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What are Hashmaps and Hashtables? Easy-to-understand explanation of basic concepts of data structures
When working with data structures, you may have come across terms like “hashmap” and “hashtable.” But what exactly do these terms mean and how do they work? In this blog post, we will provide a comprehensive and easy-to-understand explanation of these concepts.
Hashmaps
A hashmap, also known as a dictionary or associative array, is a data structure that allows you to store and retrieve data based on key-value pairs. It provides a fast and efficient way to search, insert, and delete elements. The key is used to uniquely identify the value, and the underlying mechanism that enables this efficient retrieval is called hashing.
To put it simply, hashing is the process of converting a key into a unique numeric value called a hash code. This hash code is used to determine the position or index in the underlying array where the value should be stored. Hashmaps use an array coupled with a hashing function to achieve fast access to values based on their keys.
When you need to retrieve a value from a hashmap, you provide the corresponding key, and the hashmap calculates the hash code based on that key. This hash code is then used to find the index in the underlying array, where the value associated with that key is stored. By doing this, hashmaps can directly access the element in constant time, making it an efficient data structure for data retrieval.
One important thing to note is that hashmaps use a hash function that should ideally distribute the values uniformly across the array, minimizing collisions. Collisions occur when two or more keys produce the same hash code, and they need to be resolved to guarantee correct retrieval. Various collision resolution strategies, such as chaining or open addressing, can be employed to handle collisions efficiently.
Hashtables
Hashtables, also known as hashsets or hashmaps with open addressing, are another variation of the hashmap data structure. They are similar in concept but differ in the way collisions are resolved.
Unlike hashmaps, which use chaining to handle collisions by linked lists or other data structures, hashtables employ open addressing. Open addressing deals with collisions by finding an alternative empty slot within the same array without the need for additional data structures.
When a collision occurs in a hashtable, the algorithm searches for the next available position in the array using a predetermined sequence of steps, often referred to as probing. Probing methods can vary but commonly include techniques like linear probing or quadratic probing.
While hashtables can provide a more memory-efficient solution compared to hashmaps, it can be more challenging to maintain a low rate of collisions. As the hashtable gets filled, the probability of collisions increases, which can degrade the performance of the data structure.
In conclusion, hashmaps and hashtables are data structures that enable efficient retrieval of values based on keys. Hashmaps use a hashing mechanism, coupled with an array, to achieve fast access to values, while hashtables use open addressing to handle collisions. Both structures have their own advantages and considerations, and choosing the appropriate one depends on the specific requirements of your application.
By understanding the basic concepts of hashmaps and hashtables, you can leverage these data structures to optimize your programs and algorithms when dealing with key-value pairs.
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