Linked List Search & Insert Before | Pointer Logic Explained with Dry Run | DSA & Problem Solving #8
In previous videos of this Data Structures & Algorithms series we built the core of our Linked List step by step.
Introduction to Search & Insert Before (0:00)
What is a Linked List? (1:17)
Naive Search Approach (1:30)
Search Algorithm Implementation (3:59)
Understanding insert before (5:45)
insert before (Two-Pointer Approach) (7:09)
insert before (Single-Pointer Approach) (9:27)
insert before Code Implementation (12:35)
Testing Search and Insert Before (17:06)
Conclusion & Next Steps (26:35)
We started with:
• Understanding arrays and memory
• Designing the Node structure
• Implementing push_back
• Implementing push_front
• Implementing insert at position
Now we move to two very important operations:
Search
Insert Before
These operations introduce a deeper level of thinking about pointer traversal.
Linked Lists are not about memorizing code.
They are about understanding how memory references move.
And search is the first operation where we begin exploring the list rather than just modifying its ends.
Linked List Search
Search is conceptually simple but extremely important.
The algorithm follows a fundamental traversal pattern:
Start from head
Move node by node
Compare the value
Stop when found or reach NULL
But the real learning happens when you visualize what traversal means.
Each step means following a pointer in memory.
Each node lives somewhere in heap memory.
The next pointer tells us where the next node exists.
So when we search, we are literally walking through memory addresses.
This is why Linked Lists have O(n) search complexity.
Unlike arrays, we cannot jump directly to an index.
We must follow the chain.
Insert Before
Insert before a node is where pointer logic becomes interesting.
To insert before a value we must:
• Traverse the list
• Identify the target node
• Keep track of the previous node
• Create a new node
• Adjust links carefully
If we change pointers in the wrong order, we can lose part of the list.
This is one of the most common mistakes beginners make.
That’s why in this video we slow down and perform detailed dry runs.
We trace:
head
current node
previous node
new node
And observe exactly how links change.
Why This Matters
Many students struggle with Linked Lists because they treat them like syntax problems.
But Linked Lists are really about understanding relationships between nodes in memory.
Once you internalize this idea, many advanced structures become easier to understand:
• Trees
• Graphs
• Hash table chaining
• Dynamic memory structures
Everything begins with understanding references.
Learning Strategy
While watching this video, do not watch passively.
Pause frequently.
Take a notebook.
Draw nodes as boxes.
Draw arrows representing pointers.
Predict what happens before running the program.
Then run the code and verify your reasoning.
This process trains your brain to simulate algorithms.
That skill becomes extremely valuable during coding interviews and competitive programming.
Topics Covered
• Linked List traversal
• Search algorithm in Linked List
• Insert before node logic
• Pointer tracking (previous and current)
• Memory visualization through dry run
• Edge cases in insertion
What’s Coming Next
In upcoming videos we will explore:
• Insert after node
• Deletion operations
• Count and occurrence logic
• Reversing a Linked List
• Sorting Linked List
• Doubly Linked Lists
Each step will build deeper understanding of dynamic data structures.
If you are learning DSA seriously
Remember one rule:
Don’t rush.
Strong programmers are built through reasoning, not speed.
Think through the code.
Dry run every algorithm.
Visualize memory.
That is how you master Linked Lists.
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