Nine Chapter
  • Introduction
    • Summary
  • 1.Binary Search
    • Introduction
    • 458.Last position of target
    • 600.Smallest Rectangle Enclosing Black Pixels
    • 585.Maximum Number in Mountain Sequence
    • 183.Wood Cut
    • 62.Search in Rotated Sorted Array
    • 63.Search in Rotated Sorted Array II
    • 159.Find Minimum in Rotated Sorted Array
    • 160.Find Minimum in Rotated Sorted Array II
    • 75.Find Peak Element
    • 60.Search Insert Position
    • 28.Search a 2D Matrix
    • 240. Search a 2D Matrix II
    • 14.First Position of Target
    • 74.First Bad Version
    • 875. Koko Eating Bananas
    • 1011. Capacity To Ship Packages Within D Days (M)
    • 410. Split Array Largest Sum (H)
    • 475. Heaters (M)
    • 1044. Longest Duplicate Substring (H)
  • 2.Binary Tree
    • Summary
      • 二叉树八股文:递归改迭代
      • BST
      • Frame
    • 66.Binary Tree Preorder Traversal
    • 67.🌟Binary Tree Inorder Traversal
    • 145. Binary Tree Postorder Traversal (E)
    • 98.Validate Binary Search Tree(M)
    • 85.Insert Node in a Binary Search Tree
    • 104. Maximum Depth of Binary Tree(E)
    • 235. Lowest Common Ancestor of a Binary Search Tree (E)
    • 236.Lowest Common Ancestor of Binary Tree(M)
    • 578.Lowest Common Ancestor III
    • 1120.Subtree with Maximum Average
    • 596.Minimum Subtree
    • 480.Binary Tree Paths
    • 453.Flatten Binary Tree to Linked List
    • 110.Balanced Binary Tree
    • 376.Binary Tree Path Sum
    • 246.Binary Tree Path Sum II
    • 475.Binary Tree Maximum Path Sum II
    • 124.Binary Tree Maximum Path Sum (H)
    • Path Sum (*)
      • 112. Path Sum
      • 113. Path Sum II
      • 437. Path Sum III
    • 177.Convert Sorted Array to Binary Search Tree With Minimal Height
    • 7.Binary Tree Serialization
    • 72,73.Construct Binary Tree
    • Binary Search Tree Path
    • 245.Subtree
    • 469.Identical Binary Tree
    • 87.Remove Node in Binary Search Tree
    • 116.Populating Next Right Pointers in Each Node (M)
    • 114. Flatten Binary Tree to Linked List(M)
    • 654.Maximum Binary Tree (M)
    • 105. 🌟Construct Binary Tree from Preorder and Inorder Traversal (M)
    • 106. Construct Binary Tree from Inorder and Postorder Traversal (M)
    • 652. Find Duplicate Subtrees(M)
    • 230. Kth Smallest Element in a BST (M)
    • 538&1038. Convert BST to Greater Tree
    • 450. Delete Node in a BST (M)
    • 701. Insert into a Binary Search Tree (M)
    • 96. Unique Binary Search Trees
    • 95. Unique Binary Search Trees II (M)
    • 1373. Maximum Sum BST in Binary Tree (H)
    • 297. Serialize and Deserialize Binary Tree (H)
    • 222. Count Complete Tree Nodes (M)
    • 1120. Maximum Average Subtree
    • 341. Flatten Nested List Iterator
    • 333. Largest BST Subtree (M)
    • 543. Diameter of Binary Tree
    • Binary Tree Longest Consecutive Sequence(*)
      • 298.Binary Tree Longest Consecutive Sequence
      • 549. Binary Tree Longest Consecutive Sequence II (M)
  • 3.Breadth First Search
    • Introduction
      • BFS 算法解题套路框架
      • 双向 BFS 优化
    • 102.Binary Tree Level Order Traversal (M)
    • 103. Binary Tree Zigzag Level Order Traversal (M)
    • 107.Binary Tree Level Order Traversal II(M)
    • 618.Search Graph Nodes
    • 207.Course Schedule (M)
    • 210.Course Schedule II (M)
    • 611.Knight Shortest Path
    • 598.Zombie in Matrix
    • 133.Clone Graph (M)
    • 178.Graph Valid Tree
    • 7.Binary Tree Serialization
    • 574.Build Post Office
    • 573.Build Post Office II
    • 127.Topological Sorting
