LintCode & LeetCode
  • Introduction
  • Linked List
    • Sort List
    • Merge Two Sorted Lists
    • Merge k Sorted Lists
    • Linked List Cycle
    • Linked List Cycle II
    • Add Two Numbers II
    • Add Two Numbers
    • Odd Even Linked List
    • Intersection of Two Linked Lists
    • Reverse Linked List
    • Reverse Linked List II
    • Remove Linked List Elements
    • Remove Nth Node From End of List
    • Middle of the Linked List
    • Design Linked List
      • Design Singly Linked List
      • Design Doubly Linked List
    • Palindrome Linked List
    • Remove Duplicates from Sorted List
    • Remove Duplicates from Sorted List II
    • Implement Stack Using Singly Linked List
    • Copy List with Random Pointer
  • Binary Search
    • Search in Rotated Sorted Array
    • Search in Rotated Sorted Array II
    • Search in a Sorted Array of Unknown Size
    • First Bad Version
    • Find Minimum in Rotated Sorted Array
    • Find Minimum in Rotated Sorted Array II
    • Find Peak Element
    • Search for a Range
    • Find K Closest Elements
    • Search Insert Position
    • Peak Index in a Mountain Array
    • Heaters
  • Hash Table
    • Jewels and Stones
    • Single Number
    • Subdomain Visit Count
    • Design HashMap
    • Design HashSet
    • Logger Rate Limiter
    • Isomorphic Strings
    • Minimum Index Sum of Two Lists
    • Contains Duplicate II
    • Contains Duplicate III
    • Longest Consecutive Sequence
    • Valid Sudoku
    • Distribute Candies
    • Shortest Word Distance
    • Shortest Word Distance II
  • String
    • Rotate String
    • Add Binary
    • Implement strStr()
    • Longest Common Prefix
    • Reverse Words in a String
    • Reverse Words in a String II
    • Reverse Words in a String III
    • Valid Word Abbreviation
    • Group Anagrams
    • Unique Email Addresses
    • Next Closest Time
    • License Key Formatting
    • String to Integer - atoi
    • Ransom Note
    • Multiply Strings
    • Text Justification
    • Reorder Log Files
    • Most Common Word
    • Valid Parenthesis String
    • K-Substring with K different characters
    • Find All Anagrams in a String
    • Find the Closest Palindrome
    • Simplify Path
  • Array
    • Partition Array
    • Median of Two Sorted Arrays
    • Intersection of Two Arrays
    • Intersection of Two Arrays II
    • Maximum Subarray Sum
    • Minimum Subarray Sum
    • Maximum Subarray II
    • Maximum Subarray III
    • Subarray Sum Closest
    • Subarray Sum
    • Plus One
    • Maximum Subarray Difference
    • Maximum Subarray IV
    • Subarray Sum Equals K
    • Intersection of Two Arrays
    • Intersection of Two Arrays II
    • Find Pivot Index
    • Rotate Array
    • Get Smallest Nonnegative Integer Not In The Array
    • Maximize Distance to Closest Person
    • Sort Colors
    • Next Permutation
    • Rotate Image
    • Pour Water
    • Prison Cells After N Days
    • Majority Element
    • Can Place Flowers
    • Candy
  • Matrix
    • Spiral Matrix
    • Set Matrix Zeroes
    • Diagonal Traverse
  • Queue
    • Design Circular Queue
    • Implement Queue using Stacks
    • Implement Queue by Two Stacks
    • Implement Stack using Queues
    • Moving Average from Data Stream
    • Walls and Gates
    • Open the Lock
    • Sliding Window Maximum
    • Implement Queue Using Fixed Length Array
    • Animal Shelter
  • Stack
    • Valid Parentheses
    • Longest Valid Parentheses
    • Min Stack
    • Max Stack
    • Daily Temperatures
    • Evaluate Reverse Polish Notation
    • Next Greater Element I
    • Next Greater Element II
    • Next Greater Element III
    • Largest Rectangle in Histogram
    • Maximal Rectangle
    • Car Fleet
  • Heap
    • Trapping Rain Water II
    • The Skyline Problem
    • Top K Frequent Words
    • Top K Frequent Words II
    • Top K Frequent Elements
    • Top k Largest Numbers
    • Top k Largest Numbers II
    • Minimum Cost to Hire K Workers
