Hash Collisions: Why Do Hash Tables Collide? How to Resolve Them?
Hash tables map keys to array positions using hash functions, but when different keys map to the same position, a hash collision occurs. The core reasons are either the number of keys far exceeding the array capacity or an uneven hash function. The key to resolving collisions is to ensure conflicting keys "occupy distinct positions." Common methods include: 1. **Chaining (Zipper Method)**: The most widely used approach, where each array position is a linked list. Conflicting keys are appended sequentially to the corresponding linked list (e.g., keys 5, 1, and 9 colliding would form a list: 5→1→9). This method is simple to implement, has high space utilization, and allows efficient traversal during lookups. 2. **Open Addressing**: When a collision occurs, vacant positions are sought in subsequent slots. This includes linear probing (step size 1), quadratic probing (step size as a square), and double hashing (multiple hash functions). However, it may cause clustering and is more complex to implement. 3. **Public Overflow Area**: The main array stores non-colliding keys, while colliding keys are placed in an overflow area. Lookups require traversing both the main array and the overflow area, but space allocation is difficult. The choice of collision resolution method depends on the scenario. Chaining is widely adopted due to its efficiency and versatility. Understanding hash collisions and their solutions is crucial for optimizing hash table performance.
Read MoreHash Table: How Does a Hash Table Store Data? A Diagram of Collision Resolution Methods
A hash table is a key-value storage structure that maps keys to array bucket positions through a hash function, enabling O(1) efficient lookup, insertion, and deletion. Its underlying structure is an array, where keys are converted into array indices (bucket positions) via a hash function (e.g., "key % array length"), and corresponding values are directly stored at these indices. Collisions occur when different keys yield the same hash value (e.g., student IDs 12 and 22 both %10 to 2 when the array length is 10). Two classic collision resolution methods exist: 1. **Chaining**: Each bucket stores a linked list, with colliding elements appended to the tail of the list. This is simple to implement but requires additional space. 2. **Open Addressing**: Linear probing is a common variant, where the algorithm searches for the next empty bucket (e.g., h → h+1 → h+2 ... for a hash value h). This uses only array operations but may cause clustering. The core components of a hash table are the hash function and collision handling logic, making it a foundational topic in data structure learning.
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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