    • 127.Word Ladder
    • 126. Word Ladder II
    • (LeetCode)515.Find Largest Value in Each Tree Row
    • 111. Minimum Depth of Binary Tree (E)
    • 752. Open the Lock
    • 542. 01 Matrix (M)
    • 1306. Jump Game III (M)
  • 4.Depth First Search+BackTracking
    • Summary
      • FloodFill 算法
    • 136.Palindrome Partitioning
    • 39.Combination Sum
    • 40.Combination Sum II
    • 377. Combination Sum IV
    • 77.Combinations (M)
    • 78.Subsets (M)
    • 90.Subsets II (M)
    • 46.🌟Permutations
    • 47.Permutations II
    • 582.Word Break II
    • 490.The Maze (M)
    • 51.N-Queens (H)
    • 52. N-Queens II (H)
    • 698. Partition to K Equal Sum Subsets (M)
    • 22. Generate Parentheses (M)
    • 岛屿问题
      • 200.Number of Islands (M)
      • 1254. Number of Closed Islands (M)
      • 1020. Number of Enclaves (M)
      • 695. Max Area of Island (M)
      • 1905. Count Sub Islands (M)
      • 694. Number of Distinct Islands
    • 131. Palindrome Partitioning (M)
    • 967. Numbers With Same Consecutive Differences (M)
    • 79. Word Search (M)
    • 212. Word Search II (M)
    • 472. Concatenated Words (H)
    • Page 2
    • 291. Word Pattern II
    • 17. Letter Combinations of a Phone Number (M)
  • 5.LinkedList
    • Summary
      • 单链表的倒数第 k 个节点
      • Merge two/k sorted LinkedList
      • Middle of the Linked List
      • 判断链表是否包含环
      • 两个链表是否相交 Intersection of Two Linked Lists
      • 递归反转链表
      • 如何判断回文链表
    • 599.Insert into a Cyclic Sorted List
    • 21.Merge Two Sorted Lists (E)
    • 23.Merge k Sorted Lists (H)
    • 105.Copy List with Random Pointer
    • 141.Linked List Cycle (E)
    • 142.Linked List Cycle II (M)
    • 148.Sort List (M)
    • 86.Partition List (M)
    • 83.Remove Duplicates from Sorted List(E)
    • 82.Remove Duplicates from Sorted List II (M)
    • 206.Reverse Linked List (E)
    • 92.Reverse Linked List II (M)
    • 143.Reorder List (M)
    • 19.Remove Nth Node From End of List (E)
    • 170.Rotate List
    • 🤔25.Reverse Nodes in k-Group (H)
    • 452.Remove Linked List Elements
    • 167.Add Two Numbers
    • 221.Add Two Numbers II
    • 876. Middle of the Linked List (E)
    • 160. Intersection of Two Linked Lists (E)
    • 234. Palindrome Linked List (E)
    • 2130. Maximum Twin Sum of a Linked List (M)
  • 6.Array
    • Summary
      • 前缀和思路PrefixSum
      • 差分数组 Difference Array
      • 双指针Two Pointers
      • 滑动窗口算法算法
      • Sliding windows II
      • 二分搜索Binary Search
      • 排序算法
      • 快速选择算法
    • 604.Window Sum
    • 138.Subarray Sum
    • 41.Maximum Subarray
    • 42.Maximum Subarray II
    • 43.Maximum Subarray III
    • 620.Maximum Subarray IV
    • 621.Maximum Subarray V
    • 6.Merge Two Sorted Arrays
    • 88.Merge Sorted Array
    • 547.Intersection of Two Arrays
    • 548.Intersection of Two Arrays II
    • 139.Subarray Sum Closest
    • 65.Median of two Sorted Arrays
    • 636.132 Pattern
    • 402.Continuous Subarray Sum
    • 303. Range Sum Query - Immutable (E)
    • 304.Range Sum Query 2D - Immutable (M)
    • 560. Subarray Sum Equals K (M)
    • 370. Range Addition(M)
    • 1109. Corporate Flight Bookings(M)
    • 1094. Car Pooling (M)
    • 76. Minimum Window Substring(H)
    • 567. Permutation in String (M)
    • 438. Find All Anagrams in a String(M)
    • 3. Longest Substring Without Repeating Characters (M)
    • 380. Insert Delete GetRandom O(1) (M)
    • 710. Random Pick with Blacklist (H)
    • 528. Random Pick with Weight (M)
    • 26. Remove Duplicates from Sorted Array (E)
