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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

Life-cycle hooks


Akka lets us specify code that runs in response to specific events in an actor's life, through life-cycle hooks. Akka defines the following hooks:

  • preStart(): This runs after the actor's constructor has finished but before it starts processing messages. This is useful to run initialization code that depends on the actor being fully constructed.

  • postStop(): This runs when the actor dies after it has stopped processing messages. This is useful to run cleanup code before terminating the actor.

  • preRestart(reason: Throwable, message: Option[Any]): This is called just after an actor receives an order to restart. The preRestart method has access to the exception that was thrown and to the offending message, allowing for corrective action. The default behavior of preRestart is to stop each child and then call postStop.

  • postRestart(reason:Throwable): This is called after an actor has restarted. The default behavior is to call preStart().

Let's use system hooks to persist the state...

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