a></li>
<li><a href="content/MapReduce-algorithms-ch2-20171225.pdf">Chapter 2: MapReduce Basics</a></li>
<li><a href="content/MapReduce-algorithms-ch3-20171225.pdf">Chapter 3: MapReduce Algorithm Design</a></li>
</ul>
</li>
<li>Hadoop: The Definitive Guide (4th Edition):
<ul>
<li>Chapter 1: Meet Hadoop</li>
<li>Chapter 2: MapReduce</li>
<li>Chapter 3: The Hadoop Distributed Filesystem (Focus on the mechanics of the HDFS commands and don't worry so much about learning the Java API all at once—you'll pick it up in time.)</li>
<li>Chapter 5: Hadoop I/O (Read sections "Serialization" and "File-Based Data Structures")</li>
<li>Chapter 6: Developing a MapReduce Application (Skip sections "Setting Up the Development Environment", "Writing a Unit Test with MRUnit" and "MapReduce Workflows")</li>
<li>Chapter 7: How MapReduce Works (Skip section on "Configuration Tuning")</li>
<li>Chapter 8: MapReduce Types and Formats</li>
<li>Chapter 9: MapReduce Features (Read sections on "Counters", "Sorting", and "Side Data distribution")</li>
</ul>
</ul>
<div style="padding-top: 5px"></div>
<h4>Slides</h4>
<p>
<a href="slides/didp-part01a.pptx" class="btn btn-xs btn-primary" role="button">PPTX (Mac)</a>
<a href="slides/didp-part01a.pdf" class="btn btn-xs btn-info" role="button">PDF</a> Part 1a: January 8
</p>
<p>
<a href="slides/didp-part01b.pptx" class="btn btn-xs btn-primary" role="button">PPTX (Mac)</a>
<a href="slides/didp-part01b.pdf" class="btn btn-xs btn-info" role="button">PDF</a> Part 1b: January 10
</p>
<p>
<a href="slides/didp-part01c.pptx" class="btn btn-xs btn-primary" role="button">PPTX (Mac)</a>
<a href="slides/didp-part01c.pdf" class="btn btn-xs btn-info" role="button">PDF</a> Part 1c: January 15
</p>
<p>
<a href="slides/didp-part01d.pptx" class="btn btn-xs btn-primary" role="button">PPTX (Mac)</a>
<a href="slides/didp-part01d.pdf" class="btn btn-xs btn-info" role="button">PDF</a> Part 1d: January 17</p>
<p style="padding-top: 20px"><a href="#">Back to top</a></p>
</div></section>
<section id="part02" style="padding-top:35px"><div>
<h3>Part 2: From MapReduce to Spark <small>Jan 22, 24</small></h3>
<h4>Topics</h4>
<ul>
<li>Evolution of dataflow abstractions</li>
<li>MapReduce, Pig, Dryad, Spark, Flink, etc.</li>
</ul>
<div style="padding-top: 5px"></div>
<h4>Readings</h4>
<ul>
<li>Jimmy Lin. <a href="https://arxiv.org/abs/1304.7544">Monoidify! Monoids as a Design Principle for Efficient MapReduce Algorithms.</a> <i>arXiv:1304.7544</i>.</li>
<li>Learning Spark <i>(Optional)</i>:
<ul>
<li>Chapter 1: Introduction to Data Analysis with Spark</li>
<li>Chapter 2: Downloading Spark and Getting Started (Skip section on downloading)</li>
<li>Chapter 3: Programming with RDDs</li>
<li>Chapter 4: Working with Key/Value Pairs</li>
<li>Chapter 5: Loading and Saving Your Data (Stop when you get to Structured Data with Spark SQL)</li>
</ul>
</ul>
<p>Note that the Spark book is a bit outdated since it covers Spark
1.3; we're using Spark 2.1. All the material in the book can be found
in a multitude of sources online, but you'll have to hunt around for
resources — the book is useful primarily as single reference
that gathers everything together.</p>
<div style="padding-top: 5px"></div>
<h4>Slides</h4>
<p>
<a href="slides/didp-part02a.pptx" class="btn btn-xs btn-primary" role="button">PPTX (Mac)</a>
<a href="slides/didp-part02a.pdf" class="btn btn-xs btn-info" role="button">PDF</a> Part 2a: January 22
</p>
<p>
<a href="slides/didp-part02b.pptx" class="btn btn-xs btn-primary" role="button">PPTX (Mac)</a>
