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Arrays store elements in contiguous memory locations, resulting in easily calculable addresses for the elements stored and this allows faster access to an element at a specific index. Linked lists are less rigid in their storage structure and elements are usually not stored in contiguous locations, hence they need to be stored with additional tags giving a reference to the next element. This difference in the data storage scheme decides which data structure would be more suitable for a given situation.
Visit the following resources to learn more:
- articleArrays in Python
- articleLinked List Python
- videoArray Data Structure | Illustrated Data Structures
- videoLinked List Data Structure | Illustrated Data Structures
| Characteristic | Arrays | Linked Lists |
|---|---|---|
| Definition | A collection of elements stored in contiguous memory locations. | A collection of elements (nodes) where each node points to the next node. |
| Memory Allocation | Fixed size and contiguous memory allocation. | Dynamic size with non-contiguous memory allocation. |
| Access Time | Constant time complexity O(1) for index-based access. | Linear time complexity O(n) for accessing an element by index. |
| Insertion Time | O(n) for insertion or deletion due to shifting elements. | O(1) for insertion or deletion if the position is known. |
| Deletion Time | O(n) for deletion due to shifting elements. | O(1) for deletion if the node is known. |
| Search Time | O(n) for searching an element. | O(n) for searching an element. |
| Memory Usage | Requires a fixed amount of memory; might waste space if not fully used. | More memory overhead due to storing additional pointers. |
| Dynamic Resizing | Fixed size; resizing involves creating a new array and copying elements. | Naturally dynamic; grows or shrinks as needed. |
| Cache Performance | Better cache locality due to contiguous memory. | Poor cache performance due to non-contiguous memory. |
| Implementation | Implemented directly in most programming languages. | Typically implemented using custom data structures or libraries. |
| Type of Data | Suitable for homogeneous data. | Suitable for both homogeneous and heterogeneous data. |
| Complexity of Operations | Simple and efficient for fixed-size operations. | More complex due to pointer management but flexible in size. |
| Examples | int arr[5] = {1, 2, 3, 4, 5}; in C/C++ <br> array = [1, 2, 3, 4, 5] in Python | Node class with data and next pointers in custom implementations. |