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Binary Tree (Stuff Formally Trained Programmers Know)

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Binary Tree (Stuff Formally Trained Programmers Know)

In this post, we take a look at binary trees, how they can be of use for big data professionals for data classification and data implementation.

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This post is one in a series about stuff formally trained programmers know – the rest of the series can be found here.

Binary Tree

In the previous post, we looked at the tree pattern, which is a theoretical way of structuring data with many advantages. A tree is just a theory though, so what does an actual implementation of it look like? A common data structure implementation is a binary tree.

The name binary tree gives us a hint to how it is structured, each node can have at most 2 child nodes.

Example of annotated binary tree


As a binary tree has some flexibility in it, a number of classifications have come up to have a consistent way to discuss a binary tree. Common classifications are: 

  • Full binary tree: Each node in a binary tree can have zero, one, or two child nodes. In a full binary tree, each node can only have zero or two child nodes.

  • Perfect binary tree: This is a full binary tree with the additional condition that all leaf nodes (i.e. nodes with no children) are at the same level/depth.

  • Complete binary tree: The complete binary tree is where each leaf node is as far left as possible.

  • Balanced binary tree: A balanced binary tree is a tree where the height of the tree is as small a number as possible.


While a binary tree is more than just a pattern, there are no out of the box implementations in C#, Java, or JavaScript for it. The reason is that it is a very simple data structure and so if you need just the data structure you could implement it yourself but, more importantly, you likely want more than the simple structure - you want a structure that optimizes for traversal or data management.


Wikipedia: Binary Tree

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