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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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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

Stateful actors


The behavior of the fetcher manager depends on whether it has work to give out to the fetchers:

  • If it has work to give, it needs to respond to GiveMeWork messages with a Fetcher.Fetch message

  • If it does not have work, it must ignore the GiveMeWork messages and, if work gets added, it must send a WorkAvailable message to the fetchers

Encoding the notion of state is straightforward in Akka. We specify different receive methods and switch from one to the other depending on the state. We will define the following receive methods for our fetcher manager, corresponding to each of the states:

// receive method when the queue is empty
def receiveWhileEmpty: Receive = { 
    ... 
}

// receive method when the queue is not empty
def receiveWhileNotEmpty: Receive = {
    ...
}

Note that we must define the return type of the receive methods as Receive. To switch the actor from one method to the other, we can use context.become(methodName). Thus, for instance, when the last login name is popped...

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