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This method employs an array of linked lists (chaining) to handle collisions. Each index represents a hash code, and if multiple keys hash to the same index, we store them in a linked list at that index. This helps us efficiently manage key collisions.
Time Complexity: For each operation (put, get, remove), it is O(n) in the worst case, where n is the number of keys stored at a particular index.
Space Complexity: O(N) where N is the number of distinct keys inserted into the hash map.
1class MyHashMap:
2
3 def __init__(self):
4 self.size = 1000
5 self.map = [[] for _ in range(self.size)]
6
7 def put(self, key: int, value: int) -> None:
8 index = key % self.size
9 for kv in self.map[index]:
10 if kv[0] == key:
11 kv[1] = value
12 return
13 self.map[index].append([key, value])
14
15 def get(self, key: int) -> int:
16 index = key % self.size
17 for kv in self.map[index]:
18 if kv[0] == key:
19 return kv[1]
20 return -1
21
22 def remove(self, key: int) -> None:
23 index = key % self.size
24 for i, kv in enumerate(self.map[index]):
25 if kv[0] == key:
26 del self.map[index][i]
27 returnThe Python version uses an array of lists to implement separate chaining for achieving collision resolution in the hash map.
put method updates or inserts a key-value pair.get method searches for a key at the calculated index.remove method deletes the key-value pair if present.Double hashing avoids the clustering problem by calculating two hash functions. If a collision occurs, a second hash determines the step size for probing.
Time Complexity: O(1) on average for put, get, and remove due to effective hashing; O(n) in worst-case.
Space Complexity: O(N) for storing entries with keys and values.
1class
In JavaScript's setup, judicious double hashing governs open addressing policy, augmenting efficiency usually diminished by collision instances.