These two seem very similar and have almost an identical structure. What's the difference? What are the time complexities for different operations of each?
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$\begingroup$ stackoverflow.com/q/6147242/781723 $\endgroup$– D.W. ♦Commented Jan 9 at 8:49
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5 Answers
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Heap just guarantees that elements on higher levels are greater (for max-heap) or smaller (for min-heap) than elements on lower levels, whereas BST guarantees order (from "left" to "right"). If you want sorted elements, go with BST.
Source: https://stackoverflow.com/a/6147264/781723
Heap is better at findMin/findMax (O(1)), while BST is good at all finds (O(logN)). Insert is O(logN) for both structures. If you only care about findMin/findMax (e.g. priority-related), go with heap. If you want everything sorted, go with BST.
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2$\begingroup$ I think BST is better in findMin & findMax stackoverflow.com/a/27074221/764592 $\endgroup$– YeoCommented Nov 22, 2014 at 5:50
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23$\begingroup$ I think this is just a common misconception. A binary tree can be easily modified to find min and max as pointed by Yeo. This is actually a restriction of the heap: the only efficient find is min or max. The true advantage of the heap is O(1) average insert as I explain: stackoverflow.com/a/29548834/895245 $\endgroup$ Commented Jun 20, 2015 at 5:01
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$\begingroup$ According to this video, you can have larger values on a lower level, as long as the larger is not descendant of the lower one. $\endgroup$– whoanCommented Jun 21, 2017 at 0:08
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1$\begingroup$ Heap is sorted root to leaf and BST is sorted left to right. $\endgroup$ Commented Aug 21, 2018 at 9:58
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Summary
Type BST (*) Heap
Insert average log(n) 1
Insert worst log(n) log(n) or n (***)
Find any worst log(n) n
Find max worst 1 (**) 1
Create worst n log(n) n
Delete worst log(n) log(n)
All average times on this table are the same as their worst times except for Insert.
*: everywhere in this answer, BST == Balanced BST, since unbalanced sucks asymptotically**: using a trivial modification explained in this answer***:log(n)for pointer tree heap,nfor dynamic array heap
Advantages of binary heap over a BST
average time insertion into a binary heap is
O(1), for BST isO(log(n)). This is the killer feature of heaps.There are also other heaps which reach
O(1)amortized (stronger) like the Fibonacci Heap, and even worst case, like the Brodal queue, although they may not be practical because of non-asymptotic performance: https://stackoverflow.com/questions/30782636/are-fibonacci-heaps-or-brodal-queues-used-in-practice-anywherebinary heaps can be efficiently implemented on top of either dynamic arrays or pointer-based trees, BST only pointer-based trees. So for the heap we can choose the more space efficient array implementation, if we can afford occasional resize latencies.
binary heap creation is
O(n)worst case,O(n log(n))for BST.
Advantage of BST over binary heap
search for arbitrary elements is
O(log(n)). This is the killer feature of BSTs.For heap, it is
O(n)in general, except for the largest element which isO(1).
"False" advantage of heap over BST
heap is
O(1)to find max, BSTO(log(n)).This is a common misconception, because it is trivial to modify a BST to keep track of the largest element, and update it whenever that element could be changed: on insertion of a larger one swap, on removal find the second largest. https://stackoverflow.com/questions/7878622/can-we-use-binary-search-tree-to-simulate-heap-operation (mentioned by Yeo).
Actually, this is a limitation of heaps compared to BSTs: the only efficient search is that for the largest element.
Average binary heap insert is O(1)
Sources:
- Paper: http://i.stanford.edu/pub/cstr/reports/cs/tr/74/460/CS-TR-74-460.pdf
- WSU slides: https://web.archive.org/web/20161109132222/http://www.eecs.wsu.edu/~holder/courses/CptS223/spr09/slides/heaps.pdf
Intuitive argument:
- bottom tree levels have exponentially more elements than top levels, so new elements are almost certain to go at the bottom
- heap insertion starts from the bottom, BST must start from the top
In a binary heap, increasing the value at a given index is also O(1) for the same reason. But if you want to do that, it is likely that you will want to keep an extra index up-to-date on heap operations https://stackoverflow.com/questions/17009056/how-to-implement-ologn-decrease-key-operation-for-min-heap-based-priority-queu e.g. for Dijkstra. Possible at no extra time cost.
