Pragmatic Bookshelf

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Want to write code that pushes the boundaries of speed, space savings, and scalability? Then you need more advanced data structures and algorithms. Go beyond Big O notation and evaluate the true efficiency of each algorithm you design. Pull out data structures such as B-trees, bit vectors, and Bloom filters to wrangle big data. Wield techniques like caching, randomization, and fingerprinting to tame even the most demanding applications. With simple language, clear diagrams, and practice exercises and solutions, this book makes these topics easy to grasp. Go beyond the basics and use these next-level concepts to build software that’s ready to take on the challenges of the real world. The applications you write get more complex by the day. To keep up, you need to ensure your knowledge and techniques grow, too. A mastery of data structures and algorithms lets you write software more quickly; software that works, performs, and scales. Volume 2 of the series takes your knowledge of data structures and algorithms to the next level. With this practical and easy-to-understand guide, you’ll create software that can tackle today’s challenging problems head on. This Python edition uses Python exclusively for all code examples, exercises, and solutions. Benchmark your Python code to learn its true speed. Design fast and elegant solutions by connecting different data structures together. Use Monte Carlo algorithms to push the limits of your application’s speed and memory savings in surprising ways. Wrangle big data with B-trees and other specialized algorithms. Design efficient algorithms by cleverly sprinkling in a bit of randomization. Cram tons of data into tiny bit vectors and Bloom filters. And leverage caching to make your software blazingly fast. Learn these sophisticated techniques and create great software that meets today’s challenges.