117
COConfused about Stack?
Think of plates 🍽️
The last plate you add is the first one you remove.
That's called LIFO.
Follow for more simple coding concepts 👨💻
#DataStructures #Stack #LIFO #codingConcepts #ComputerScience
@coding.jrise123
#What Is The Difference Between A Stack And A Queue
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YOQuickSort is a sorting algorithm based on the Divide and Conquer that picks an element as a pivot and partitions the given array around the picked pivot by placing the pivot in its correct position in the sorted array. .
There are mainly three steps in the algorithm:
Choose a Pivot: Select an element from the array as the pivot. The choice of pivot can vary (e.g., first element, last element, random element, or median).
Partition the Array: Re arrange the array around the pivot. After partitioning, all elements smaller than the pivot will be on its left, and all elements greater than the pivot will be on its right.
Recursively Call: Recursively apply the same process to the two partitioned sub-arrays.
Base Case: The recursion stops when there is only one element left in the sub-array, as a single element is already sorted.
#programming #coding #datastructure #algorrithms #softwareengineer
@yo07_dev
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RUFollow ➡ @Rubix_Codes
Heap Sort Visualization
For More Updates✨
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Heap Sort is a comparison-based sorting algorithm that uses a binary heap data structure to sort elements. It works by first building a max-heap from the input array, then repeatedly extracting the maximum element from the heap and placing it at the end of the array. The heap property is maintained after each extraction using the heapify operation.
Key Characteristics:
• Time Complexity: O(n log n) in all cases (best, average, worst)
• Space Complexity: O(1) - in-place sorting algorithm
• Not stable - doesn't preserve relative order of equal elements
• Guaranteed O(n log n) performance - no worst-case degradation
• Excellent cache performance for building heap
• More complex to implement than simple sorts
Best Use Cases:
• When guaranteed O(n log n) performance is required
• Memory-constrained environments (in-place sorting)
• Systems where worst-case performance must be avoided
• Embedded systems with limited memory
• When memory allocation overhead must be minimized
#heapsort #algorithms #sorting
@rubix_codes
294
TE✅ Solved LeetCode Problem 696 – Count Binary Substrings
Today I solved LeetCode 696, a great problem focused on string manipulation and pattern recognition. The task is to count the number of non-empty substrings that have equal numbers of consecutive 0’s and 1’s, and all the 0’s and 1’s in those substrings are grouped consecutively.
🔹 This problem helps improve understanding of:
String traversal techniques
Counting consecutive characters
Optimizing from brute-force to efficient linear solution (O(n))
💡 The key idea is to track consecutive groups of 0’s and 1’s and count valid pairs based on the minimum of adjacent group lengths.
Another step forward in mastering Data Structures & Algorithms 🚀
Consistency is the key!
#leetcode #dsa #softwareengineer #programmer #java
@technogeek.17
93.9K
ONStack Data Structure #datastructures
@onjsdev
140
BL#software #javascript #datastructuresandalgorithms
@blezolad.dev
177
ONStacks are one of the most fundamental data structures in computer science, but their importance goes far beyond push and pop operations. Internally, stacks power recursion through the call stack, where each function call is stored until it returns. They are also essential for parsing expressions, checking balanced parentheses, evaluating postfix and prefix expressions, and implementing undo/redo features in applications. Many advanced algorithms rely on stack-based patterns like monotonic stacks, which help solve problems such as Next Greater Element, Daily Temperatures, and Largest Rectangle in Histogram efficiently. Stacks can be implemented using arrays or linked lists, and in most modern languages, they are built on top of dynamic arrays. While simple in structure, stacks form the backbone of many algorithmic techniques and system-level operations.
#coding #explore #reels #algorithms #ProgrammingLife
@onebyte.atatime
234
PRA stack is a linear data structure that follows the rule:
Last item added is the first item removed.
Think of it like a pile of books.
You add a book on top.
You remove a book from the top.
You never access the middle directly.
Stack Methods:
Push
Adds an element to the top of the stack.
Pop
Removes the top element from the stack.
Peek or Top
Returns the top element without removing it.
IsEmpty
Checks if the stack has no elements.
