How does the use of a B-tree index affect data search speed?

B-tree is a balanced search tree widely used in databases and file systems for data indexing. Its use significantly speeds up search compared to sequential scanning.

Main advantages of B-tree for searching:

• Logarithmic search complexity: search, insert, and delete operations are performed in O(log n), where n is the number of elements.
• High branching factor: each node can contain multiple keys, reducing the tree's height and the number of disk accesses.
• Disk storage optimization: B-tree nodes typically match the size of disk blocks, reducing the number of read operations.

As a result, using a B-tree index allows quick access to needed records, minimizing input/output operations and key comparisons.

Example in Go (simplified):

// In real projects, ready-made libraries or databases with B-trees are used
// Here is a simple illustration of a node structure

type BTreeNode struct {
    keys []int
    children []*BTreeNode
    leaf bool
}

// Searching for a key in a B-tree will be performed in O(log n)

Thus, a B-tree based index significantly increases search speed compared to linear traversal.