Hash Map

Subtitle / Summary The key to LeetCode 105 is not memorizing that preorder and inorder can reconstruct a tree. It is understanding what each traversal contributes: preorder tells you the root, inorder tells you the left/right boundary. Once those roles are clear, the whole problem becomes a clean indexed divide-and-conquer. Reading time: 12-15 min Tags: Hot100, binary tree, divide and conquer, hash map, preorder SEO keywords: Construct Binary Tree from Preorder and Inorder Traversal, preorder, inorder, divide and conquer, hash map, LeetCode 105 Meta description: Learn LeetCode 105 from traversal roles, indexed recursion, and hash-map root lookup, with runnable multi-language implementations. A — Algorithm Problem Restatement Given the preorder traversal preorder and inorder traversal inorder of a binary tree, reconstruct the tree and return its root. ...

Subtitle / Summary Clone Graph is not a traversal-only problem. The real challenge is preserving graph structure while avoiding duplicate copies in the presence of cycles. The stable solution is a traversal plus a hash map from original nodes to cloned nodes. Reading time: 12-15 min Tags: graph, dfs, bfs, hash map, deep copy SEO keywords: Clone Graph, graph deep copy, DFS, BFS, LeetCode 133 Meta description: Deep-copy an undirected graph with a node-to-node map, explaining why memoization is mandatory and how DFS/BFS versions work, with runnable code in six languages. Target Readers LeetCode learners practicing graph traversal and deep-copy patterns Engineers who duplicate object graphs, workflow graphs, or topology graphs Developers who want one reusable template for “clone with cycles” Background / Motivation Many “copy” problems are actually identity-preservation problems. ...

Subtitle / Summary This is not a memorization question. It is core cache-engineering practice: satisfy fast lookup and least-recently-used eviction at the same time, both in constant average time. We derive the optimal structure from naive approaches and provide runnable implementations. Reading time: 14-18 min Tags: LRU, hash map, doubly linked list, system design SEO keywords: LRU Cache, LeetCode 146, hash map, doubly linked list, O(1) Meta description: Build an LRU cache with hash map + doubly linked list to achieve O(1) average get/put, with engineering use cases, pitfalls, and six-language implementations. A — Algorithm (Problem & Algorithm) Problem Restatement Design and implement LRUCache: ...

Subtitle / Summary This is Hot100 article #1 for the series: Subarray Sum Equals K. We reduce the naive O(n^2) approach to O(n) with prefix sum plus a frequency hash map, then map the same pattern to real engineering scenarios. Reading time: 12-15 min Tags: Hot100, prefix sum, hash map SEO keywords: Subarray Sum Equals K, prefix sum, hash map, O(n), Hot100 Meta description: O(n) counting of subarrays with sum k using prefix sum + hash map, with complexity analysis and runnable multi-language code. Target Readers Hot100 learners who want stable reusable templates Intermediate engineers who want to transfer counting patterns to real data pipelines Interview prep readers who want to master prefix sum + hash map Background / Motivation “Count subarrays whose sum equals k” is one of the most classic counting problems. It appears in log analytics, risk threshold hits, and transaction sequence statistics. The two-loop brute force method is straightforward, but slows down quickly as input grows. So we need an O(n) method that scales. ...

Subtitle / Summary The constraint “the path can start anywhere, but must go downward” makes root-to-leaf DP insufficient. This ACERS guide explains prefix sums on trees: convert any downward path into a difference of two prefix sums, maintain a frequency hash map during one DFS, and finish in O(n). Reading time: 12–15 min Tags: binary tree, prefix sum, DFS, hash map SEO keywords: Path Sum III, tree prefix sum, prefix-sum hash, LeetCode 437 Meta description: Count downward paths whose sum equals targetSum in O(n) via prefix sum + hash map, with derivation, tradeoffs, and multi-language implementations. A — Algorithm (Problem & Algorithm) Problem Restatement Given the root root of a binary tree and an integer targetSum, return the number of downward paths whose node values sum to targetSum. The path does not need to start at the root or end at a leaf, but it must go downward (parent → child). ...