Swift Algorithm Club: Swift Binary Search Tree Data Structure
- Getting Started
- Binary Tree Data Structure
- Left Child
- Right Child
- Leaf Node
- Binary Tree Implementation in Swift
- Value Semantics
- Example: Sequence of Arithmetical Operations
- Getting The Count
- Binary Search Trees
- “Always Sorted” Property
- Challenge: Implementing Insertion
- Copy on Write
- Insertion Time Complexity
- Traversal Algorithms
- In-order Traversal
- Pre-order Traversal
- Post-order Traversal
- Mini Challenge
- Where To Go From Here?
Where To Go From Here?
I hope you enjoyed this tutorial on making a Swift Binary Tree data structure!
This was just one of the many algorithms in the Swift Algorithm Club repository. If you're interested in more, check out the repo.
It's in your best interest to know about algorithms and data structures - they're solutions to many real world problems, and are frequently asked as interview questions. Plus it's fun!
So stay tuned for many more tutorials from the Swift Algorithm club in the future. In the meantime, if you have any questions on implementing trees in Swift, please join the forum discussion below!
If you enjoyed what you learned in this tutorial, why not check out our Data Structures and Algorithms in Swift book, available on our store?
In Data Structures and Algorithms in Swift, you’ll learn how to implement the most popular and useful data structures and when and why you should use one particular datastructure or algorithm over another. This set of basic data structures and algorithms will serve as an excellent foundation for building more complex and special-purpose constructs.
As well, the high-level expressiveness of Swift makes it an ideal choice for learning these core concepts without sacrificing performance.
- You’ll start with the fundamental structures of linked lists, queues and stacks, and see how to implement them in a highly Swift-like way.
- Move on to working with various types of trees, including general purpose trees, binary trees, AVL trees, binary search trees and tries.
- Go beyond bubble and insertion sort with better-performing algorithms, including mergesort, radix sort, heap sort and quicksort.
- Learn how to construct directed, non-directed and weighted graphs to represent many real-world models, and traverse graphs and trees efficiently with breadth-first, depth-first, Dijkstra’s and Prim’s algorithms to solve problems such as finding the shortest path or lowest cost in a network.
- And much, much more!
By the end of this book, you’ll have hands-on experience solving common issues with data structures and algorithms — and you’ll be well on your way to developing your own efficient and useful implementations.