LeetCode 0677. Map Sum Pairs Solution in Java, C++, Python & More | Explanation + Code

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0677. Map Sum Pairs

Description

Design a map that allows you to do the following:

  • Maps a string key to a given value.
  • Returns the sum of the values that have a key with a prefix equal to a given string.

Implement the MapSum class:

  • MapSum() Initializes the MapSum object.
  • void insert(String key, int val) Inserts the key-val pair into the map. If the key already existed, the original key-value pair will be overridden to the new one.
  • int sum(string prefix) Returns the sum of all the pairs' value whose key starts with the prefix.

 

Example 1:

Input
["MapSum", "insert", "sum", "insert", "sum"]
[[], ["apple", 3], ["ap"], ["app", 2], ["ap"]]
Output
[null, null, 3, null, 5]

Explanation
MapSum mapSum = new MapSum();
mapSum.insert("apple", 3);  
mapSum.sum("ap");           // return 3 (apple = 3)
mapSum.insert("app", 2);    
mapSum.sum("ap");           // return 5 (apple + app = 3 + 2 = 5)

 

Constraints:

  • 1 <= key.length, prefix.length <= 50
  • key and prefix consist of only lowercase English letters.
  • 1 <= val <= 1000
  • At most 50 calls will be made to insert and sum.

Solutions

Solution 1: Hash Table + Trie

We use a hash table d to store key-value pairs and a trie t to store the prefix sums of the key-value pairs. Each node in the trie contains two pieces of information:

  • val: the total sum of the values of the key-value pairs with this node as the prefix
  • children: an array of length 26 that stores the child nodes of this node

When inserting a key-value pair (key, val), we first check if the key exists in the hash table. If it does, the val of each node in the trie needs to subtract the original value of the key and then add the new value. If it does not exist, the val of each node in the trie needs to add the new value.

When querying the prefix sum, we start from the root node of the trie and traverse the prefix string. If the current node's child nodes do not contain the character, it means the prefix does not exist in the trie, and we return 0. Otherwise, we continue to traverse the next character until we finish traversing the prefix string and return the val of the current node.

In terms of time complexity, the time complexity of inserting a key-value pair is O(n), where n is the length of the key. The time complexity of querying the prefix sum is O(m), where m is the length of the prefix.

The space complexity is O(n × m × C), where n and m are the number of keys and the maximum length of the keys, respectively; and C is the size of the character set, which is 26 in this problem.

PythonJavaC++GoTypeScriptRustJavaScriptC#
class Trie: def __init__(self): self.children: List[Trie | None] = [None] * 26 self.val: int = 0 def insert(self, w: str, x: int): node = self for c in w: idx = ord(c) - ord('a') if node.children[idx] is None: node.children[idx] = Trie() node = node.children[idx] node.val += x def search(self, w: str) -> int: node = self for c in w: idx = ord(c) - ord('a') if node.children[idx] is None: return 0 node = node.children[idx] return node.val class MapSum: def __init__(self): self.d = defaultdict(int) self.tree = Trie() def insert(self, key: str, val: int) -> None: x = val - self.d[key] self.d[key] = val self.tree.insert(key, x) def sum(self, prefix: str) -> int: return self.tree.search(prefix) # Your MapSum object will be instantiated and called as such: # obj = MapSum() # obj.insert(key,val) # param_2 = obj.sum(prefix)(code-box)

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