    • 27. Remove Element (E)
    • 283. Move Zeroes (E)
    • 659. Split Array into Consecutive Subsequences (M)
    • 4. Median of Two Sorted Arrays (H)
    • 48. Rotate Image (M)
    • 54. Spiral Matrix (M)
    • 59. Spiral Matrix II (M)
    • 918. Maximum Sum Circular Subarray
    • 128. Longest Consecutive Sequence (M)
    • 238. Product of Array Except Self (M)
    • 1438. Longest Continuous Subarray With Absolute Diff Less Than or Equal to Limit (M)
    • 1151. Minimum Swaps to Group All 1's Together (M)
    • 2134. Minimum Swaps to Group All 1's Together II
    • 2133. Check if Every Row and Column Contains All Numbers
    • 632. Smallest Range Covering Elements from K Lists (H)
    • 36. Valid Sudoku (M)
    • 383. Ransom Note
    • 228. Summary Ranges
  • 7.Two pointers
    • Summary
      • Two Sum
      • 2Sum 3Sum 4Sum 问题
    • 1.Two Sum I
    • 170.Two Sum III - Data structure design
    • 167.Two Sum II- Input array is sorted
    • 609.Two Sum - Less than or equal to target
    • 610.Two Sum - Difference equals to targe
    • 587.Two Sum - Unique pairs
    • 533.Two Sum - Closest to target
    • 443.Two Sum - Greater than target
    • 653. Two Sum IV - Input is a BST (M)
    • 57.3Sum
    • 59.3Sum Closest
    • 58.4Sum
    • 148.Sort Colors
    • 143.Sort Colors II
    • 31.Partition Array
    • 625.Partition Array II
    • 382.Triangle Count
      • 611. Valid Triangle Number
    • 521.Remove Duplicate Numbers in Array
    • 167. Two Sum II - Input Array Is Sorted (E)
    • 870. Advantage Shuffle (M)
    • 9. Palindrome Number (E)
    • 125. Valid Palindrome(E)
    • 5. Longest Palindromic Substring (M)
    • 42. Trapping Rain Water
    • 11. Container With Most Water (M)
    • 658. Find K Closest Elements (M)
    • 392. Is Subsequence
  • 8.Data Structure
    • Summary
      • 数据结构的存储方式
      • 单调栈
      • 单调队列
      • 二叉堆 Binary Heap
      • TreeMap
      • TreeSet
      • 🌟Trie
      • Trie Application
    • 155. Min Stack (E)
    • 716. Max Stack (E)
    • 1648. Sell Diminishing-Valued Colored Balls
    • 232. Implement Queue using Stacks (E)
    • 225. Implement Stack using Queues(E)
    • 84.Largest Rectangle in Histogram
    • 128.Hash Function
    • Max Tree
    • 544.Top k Largest Numbers
    • 545.Top k Largest Numbers II
    • 613.High Five
    • 606.Kth Largest Element II
    • 5.Kth Largest Element
    • 129.Rehashing
    • 4.Ugly Number II
    • 517.Ugly Number
    • 28. Implement strStr()
    • 594.strStr II
    • 146.LRU Cache
    • 460.LFU Cache
    • 486.Merge k Sorted Arrays
    • 130.Heapify
    • 215. Kth Largest Element in an Array (M)
    • 612.K Closest Points
    • 692. Top K Frequent Words
    • 347.Top K Frequent Elements
    • 601.Flatten 2D Vector
    • 540.Zigzag Iterator
    • 541.Zigzag Iterator II
    • 423.Valid Parentheses
    • 488.Happy Number
    • 547.Intersection of Two Arrays
    • 548.Intersection of Two Arrays II
    • 627.Longest Palindrome
    • 638.Strings Homomorphism
    • 138.Subarray Sum
    • 647.Substring Anagrams
    • 171.Anagrams
    • 739. Daily Temperatures(M)
    • 496. Next Greater Element I (E)
    • 503. Next Greater Element II(M)
    • 316. Remove Duplicate Letters(M) & 1081. Smallest Subsequence of Distinct Characters
    • 239. Sliding Window Maximum (H)
    • 355. Design Twitter (M)
    • 895. Maximum Frequency Stack (H)
    • 20. Valid Parentheses (E)
    • 921. Minimum Add to Make Parentheses Valid (M)
    • 1541. Minimum Insertions to Balance a Parentheses String (M)
