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However, hash tables have a much better average-case time complexity than self-balancing binary search trees of O(1), and their worst-case performance is highly unlikely when a good hash function is used. A self-balancing binary search tree can be used to implement the buckets for a hash table that uses separate chaining.
In a well-dimensioned hash table, the average time complexity for each lookup is independent of the number of elements stored in the table. Many hash table designs also allow arbitrary insertions and deletions of key–value pairs, at amortized constant average cost per operation. [3] [4] [5] Hashing is an example of a space-time tradeoff.
Although std::map is typically implemented using a self-balancing binary search tree, C++11 defines a second map called std::unordered_map, which has the algorithmic characteristics of a hash table. This is a common vendor extension to the Standard Template Library (STL) as well, usually called hash_map , available from such implementations as ...
Binary tree; Cartesian tree; Conc-tree list; Left-child right-sibling binary tree; ... Distributed hash table; Double hashing; Dynamic perfect hash table; Hash array ...
In computer science, a hash tree (or hash trie) is a persistent data structure that can be used to implement sets and maps, intended to replace hash tables in purely functional programming. In its basic form, a hash tree stores the hashes of its keys, regarded as strings of bits, in a trie, with the actual keys and (optional) values stored at ...
In computer science, a hashed array tree (HAT) is a dynamic array data-structure published by Edward Sitarski in 1996, [1] maintaining an array of separate memory fragments (or "leaves") to store the data elements, unlike simple dynamic arrays which maintain their data in one contiguous memory area. Its primary objective is to reduce the amount ...
A hash tree allows efficient and secure verification of the contents of a large data structure. A hash tree is a generalization of a hash list and a hash chain. Demonstrating that a leaf node is a part of a given binary hash tree requires computing a number of hashes proportional to the logarithm of the number of leaf nodes in the tree. [1]
This unsorted tree has non-unique values (e.g., the value 2 existing in different nodes, not in a single node only) and is non-binary (only up to two children nodes per parent node in a binary tree). The root node at the top (with the value 2 here), has no parent as it is the highest in the tree hierarchy.