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A B+ tree can be viewed as a B-tree in which each node contains only keys (not key–value pairs), and to which an additional level is added at the bottom with linked leaves. The primary value of a B+ tree is in storing data for efficient retrieval in a block-oriented storage context — in particular, filesystems.
The term B-tree may refer to a specific design or 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.
Database tables and indexes may be stored on disk in one of a number of forms, including ordered/unordered flat files, ISAM, heap files, hash buckets, or B+ trees. Each form has its own particular advantages and disadvantages. The most commonly used forms are B-trees and ISAM.
The cluster configuration defines the data layout in the tables that are parts of the cluster. A cluster can be keyed with a B-tree index or a hash table. The data block where the table record is stored is defined by the value of the cluster key.
LSM trees, like other search trees, maintain key-value pairs. LSM trees maintain data in two or more separate structures, each of which is optimized for its respective underlying storage medium; data is synchronized between the two structures efficiently, in batches. One simple version of the LSM tree is a two-level LSM tree. [2]
The key–value model can be extended to a discretely ordered model that maintains keys in lexicographic order. This extension is computationally powerful, in that it can efficiently retrieve selective key ranges. [28] Key–value stores can use consistency models ranging from eventual consistency to serializability. Some databases support ...
A tabular data card proposed for Babbage's Analytical Engine showing a key–value pair, in this instance a number and its base-ten logarithm. A key–value database, or key–value store, is a data storage paradigm designed for storing, retrieving, and managing associative arrays, and a data structure more commonly known today as a dictionary or hash table.
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.