堆（英語：Heap）是電腦科學中的一種特別的樹狀資料結構。若是滿足以下特性，即可稱為堆：“給定堆中任意節點 P 和 C，若 P 是 C 的母節點，那麼 P 的值會小於等於（或大於等於） C 的值”。若母節點的值恆小於等於子節點的值，此堆稱為最小堆（英語：min heap）；反之，若母節點的值恆大於等於子節點的值，此堆稱為最大堆（英語：max heap）。在堆中最頂端的那一個節點，稱作根節點（英語：root node），根節點本身沒有母節點（英語：parent node）。

性質

• 任意節點小於（或大於）它的所有後裔，最小元（或最大元）在堆的根上（堆序性
  ）。
• 堆總是一棵完全樹。即除了最底層，其他層的節點都被元素填滿，且最底層儘可能地從左到右填入。

Comparison of theoretic bounds for variants

 根據上圖可以看出斐波那契堆和嚴格-斐波那契堆的效率比較高。

應用

The heap data structure has many applications.

• Heapsort: One of the best sorting methods being in-place and with no quadratic worst-case scenarios.
• Selection algorithms: A heap allows access to the min or max element in constant time, and other selections (such as median or kth-element) can be done in sub-linear time on data that is in a heap.[17]
• Graph algorithms: By using heaps as internal traversal data structures, run time will be reduced by polynomial order. Examples of such problems are 
  Prim's minimal-spanning-tree algorithm and Dijkstra's shortest-path algorithm.
• Priority Queue: A priority queue is an abstract concept like "a list" or "a map"; just as a list can be implemented with a linked list or an array, a priority queue can be implemented with a heap or a variety of other methods.
• K-way merge: A heap data structure is useful to merge many already-sorted input streams into a single sorted output stream. Examples of the need for merging include external sorting and streaming results from distributed data such as a log structured merge tree. The inner loop is obtaining the min element, replacing with the next element for the corresponding input stream, then doing a sift-down heap operation. (Alternatively the replace function.) (Using extract-max and insert functions of a priority queue are much less efficient.)
• Order statistics: The Heap data structure can be used to efficiently find the kth smallest (or largest) element in an array.

1.堆（通常是二叉堆）常用於排序。這種演算法稱作堆排序。 

2.優先順序佇列可以用堆實現。