Tech Interviews: DSA Patterns

Confirm JavaScript is a valid interview language at top-tier tech companies, and learn the language-level quirks that trip up candidates in coding rounds.

Memorize the time and space complexity of JavaScript's built-in methods, and learn to roll your own priority queue, LRU cache, and linked-list node — things JS doesn't give you for free.

Recognize when a problem can be solved by trading space for time with a hash map or set, and internalize the canonical patterns.

Recognize when a problem's structure lets you shrink a search space with two coordinated pointers instead of a nested loop.

Master the fixed and variable sliding window patterns for problems involving contiguous subarrays or substrings.

Recognize when LIFO or FIFO order unlocks an O(n) solution, and master the monotonic stack pattern that solves 'next greater' problems.

Apply binary search beyond sorted arrays — recognize when the answer space is monotonic and search it in O(log n).

Master DFS and BFS traversals of binary trees and recognize when to use each based on problem structure.

Master the BST invariant, inorder-based techniques, and how to construct trees from traversal sequences.

Solve the harder tree problems — path sums, LCA on arbitrary trees, tree DP — by returning richer information from recursive calls.

Master two specialized data structures that unlock efficient solutions for prefix search, top-K, and streaming median problems.

Master the backtracking template for generating all solutions, finding one, or searching a combinatorial space with pruning.

Master BFS for shortest-path problems on unweighted graphs and 2D grids, including multi-source BFS.

Master DFS for connectivity, cycle detection, and topological ordering of directed graphs.

Master Dijkstra for weighted shortest path, Union-Find for connectivity queries, and MST algorithms for graph-based optimization.

Master the mental model for 1D DP: defining the state, writing the recurrence, choosing top-down vs bottom-up, and optimizing space.

Apply DP to 2D state spaces — grid problems, two-string problems, and knapsack variants — and learn when to collapse 2D tables to 1D.

Recognize when a local-optimum choice solves the global problem, and master the interval-scheduling patterns.

Master the bit tricks and math identities that unlock elegant O(1) or O(n) solutions to problems that look much harder.

Develop the meta-skill of recognizing which pattern a big-tech hard belongs to, and execute the 45-minute round confidently under pressure.