<a href="slides/didp-part02b.pdf" class="btn btn-xs btn-info" role="button">PDF</a> Part 2b: January 24
</p>
<p style="padding-top: 20px"><a href="#">Back to top</a></p>
</div></section>
<section id="part03" style="padding-top:35px"><div>
<h3>Part 3: Analyzing Text <small>Jan 29, 31</small></h3>
<h4>Topics</h4>
<ul>
<li>Language models and machine translation</li>
<li>Inverted indexing and search</li>
</ul>
<div style="padding-top: 5px"></div>
<h4>Readings</h4>
<ul>
<li>Data-Intensive Text Processing with MapReduce — <a href="content/MapReduce-algorithms-ch4-20171225.pdf">Chapter 4: Inverted Indexing for Text Retrieval</a></li>
</ul>
<div style="padding-top: 5px"></div>
<h4>Slides</h4>
<p>
<a href="slides/didp-part03a.pptx" class="btn btn-xs btn-primary" role="button">PPTX (Mac)</a>
<a href="slides/didp-part03a.pdf" class="btn btn-xs btn-info" role="button">PDF</a> Part 3a: January 29
</p>
<p>
<a href="slides/didp-part03b.pptx" class="btn btn-xs btn-primary" role="button">PPTX (Mac)</a>
<a href="slides/didp-part03b.pdf" class="btn btn-xs btn-info" role="button">PDF</a> Part 3b: January 31
</p>
<p style="padding-top: 20px"><a href="#">Back to top</a></p>
</div></section>
<section id="part04" style="padding-top:35px"><div>
<h3>Part 4: Analyzing Graphs <small>Feb 5, 7</small></h3>
<h4>Topics</h4>
<ul>
<li>Graph representations</li>
<li>Parallel breadth-first search</li>
<li>PageRank and random walks</li>
<li>Issues and challenges with dataflow abstractions</li>
</ul>
<div style="padding-top: 5px"></div>
<h4>Readings</h4>
<ul>
<li>Data-Intensive Text Processing with MapReduce — <a href="content/MapReduce-algorithms-ch5-20171225.pdf">Chapter 5: Graph Algorithms</a></li>
</ul>
<div style="padding-top: 5px"></div>
<h4>Slides</h4>
<p>
<a href="slides/didp-part04a.pptx" class="btn btn-xs btn-primary" role="button">PPTX (Mac)</a>
<a href="slides/didp-part04a.pdf" class="btn btn-xs btn-info" role="button">PDF</a> Part 4a: February 5
</p>
<p>
<a href="slides/didp-part04b.pptx" class="btn btn-xs btn-primary" role="button">PPTX (Mac)</a>
<a href="slides/didp-part04b.pdf" class="btn btn-xs btn-info" role="button">PDF</a> Part 4b: February 11
</p>
<p style="padding-top: 20px"><a href="#">Back to top</a></p>
</div></section>
<section id="part05" style="padding-top:35px"><div>
<h3>Part 5: Analyzing Relational Data <small>Feb 12, 14, 26</small></h3>
<h4>Topics</h4>
<ul>
<li>OLTP vs. OLAP</li>
<li>Data warehousing and data lakes, ETL</li>
<li>SQL-on-Hadoop: relational data processing with MapReduce and Spark</li>
<li>Optimizations for relational processing: row vs. column stores, vectorized processing</li>
<li>Semistructured data and record reconstruction (Parquet)</li>
</ul>
<div style="padding-top: 5px"></div>
<h4>Readings</h4>
<ul>
<li>Data-Intensive Text Processing with MapReduce — <a href="content/MapReduce-algorithms-ch6-20171225.pdf">Chapter 6: Processing Relational Data</a></li>
<li><a href="http://homes.cs.washington.edu/~billhowe/mapreduce_a_major_step_backwards.html">MapReduce: A major step backwards</a></li>
<li>Chaudhuri et al. (2011) <a href="http://dl.acm.org/citation.cfm?id=1978562">An overview of business intelligence technology</a>, <i>CACM</i>, 54(8):88-98.</li>
</ul>
<h4>Slides</h4>
<p>
<a href="slides/didp-part05a.pptx" class="btn btn-xs btn-primary" role="button">PPTX (Mac)</a>
<a href="slides/didp-part05a.pdf" class="btn btn-xs btn-info" role="button">PDF</a> Part 5a: February 12
</p>
<p>
<a href="slides/didp-part05b.pptx" class="btn btn-xs btn-primary" role="button">PPTX (Mac)</a>
<a href="slides/didp-part05b.pdf" class="btn btn-xs btn-info" role="button">PDF</a> Part 5b: February 14
