Need fast lookup · Count occurrences · Find duplicates · Check membership

Use a hash map or set to achieve O(1) average lookup, insertion, and deletion. Essential for problems requiring fast membership testing or counting distinct elements.

Sorted array · Find pairs · Container problems · Reverse strings

Move two pointers from opposite ends or at different speeds to solve problems in O(n) time without extra space. Perfect for sorted arrays and problems requiring pair comparisons.

Substring or subarray · Contiguous elements · Length constraints

Maintain a window of elements that expands and contracts. Converts nested loop O(n²) problems into O(n) by reusing computations from the previous window.

Sorted data · Find target · Minimize or maximize · Search space halving

Divide search space in half each iteration to achieve O(log n). Works on sorted arrays and problem domains where you can eliminate half the remaining options at each step.

Matching pairs · Brackets or parentheses · LIFO order · Undo operations

Last-in-first-out data structure. Use for problems requiring matching (brackets, tags), function call stacks, or operations where recent items matter most.

FIFO order · Level-order traversal · Breadth-first search · Task scheduling

First-in-first-out data structure. Essential for BFS algorithms and any problem where you process elements in the order they were added.

Sequential access · Insertion at head · Reversal needed · Cycle detection

Nodes containing data and pointers. Better than arrays for frequent insertions/deletions at known positions. Enable in-place operations without shifting elements.

Tree or graph · All paths · Topological sort · Backtracking

Explore deeply before backtracking. Use recursion or an explicit stack. Essential for finding all paths, detecting cycles, and problems requiring exhaustive search.

Shortest path · Level-order · Nearest neighbor · Graph traversal

Explore level by level using a queue. Finds shortest paths in unweighted graphs and processes nodes closest to the start first.

Top K elements · Median finding · Task scheduling · Dijkstra's algorithm

Efficiently retrieve min or max element in O(log n). Use for problems needing sorted access or maintaining a stream of top/bottom elements.