# Red-Black Trees

We said in the previous section that we really like 2-3 trees because they always remain balanced, but we also don't like them because they are hard to implement. But why not both? Why not create a tree that is implemented using a BST, but is structurally identical to a 2-3 tree and thus stays balanced? (Note that in this chapter we will be honing in on 2-3 Trees specifically, not 2-3-4 trees)

Enter the Red-Black Tree

We are going to create this tree by looking at a 2-3 tree and asking ourselves what kind of modifications we can make in order to convert it into a BST.

For a 2-3 tree that only has 2-nodes (nodes with 2 children), we already have a BST, so we don't need to make any modifications!

However, what happens when we get a 3-node?

One thing we could do is create a "glue" node that doesn't hold any information and only serves to show that its 2 children are actually a part of one node.

However, this is a very inelegant solution because we are taking up more space and the code will be ugly. So, instead of using glue nodes we will use glue links instead!

We choose arbitrarily to make the left element a child of the right one. This results in a left-leaning tree. We show that a link is a glue link by making it red. Normal links are black. Because of this, we call these structures left-leaning red-black trees (LLRB). We will be using left-leaning trees in 61B.

## Properties of LLRB's

Here are the properties of LLRB's:

• 1-1 correspondence with 2-3 trees.
• No node has 2 red links.
• There are no red right-links.
• Every path from root to leaf has same number of black links (because 2-3 trees have same number of links to every leaf).
• Height is no more than 2x height of corresponding 2-3 tree.

## Inserting into LLRB

We can always insert into a LLRB tree by inserting into a 2-3 tree and converting it using the scheme from above. However, this would be contrary to our original purpose of creating a LLRB, which was to avoid the complicate code of a 2-3 tree! Instead, we insert into the LLRB as we would with a normal BST. However, this could break its 1-1 mapping to a 2-3 tree, so we will use rotations to massage the tree back into a proper structure.

We will go over the different tasks we need to address when inserting into a LLRB below.

1. Task 1: insertion color: because in a 2-3 tree, we are always inserting by adding to a leaf node, the color of the link we add should always be red.
2. Task 2: insertion on the right: recall, we are using left-leaning red black trees, which means we can never have a right red link. If we insert on the right, we will need to use a rotation in order to maintain the LLRB invariant.
However, if we were to insert on the right with a red link and the left child is also a red link, then we will temporarily allow it for purposes that will become clearer in task 3.

3. Task 3: double insertion on the left: If there are 2 left red links, then we have a 4-node which is illegal. First, we will rotate to create the same tree seen in task 2 above. Then, in both situations, we will flip the colors of all edges touching S. This is equivalent to pushing up the middle node in a 2-3 tree.

You may need to go through a series of rotations in order to complete the transformation. The process is: while the LLRB tree does not satisfy the 1-1 correspondence with a 2-3 tree or breaks the LLRB invariants, perform task 1, 2, or 3 depending on the condition of the tree until you get a legal LLRB.

Here is a summary of all the operations:

• When inserting: Use a red link.

• If there is aright leaning “3-node”, we have a Left Leaning Violation

• Rotate left the appropriate node to fix.
• If there are two consecutive left links, we have an incorrect 4 Node Violation!

• Rotate right the appropriate node to fix.
• If there are any nodes with two red children, we have a temporary 4 Node.

• Color flip the node to emulate the split operation.

## Runtime

Because a left-leaning red-black tree has a 1-1 correspondence with a 2-3 tree and will always remain within 2x the height of its 2-3 tree, the runtimes of the operations will take $\log N$ time.

Here's the abstracted code for insertion into a LLRB:

private Node put(Node h, Key key, Value val) {
if (h == null) { return new Node(key, val, RED); }

int cmp = key.compareTo(h.key);
if (cmp < 0)      { h.left  = put(h.left,  key, val); }
else if (cmp > 0) { h.right = put(h.right, key, val); }
else              { h.val   = val;                    }

if (isRed(h.right) && !isRed(h.left))      { h = rotateLeft(h);  }
if (isRed(h.left)  &&  isRed(h.left.left)) { h = rotateRight(h); }
if (isRed(h.left)  &&  isRed(h.right))     { flipColors(h);      }

return h;
}


Look how short and sweet!

## Summary

• Binary search trees are simple, but they are subject to imbalance which leads to crappy runtime.

• 2-3 Trees (B Trees) are balanced, but painful to implement and relatively slow.

• LLRBs insertion is simple to implement (but deletion is hard).

• Works by maintaining mathematical bijection with a 2-3 trees.
• Java’s TreeMap is a red-black tree (but not left leaning).

• LLRBs maintain correspondence with 2-3 tree, Standard Red-Black trees maintain correspondence with 2-3-4 trees.

• Allows glue links on either side (see Red-Black Tree).

• More complex implementation, but significantly faster.