How to Handle Hash Table Collisions? Understand Hash Resolution Methods in Data Structures Simply
Hash tables map keys to array slots via hash functions, and hash collisions occur when different keys map to the same slot. The core solution is to find new positions for colliding elements, with the main methods as follows: 1. **Chaining (Separate Chaining)**: Each slot stores a linked list, where colliding elements are appended to the list. For example, keys 1, 6, and 11 hashing to the same slot form a linked list [1, 6, 11]. **Advantages**: Simple implementation, suitable for dynamic data. **Disadvantages**: Extra space for linked lists; worst-case O(n) lookup. 2. **Open Addressing**: When collisions occur, empty slots are probed. Includes linear probing (i+1, wrapping around) and quadratic probing (i+1², etc.). For example, key 6 hashing to slot 1 (collision) probes slot 2; key 11 probes slot 3. **Advantages**: High space utilization. **Disadvantages**: Linear probing causes primary clustering; quadratic probing requires a larger array. Other methods: Double hashing (multiple hash functions) and common overflow area (primary table + overflow table), suitable for low-collision scenarios. Selection depends on the use case: Chaining (e.g., Java HashMap) suits dynamic, large datasets; Open addressing is better for fixed-size arrays with few collisions.
Read MoreHow Does a Hash Table Store Data? Hash Functions Simplify Lookup
The article uses the analogy of searching for a book to introduce the problem: sequential search of data (such as arrays) is inefficient, while a hash table is an efficient storage tool. The core of a hash table is the hash function, which maps data to "buckets" (array positions) to enable fast access and storage. The hash function converts data into a hash value (bucket number), for example, taking the last two digits of a student ID to get the hash value. When storing, the hash value is first calculated to locate the bucket. If multiple data items have the same hash value (collision), it can be resolved using the linked list method (chaining within the bucket) or open addressing (finding the next empty bucket). When searching, the hash value is directly calculated to locate the bucket, and then the search is performed within the bucket, eliminating the need to traverse all data, resulting in extremely fast speed. Hash tables are widely used (such as in address books and caches), with their core being the use of a hash function to transform the search from "scanning through everything" to "direct access".
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