    • 32. Longest Valid Parentheses (H)
    • Basic Calculator (*)
      • 224. Basic Calculator
      • 227. Basic Calculator II (M)
    • 844. Backspace String Compare
    • 295. Find Median from Data Stream
    • 208. Implement Trie (Prefix Tree)
    • 461.Kth Smallest Numbers in Unsorted Array
    • 1152.Analyze user website visit pattern
    • 811. Subdomain Visit Count (M)
    • 71. Simplify Path (M)
    • 362. Design Hit Counter
  • 9.Dynamic Programming
    • Summary
      • 最优子结构 Optimal Sustructure
      • 子序列解题模板
      • 空间压缩
      • 背包问题
        • Untitled
      • 股票买卖问题
      • KMP
    • 109.Triangle
    • 110.Minimum Path Sum
    • 114.Unique Paths
    • 115.Unique Paths II
    • 70.Climbing Stairs
    • 272.Climbing StairsII
    • 116.Jump Game
    • 117.Jump Game II
    • 322.Coin Change
    • 518. Coin Change 2 ()
    • Backpack I~VI
      • LintCode 563.Backpack V (M)
    • Best Time to Buy and Sell Stock(*)
      • 121. Best Time to Buy and Sell Stock
      • 122. Best Time to Buy and Sell Stock II (M)
      • 123. Best Time to Buy and Sell Stock III (H)
      • 188. Best Time to Buy and Sell Stock IV (H)
      • 309. Best Time to Buy and Sell Stock with Cooldown (M)
      • 714. Best Time to Buy and Sell Stock with Transaction Fee (M)
    • 394.Coins in a line
    • 395.Coins in a Line II
    • 509. Fibonacci Number (E)
    • 931. Minimum Falling Path Sum (M)
    • 494. Target Sum (M)
    • 72. Edit Distance (H)
    • 300.Longest Increasing Subsequence
    • 1143. Longest Common Subsequence (M)
    • 718. Maximum Length of Repeated Subarray
    • 583. Delete Operation for Two Strings (M)
    • 712. Minimum ASCII Delete Sum for Two Strings(M)
    • 53. Maximum Subarray (E)
    • 516. Longest Palindromic Subsequence (M)
    • 1312. Minimum Insertion Steps to Make a String Palindrome (H)
    • 416. Partition Equal Subset Sum (M)
    • 64. Minimum Path Sum(M)
    • 651. 4 Keys Keyboards (M)
    • House Robber (*)
      • 198. House Robber (M)
      • 213. House Robbber II
      • 337. House Robber III (M)
    • Word Break (*)
      • 139.Word Break (M)
    • 140. Word Break II (H)
    • 828. Count Unique Characters of All Substrings of a Given String (H)
    • 174. Dungeon Game (H)
    • 1567. Maximum Length of Subarray With Positive Product (M)
  • 10. Graph
    • Introduction
      • 有向图的环检测
      • 拓扑排序
      • 二分图判定
      • Union-Find
      • 最小生成树(Minimum Spanning Tree)算法
        • KRUSKAL 最小生成树算法
        • Prim 最小生成树算法
      • Dijkstra 最短路径算法
      • BFS vs DFS
    • 797. All Paths From Source to Target (M)
    • 785. Is Graph Bipartite? (M)
    • 886. Possible Bipartition (M)
    • 130. Surrounded Regions (M)
    • 990. Satisfiability of Equality Equations (M)
    • 721. Accounts Merge (M)
    • 323. Number of Connected Components in an Undirected Graph (M)
    • 261. Graph Valid Tree
    • 1135. Connecting Cities With Minimum Cost
    • 1584. Min Cost to Connect All Points (M)
    • 277. Find the Celebrity (M)
    • 743. Network Delay Time (M)
    • 1631. Path With Minimum Effort (M)
    • 1514. Path with Maximum Probability (M)
    • 589.Connecting Graph
    • 🌟787. Cheapest Flights Within K Stops (M)
    • 2050. Parallel Courses III (H)
    • 1293. Shortest Path in a Grid with Obstacles Elimination (H)
    • 864. Shortest Path to Get All Keys (H)
    • 269. Alien Dictionary (H)
    • 1192. Critical Connections in a Network (H)
    • 529. Minesweeper (M)
  • 11.Math
    • Page 1
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  1. 8.Data Structure