    • Kth Largest Element in an Array
    • Kth Smallest Number in Sorted Matrix
    • Kth Smallest Sum In Two Sorted Arrays
    • K Closest Points to the Origin
    • Merge K Sorted Lists
    • Merge K Sorted Arrays
    • Top K Frequent Words - Map Reduce
  • Data Structure & Design
    • Hash Function
    • Heapify
    • LRU Cache
    • LFU Cache
    • Rehashing
    • Stack Sorting
    • Animal Shelter
    • Sliding Window Maximum
    • Moving Average from Data Stream
    • Find Median from Data Stream
    • Sliding Window Median
    • Design Hit Counter
    • Read N Characters Given Read4 II - Call multiple times
    • Read N Characters Given Read4
    • Flatten 2D Vector
    • Flatten Nested List Iterator
    • Design Search Autocomplete System
    • Time Based Key-Value Store
    • Design Tic-Tac-Toe
    • Insert Delete GetRandom O(1)
  • Union Find
    • Find the Connected Component in the Undirected Graph
    • Find the Weak Connected Component in the Directed Graph
    • Graph Valid Tree
    • Number of Islands
    • Number of Islands II
    • Surrounded Regions
    • Most Stones Removed with Same Row or Column
    • Redundant Connection
  • Trie
    • Implement Trie
    • Add and Search Word
    • Word Search II
    • Longest Word in Dictionary
    • Palindrome Pairs
    • Trie Serialization
    • Trie Service
    • Design Search Autocomplete System
    • Typeahead
  • Trees
    • Binary Tree Inorder Traversal
    • Binary Tree Postorder Traversal
    • Binary Tree Preorder Traversal
    • Binary Tree Level Order Traversal
    • Binary Tree Zigzag Level Order Traversal
    • Binary Tree Vertical Order Traversal
    • N-ary Tree Level Order Traversal
    • N-ary Tree Preorder Traversal
    • N-ary Tree Postorder Traversal
    • Construct Binary Tree from Preorder and Inorder Traversal
    • Populating Next Right Pointers in Each Node
    • Populating Next Right Pointers in Each Node II
    • Maximum Depth of Binary Tree
    • Symmetric Tree
    • Validate Binary Search Tree
    • Convert Sorted Array to Binary Search Tree
    • Path Sum
    • Path Sum II
    • Path Sum III
    • Binary Tree Maximum Path Sum
    • Kth Smallest Element in a BST
    • Same Tree
    • Lowest Common Ancestor of a Binary Tree
    • Lowest Common Ancestor of a Binary Search Tree
    • Nested List Weight Sum II
    • BST Node Distance
    • Minimum Distance (Difference) Between BST Nodes
    • Closet Common Manager
    • N-ary Tree Postorder Traversal
    • Serialize and Deserialize Binary Tree
    • Serialize and Deserialize N-ary Tree
    • Diameter of a Binary Tree
    • Print Binary Trees
  • Segment Tree
    • Segment Tree Build
    • Range Sum Query - Mutable
  • Binary Indexed Tree
  • Graph & Search
    • Clone Graph
    • N Queens
    • Six Degrees
    • Number of Islands
    • Number of Distinct Islands
    • Word Search
    • Course Schedule
    • Course Schedule II
    • Word Ladder
    • Redundant Connection
    • Redundant Connection II
    • Longest Increasing Path in a Matrix
    • Reconstruct Itinerary
    • The Maze
    • The Maze II
    • The Maze III
    • Topological Sorting
    • Island Perimeter
    • Flood Fill
    • Cheapest Flights Within K Stops
    • Evaluate Division
    • Alien Dictionary
    • Cut Off Trees for Golf Event
    • Jump Game II
    • Most Stones Removed with Same Row or Column
  • Backtracking
    • Subsets
    • Subsets II
    • Letter Combinations of a Phone Number
    • Permutations
    • Permutations II
    • Combinations
    • Combination Sum
    • Combination Sum II
    • Combination Sum III
    • Combination Sum IV
    • N-Queens
    • N-Queens II
    • Generate Parentheses
    • Subsets of Size K
  • Two Pointers
    • Two Sum II
    • Triangle Count
    • Trapping Rain Water
    • Container with Most Water
    • Minimum Size Subarray Sum
    • Minimum Window Substring
    • Longest Substring Without Repeating Characters
    • Longest Substring with At Most K Distinct Characters