GCC C++ standard library insert benchmark on real hardware
I benchmarked the C++ std::set (Red-black tree BST) and std::priority_queue (dynamic array heap) insert to see if I was right about the insert times, and this is what I got:
- benchmark code
- plot script
- plot data
- tested on Ubuntu 19.04, GCC 8.3.0 in a Lenovo ThinkPad P51 laptop with CPU: Intel Core i7-7820HQ CPU (4 cores / 8 threads, 2.90 GHz base, 8 MB cache), RAM: 2x Samsung M471A2K43BB1-CRC (2x 16GiB, 2400 Mbps), SSD: Samsung MZVLB512HAJQ-000L7 (512GB, 3,000 MB/s)
So clearly:
heap insert time is basically constant.
We can clearly see dynamic array resize points. Since we are averaging every 10k inserts to be able to see anything at all above system noise, those peaks are in fact about 10k times larger than shown!
The zoomed graph excludes essentially only the array resize points, and shows that almost all inserts fall under 25 nanoseconds.
BST is logarithmic. All inserts are much slower than the average heap insert.
BST vs hashmap detailed analysis at: https://stackoverflow.com/questions/18414579/what-data-structure-is-inside-stdmap-in-c/51945119#51945119
GCC C++ standard library insert benchmark on gem5
gem5 is a full system simulator, and therefore provides an infinitely accurate clock with with m5 dumpstats. So I tried to use it to estimate timings for individual inserts.
Interpretation:
heap is still constant, but now we see in more detail that there are a few lines, and each higher line is more sparse.
This must correspond to memory access latencies are done for higher and higher inserts.
TODO I can't really interpret the BST fully one as it does not look so logarithmic and somewhat more constant.
With this greater detail however we can see can also see a few distinct lines, but I'm not sure what they represent: I would expect the bottom line to be thinner, since we insert top bottom?
Benchmarked with this Buildroot setup on an aarch64 HPI CPU.
BST cannot be efficiently implemented on an array
Heap operations only need to bubble up or down a single tree branch, so O(log(n)) worst case swaps, O(1) average.
Keeping a BST balanced requires tree rotations, which can change the top element for another one, and would require moving the entire array around (O(n)).
Heaps can be efficiently implemented on an array
Parent and children indexes can be computed from the current index as shown here.
There are no balancing operations like BST.
Delete min is the most worrying operation as it has to be top down. But it can always be done by "percolating down" a single branch of the heap as explained here. This leads to an O(log(n)) worst case, since the heap is always well balanced.
If you are inserting a single node for every one you remove, then you lose the advantage of the asymptotic O(1) average insert that heaps provide as the delete would dominate, and you might as well use a BST. Dijkstra however updates nodes several times for each removal, so we are fine.
Dynamic array heaps vs pointer tree heaps
Heaps can be efficiently implemented on top of pointer heaps: https://stackoverflow.com/questions/19720438/is-it-possible-to-make-efficient-pointer-based-binary-heap-implementations
The dynamic array implementation is more space efficient. Suppose that each heap element contains just a pointer to a struct:
the tree implementation must store three pointers for each element: parent, left child and right child. So the memory usage is always
4n(3 tree pointers + 1structpointer).Tree BSTs would also need further balancing information, e.g. black-red-ness.
the dynamic array implementation can be of size
2njust after a doubling. So on average it is going to be1.5n.
On the other hand, the tree heap has better worst case insert, because copying the backing dynamic array to double its size takes O(n) worst case, while the tree heap just does new small allocations for each node.
Still, the backing array doubling is O(1) amortized, so it comes down to a maximum latency consideration. Mentioned here.
Philosophy
BSTs maintain a global property between a parent and all descendants (left smaller, right bigger).
The top node of a BST is the middle element, which requires global knowledge to maintain (knowing how many smaller and larger elements are there).
This global property is more expensive to maintain (log n insert), but gives more powerful searches (log n search).
Heaps maintain a local property between parent and direct children (parent > children).
The top note of a heap is the big element, which only requires local knowledge to maintain (knowing your parent).
Comparing BST vs Heap vs Hashmap:
BST: can either be either a reasonable:
heap: is just a sorting machine. Cannot be an efficient unordered set, because you can only check for the smallest/largest element fast.
hash map: can only be an unordered set, not an efficient sorting machine, because the hashing mixes up any ordering.