355. Design Twitter (M)

https://leetcode.com/problems/design-twitter/

Design a simplified version of Twitter where users can post tweets, follow/unfollow another user, and is able to see the 10 most recent tweets in the user's news feed.

Implement the Twitter class:

  • Twitter() Initializes your twitter object.

  • void postTweet(int userId, int tweetId) Composes a new tweet with ID tweetId by the user userId. Each call to this function will be made with a unique tweetId.

  • List<Integer> getNewsFeed(int userId) Retrieves the 10 most recent tweet IDs in the user's news feed. Each item in the news feed must be posted by users who the user followed or by the user themself. Tweets must be ordered from most recent to least recent.

  • void follow(int followerId, int followeeId) The user with ID followerId started following the user with ID followeeId.

  • void unfollow(int followerId, int followeeId) The user with ID followerId started unfollowing the user with ID followeeId.

Example 1:

Input
["Twitter", "postTweet", "getNewsFeed", "follow", "postTweet", "getNewsFeed", "unfollow", "getNewsFeed"]
[[], [1, 5], [1], [1, 2], [2, 6], [1], [1, 2], [1]]
Output
[null, null, [5], null, null, [6, 5], null, [5]]

Explanation
Twitter twitter = new Twitter();
twitter.postTweet(1, 5); // User 1 posts a new tweet (id = 5).
twitter.getNewsFeed(1);  // User 1's news feed should return a list with 1 tweet id -> [5]. return [5]
twitter.follow(1, 2);    // User 1 follows user 2.
twitter.postTweet(2, 6); // User 2 posts a new tweet (id = 6).
twitter.getNewsFeed(1);  // User 1's news feed should return a list with 2 tweet ids -> [6, 5]. Tweet id 6 should precede tweet id 5 because it is posted after tweet id 5.
twitter.unfollow(1, 2);  // User 1 unfollows user 2.
twitter.getNewsFeed(1);  // User 1's news feed should return a list with 1 tweet id -> [5], since user 1 is no longer following user 2.

Constraints:

  • 1 <= userId, followerId, followeeId <= 500

  • 0 <= tweetId <= 104

  • All the tweets have unique IDs.

  • At most 3 * 104 calls will be made to postTweet, getNewsFeed, follow, and unfollow.

Solution:

Twitter 和微博功能差不多,我们主要要实现这样几个 API:

class Twitter {

    /** user 发表一条 tweet 动态 */
    public void postTweet(int userId, int tweetId) {}
    
    /** 返回该 user 关注的人(包括他自己)最近的动态 id,
    最多 10 条,而且这些动态必须按从新到旧的时间线顺序排列。*/
    public List<Integer> getNewsFeed(int userId) {}
    
    /** follower 关注 followee,如果 Id 不存在则新建 */
    public void follow(int followerId, int followeeId) {}
    
    /** follower 取关 followee,如果 Id 不存在则什么都不做 */
    public void unfollow(int followerId, int followeeId) {}
}

举个具体的例子,方便大家理解 API 的具体用法:

Twitter twitter = new Twitter();

twitter.postTweet(1, 5);
// 用户 1 发送了一条新推文 5

twitter.getNewsFeed(1);
// return [5],因为自己是关注自己的

twitter.follow(1, 2);
// 用户 1 关注了用户 2

twitter.postTweet(2, 6);
// 用户2发送了一个新推文 (id = 6)

twitter.getNewsFeed(1);
// return [6, 5]
// 解释:用户 1 关注了自己和用户 2,所以返回他们的最近推文
// 而且 6 必须在 5 之前,因为 6 是最近发送的

twitter.unfollow(1, 2);
// 用户 1 取消关注了用户 2

twitter.getNewsFeed(1);
// return [5]

这个场景在我们的现实生活中非常常见。拿朋友圈举例,比如我刚加到女神的微信,然后我去刷新一下我的朋友圈动态,那么女神的动态就会出现在我的动态列表,而且会和其他动态按时间排好序。只不过 Twitter 是单向关注,微信好友相当于双向关注。除非,被屏蔽…

这几个 API 中大部分都很好实现,最核心的功能难点应该是 getNewsFeed,因为返回的结果必须在时间上有序,但问题是用户的关注是动态变化的,怎么办?

