1. |
You can’t find an arbitrary element in a heap in O(log N). |
1. You can find an arbitrary element in a Red-Black Tree in O(log N). |
2. |
Implement using a complete binary tree. |
2. Implement using a self-balancing binary search tree. |
3. |
Ordering, parent < children in Min Heap and parent > children in Max Heap. |
3. Ordering, left child < parent < right child. |
4. |
The structure is implicit, the root is at position 0, and children of the root are at 1 and 2, so no overhead per element. |
4. Pointers are used to represent the structure of the tree, so overhead per element. |
5. |
Typical use, priority queue, and heap sort. |
5. Typical use, TreeSet, TreeMap, and Hashmap in the Java Collections Library and ordered map in C++ STL. |
6. |
Heap does not support efficient search operation |
Red-black tree provides efficient search operations due to its ordered binary search tree data structure. |
7. |
Requires less space than red-black tree. |
Requires more space as it stores data of colour. |
8. |
Specialized data structure. |
General-purpose data structure. |
9. |
Heap has a simpler deletion operation compared to red-black tree since it only involves removing the root node and then fixing the heap property by swapping nodes. |
Red-black tree, on the other hand, requires more complex rotations and color changes to maintain the balance property. |