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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
Languages
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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

Fault tolerance


Real programs fail, and they fail in unpredictable ways. Akka, and the Scala community in general, favors planning explicitly for failure rather than trying to write infallible applications. A fault tolerant system is a system that can continue to operate when one or more of its components fails. The failure of an individual subsystem does not necessarily mean the failure of the application. How does this apply to Akka?

The actor model provides a natural unit to encapsulate failure: the actor. When an actor throws an exception while processing a message, the default behavior is for the actor to restart, but the exception does not leak out and affect the rest of the system. For instance, let's introduce an arbitrary failure in the response interpreter. We will modify the receive method to throw an exception when it is asked to interpret the response for misto, one of Martin Odersky's followers:

// ResponseInterpreter.scala
def receive = {
  case InterpretResponse("misto", r...
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