Programming Algorithms: Trees

This is a snippet of the "Trees" chapter of the book.

Couldn't resist adding this xkcd

Balancing a binary tree is the infamous interview problem that has all that folklore and debate associated with it. To tell you the truth, like the other 99% of programmers, I never had to perform this task for some work-related project. And not even due to the existence of ready-made libraries, but because self-balancing binary trees are, actually, pretty rarely used. But trees, in general, are ubiquitous even if you may not recognize their presence. The source code we operate with, at some stage of its life, is represented as a tree (a popular term here is Abstract Syntax Tree or AST, but the abstract variant is not the only one the compilers process). The directory structure of the file system is the tree. The object-oriented class hierarchy is likewise. And so on. So, returning to interview questions, trees indeed are a good area as they allow to cover a number of basic points: linked data structures, recursion, complexity. But there's a much better task, which I have encountered a lot in practice and also used quite successfully in the interview process: breadth-first tree traversal. We'll talk about it a bit later.

Similar to how hash-tables can be thought of as more sophisticated arrays (they are sometimes even called "associative arrays"), trees may be considered an expansion of linked lists. Although technically, a few specific trees are implemented not as a linked data structure but are based on arrays, the majority of trees are linked. Like hash-tables, some trees also allow for efficient access to the element by key, representing an alternative key-value implementation option.

Basically, a tree is a recursive data structure that consists of nodes. Each node may have zero or more children. If the node doesn't have a parent, it is called the root of the tree. And the constraint on trees is that the root is always single. Graphs may be considered a generalization of trees that don't impose this constraint, and we'll discuss them in a separate chapter. In graph terms, a tree is an acyclic directed single-component graph. Directed means that there's a one-way parent-child relation. And acyclic means that a child can't have a connection to the parent neither directly, nor through some other nodes (in the opposite case, what will be the parent and what — the child?) The recursive nature of trees manifests in the fact that if we extract an arbitrary node of the tree with all of its descendants, the resulting part will remain a tree. We can call it a subtree. Besides parent-child or, more generally, ancestor-descendant "vertical" relationships that apply to all the nodes in the tree, we can also talk about horizontal siblings — the set of nodes that have the same parent/ancestor.

Another important tree concept is the distinction between terminal (leaf) and nonterminal (branch) nodes. Leaf nodes don't have any children. In some trees, the data is stored only in the leaves with branch nodes serving to structure the tree in a certain manner. In other trees, the data is stored in all nodes without any distinction.

More details about of the book may be found on its website.