这里就涉及到算法了:如果我们把每个用户各自的推文存储在链表里,每个链表节点存储文章 id 和一个时间戳 time(记录发帖时间以便比较),而且这个链表是按 time 有序的,那么如果某个用户关注了 k 个用户,我们就可以用合并 k 个有序链表的算法合并出有序的推文列表,正确地 getNewsFeed 了!

具体的算法等会讲解。不过,就算我们掌握了算法,应该如何编程表示用户 user 和推文动态 tweet 才能把算法流畅地用出来呢?这就涉及简单的面向对象设计了,下面我们来由浅入深,一步一步进行设计。

二、面向对象设计

根据刚才的分析,我们需要一个 User 类,储存 user 信息,还需要一个 Tweet 类,储存推文信息,并且要作为链表的节点。所以我们先搭建一下整体的框架:

class Twitter {
    private static int timestamp = 0;
    private static class Tweet {}
    private static class User {}

    /* 还有那几个 API 方法 */
    public void postTweet(int userId, int tweetId) {}
    public List<Integer> getNewsFeed(int userId) {}
    public void follow(int followerId, int followeeId) {}
    public void unfollow(int followerId, int followeeId) {}
}

之所以要把 Tweet 和 User 类放到 Twitter 类里面,是因为 Tweet 类必须要用到一个全局时间戳 timestamp,而 User 类又需要用到 Tweet 类记录用户发送的推文,所以它们都作为内部类。不过为了清晰和简洁,下文会把每个内部类和 API 方法单独拿出来实现。

1、Tweet 类的实现

根据前面的分析,Tweet 类很容易实现:每个 Tweet 实例需要记录自己的 tweetId 和发表时间 time,而且作为链表节点,要有一个指向下一个节点的 next 指针。

class Tweet {
    private int id;
    private int time;
    private Tweet next;

    // 需要传入推文内容(id)和发文时间
    public Tweet(int id, int time) {
        this.id = id;
        this.time = time;
        this.next = null;
    }
}

2、User 类的实现

我们根据实际场景想一想,一个用户需要存储的信息有 userId,关注列表,以及该用户发过的推文列表。其中关注列表应该用集合(Hash Set)这种数据结构来存,因为不能重复,而且需要快速查找;推文列表应该由链表这种数据结构储存,以便于进行有序合并的操作。画个图理解一下:

除此之外,根据面向对象的设计原则,「关注」「取关」和「发文」应该是 User 的行为,况且关注列表和推文列表也存储在 User 类中,所以我们也应该给 User 添加 follow,unfollow 和 post 这几个方法:

// static int timestamp = 0
class User {
    private int id;
    public Set<Integer> followed;
    // 用户发表的推文链表头结点
    public Tweet head;

    public User(int userId) {
        followed = new HashSet<>();
        this.id = userId;
        this.head = null;
        // 关注一下自己
        follow(id);
    }

    public void follow(int userId) {
        followed.add(userId);
    }

    public void unfollow(int userId) {
        // 不可以取关自己
        if (userId != this.id)
            followed.remove(userId);
    }

    public void post(int tweetId) {
        Tweet twt = new Tweet(tweetId, timestamp);
        timestamp++;
        // 将新建的推文插入链表头
        // 越靠前的推文 time 值越大
        twt.next = head;
        head = twt;
    }
}

3、几个 API 方法的实现

class Twitter {
    private static int timestamp = 0;
    private static class Tweet {...}
    private static class User {...}

    // 我们需要一个映射将 userId 和 User 对象对应起来
    private HashMap<Integer, User> userMap = new HashMap<>();

