## Check-in Exercise

Linked here.

## BSTs

**Depth** We define the depth of a *node* as how far it is from the root. For consistency, we say the root has a depth of 0.

**Height** We define the height of a tree as the depth of the deepest node.

Notice that depending on how we insert into our BST, our height could vary drastically. We say a tree is “spindly” if it has height close to N and a tree is “bushy” if its height is closer to logN. For operations such as getting a node, we want to have the height to be as small as possible, thus favoring “bushy” BSTs

## B-Trees

Two specific B-Trees in this course are 2-3 Trees (A B-Tree where each node has 2 or 3 children), and 2-3-4/2-4 Trees (A B-Tree where each node has 2, 3, or 4 children). The key idea of a B-Tree is to over stuff the nodes at the bottom to prevent increaseing the height of the tree. This allows us to ensure a max height of logN.

Make sure you know how to insert into a B-Tree. Refer back to lecture slides for examples.

With our restriction on height, we get that the runtime for contains and add are both THETA(LogN)

### B-Tree invariants

Because of how we add to our tree, we get two nice invariants for B-Trees:

- All leaves must be the same distance from the source
- A non-leaf node with k items mut has exactly k+1 children.

## Practice Problems

- Draw the 2-3 tree that results when you insert the keys A B C D E F G in order.
- How many compares does it take in the worst case to decide whether to go left, middle, or right from a 3 node?
- Problem 5 of the Fall 2014 midterm.
- Problem 1c, e of the Spring 2018 Midterm 2
- Problem 8b of the Spring 2016 Midterm 2