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data-structures-expert

Expert-level data structures covering arrays, linked lists, trees, heaps, hash tables, graphs, and advanced structures like segment trees, tries, and union-find.

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Data Structures Expert

Before Starting

  1. What operations need to be fast? (insert, delete, search, range query)
  2. Memory constraints?
  3. Ordered or unordered data?

Core Expertise Areas

Linear Structures

Array: O(1) random access, O(n) insert/delete middle, cache-friendly. Linked list: O(1) insert/delete at known position, O(n) search, no random access. Stack: LIFO, O(1) push/pop. Applications: function calls, expression parsing, DFS. Queue: FIFO, O(1) enqueue/dequeue. Applications: BFS, scheduling, buffers. Deque: double-ended queue, O(1) both ends. Sliding window problems.

Trees

BST: O(log n) avg search/insert/delete, O(n) worst case (degenerate). AVL tree: height-balanced BST, O(log n) guaranteed, rotation on insert/delete. Red-black tree: O(log n) guaranteed, fewer rotations than AVL, used in STL map. B-tree: multi-way tree for disk storage, minimizes disk reads, used in databases. Segment tree: range queries and updates, O(log n), build O(n). Trie: prefix tree for strings, O(m) operations where m is string length.

Heaps

Binary heap: complete binary tree, O(log n) insert/delete-max, O(1) peek. Min-heap vs max-heap: heap property direction determines which is O(1) to access. Fibonacci heap: O(1) amortized insert and decrease-key, O(log n) delete-min. Applications: priority queue, Dijkstra, Prim, heap sort, k-th largest.

Hash Tables

Hash function: maps key to index, good function minimizes collisions. Collision resolution: chaining (linked list per bucket), open addressing (probing). Load factor: ratio of elements to buckets, resize when exceeds threshold. O(1) average insert/search/delete, O(n) worst case with many collisions.

Union-Find

Disjoint set union: tracks connected components. Path compression and union by rank: nearly O(1) amortized per operation. Applications: Kruskal MST, cycle detection, dynamic connectivity.

Best Practices

  • Choose structure based on dominant operations in your use case
  • Consider cache performance for large datasets
  • Use lazy deletion where appropriate
  • Understand amortized vs worst-case complexity

Common Pitfalls

PitfallFix
Using linked list for random accessUse array or dynamic array instead
Hash table with bad hash functionTest distribution of hash values
BST degenerating to O(n)Use balanced variant like AVL or RB tree
Segment tree off-by-oneUse 1-indexed and carefully define ranges

Related Skills

  • algorithms-cs-expert
  • python-expert
  • java-expert

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

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