Data Structures and Algorithms
- Introduction to Data Structures and Algorithms
- Time and Space Complexity Analysis
- Big-O, Big-Theta, and Big-Omega Notations
- Recursion and Backtracking
- Divide and Conquer Algorithm
- Dynamic Programming: Memoization vs. Tabulation
- Greedy Algorithms and Their Use Cases
- Understanding Arrays: Types and Operations
- Linear Search vs. Binary Search
- Sorting Algorithms: Bubble, Insertion, Selection, and Merge Sort
- QuickSort: Explanation and Implementation
- Heap Sort and Its Applications
- Counting Sort, Radix Sort, and Bucket Sort
- Hashing Techniques: Hash Tables and Collisions
- Open Addressing vs. Separate Chaining in Hashing
- DSA Questions for Beginners
- Advanced DSA Questions for Competitive Programming
- Top 10 DSA Questions to Crack Your Next Coding Test
- Top 50 DSA Questions Every Programmer Should Practice
- Top Atlassian DSA Interview Questions
- Top Amazon DSA Interview Questions
- Top Microsoft DSA Interview Questions
- Top Meta (Facebook) DSA Interview Questions
- Netflix DSA Interview Questions and Preparation Guide
- Top 20 DSA Interview Questions You Need to Know
- Top Uber DSA Interview Questions and Solutions
- Google DSA Interview Questions and How to Prepare
- Airbnb DSA Interview Questions and How to Solve Them
- Mobile App DSA Interview Questions and Solutions
DSA Interview Questions
- DSA Questions for Beginners
- Advanced DSA Questions for Competitive Programming
- Top 10 DSA Questions to Crack Your Next Coding Test
- Top 50 DSA Questions Every Programmer Should Practice
- Top Atlassian DSA Interview Questions
- Top Amazon DSA Interview Questions
- Top Microsoft DSA Interview Questions
- Top Meta (Facebook) DSA Interview Questions
- Netflix DSA Interview Questions and Preparation Guide
- Top 20 DSA Interview Questions You Need to Know
- Top Uber DSA Interview Questions and Solutions
- Google DSA Interview Questions and How to Prepare
- Airbnb DSA Interview Questions and How to Solve Them
- Mobile App DSA Interview Questions and Solutions
Introduction to High-Level System Design
System Design Fundamentals
- Functional vs. Non-Functional Requirements
- Scalability, Availability, and Reliability
- Latency and Throughput Considerations
- Load Balancing Strategies
Architectural Patterns
- Monolithic vs. Microservices Architecture
- Layered Architecture
- Event-Driven Architecture
- Serverless Architecture
- Model-View-Controller (MVC) Pattern
- CQRS (Command Query Responsibility Segregation)
Scaling Strategies
- Vertical Scaling vs. Horizontal Scaling
- Sharding and Partitioning
- Data Replication and Consistency Models
- Load Balancing Strategies
- CDN and Edge Computing
Database Design in HLD
- SQL vs. NoSQL Databases
- CAP Theorem and its Impact on System Design
- Database Indexing and Query Optimization
- Database Sharding and Partitioning
- Replication Strategies
API Design and Communication
Caching Strategies
- Types of Caching
- Cache Invalidation Strategies
- Redis vs. Memcached
- Cache-Aside, Write-Through, and Write-Behind Strategies
Message Queues and Event-Driven Systems
- Kafka vs. RabbitMQ vs. SQS
- Pub-Sub vs. Point-to-Point Messaging
- Handling Asynchronous Workloads
- Eventual Consistency in Distributed Systems
Security in System Design
Observability and Monitoring
- Logging Strategies (ELK Stack, Prometheus, Grafana)
- API Security Best Practices
- Secure Data Storage and Access Control
- DDoS Protection and Rate Limiting
Real-World System Design Case Studies
- Distributed locking (Locking and its Types)
- Memory leaks and Out of memory issues
- HLD of YouTube
- HLD of WhatsApp
System Design Interview Questions
- Adobe System Design Interview Questions
- Top Atlassian System Design Interview Questions
- Top Amazon System Design Interview Questions
- Top Microsoft System Design Interview Questions
- Top Meta (Facebook) System Design Interview Questions
- Top Netflix System Design Interview Questions
- Top Uber System Design Interview Questions
- Top Google System Design Interview Questions
- Top Apple System Design Interview Questions
- Top Airbnb System Design Interview Questions
- Top 10 System Design Interview Questions
- Mobile App System Design Interview Questions
- Top 20 Stripe System Design Interview Questions
- Top Shopify System Design Interview Questions
- Top 20 System Design Interview Questions
- Top Advanced System Design Questions
- Most-Frequented System Design Questions in Big Tech Interviews
- What Interviewers Look for in System Design Questions
- Critical System Design Questions to Crack Any Tech Interview
- Top 20 API Design Questions for System Design Interviews
- Top 10 Steps to Create a System Design Portfolio for Developers
Linked Lists: Types and Implementations
Introduction to Linked Lists
Linked lists are a key building block in computer science that help store and manage data in a smart way. Imagine a treasure hunt where each clue leads you to the next—linked lists work like that! Each piece of data, called a node, holds a value and points to the next node. If you want to get better at coding and ace tech interviews, learning about linked lists is a must. Sign up for free course updates by filling out this quick form and get the latest tips and resources sent right to you.
