Chapter 4. Parallel Collections and Futures

Data science often involves processing medium or large amounts of data. Since the previously exponential growth in the speed of individual CPUs has slowed down and the amount of data continues to increase, leveraging computers effectively must entail parallel computation.

In this chapter, we will look at ways of parallelizing computation and data processing over a single computer. Virtually all new computers have more than one processing unit, and distributing a calculation over these cores can be an effective way of hastening medium-sized calculations.

Parallelizing calculations over a single chip is suitable for calculations involving gigabytes or a few terabytes of data. For larger data flows, we must resort to distributing the computation over several computers in parallel. We will discuss Apache Spark, a framework for parallel data processing in Chapter 10, Distributed Batch Processing with Spark.

In this book, we will look at three common ways of leveraging parallel architectures in a single machine: parallel collections, futures, and actors. We will consider the first two in this chapter, and leave the study of actors to Chapter 9, Concurrency with Akka.