The arbitrary storage of leaf nodes and its location on the actual disk will not link up or respond to the index order or any logical positioning. Therefore, a database search algorithm is used with the right structure, to change the position and quickly output a balanced search tree or, in short, a B-tree. Each branch of data will have its leaf nodes that link or refer to the memory location that is used to store each and every table of the database.
B-tree allows sequential access, deletions, and insertions, based on the general binary search tree in a node that can have two or more children. When data is removed or added, the number of child nodes will change, and in order to maintain a specific range, some internal nodes may join or split. Every internal node within a B-tree structure will have a number of keys. Each of these keys will have a value and will be divided into subtrees. B-tree is a good way to search for data with a key associated to find the right data, but it is inefficient at search data with a query string; it uses an index to speed up this searching. B-trees are an efficient way of searching data with a key, and when executed correctly, they become very quick. This is an explanation about B-tree and how it is applied with SQLite.