Unlike arrays, which keep data in a straight line in memory, linked lists can scatter their nodes anywhere, connecting them with pointers. This setup makes it super easy to add or remove items without moving everything else around. But there’s a catch—finding a specific item takes longer because you have to follow the pointers step by step, unlike arrays where you can jump straight to what you need. Linked lists shine when you need a list that grows or shrinks a lot, like in games or music playlists.
They’re also a big deal in coding interviews. Problems about linked lists pop up all the time because they test how well you understand data structures. Want to get ready? Take a look at these Top 20 DSA Interview Questions to practice and boost your skills.
Types of Linked Lists
There are different flavors of linked lists, each with its own special twist. Let’s break down the three main ones: singly, doubly, and circular linked lists. Each type has a unique way of linking nodes, making them handy for different tasks.

Singly Linked List
A singly linked list is the simplest kind. Each node has two parts: the data (like a number or word) and a pointer that shows the way to the next node. The last node points to nothing (null), telling you the list is over.
Think of it like a one-way road—you can only move forward. Adding a new node is quick if you’re at the start, but it takes more steps to reach the end. You might use this in a program where you only need to go one direction, like tracking scores in a game. For a deeper dive into these concepts, check out some essential DSA courses.
Here’s what makes singly linked lists special:
- One pointer per node, keeping it simple.
- Great for moving forward through data.
- Takes less memory than other types.
Doubly Linked List
A doubly linked list steps things up a bit. Each node still has data, but now it has two pointers—one to the next node and one to the previous node. It’s like a two-way street where you can go forward or backward.
This makes some tasks easier, like removing a node, because you can find its neighbors fast. It’s perfect for things like a music app where you want to skip back to the last song. If you’re prepping for a big tech interview, like at Amazon, mastering this could help—see these Amazon DSA Questions for practice.
Key features of doubly linked lists:
- Two pointers let you travel both ways.
- Easier to delete or rearrange nodes.
- Uses more memory because of the extra pointer.
Circular Linked List
A circular linked list is a fun twist. Instead of ending with a null pointer, the last node loops back to the first one, making a circle. It can be singly (one-way) or doubly (two-way), depending on what you need.
Picture a merry-go-round—it keeps going around without a clear start or stop. This is awesome for things like a playlist that repeats or a game where players take turns in a loop. Curious about real-world uses? Explore more in a crash course on data structures.
What sets circular linked lists apart:
- No end—just keeps circling back.
- Handy for repeating patterns or cycles.
- Needs careful handling to avoid getting stuck in a loop.
Implementations of Linked Lists
Now that we know the types, let’s see how to build them. We’ll use Python because it’s easy to read, but the ideas work in any language. Each type needs a node class and a list class to manage it.
Implementing a Singly Linked List
For a singly linked list, you start with a node that holds data and a next pointer. Then, you make a class to control the whole list—like adding or removing nodes.
Here’s a simple setup in Python:
class Node:
class Node:
def __init__(self, data):
self.data = data
self.next = None
class LinkedList:
def __init__(self):
self.head = None
def add_to_start(self, data):
new_node = Node(data)
new_node.next = self.head
self.head = new_node
def show_list(self):
current = self.head
while current:
print(current.data, end=" -> ")
current = current.next
print("None")
Adding to the start is fast—just point the new node to the old head. Want to code this yourself? A DSA course can guide you through hands-on examples.
