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The term B-tree may refer to a specific design or it may refer to a general class of designs. In the narrow sense, a B-tree stores keys in its internal nodes but need not store those keys in the records at the leaves. The general class includes variations such as the B+ tree, the B * tree and the B *+ tree.
Memory based B+ tree implementation as C++ template library; 2019 improvement of previous; Stream based B+ tree implementation as C++ template library; Open Source JavaScript B+ Tree Implementation; Perl implementation of B+ trees; Java/C#/Python implementations of B+ trees; C# B+ tree and related "A-List" data structures; File based B+Tree in ...
In computer science, a 2–3–4 tree (also called a 2–4 tree) is a self-balancing data structure that can be used to implement dictionaries. The numbers mean a tree where every node with children (internal node) has either two, three, or four child nodes: a 2-node has one data element, and if internal has two child nodes;
To turn a regular search tree into an order statistic tree, the nodes of the tree need to store one additional value, which is the size of the subtree rooted at that node (i.e., the number of nodes below it). All operations that modify the tree must adjust this information to preserve the invariant that size[x] = size[left[x]] + size[right[x]] + 1
In computer science, a 2–3 tree is a tree data structure, where every node with children (internal node) has either two children (2-node) and one data element or three children (3-node) and two data elements. A 2–3 tree is a B-tree of order 3. [1] Nodes on the outside of the tree have no children and one or two data elements.
Patrick O'Neil published a paper about this implementation in 1987. [25] This implementation is a hybrid between the basic bitmap index (without compression) and the list of Row Identifiers (RID-list). Overall, the index is organized as a B+tree. When the column cardinality is low, each leaf node of the B-tree would contain long list of RIDs.
Btrfs is structured as several layers of such trees, all using the same B-tree implementation. The trees store generic items sorted by a 136-bit key. The most significant 64 bits of the key are a unique object id. The middle eight bits are an item type field: its use is hardwired into code as an item filter in tree lookups.
Bagwell [1] presented a time and space efficient solution for tries named Array Mapped Tree (AMT). The Hash array mapped trie (HAMT) is based on AMT. The compact trie node representation uses a bitmap to mark every valid branch – a bitwise trie with bitmap.