order of a B+ tree is 7, each internal node may have between and 7 children, while the root may have between 2 and 7. In the situation where a B+ tree is empty or contains exactly 1 node, the root instead becomes the single leaf. In this case, the number of keys must satisfy . A concrete example of these bounds is depicted in the table below: Intervals in internal nodes By definition, each value contained within the B+ tree is a key contained in exactly one leaf node. Each key is required to be directly comparable with every other key, which forms a total order. This enables each leaf node to keep all of its keys sorted at all times, which then enables each internal node to construct an ordered collection of intervals representing the contiguous extent of values contained in a given leaf. Internal nodes higher in the tree can then construct their own intervals, which recursively aggregate the intervals contained in their own child internal nodes. Eventually, the root of a B+ Tree represents the whole range of values in the tree, where every internal node represents a subinterval. For this recursive interval information to be retained, internal nodes must additionally contain copies of keys for representing the least element within the interval covered by the child with index (which may itself be an internal node, or a leaf). Characteristics For a -order B+ tree with levels of index: The maximum number of records stored is The minimum number of records stored is The minimum number of keys is The maximum number of keys is The space required to store the tree is Inserting a record requires operations Finding a record requires operations Removing a (previously located) record requires operations Performing a range query with k elements occurring within the range requires operations The B+ tree structure expands/contracts as the number of records increases/decreases. There are no restrictions on the size of B+ trees. Thus, increasing usability of a database system. Any change in structure does not affect performance due to balanced tree properties. The data is stored in the leaf nodes and more branching of internal nodes helps to reduce the tree's height, thus, reduce search time. As a result, it works well in secondary storage devices. Searching becomes extremely simple because all records are stored only in the leaf node and are sorted sequentially