    • Longest Substring with At Most Two Distinct Characters
    • Fruit Into Baskets
    • Nuts & Bolts Problem
    • Valid Palindrome
    • The Smallest Difference
    • Reverse String
    • Remove Element
    • Max Consecutive Ones
    • Max Consecutive Ones II
    • Remove Duplicates from Sorted Array
    • Remove Duplicates from Sorted Array II
    • Move Zeroes
    • Longest Repeating Character Replacement
    • 3Sum With Multiplicity
    • Merge Sorted Array
    • 3Sum Smaller
    • Backspace String Compare
  • Mathematics
    • Ugly Number
    • Ugly Number II
    • Super Ugly Number
    • Sqrt(x)
    • Random Number 1 to 7 With Equal Probability
    • Pow(x, n)
    • Narcissistic Number
    • Rectangle Overlap
    • Happy Number
    • Add N Days to Given Date
    • Reverse Integer
    • Greatest Common Divisor or Highest Common Factor
  • Bit Operation
    • IP to CIDR
  • Random
    • Random Pick with Weight
    • Random Pick Index
    • Linked List Random Node
  • Dynamic Programming
    • House Robber
    • House Robber II
    • House Robber III
    • Longest Increasing Continuous Subsequence
    • Longest Increasing Continuous Subsequence II
    • Coins in a Line
    • Coins in a Line II
    • Coins in a Line III
    • Maximum Product Subarray
    • Longest Palindromic Substring
    • Stone Game
    • Burst Balloons
    • Perfect Squares
    • Triangle
    • Pascal's Triangle
    • Pascal's Triangle II
    • Min Cost Climbing Stairs
    • Climbing Stairs
    • Unique Paths
    • Unique Paths II
    • Minimum Path Sum
    • Word Break
    • Word Break II
    • Range Sum Query - Immutable
    • Decode Ways
    • Edit Distance
    • Unique Binary Search Trees
    • Unique Binary Search Trees II
    • Maximal Rectangle
    • Maximal Square
    • Regular Expression Matching
    • Wildcard Matching
    • Flip Game II
    • Longest Increasing Subsequence
    • Target Sum
    • Partition Equal Subset Sum
    • Coin Change
    • Jump Game
    • Can I Win
    • Maximum Sum Rectangle in a 2D Matrix
    • Cherry Pick
  • Knapsack
    • Backpack
    • Backpack II
    • Backpack III
    • Backpack IV
    • Backpack V
    • Backpack VI
    • Backpack VII
    • Coin Change
    • Coin Change II
  • High Frequency
    • 2 Sum Closest
    • 3 Sum
    • 3 Sum Closest
    • Sort Colors II
    • Majority Number
    • Majority Number II
    • Majority Number III
    • Best Time to Buy and Sell Stock
    • Best Time to Buy and Sell Stock II
    • Best Time to Buy and Sell Stock III
    • Best Time to Buy and Sell Stock IV
    • Two Sum
    • Two Sum II - Input array is sorted
    • Two Sum III - Data structure design
    • Two Sum IV - Input is a BST
    • 4 Sum
    • 4 Sum II
  • Sorting
  • Greedy
    • Jump Game II
    • Remove K Digits
  • Minimax
    • Nim Game
    • Can I Win
  • Sweep Line & Interval
    • Meeting Rooms
    • Meeting Rooms II
    • Merge Intervals
    • Insert Interval
    • Number of Airplanes in the Sky
    • Exam Room
    • Employee Free Time
    • Closest Pair of Points
    • My Calendar I
    • My Calendar II
    • My Calendar III
    • Add Bold Tag in String
  • Other Algorithms and Data Structure
    • Huffman Coding
    • Reservoir Sampling
    • Bloom Filter
    • External Sorting
    • Construct Quad Tree
  • Company Tag
    • Google
      • Guess the Word
      • Raindrop on Sidewalk
    • Airbnb
      • Display Pages (Pagination)
    • Amazon
  • Problem Solving Summary
    • String or Array Rotation
    • Tips for Avoiding Bugs
    • Substring or Subarray Search
    • Sliding Window
    • K Sums
    • Combination Sum Series
    • Knapsack Problems
    • Depth-first Search
    • Large Number Operation
    • Implementation - Simulation
    • Monotonic Stack & Queue
    • Top K Problems
    • Java Interview Tips
      • OOP in Java
      • Conversion in Java
      • Data Structures in Java
    • Algorithm Optimization Tips
  • Reference
Powered by GitBook
On this page