Here’s a quick look at operations:
Operation | Time Needed |
Add to Start | O(1) |
Add to End | O(n) |
Remove a Node | O(n) |
Implementing a Doubly Linked List
A doubly linked list needs a node with two pointers—next and prev. The list class updates both when you add or remove something.
Here’s how it starts:
class Node:
def __init__(self, data):
self.data = data
self.next = None
self.prev = None
class DoublyLinkedList:
def __init__(self):
self.head = None
def add_to_start(self, data):
new_node = Node(data)
new_node.next = self.head
if self.head:
self.head.prev = new_node
self.head = new_node
Going both ways makes it flexible, but it takes more work to manage. Prepping for a Meta interview? Check out Meta DSA Questions to see how this fits in.
Key operations:
- Adding or removing is O(1) if you know the spot.
- Takes extra space for the prev pointer.
Implementing a Circular Linked List
For a circular linked list, you tweak a singly or doubly list so the last node points back to the head. Here’s a singly version:
class Node:
def __init__(self, data):
self.data = data
self.next = None
class CircularLinkedList:
def __init__(self):
self.head = None
def add_to_end(self, data):
new_node = Node(data)
if not self.head:
self.head = new_node
new_node.next = self.head
else:
current = self.head
while current.next != self.head:
current = current.next
current.next = new_node
new_node.next = self.head
The loop makes it unique—perfect for cycling tasks. For more coding tips, try a web development course that covers practical projects.
Notable points:
- Loops back to the start.
- Great for round-and-round jobs.
Applications of Linked Lists
Linked lists aren’t just theory—they’re used everywhere! They’re perfect when you need a list that changes a lot or doesn’t need fast lookups.
Think about a music app. Songs in your playlist are nodes, and you can add or skip tracks easily with a linked list. Or in a game, tracking player moves works well with this setup. Even operating systems use linked lists to manage memory chunks. Want to see how this applies to coding jobs? Look at Atlassian DSA Questions for real examples.
Common uses:
- Playlists that grow or shrink.
- Back-and-forth features like undo in apps.
- Managing tasks that cycle, like scheduling.

Advantages and Disadvantages
Linked lists have their ups and downs, so let’s weigh them. They’re awesome for some jobs but not perfect for everything.
They’re great because they can grow as big as you need without planning ahead, unlike arrays. Adding or removing items is quick if you’re at the right spot. But they’re slow to search through since you can’t jump to the middle—you have to walk the whole path. If you’re aiming for a Netflix gig, understanding this trade-off is key; peek at Netflix DSA Questions.
Here’s a comparison:
Feature | Linked List | Array |
Size | Grows Easy | Fixed |
Access Speed | Slow (O(n)) | Fast (O(1)) |
Add/Remove | Fast (O(1)) | Slow (O(n)) |
In short, linked lists are your go-to when you need flexibility over speed. They’ve been around since the 1950s, when computer pioneers like Allen Newell used them in early programming languages. Today, they’re still a vital tool for coders everywhere.

What’s the easiest type of linked list to use?
The singly linked list is the easiest because it’s super simple—each node just points to the next one. It’s perfect for basic tasks like making a to-do list in code or tracking steps in a game. If you’re new to this and want to practice, the DSA Course is a great place to start with clear examples.
Why would I pick a doubly linked list over a singly one?
A doubly linked list lets you move backward and forward, thanks to its two pointers per node. This makes it awesome for things like a photo gallery where you swipe both ways. To get hands-on with this, try the Web Development Course—it’s packed with projects to help you learn.
Are circular linked lists really useful?
Yes, they’re super helpful for things that repeat, like a playlist on loop or a game where turns go round and round. They save you from restarting at the end—just keep going! Curious to explore more? The Design DSA Combined Course dives into cool uses like this.
How do linked lists help in real coding jobs?
They’re used in tons of places—like managing memory in your computer or keeping browser history so you can go back. They’re a big deal in tech interviews too, so knowing them can land you a job. Check out the Master DSA Web Dev System Design Course to see how they fit into real projects.
Can I learn linked lists fast for an interview?
Absolutely! Start with the basics—singly, doubly, circular—and practice a few problems. They’re not hard once you get the hang of pointers. For a quick boost, the Data Science Course offers fast-track lessons on this and more.

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