Английская Википедия:Comparison of data structures
This is a comparison of the performance of notable data structures, as measured by the complexity of their logical operations. For a more comprehensive listing of data structures, see List of data structures.
The comparisons in this article are organized by abstract data type. As a single concrete data structure may be used to implement many abstract data types, some data structures may appear in multiple comparisons (for example, a hash map can be used to implement an associative array or a set).
Lists
A list or sequence is an abstract data type that represents a finite number of ordered values, where the same value may occur more than once. Lists generally support the following operations:
- peek: access the element at a given index.
- insert: insert a new element at a given index. When the index is zero, this is called prepending; when the index is the last index in the list it is called appending.
- delete: remove the element at a given index.
Шаблон:List data structure comparison
Maps
Maps store a collection of (key, value) pairs, such that each possible key appears at most once in the collection. They generally support three operations:[1]
- Insert: add a new (key, value) pair to the collection, mapping the key to its new value. Any existing mapping is overwritten. The arguments to this operation are the key and the value.
- Remove: remove a (key, value) pair from the collection, unmapping a given key from its value. The argument to this operation is the key.
- Lookup: find the value (if any) that is bound to a given key. The argument to this operation is the key, and the value is returned from the operation.
Unless otherwise noted, all data structures in this table require O(n) space.
| Data structure | Lookup, removal | Insertion | Ordered | ||
|---|---|---|---|---|---|
| average | worst case | average | worst case | ||
| Association list | O(n) | O(n) | O(1) | O(1) | Шаблон:No |
| B-treeШаблон:Sfn | O(log n) | O(log n) | O(log n) | O(log n) | Шаблон:Yes |
| Hash table | O(1) | O(n) | O(1) | O(n) | Шаблон:No |
| Unbalanced binary search tree | O(log n) | O(n) | O(log n) | O(n) | Шаблон:Yes |
Integer keys
Some map data structures offer superior performance in the case of integer keys. In the following table, let Шаблон:Mvar be the number of bits in the keys.
| Data structure | Lookup, removal | Insertion | Space | ||
|---|---|---|---|---|---|
| average | worst case | average | worst case | ||
| Fusion tree | Шаблон:Data missing | O(log m n) | Шаблон:Data missing | Шаблон:Data missing | O(n) |
| Van Emde Boas tree | O(log log m) | O(log log m) | O(log log m) | O(log log m) | O(m) |
| X-fast trie | O(n log m)Шаблон:Efn | Шаблон:Data missing | O(log log m) | O(log log m) | O(n log m) |
| Y-fast trie | O(log log m)Шаблон:Efn | Шаблон:Data missing | O(log log m)Шаблон:Efn | Шаблон:Data missing | O(n) |
Priority queues
A priority queue is an abstract data-type similar to a regular queue or stack. Each element in a priority queue has an associated priority. In a priority queue, elements with high priority are served before elements with low priority. Priority queues support the following operations:
- insert: add an element to the queue with an associated priority.
- find-max: return the element from the queue that has the highest priority.
- delete-max: remove the element from the queue that has the highest priority.
Priority queues are frequently implemented using heaps.
Heaps
A (max) heap is a tree-based data structure which satisfies the Шаблон:Dfni: for any given node C, if P is a parent node of C, then the key (the value) of P is greater than or equal to the key of C.
In addition to the operations of an abstract priority queue, the following table lists the complexity of two additional logical operations:
- increase-key: updating a key.
- meld: joining two heaps to form a valid new heap containing all the elements of both, destroying the original heaps.
Notes
References