Was this helpful?

Design

Hard

Design a search autocomplete system for a search engine. Users may input a sentence (at least one word and end with a special character'#'). Foreach characterthey typeexcept '#', you need to return thetop 3historical hot sentences that have prefix the same as the part of sentence already typed. Here are the specific rules:

  1. The hot degree for a sentence is defined as the number of times a user typed the exactly same sentence before.

  2. The returned top 3 hot sentences should be sorted by hot degree (The first is the hottest one). If several sentences have the same degree of hot, you need to use ASCII-code order (smaller one appears first).

  3. If less than 3 hot sentences exist, then just return as many as you can.

  4. When the input is a special character, it means the sentence ends, and in this case, you need to return an empty list.

Your job is to implement the following functions:

The constructor function:

AutocompleteSystem(String[] sentences, int[] times):This is the constructor. The input ishistorical data.Sentencesis a string array consists of previously typed sentences.Timesis the corresponding times a sentence has been typed. Your system should record these historical data.

Now, the user wants to input a new sentence. The following function will provide the next character the user types:

List<String> input(char c):The inputcis the next character typed by the user. The character will only be lower-case letters ('a'to'z'), blank space (' ') or a special character ('#'). Also, the previously typed sentence should be recorded in your system. The output will be thetop 3historical hot sentences that have prefix the same as the part of sentence already typed.

Example: Operation:AutocompleteSystem(["i love you", "island","ironman", "i love leetcode"], [5,3,2,2]) The system have already tracked down the following sentences and their corresponding times: "i love you":5times "island":3times "ironman":2times "i love leetcode":2times Now, the user begins another search:

Operation:input('i') Output:["i love you", "island","i love leetcode"] Explanation: There are four sentences that have prefix"i". Among them, "ironman" and "i love leetcode" have same hot degree. Since' 'has ASCII code 32 and'r'has ASCII code 114, "i love leetcode" should be in front of "ironman". Also we only need to output top 3 hot sentences, so "ironman" will be ignored.

Operation:input(' ') Output:["i love you","i love leetcode"] Explanation: There are only two sentences that have prefix"i ".

Operation:input('a') Output:[] Explanation: There are no sentences that have prefix"i a".

Operation:input('#') Output:[] Explanation: The user finished the input, the sentence"i a"should be saved as a historical sentence in system. And the following input will be counted as a new search.

Note:

  1. The input sentence will always start with a letter and end with '#', and only one blank space will exist between two words.

  2. The number of complete sentences that to be searched won't exceed 100. The length of each sentence including those in the historical data won't exceed 100.

  3. Please use double-quote instead of single-quote when you write test cases even for a character input.

  4. Please remember to RESET your class variables declared in class AutocompleteSystem, as static/class variables are

Solution & Analysis

Trie + PriorityQueue (Min-Heap)

249 ms, faster than 76.43%

Trie + PriorityQueue (Max-Heap)

LeetCode Official

Trie + List

Last updated 5 years ago

Was this helpful?

persisted across multiple test cases . Please see for more details.

class AutocompleteSystem {

    class TrieNode {
        Map<Character, TrieNode> children; 
        Map<String, Integer> counts;
        boolean isWord;

        public TrieNode () {
            children = new HashMap<>();
            counts = new HashMap<>();
            isWord = false;
        }
    }

    TrieNode root;
    String prefix;

    public AutocompleteSystem(String[] sentences, int[] times) {
        root = new TrieNode();
        prefix = "";

        for (int i = 0; i < sentences.length; i++) {
            add(sentences[i], times[i]);
        }
    }

    private void add(String s, int count) {
        TrieNode curr = root;
        for (char c: s.toCharArray()) {
            curr.children.putIfAbsent(c, new TrieNode());
            curr = curr.children.get(c);
            curr.counts.put(s, curr.counts.getOrDefault(s, 0) + count);
        }
        curr.isWord = true;
    }

    public List<String> input(char c) {
        if (c == '#') {
            add(prefix, 1);
            prefix = "";
            return new ArrayList<String>();
        }
        prefix = prefix + c;

        TrieNode curr = root;

        for (char ch: prefix.toCharArray()) {
            if (!curr.children.containsKey(ch)) {
                return new ArrayList<String>();
            }
            curr = curr.children.get(ch);
        }

        Comparator<Map.Entry<String, Integer>> cmp = new Comparator<Map.Entry<String, Integer>>() {
            public int compare(Map.Entry<String, Integer> a, Map.Entry<String, Integer> b) {
                return a.getValue() == b.getValue() ? b.getKey().compareTo(a.getKey()) : a.getValue() - b.getValue();
            }
        };
        PriorityQueue<Map.Entry<String, Integer>> pq = new PriorityQueue<>(cmp);
        int k = 3; 
        for (Map.Entry<String, Integer> entry: curr.counts.entrySet()) {
            pq.offer(entry);
            while (!pq.isEmpty() && pq.size() > k) {
                pq.poll();
            }
        }

        ArrayList<String> res = new ArrayList<>();
        while (!pq.isEmpty()) {
            res.add(0, pq.poll().getKey());
        }
        return res;
    }
}

