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Scala for Data Science

You're reading from   Scala for Data Science Leverage the power of Scala with different tools to build scalable, robust data science applications

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Product type Paperback
Published in Jan 2016
Publisher
ISBN-13 9781785281372
Length 416 pages
Edition 1st Edition
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Author (1):
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Pascal Bugnion Pascal Bugnion
Author Profile Icon Pascal Bugnion
Pascal Bugnion
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Toc

Table of Contents (17) Chapters Close

Preface 1. Scala and Data Science FREE CHAPTER 2. Manipulating Data with Breeze 3. Plotting with breeze-viz 4. Parallel Collections and Futures 5. Scala and SQL through JDBC 6. Slick – A Functional Interface for SQL 7. Web APIs 8. Scala and MongoDB 9. Concurrency with Akka 10. Distributed Batch Processing with Spark 11. Spark SQL and DataFrames 12. Distributed Machine Learning with MLlib 13. Web APIs with Play 14. Visualization with D3 and the Play Framework A. Pattern Matching and Extractors Index

Routing


In the previous example, we created four fetchers and dispatched messages to them, one after the other. We have a pool of identical actors among which we distribute tasks. Manually routing the messages to the right actor to maximize the utilization of our pool is painful and error-prone. Fortunately, Akka provides us with several routing strategies that we can use to distribute work among our pool of actors. Let's rewrite the previous example with automatic routing. You can find the code examples for this section in the chap09/fetchers_routing directory in the sample code provided with this book (https://github.com/pbugnion/s4ds). We will reuse the same definition of Fetchers and its companion object as we did in the previous section.

Let's start by importing the routing package:

// FetcherDemo.scala
import akka.routing._

A router is an actor that forwards the messages that it receives to its children. The easiest way to define a pool of actors is to tell Akka to create a router and...

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