Linked lists are versatile and widely-used data structures in programming. Just like arrays, they allow storing data sequentially. However, there are important differences in how linked lists allocate memory and store the data elements.
How Linked Lists Work
In a linked list, each data element is contained inside a node object. This node object contains the data value as well as pointers to the next and previous node. This forms a chain-like structure where nodes are linked via these pointer references.
Unlike arrays, linked list elements are not stored next to each other in memory. This has some major benefits:
Dynamic size: Linked lists can grow or shrink in size freely as nodes are added/removed. Arrays require pre-allocated static memory.
Efficiency: Linked lists prevent wasted unused memory slots. New nodes can be created as needed.
Flexibility: Easy to insert or delete nodes without shifting other elements (unlike arrays).
However, there are also downsides. Accessing a random element requires traversing pointers from the head node. So linked lists lose out performance-wise for indexing and random access compared to arrays.
| Operation | Linked List | Array |
|---|---|---|
| Indexing | O(N) | O(1) |
| Insert/Delete | O(1) | O(N) |
| Memory Allocation | Efficient | Fixed |
Nonetheless, linked lists open up many use cases such as:
Types of Linked Lists
There are different linked list variants designed for specific purposes:
Singly Linked Lists
The simplest type with each node pointing to the next. Used for:
- Stacks/Queues
- Polynomial representation
- File systems
- Browser history
And more such as AI decision trees, memory management, implementing CPU scheduler etc.
Doubly Linked Lists
Nodes contain references to both next and previous nodes. Use cases:
- Music/Video playlists
- Recently viewed products
- Undo/redo functionality
- Operating system process scheduling
- In-memory databases
Circular Linked Lists
The last node loops back to the first node in a non-ending loop. Applications:
- Implementing circular queues
- Traffic light systems
- Multiplayer gaming
- Round robin scheduling
- Image and video editing tools
Skip Lists
Special tiered structure to enable faster searches, inserts and deletes. Used for:
- Database indexing
- Caches
- Lookups/Dictionaries
And more like self-balancing trees, priority queues etc.
As we can see, different variants are optimized for certain use cases. Let‘s look at an example implementation next.
Linked List Implementation in Python
Here is some sample code to create a basic singly linked list and add some nodes:
class Node:
def __init__(self, data=None):
self.data = data
self.next = None
class SinglyLinkedList:
def __init__(self):
self.head = None
def insert_at_beginning(self, data):
new_node = Node(data)
new_node.next = self.head
self.head = new_node
linked_list = SinglyLinkedList()
linked_list.insert_at_beginning(10)
linked_list.insert_at_beginning(20)We can see methods like insert_at_beginning allow easily adding nodes in O(1) time.
Conclusion
The flexibility of linked lists makes them a versatile tool for tasks involving dynamic data and memory management, unde/redo capability, caching, system scheduling, route planning, and much more.
Choosing the right linked list variant like circular, doubly, or skip lists further optimizes efficiency and performance based on the use case. With practice, linked lists can become indispensable weapons in any programmer‘s arsenal!
