Data Structures
Understand arrays, trees, graphs, heaps, hash tables, and choosing the right structure.
Choosing the wrong structure is a sharp signal in coding rounds and design discussions: it leads to wrong complexity, brittle code, or production slowness that's hard to fix later. At senior levels, interviewers listen for whether you reason from constraints to structure, not the other way around.
These topics build a precise mental model of arrays, trees, heaps, hashes, and when each fits—so you can choose and justify your choices under real constraints, reason about trade-offs, and explain your design clearly. You'll signal that you think in data shapes, not just APIs.
Topics in this category
Arrays: Data Structure Fundamentals & Complexity
Read →Master arrays: the most fundamental data structure. Learn operations, time complexity, dynamic arrays, and when to use arrays vs other structures.
B-Tree Explained for Database Indexes (B+ Tree Included)
Read →A clear guide to B-trees for DB indexes: how inserts/splits work, why high fanout matters, and how B+ trees enable fast range queries.
Bloom Filters: Fundamentals, Operations & Complexity
Read →Master Bloom filters: space-efficient probabilistic data structures for membership testing. Learn false positives, hash functions, and real-world applications.