Topic
algorithms
26 articles across 9 sub-topics
Sub-topic
12 articles
Two Pointer Technique: Solving Pair and Partition Problems in O(n)
TLDR: Place one pointer at the start and one at the end of a sorted array. Move them toward each other based on a comparison condition. Every classic pair/partition problem that naively runs in O(n²) collapses to O(n) with this one idea — and masteri...
Tries (Prefix Trees): The Data Structure Behind Autocomplete
TLDR: A Trie stores strings character by character in a tree, so every string sharing a common prefix shares those nodes. Insert and search are O(L) where L is the word length. Tries beat HashMaps on prefix queries — startsWith in O(L) with zero coll...
Sliding Window Technique: From O(n·k) Scans to O(n) in One Pass
TLDR: Instead of recomputing a subarray aggregate from scratch on every shift, maintain it incrementally — add the incoming element, remove the outgoing element. For a fixed window this costs O(1) per shift. For a variable window, expand the right bo...
Merge Intervals Pattern: Solve Scheduling Problems with Sort and Sweep
TLDR: Sort intervals by start time, then sweep left-to-right and merge any interval whose start ≤ the current running end. O(n log n) time, O(n) space. One pattern — three interview problems solved. 📖 When Two Meetings Overlap: The Scheduling Prob...
In-Place Reversal of a Linked List: The 3-Pointer Dance Every Interviewer Expects
TLDR: Reversing a linked list in O(1) space requires three pointers — prev, curr, and next. Each step: save next, flip curr.next to point backward, advance both prev and curr. Learn this once and you unlock four reversal variants that appear constant...
Fast and Slow Pointer: Floyd's Cycle Detection Algorithm Explained
TLDR: Move a slow pointer one step and a fast pointer two steps through a linked structure. If they ever meet, a cycle exists. Then reset one pointer to the head and advance both one step at a time — where they meet next is the cycle's start node. Th...
Sub-topic
5 articles
HyperLogLog Explained: Counting Billions of Unique Items with 12 KB
TLDR: HyperLogLog estimates the number of distinct elements in a dataset using ~12 KB of memory regardless of cardinality — with ±0.81% error. The insight: if you hash every element to a random bit string, the maximum length of leading zeros you obse...
Understanding Inverted Index and Its Benefits in Software Development
TLDR TLDR: An Inverted Index maps every word to the list of documents containing it — the same structure as the back-of-the-book index. It is the core data structure behind every full-text search engine, including Elasticsearch, Lucene, and PostgreS...
The Ultimate Data Structures Cheat Sheet
TLDR: Data structures are tools. Picking the right one depends on what operation you do most: lookup, insert, delete, ordered traversal, top-k, prefix search, or graph navigation. Start from operation frequency, not from habit. 📖 Why Structure Cho...
Tree Data Structure Explained: Concepts, Implementation, and Interview Guide
TLDR: Trees are hierarchical data structures used everywhere — file systems, HTML DOM, databases, and search algorithms. Understanding Binary Trees, BSTs, and Heaps gives you efficient $O(\log N)$ search, insertion, and deletion — and helps you ace a...
Mastering Binary Tree Traversal: A Beginner's Guide
TLDR: Binary tree traversal is about visiting every node in a controlled order. Learn pre-order, in-order, post-order, and level-order, and you can solve many interview and production problems cleanly. 📖 Four Ways to Walk a Tree — and Why the Orde...
Sub-topic
3 articles
Bloom Filters Explained: Membership Testing with Zero False Negatives
TLDR: A Bloom filter is a bit array of m bits + k independent hash functions that sets k bits on insert and checks those same k bits on lookup. If any checked bit is 0, the element is definitely not in the set — false negatives are mathematically imp...
Probabilistic Data Structures: A Practical Guide to Bloom Filters, HyperLogLog, and Count-Min Sketch
TLDR: Probabilistic data structures trade a small, bounded probability of being wrong for orders-of-magnitude better memory efficiency and O(1) speed. Bloom Filters answer "definitely not in this set" in constant time with zero false negatives. Hyper...
How Bloom Filters Work: The Probabilistic Set
TLDR TLDR: A Bloom Filter is a bit array + multiple hash functions that answers "Is X in the set?" in $O(1)$ constant space. It can return false positives (say "yes" when the answer is "no") but never false negatives (never says "no" when the answer...
Sub-topic
1 article
Count-Min Sketch Explained: Frequency Estimation at Streaming Scale
TLDR: Count-Min Sketch (CMS) is a fixed-size d × w counter matrix that estimates how often any element has appeared in a stream. Insert: hash the element with each of the d hash functions to get one column per row, increment those d counters. Query: ...
Sub-topic
1 article
Big O Notation Explained: Time Complexity, Space Complexity, and Why They Matter in Interviews
TLDR: Big O notation describes how an algorithm's resource usage grows as input size grows — not how fast it runs on your laptop. Learn to identify the 7 complexity classes (O(1) through O(n!)), derive time and space complexity by counting loops and ...
Sub-topic
1 article
Binary Search Patterns: Five Variants Every Senior Engineer Knows
TLDR: Binary search has five patterns beyond the classic "find the target": leftmost position, rightmost position, rotated array search, minimum in rotated array, and 2D matrix search. The root of every off-by-one bug is a mismatched loop condition a...
Sub-topic
1 article
What are Hash Tables? Basics Explained
TLDR: A hash table gives you near-O(1) lookups, inserts, and deletes by using a hash function to map keys to array indices. The tradeoff: collisions (when two keys hash to the same slot) must be handled, and a full hash table must be resized. 📖 Th...
Sub-topic
1 article
Exploring Different Types of Binary Trees
TLDR: A Binary Tree has at most 2 children per node, but the shape of the tree determines performance. A Full tree has 0 or 2 children. A Complete tree fills left-to-right. A Perfect tree is a symmetric triangle. A Degenerate tree becomes a linked li...
Sub-topic
1 article
Exploring Backtracking Techniques in Data Structures
TLDR: Backtracking is "Recursion with Undo." You try a path, explore it deeply, and if it fails, you undo your last decision and try the next option. It explores the full search space but prunes invalid branches early, making it far more efficient th...