Hash tables employ hashing functions to store and retrieve data from "summary" of Data Structures and Algorithms in Python by Michael T. Goodrich,Roberto Tamassia,Michael H. Goldwasser
Hash tables, a fundamental data structure in computer science, utilize hashing functions to efficiently store and retrieve data. The primary purpose of a hash table is to map keys to values, allowing for quick access to stored information. The process begins with the application of a hashing function to the key, which generates a unique hash code corresponding to a specific location within the table. By utilizing hashing functions, hash tables can achieve constant-time complexity for both insertion and retrieval operations in the best-case scenario. This is made possible by the direct mapping of keys to their respective hash codes, enabling immediate access to the corresponding values without the need for sequential search. In the event of hash collisions, where multiple keys map to the same hash code, hash tables employ collision resolution strategies to handle such conflicts. One common approach is separate chaining, where each hash table entry contains a linked list or other data structure to store multiple values associated with the same hash code. Another collision resolution technique is open addressing, which involves searching for an alternative location within the hash table when a collision occurs. This may involve probing methods such as linear probing, quadratic probing, or double hashing to find an available slot for the key-value pair.- The use of hashing functions in hash tables enables efficient data storage and retrieval by optimizing access to information based on the keys provided. By leveraging the unique hash codes generated through hashing functions, hash tables can achieve fast and constant-time operations, making them a valuable data structure for a wide range of applications.