/**
 * Your AutocompleteSystem object will be instantiated and called as such:
 * AutocompleteSystem obj = new AutocompleteSystem(sentences, times);
 * List<String> param_1 = obj.input(c);
 */
class AutocompleteSystem {
    class TrieNode {
        Map<Character, TrieNode> next;
        Map<String, Integer> count;
        boolean isWord;

        public TrieNode() {
            next = new HashMap<>();
            count = new HashMap<>();
            isWord = false;
        }
     }

    TrieNode root;
    String prefix;

    public AutocompleteSystem(String[] sentences, int[] times) {
        root = new TrieNode();
        prefix = "";

        for (int i = 0; i < sentences.length; i++) {
            add(sentences[i], times[i]);
        }
    }

    private void add(String str, int count) {
        char[] chas = str.toCharArray();
        TrieNode node = root;

        for (char c: chas) {
            TrieNode nextNode = node.next.get(c);
            if (nextNode == null) {
                nextNode = new TrieNode();
                node.next.put(c, nextNode);
            }
            node = nextNode;
            node.count.put(str, node.count.getOrDefault(str, 0) + count);
        }

        node.isWord = true;
    }

    public List<String> input(char c) {
        if (c == '#') {
            add(prefix, 1);
            prefix = "";
            return new ArrayList<>();
        }

        prefix = prefix + c;
        // System.out.println(prefix);
        TrieNode node = root;
        for (char cc: prefix.toCharArray()) {
            node = node.next.get(cc);
            if (node == null) return new ArrayList<>();
        }

        PriorityQueue<Pair> pq = new PriorityQueue<>((o1, o2) -> (o1.count == o2.count ? o1.str.compareTo(o2.str) : o2.count - o1.count));

        for (String str: node.count.keySet()) {
            pq.add(new Pair(str, node.count.get(str)));
        }

        List<String> res = new ArrayList<>();
        for (int i = 0; i < 3 && !pq.isEmpty(); i++) {
            Pair pair = pq.poll();
            // System.out.println(pair.str + " " + pair.count);
            res.add(pair.str);
        }

        return res;
    }

    class Pair {
        String str;
        int count;
        public Pair(String str, int count) {
            this.str = str;
            this.count = count;
        }
    }

}
public class AutocompleteSystem {
    class Node {
        Node(String st, int t) {
            sentence = st;
            times = t;
        }
        String sentence;
        int times;
    }
    class Trie {
        int times;
        Trie[] branches = new Trie[27];
    }
    public int int_(char c) {
        return c == ' ' ? 26 : c - 'a';
    }
    public void insert(Trie t, String s, int times) {
        for (int i = 0; i < s.length(); i++) {
            if (t.branches[int_(s.charAt(i))] == null)
                t.branches[int_(s.charAt(i))] = new Trie();
            t = t.branches[int_(s.charAt(i))];
        }
        t.times += times;
    }
    public List < Node > lookup(Trie t, String s) {
        List < Node > list = new ArrayList < > ();
        for (int i = 0; i < s.length(); i++) {
            if (t.branches[int_(s.charAt(i))] == null)
                return new ArrayList < Node > ();
            t = t.branches[int_(s.charAt(i))];
        }
        traverse(s, t, list);
        return list;
    }
    public void traverse(String s, Trie t, List < Node > list) {
        if (t.times > 0)
            list.add(new Node(s, t.times));
        for (char i = 'a'; i <= 'z'; i++) {
            if (t.branches[i - 'a'] != null)
                traverse(s + i, t.branches[i - 'a'], list);
        }
        if (t.branches[26] != null)
            traverse(s + ' ', t.branches[26], list);
    }
    Trie root;
    public AutocompleteSystem(String[] sentences, int[] times) {
        root = new Trie();
        for (int i = 0; i < sentences.length; i++) {
            insert(root, sentences[i], times[i]);
        }
    }
    String cur_sent = "";
    public List < String > input(char c) {
        List < String > res = new ArrayList < > ();
        if (c == '#') { 
            insert(root, cur_sent, 1);
            cur_sent = "";
        } else {
            cur_sent += c;
            List < Node > list = lookup(root, cur_sent);
            Collections.sort(list, (a, b) -> a.times == b.times ? a.sentence.compareTo(b.sentence) : b.times - a.times);
            for (int i = 0; i < Math.min(3, list.size()); i++)
                res.add(list.get(i).sentence);
        }
        return res;
    }
}
  1. Data Structure & Design

Design Search Autocomplete System

PreviousFlatten Nested List IteratorNextTime Based Key-Value Store
  • Solution & Analysis
  • Trie + PriorityQueue (Min-Heap)
  • Trie + PriorityQueue (Max-Heap)
  • LeetCode Official
here