</p>
<p>
<a href="slides/didp-part05c.pptx" class="btn btn-xs btn-primary" role="button">PPTX (Mac)</a>
<a href="slides/didp-part05c.pdf" class="btn btn-xs btn-info" role="button">PDF</a> Part 5c: February 26
</p>
<p style="padding-top: 20px"><a href="#">Back to top</a></p>
</div></section>
<section id="part06" style="padding-top:35px"><div>
<h3>Part 6: Data Mining and Machine Learning <small>Feb 28, Mar 5, 7, 12</small></h3>
<h4>Topics</h4>
<ul>
<li>Supervised machine learning: binary classification</li>
<li>Logistic regression, gradient descent, stochastic gradient descent, ensemble methods</li>
<li>Production machine learning pipelines</li>
<li>Hashing: minhash, random projections, etc.</li>
<li>Clustering: <i>k</i>-means, Gaussian mixture models</li>
</ul>
<p style="padding-top: 5px"></p>
<h4>Readings</h4>
<ul>
<li>Tom Mitchell. <a href="https://www.cs.cmu.edu/~tom/mlbook/NBayesLogReg.pdf">Naive Bayes and Logistic Regression</a>. (This book chapter serves as supplemental reading and goes into classification in more detail than in lecture.)</li>
<li>Deisenroth et al., <i>Mathematics for Machine Learning</i>: Chapter 12, <a href="https://mml-book.github.io/book/chapter12.pdf">Classification with Support Vector Machines</a>. (Optional supplemental reading)</li>
<li>Deisenroth et al., <i>Mathematics for Machine Learning</i>: Chapter 11, <a href="https://mml-book.github.io/book/chapter11.pdf">Density Estimation with Gaussian Mixture Models</a>. (This book chapter serves as supplemental reading and goes into clustering with Gaussian mixture models in more detail than in lecture.)</li>
<li>Jimmy Lin and Dmitriy Ryaboy. <a href="https://dl.acm.org/citation.cfm?id=2481247">Scaling Big Data Mining Infrastructure: The Twitter Experience</a>, SIGKDD Explorations, 14(2):6-19, 2012.</li>
</ul>
<p style="padding-top: 5px"></p>
<h4>Slides</h4>
<p>
<a href="slides/didp-part06a.pptx" class="btn btn-xs btn-primary" role="button">PPTX (Mac)</a>
<a href="slides/didp-part06a.pdf" class="btn btn-xs btn-info" role="button">PDF</a> Part 6a: February 28
</p>
<p>
<a href="slides/didp-part06b.pptx" class="btn btn-xs btn-primary" role="button">PPTX (Mac)</a>
<a href="slides/didp-part06b.pdf" class="btn btn-xs btn-info" role="button">PDF</a> Part 6b: March 5
</p>
<p>
<a href="slides/didp-part06c.pptx" class="btn btn-xs btn-primary" role="button">PPTX (Mac)</a>
<a href="slides/didp-part06c.pdf" class="btn btn-xs btn-info" role="button">PDF</a> Part 6c: March 7
</p>
<p>
<a href="slides/didp-part06d.pptx" class="btn btn-xs btn-primary" role="button">PPTX (Mac)</a>
<a href="slides/didp-part06d.pdf" class="btn btn-xs btn-info" role="button">PDF</a> Part 6d: March 12
</p>
<p style="padding-top: 20px"><a href="#">Back to top</a></p>
</div></section>
<section id="part07" style="padding-top:35px"><div>
<h3>Part 7: Mutable State <small>Mar 14, 19</small></h3>
<h4>Topics</h4>
<ul>
<li>Bigtable/HBase: Log-structure merge trees</li>
<li>Distributed hash tables</li>
<li>Consistency, latency, and availability tradeoffs</li>
</ul>
<p style="padding-top: 5px"></p>
<h4>Readings</h4>
<ul>
<li>The <a href="https://ai.google/research/pubs/pub27898">original Bigtable paper</a>.</li>
<li>The <a href="https://dl.acm.org/citation.cfm?id=383071">original DHT paper</a>.</li>
<li>Daniel Abadi. <a href="https://ieeexplore.ieee.org/document/6127847">Consistency Tradeoffs in Modern Distributed Database System Design</a>, Computer, 45(2):37-42, 2012.</li>
</ul>
<p style="padding-top: 5px"></p>
<h4>Slides</h4>
<p>
<a href="slides/didp-part07a.pptx" class="btn btn-xs btn-primary" role="button">PPTX (Mac)</a>