Size
Returns how many elements are in the stack.
Time Complexity:
Push
O(1) because adding to the top takes constant time.
Pop
O(1) because removing from the top takes constant time.
Peek
O(1) because you only look at the top element..
Search
O(n) in most implementations because you may need to check each element.
Where It’s Used:
Undo and redo in editors
Function calls in programs using the call stack
Expression evaluation and bracket checking
@project.maang.2026
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KRSQL vs NoSQL: The 5 Core Differences Visualized
Data architecture is the silent backbone of every massive app. Let's visualize how Relational and Document databases handle structure, scaling, and relationships fundamentally differently. Whether you are building a rigid financial system or a massive fluid social network, choosing the right database changes everything.
#programming #coding #webdevelopment #database #tech
@kreggscode
223
BAYour code works at n=10.
At n=1,000,000 it might kill your server. 🧵
Most devs never think about scale until production is on fire. Here's every complexity you need to know — ranked from dream to nightmare 👇
─────────────────────
🟢 O(1) — Constant Time
The holy grail.
Doesn't matter if you have 1 item or 1 billion — same speed, always. HashMap lookup, array indexing, stack push. When you write O(1) code, you've won.
─────────────────────
🔵 O(log n) — Logarithmic Time
Nearly as good as O(1).
Double the input → add only 1 extra step. Binary search on 1 billion records takes just 30 comparisons. This is why database indexes feel like magic. If your interviewer asks you to optimize, this is what they want.
─────────────────────
🟡 O(n) — Linear Time
The honest baseline.
One pass through the data. Touch every element once. Often completely unavoidable — if you must read all n items, O(n) IS optimal. Don't feel bad about linear. Feel bad about anything worse.
─────────────────────
🟠 O(n log n) — Linearithmic Time
Best possible for sorting.
Merge sort, quicksort, heapsort all live here. Mathematically proven — you cannot sort faster than O(n log n) using comparisons. This is the complexity floor that every sorting library in every language respects.
─────────────────────
🔴 O(n²) — Quadratic Time
Works in dev. Dies in prod.
Two nested loops over n. At n=1,000 it's fine. At n=100,000 it's 10 billion operations. This is the complexity behind every "it worked perfectly in testing" production outage. Check your loops.
─────────────────────
🟣 O(2ⁿ) — Exponential Time
More operations than atoms in the universe.
At n=50 that's over 1 quadrillion operations. At n=300 it exceeds the number of atoms in the observable universe. No, seriously. The only way to survive this is dynamic programming and memoization.
─────────────────────
The numbers at n = 1,000:
→ O(1) = 1 operation
→ O(log n) = ~10 operations
→ O(n) = 1,000 operations
→ O(n log n) = ~10,000 operations
→ O(n²) = 1,000,000 operations
→ O(2ⁿ) = 10^301 operations ☠️
────────────────────
Save it for interview ON!!
#programming #coding #softwaredevelopment #algorithms #datastructures
@backendbrilliance
5.0K
COWhat is a table in a database?
A table is the core structure used to organize data in rows and columns.
Each row represents a single record, and each column defines a specific attribute of that data.
Think of it like an Excel sheet — but with strict rules, data types, and constraints that ensure consistency and reliability.
Understanding tables is the foundation of learning SQL, backend development, and database design.
Day 3 of the Database Fundamentals Series.
Next: Primary Key.
Follow for more @codexjava_ 👆
#database #softwareengineering #sql #backenddevelopment #developerlife
@codexjava_
153
TE🔥 LeetCode 1545 – Find Kth Bit in Nth Binary String
Today’s problem was all about 🧠 Recursion + Pattern Observation
Instead of building the entire binary string (which grows exponentially 🚀), I learned how to:
✔ Identify the middle element pattern
✔ Use symmetry property
✔ Apply recursion smartly
✔ Optimize without generating full string
💡 Key Insight:
The string follows a recursive pattern:
Sn = Sn-1 + "1" + reverse(invert(Sn-1))
This problem really improved my logical thinking and recursive approach 💪
Consistency is the key 🔑
Day by day
#dsa #viralpost #softwareengineer #newarrival #programming
@technogeek.17
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