Red-Black Trees

Binary search trees work best when they are balanced or the path length from root to any leaf is within some bounds. The red-black tree algorithm is a method for balancing trees. The name derives from the fact that each node is colored red or black, and the color of the node is instrumental in determining the balance of the tree. During insert and delete operations nodes may be rotated to maintain tree balance. Both average and worst-case insert, delete, and search time is O(lgn). For details, consult Cormen [2009].


A red-black tree is a balanced binary search tree with the following properties:

  1. Every node is colored red or black.
  2. Every leaf is a sentinel node, and is colored black.
  3. If a node is red, then both its children are black.
  4. Every simple path from a node to a descendant leaf contains the same number of black nodes.
  5. The root is always black.

The number of black nodes on a path from root to leaf is known as the black-height of a tree. The above properties guarantee that any path from the root to a leaf is no more than twice as long as any other path. To see why this is true, consider a tree with a black-height of three. The shortest distance from root to leaf is two (B-B-B). The longest distance from root to leaf is four (B-R-B-R-B). It is not possible to insert more black nodes as this would violate property 4. Since red nodes must have black children (property 3), having two red nodes in a row is not allowed. The largest path we can construct consists of an alternation of red and black nodes.

In general, given a tree with a black-height of n, the shortest distance from root to leaf is n- 1, and the longest distance is 2(n- 1). All operations on the tree must maintain the properties listed above. In particular, operations that insert or delete nodes from the tree must abide by these rules.


To insert a node, search the tree for an insertion point and add the node to the tree. The new node replaces an existing sentinel node at the bottom of the tree, and has two sentinel nodes as children. In the implementation, a sentinel node is simply a pointer to a common sentinel node that is colored black. After insertion the new node is colored red. Then the parent of the node is examined to determine if the red-black tree properties have been maintained. If necessary, make adjustments to balance the tree.

The black-height property (property 4) is preserved when we insert a red node with two sentinel children. We must also ensure that both children of a red node are black (property 3). Although both children of the new node are black (they're sentinel), consider the case where the parent of the new node is red. Inserting a red node under a red parent would violate this property. There are two cases to consider.

Red Parent, Red Uncle

Figure 3-6 illustrates a red-red violation. Node X is the newly inserted node, and both parent and uncle are colored red. A simple recoloring removes the red-red violation. After recoloring the grandparent (node B) must be checked for validity, as its parent may also be red and we can't have two red nodes in a row. This has the effect of propagating a red node up the tree. On completion the root of the tree is marked black. If it was originally red the black-height of the tree increases by one.

Figure 3-6: Insertion - Red Parent, Red Uncle

Red Parent, Black Uncle

Figure 3-7 illustrates a red-red violation where the uncle is colored black. If we attempt to recolor nodes, changing node A to black, the tree is out of balance since we've increased the black-height of the left branch without changing the right branch. If we also change node B to red, then the black-height of both branches is reduced and the tree is still out of balance. If we start over and change node A to black and node C to red the situation is worse since we've increased the black-height of the left branch, and decreased the black-height of the right branch. To solve this problem we will rotate and recolor the nodes as shown. At this point the algorithm terminates since the top of the subtree (node A) is colored black and no red-red conflicts were introduced.

Figure 3-7: Insertion - Red Parent, Black Uncle


To insert a node we may have to recolor or rotate to preserve the red-black tree properties. If rotation is done, the algorithm terminates. For simple recolorings we're left with a red node at the head of the subtree and must travel up the tree one step and repeat the process to ensure the black-height properties are preserved. In the worst case we must go all the way to the root. Timing for insertion is O(lgn). The technique and timing for deletion is similar.

Implementation in C

An ANSI-C implementation for red-black trees is included. Typedefs recType, keyType, and comparison operators compLT and compEQ should be altered to reflect the data stored in the tree. Typedef NodeType defines each node and consists of left, right, and parent pointers designating each child and the parent. The node color is stored in color, and is either RED or BLACK. All leaf nodes of the tree are SENTINEL nodes, to simplify coding. The tree is based at root, and initially is a SENTINEL node.

Function rbtInsert allocates a new node and inserts it in the tree. Subsequently, it calls insertFixup to ensure that the red-black tree properties are maintained. Function rbtErase deletes a node from the tree. To maintain red-black tree properties, deleteFixup is called. Function rbtFind searches the tree for a particular value.

Consider an industrial-strength version of the red-black tree algorithm. Available as three separate files:

Modifications include:

In addition there is total isolation between the driver and red-black tree data structures. For example, the driver does not have access to the NodeType data structure or the inner-workings of the algorithm. Finally, a version in C++.

Implementation in Visual Basic

The red-black tree algorithm has been implemented as objects, using a module for the algorithm, and a class for the nodes. It has also been implemented as a class, using arrays. The array implementation is recommended.