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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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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

Functional FIFO queues


We know by now that all this mutating won't work when we deal with persistent data structures (also known as versioned data structures). How can we implement these queues so that when an element is enqueued or dequeued, the earlier version of the data structure would be preserved?

The design is beautiful; it involves two lists. The following diagram shows two lists, namely in and out:

The out list holds the elements that will be popped out. We just remove the head element and return it. The in list is where new elements are inserted, that is, prepended. As we have already seen, both list prepend and head removal are O(1). For example, given the preceding diagram, when we remove the element 9, we get another version of the persistent queue: V1.

The following figure shows the persistence in action; note the indices for both the versions:

Now let's pop out another element: 7. This will make the out list empty. Of course, this will create another version of the out list...

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