HashTable:
Hashtable is designed to use a special function called the Hash function which is used to map a given value with a particular key for faster access of elements. It is used where fast lookups are required.(Under reasonable assumptions, average time for element lookup in a hash table is O(1) ). Dictionary in Python is implemented using HashTables. Java also implements HashTable class.
Some applications of hashing can be found here.
Bloom Filter:
A Bloom filter is a space-efficient probabilistic data structure that is used to test whether an element is a member of a set. It is used where we just need to know the element belongs to the object or not. A bloom filter uses k hash functions and array of n bits, where array bit set to 0, means element doesn’t exist and 1 indicates that element is present. Some applications of bloom filters are:
- Google Bigtable, Apache HBase and Apache Cassandra and PostgreSQL use Bloom filters to reduce the disk lookups for non-existent rows or columns. Avoiding costly disk lookups considerably increases the performance of a database query operation.
- The Google Chrome web browser used to use a Bloom filter to identify malicious URLs. Any URL was first checked against a local Bloom filter, and only if the Bloom filter returned a positive result was a full check of the URL performed (and the user warned, if that too returned a positive result).
Let’s see the difference between hashtables and bloom filters:
| S No. | Hash Tables | Bloom Filters |
|---|---|---|
| 1 | In hash table the object gets stored to the bucket(index position in the hashtable) the hash function maps to. | Bloom filters doesn’t store the associated object. It just tells whether it is there in the bloom filter or not. |
| 2 | Hash tables are less space efficient. | Bloom filters are more space efficient. it’s size is even the less than the associated object which it is mapping. |
| 3 | Supports deletions. | It is not possible to delete elements from bloom filters. |
| 4 | Hashtables give accurate results. | Bloom filters have small false positive probability. ( False positive means it might be in bloom filter but actually it is not.) |
| 5 | In a hashtable either we should implement multiple hash functions or have a strong hash function to minimize collisions. | A bloom filter uses many hash functions. There is no need to handle collisions. |
| 6 | Hashtables are used in compiler operations, programming languages(hash table based data structures),password verification, etc. | Bloom filters find application in network routers, in web browsers(to detect the malicious urls), in password checkers(to not a set a weak or guessable or list of forbidden passwords), etc. |
HashTables and bloom filters are closely related to each other, therefore, it is wise to compare these two data structures and use them wisely as per your application/need demands.