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A queue has two ends, the top, which is the only position at which the push operation may occur, and the bottom, which is the only position at which the pop operation may occur. A queue may be implemented as circular buffers and linked lists, or by using both the stack pointer and the base pointer.
C++'s Standard Template Library provides the class templates std::deque and std::list, for the multiple array and linked list implementations, respectively. As of Java 6, Java's Collections Framework provides a new Deque interface that provides the functionality of insertion and removal at both ends.
array implements a compile-time non-resizable array. vector implements an array with fast random access and an ability to automatically resize when appending elements. deque implements a double-ended queue with comparatively fast random access. list implements a doubly linked list. forward_list implements a singly linked list.
One example application of the double-ended priority queue is external sorting. In an external sort, there are more elements than can be held in the computer's memory. The elements to be sorted are initially on a disk and the sorted sequence is to be left on the disk. The external quick sort is implemented using the DEPQ as follows:
While priority queues are often implemented using heaps, they are conceptually distinct from heaps. A priority queue is an abstract data type like a list or a map; just as a list can be implemented with a linked list or with an array, a priority queue can be implemented with a heap or another method such as an ordered array.
Circular buffering makes a good implementation strategy for a queue that has fixed maximum size. Should a maximum size be adopted for a queue, then a circular buffer is a completely ideal implementation; all queue operations are constant time. However, expanding a circular buffer requires shifting memory, which is comparatively costly.
A hash table may use linked lists to store the chains of items that hash to the same position in the hash table. A heap shares some of the ordering properties of a linked list, but is almost always implemented using an array. Instead of references from node to node, the next and previous data indexes are calculated using the current data's index.
The d-ary heap consists of an array of n items, each of which has a priority associated with it. These items may be viewed as the nodes in a complete d-ary tree, listed in breadth first traversal order: the item at position 0 of the array (using zero-based numbering) forms the root of the tree, the items at positions 1 through d are its children, the next d 2 items are its grandchildren, etc.