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Learning Functional Data Structures and Algorithms

You're reading from   Learning Functional Data Structures and Algorithms Learn functional data structures and algorithms for your applications and bring their benefits to your work now

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Product type Paperback
Published in Feb 2017
Publisher Packt
ISBN-13 9781785888731
Length 318 pages
Edition 1st Edition
Languages
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Authors (2):
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Raju Kumar Mishra Raju Kumar Mishra
Author Profile Icon Raju Kumar Mishra
Raju Kumar Mishra
Atul S. Khot Atul S. Khot
Author Profile Icon Atul S. Khot
Atul S. Khot
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Toc

Table of Contents (14) Chapters Close

Preface 1. Why Functional Programming? 2. Building Blocks FREE CHAPTER 3. Lists 4. Binary Trees 5. More List Algorithms 6. Graph Algorithms 7. Random Access Lists 8. Queues 9. Streams, Laziness, and Algorithms 10. Being Lazy - Queues and Deques 11. Red-Black Trees 12. Binomial Heaps 13. Sorting

Persistent data structures


If we follow the second copy-as-much-needed-and-share strategy, a thread holding a reference to the original lists will never be surprised by any changes. From the thread's point of view, nothing has changed and things continue as before.

However, the change needs to happen somewhere. How else would we grow/shrink data structures? The change does indeed happen, but only at creation time.

Let's look at what we mean when we say creation time. Consider the following snippet:

scala> :paste 
 
@scala.annotation.tailrec 
def print(low: Int, high: Int): Unit = 
  if (low > high) 
    println("Done") 
  else { 
    println(low) 
    print(low+1, high) 
  } 
scala> print(1, 10) 
1 
2 
... 
10 
Done 

We are printing a range of numbers. As we know, this needs some kind of looping. However, there is no counter variable that is getting changed (in other words, mutated).

The change happens just...

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