The index leaf nodes are stored in an arbitrary order—the position on the disk does not correspond to the logical position according to the index order. It is like a telephone directory with shuffled pages. If you search for “Smith” but first open the directory at “Robinson”, it is by no means granted that Smith follows Robinson. A database needs a second structure to find the entry among the shuffled pages quickly: a balanced search tree—in short: the B-tree.

## Figure 1.2 B-tree Structure

Figure 1.2 shows an example index with 30 entries. The doubly linked list establishes the logical order between the leaf nodes. The root and branch nodes support quick searching among the leaf nodes.

The figure highlights a branch node and the leaf nodes it refers to. Each branch node entry corresponds to the biggest value in the respective leaf node. Take the first leaf node as an example: the biggest value in this node is 46, which is thus stored in the corresponding branch node entry. The same is true for the other leaf nodes so that in the end the branch node has the values 46, 53, 57 and 83. According to this scheme, a branch layer is built up until all the leaf nodes are covered by a branch node.

#### On my Own Behalf

I offer training, tuning and consulting. Buying my book “SQL Performance Explained” (from €9.95) also supports my work on this website.

The next layer is built similarly, but on top of the first branch node level. The procedure repeats until all keys fit into a single node, the *root node*. The structure is a *balanced search tree* because the tree depth is equal at every position; the distance between root node and leaf nodes is the same everywhere.

#### Note

A B-tree is a balanced tree—not a binary tree.

Once created, the database maintains the index automatically. It applies every `insert`

, `delete`

and `update`

to the index and keeps the tree in balance, thus causing maintenance overhead for write operations. Chapter 8, “*Modifying Data*”, explains this in more detail.

## Figure 1.3 B-Tree Traversal

Figure 1.3 shows an index fragment to illustrate a search for the key “57”. The tree traversal starts at the root node on the left-hand side. Each entry is processed in ascending order until a value is greater than or equal to (>=) the search term (57). In the figure it is the entry 83. The database follows the reference to the corresponding branch node and repeats the procedure until the tree traversal reaches a leaf node.

#### Important

The B-tree enables the database to find a leaf node quickly.

The tree traversal is a very efficient operation—so efficient that I refer to it as the *first power of indexing*. It works almost instantly—even on a huge data set. That is primarily because of the tree balance, which allows accessing all elements with the same number of steps, and secondly because of the logarithmic growth of the tree depth. That means that the tree depth grows very slowly compared to the number of leaf nodes. Real world indexes with millions of records have a tree depth of four or five. A tree depth of six is hardly ever seen. The box “*Logarithmic Scalability*” describes this in more detail.