Doubly-linked list
A doubly linked list can be seen as subset of the heap where first item has greatest priority, so let's compare them here as well:
- insertion:
- position:
- doubly linked list: the inserted item must be either the first or last, as we only have pointers to those elements.
- binary heap: the inserted item can end up in any position. Less restrictive than linked list.
- time:
- doubly linked list:
O(1)worst case since we have pointers to the items, and the update is really simple - binary heap:
O(1)average, thus worse than linked list. Tradeoff for having more general insertion position.
- doubly linked list:
- position:
- search:
O(n)for both
An use case for this is when the key of the heap is the current timestamp: in that case, new entries will always go to the beginning of the list. So we can even forget the exact timestamp altogether, and just keep the position in the list as the priority.
This can be used to implement an LRU cache. Just like for heap applications like Dijkstra, you will want to keep an additional hashmap from the key to the corresponding node of the list, to find which node to update quickly.
Comparison of different Balanced BST
Although the asymptotic insert and find times for all data structures that are commonly classified as "Balanced BSTs" that I've seen so far is the same, different BBSTs do have different trade-offs. I haven't fully studied this yet, but it would be good to summarize these trade-offs here:
- Red-black tree. Appears to be the most commonly used BBST as of 2019, e.g. it is the one used by the GCC 8.3.0 C++ implementation
- AVL tree. Appears to be a bit more balanced than BST, so it could be better for find latency, at the cost of slightly more expensive finds. Wiki summarizes: "AVL trees are often compared with red–black trees because both support the same set of operations and take [the same] time for the basic operations. For lookup-intensive applications, AVL trees are faster than red–black trees because they are more strictly balanced. Similar to red–black trees, AVL trees are height-balanced. Both are, in general, neither weight-balanced nor mu-balanced for any mu < 1/2; that is, sibling nodes can have hugely differing numbers of descendants."
- WAVL. The original paper mentions advantages of that version in terms of bounds on rebalancing and rotation operations.
See also
Similar question: https://stackoverflow.com/questions/6147242/heap-vs-binary-search-tree-bst
answered Apr 9, 2015 at 6:56
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1$\begingroup$ Great answer. Common application of heap are median, k min, top k elements. For these most common operation remove min then insert(usually we have small heap with few pure insert operations). So seems like in practice, for these algorithms it does not outperform BST. $\endgroup$– yuraCommented Jun 19, 2016 at 19:25
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1$\begingroup$ Exceptional answer!!! By using deque as underlying heap structure, you can dramatically reduce resize times, although it still be O(n) worst case since it needs to realloc (smaller) array of pointers to chunks. $\endgroup$– BulatCommented Aug 22, 2018 at 9:54
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Both binary search trees and binary heaps are tree-based data structures.
Heaps require the nodes to have a priority over their children. In a max heap, each node's children must be less than itself. This is the opposite for a min heap.
Max Heap:
Binary search trees (BST) follow a specific ordering (pre-order, in-order, post-order) among sibling nodes. The tree must be sorted, unlike heaps.
Binary Search Tree:
BST have average of $O(\log n)$ for insertion, deletion, and search.
Binary Heaps have average $O(1)$ for findMin/findMax and $O(\log n)$ for insertion and deletion.
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1$\begingroup$ @FrankW Extraction is $O(\log n)$, no? $\endgroup$– flow2kCommented Aug 6, 2018 at 22:57
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With data structure one has to distinguish levels of concern.
The abstract data structures (objects stored, their operations) in this question are different. One implements a priority queue, the other a set. A priority queue is not interested in finding an arbitrary element, only the one with largest priority.
The concrete implementation of the structures. Here on first sight both are (binary) trees, with different structural properties though. Both the relative ordering of the keys and the possible global structures differ. (Somewhat imprecise, in a
BSTkeys are ordered left-to-right, in a heap they are ordered top-down.) As IPlant correctly remarks an heap should also be "complete".There is a final difference in the low level implementation. A (unbalanced) binary search-tree has a standard implementation using pointers. A binary heap to the contrary has an efficient implementation using an array (precisely because of the restricted structure).
answered Jun 20, 2014 at 13:54
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On top of the previous answers, the heap must have the heap structure property; the tree must be full, and the bottom most layer, which cannot always be full, must be filled leftmost to rightmost with no gaps.