<a href="slides/didp-part07a.pdf" class="btn btn-xs btn-info" role="button">PDF</a> Part 7a: March 14
</p>
<p>
<a href="slides/didp-part07b.pptx" class="btn btn-xs btn-primary" role="button">PPTX (Mac)</a>
<a href="slides/didp-part07b.pdf" class="btn btn-xs btn-info" role="button">PDF</a> Part 7b: March 19
</p>
<p style="padding-top: 20px"><a href="#">Back to top</a></p>
</div></section>
<section id="part08" style="padding-top:35px"><div>
<h3>Part 8: Analyzing Graphs, Redux <small>Mar 21, 26</small></h3>
<h4>Topics</h4>
<ul>
<li>Bulk synchronous parallel: "think like a vertex" (Giraph)</li>
<li>Alternative approaches: GraphX</li>
</ul>
<div style="padding-top: 5px"></div>
<h4>Readings</h4>
<ul>
<li>Sherif Sakr. <a href="https://link.springer.com/chapter/10.1007/978-3-319-38776-5_4">Large-Scale Graph Processing Systems</a>, 2016.</li>
</ul>
<p style="padding-top: 5px"></p>
<h4>Slides</h4>
<p>
<a href="slides/didp-part08a.pptx" class="btn btn-xs btn-primary" role="button">PPTX (Mac)</a>
<a href="slides/didp-part08a.pdf" class="btn btn-xs btn-info" role="button">PDF</a> Part 8a: March 21
</p>
<p>
<a href="slides/didp-part08b.pptx" class="btn btn-xs btn-primary" role="button">PPTX (Mac)</a>
<a href="slides/didp-part08b.pdf" class="btn btn-xs btn-info" role="button">PDF</a> Part 8b: March 26
</p>
<p style="padding-top: 20px"><a href="#">Back to top</a></p>
</div></section>
<section id="part09" style="padding-top:35px"><div>
<h3>Part 9: Real-Time Analytics <small>Mar 28, Apr 2</small></h3>
<h4>Topics</h4>
<ul>
<li>Stream processing semantics, issues, and frameworks</li>
<li>Probabilistic data structures (hyerloglog counters, bloom filters, count-min sketches, etc.)</li>
<li>Integrating batch and stream processing</li>
</ul>
<div style="padding-top: 5px"></div>
<h4>Readings</h4>
<ul>
<li>Zaharia et al. <a href="https://dl.acm.org/citation.cfm?id=2522737">Discretized Streams: Fault-Tolerant Streaming Computation at Scale</a>, <i>SOSP 2013</i>.</li>
<li>Kulkarni et al. <a href="https://dl.acm.org/citation.cfm?id=2742788">Twitter Heron: Stream Processing at Scale</a>, <i>SIGMOD 2015</i>.</li>
<li>Apache Beam: The world beyond batch: <a href="https://www.oreilly.com/ideas/the-world-beyond-batch-streaming-101">Streaming 101</a>, <a href="https://www.oreilly.com/ideas/the-world-beyond-batch-streaming-102">Streaming 102</a>.</li>
<li>If you're interested, here's <a href="https://cs.uwaterloo.ca/~jimmylin/publications/Lin_IEEE2017b.pdf">my rant about the Lambda and Kappa architectures</a>.</li>
</ul>
<p style="padding-top: 5px"></p>
<h4>Slides</h4>
<p>
<a href="slides/didp-part09a.pptx" class="btn btn-xs btn-primary" role="button">PPTX (Mac)</a>
<a href="slides/didp-part09a.pdf" class="btn btn-xs btn-info" role="button">PDF</a> Part 9a: March 28
</p>
<p>
<a href="slides/didp-part09b.pptx" class="btn btn-xs btn-primary" role="button">PPTX (Mac)</a>
<a href="slides/didp-part09b.pdf" class="btn btn-xs btn-info" role="button">PDF</a> Part 9b: April 2
</p>
<p style="padding-top: 20px"><a href="#">Back to top</a></p>
</div></section>
<section id="part10" style="padding-top:35px"><div>
<h3>Part 10: Looking Ahead <small>Apr 4</small></h3>
<p style="padding-top: 5px"></p>
<h4>Slides</h4>
<p>
<a href="slides/didp-bonus.pptx" class="btn btn-xs btn-primary" role="button">PPTX (Mac)</a>
<a href="slides/didp-bonus.pdf" class="btn btn-xs btn-info" role="button">PDF</a> Bonus: April 4
</p>
<p style="padding-top: 20px"><a href="#">Back to top</a></p>
</div></section>
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