    /** user 发表一条 tweet 动态 */
    public void postTweet(int userId, int tweetId) {
        // 若 userId 不存在,则新建
        if (!userMap.containsKey(userId))
            userMap.put(userId, new User(userId));
        User u = userMap.get(userId);
        u.post(tweetId);
    }
    
    /** follower 关注 followee */
    public void follow(int followerId, int followeeId) {
        // 若 follower 不存在,则新建
		if(!userMap.containsKey(followerId)){
			User u = new User(followerId);
			userMap.put(followerId, u);
		}
        // 若 followee 不存在,则新建
		if(!userMap.containsKey(followeeId)){
			User u = new User(followeeId);
			userMap.put(followeeId, u);
		}
		userMap.get(followerId).follow(followeeId);
    }
    
    /** follower 取关 followee,如果 Id 不存在则什么都不做 */
    public void unfollow(int followerId, int followeeId) {
        if (userMap.containsKey(followerId)) {
            User flwer = userMap.get(followerId);
            flwer.unfollow(followeeId);
        }
    }

    /** 返回该 user 关注的人(包括他自己)最近的动态 id,
    最多 10 条,而且这些动态必须按从新到旧的时间线顺序排列。*/
    public List<Integer> getNewsFeed(int userId) {
        // 需要理解算法,见下文
    }
}

三、算法设计

实现合并 k 个有序链表的算法需要用到优先级队列(Priority Queue),这种数据结构是「二叉堆」最重要的应用,你可以理解为它可以对插入的元素自动排序。乱序的元素插入其中就被放到了正确的位置,可以按照从小到大(或从大到小)有序地取出元素。

PriorityQueue pq
# 乱序插入
for i in {2,4,1,9,6}:
    pq.add(i)
while pq not empty:
    # 每次取出第一个(最小)元素
    print(pq.pop())

# 输出有序:1,2,4,6,9

借助这种牛逼的数据结构支持,我们就很容易实现这个核心功能了。注意我们把优先级队列设为按 time 属性从大到小降序排列,因为 time 越大意味着时间越近,应该排在前面:

public List<Integer> getNewsFeed(int userId) {
    List<Integer> res = new ArrayList<>();
    if (!userMap.containsKey(userId)) return res;
    // 关注列表的用户 Id
    Set<Integer> users = userMap.get(userId).followed;
    // 自动通过 time 属性从大到小排序,容量为 users 的大小
    PriorityQueue<Tweet> pq = 
        new PriorityQueue<>(users.size(), (a, b)->(b.time - a.time));

    // 先将所有链表头节点插入优先级队列
    for (int id : users) {
        Tweet twt = userMap.get(id).head;
        if (twt == null) continue;
        pq.add(twt);
    }

    while (!pq.isEmpty()) {
        // 最多返回 10 条就够了
        if (res.size() == 10) break;
        // 弹出 time 值最大的(最近发表的)
        Tweet twt = pq.poll();
        res.add(twt.id);
        // 将下一篇 Tweet 插入进行排序
        if (twt.next != null) 
            pq.add(twt.next);
    }
    return res;
}

这个过程是这样的,下面是我制作的一个 GIF 图描述合并链表的过程。假设有三个 Tweet 链表按 time 属性降序排列,我们把他们降序合并添加到 res 中。注意图中链表节点中的数字是 time 属性,不是 id 属性:

至此,这道一个极其简化的 Twitter 时间线功能就设计完毕了。

四、最后总结

本文运用简单的面向对象技巧和合并 k 个有序链表的算法设计了一套简化的时间线功能,这个功能其实广泛地运用在许多社交应用中。

我们先合理地设计出 User 和 Tweet 两个类,然后基于这个设计之上运用算法解决了最重要的一个功能。可见实际应用中的算法并不是孤立存在的,需要和其他知识混合运用,才能发挥实际价值。

当然,实际应用中的社交 App 数据量是巨大的,考虑到数据库的读写性能,我们的设计可能承受不住流量压力,还是有些太简化了。而且实际的应用都是一个极其庞大的工程,比如下图,是 Twitter 这样的社交网站大致的系统结构:

我们解决的问题应该只能算 Timeline Service 模块的一小部分,功能越多,系统的复杂性可能是指数级增长的。所以说合理的顶层设计十分重要,其作用是远超某一个算法的。

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