Massive modern datasets make traditional data structures and algorithms grind to a halt. This fun and practical guide introduces cutting-edge techniques that can reliably handle even the largest distributed datasets.

In Algorithms and Data Structures for Massive Datasets you will learn:

Probabilistic sketching data structures for practical problems
Choosing the right database engine for your application
Evaluating and designing efficient on-disk data structures and algorithms
Understanding the algorithmic trade-offs involved in massive-scale systems
Deriving basic statistics from streaming data
Correctly sampling streaming data
Computing percentiles with limited space resources

Algorithms and Data Structures for Massive Datasets reveals a toolbox of new methods that are perfect for handling modern big data applications. You’ll explore the novel data structures and algorithms that underpin Google, Facebook, and other enterprise applications that work with truly massive amounts of data. These effective techniques can be applied to any discipline, from finance to text analysis. Graphics, illustrations, and hands-on industry examples make complex ideas practical to implement in your projects—and there’s no mathematical proofs to puzzle over. Work through this one-of-a-kind guide, and you’ll find the sweet spot of saving space without sacrificing your data’s accuracy.

About the technology

Standard algorithms and data structures may become slow—or fail altogether—when applied to large distributed datasets. Choosing algorithms designed for big data saves time, increases accuracy, and reduces processing cost. This unique book distills cutting-edge research papers into practical techniques for sketching, streaming, and organizing massive datasets on-disk and in the cloud.

About the book

Algorithms and Data Structures for Massive Datasets introduces processing and analytics techniques for large distributed data. Packed with industry stories and entertaining illustrations, this friendly guide makes even complex concepts easy to understand. You’ll explore real-world examples as you learn to map powerful algorithms like Bloom filters, Count-min sketch, HyperLogLog, and LSM-trees to your own use cases.

What's inside

Probabilistic sketching data structures
Choosing the right database engine
Designing efficient on-disk data structures and algorithms
Algorithmic tradeoffs in massive-scale systems
Computing percentiles with limited space resources

About the reader

Examples in Python, R, and pseudocode.

About the author

Dzejla Medjedovic earned her PhD in the Applied Algorithms Lab at Stony Brook University, New York. Emin Tahirovic earned his PhD in biostatistics from University of Pennsylvania. Illustrator Ines Dedovic earned her PhD at the Institute for Imaging and Computer Vision at RWTH Aachen University, Germany.



Table of Contents

1 Introduction
PART 1 HASH-BASED SKETCHES
2 Review of hash tables and modern hashing
3 Approximate membership: Bloom and quotient filters
4 Frequency estimation and count-min sketch
5 Cardinality estimation and HyperLogLog
PART 2 REAL-TIME ANALYTICS
6 Streaming data: Bringing everything together
7 Sampling from data streams
8 Approximate quantiles on data streams
PART 3 DATA STRUCTURES FOR DATABASES AND EXTERNAL MEMORY ALGORITHMS
9 Introducing the external memory model
10 Data structures for databases: B-trees, Bε-trees, and LSM-trees
11 External memory sorting