Hashing is a technique that transforms data into a fixed-length value, called a hash or a key, that can be used to access and store data efficiently. Hashing can help you optimize data access and storage in various ways, such as:
One of the main benefits of hashing is that it can reduce the time it takes to search for data in a large collection. Instead of scanning the whole collection sequentially or using a complex sorting algorithm, you can use a hash function to map each data item to a specific index in an array, called a hash table. Then, you can simply look up the index corresponding to the hash of the data you want to find. This can make the search operation much faster, especially if the hash function distributes the data evenly across the hash table.
??A good hashing #algorithm is only as good as the data it operates on. ??By definition, a hashing operation should be completed to O(1) in #Big???Oh notation, because it has zero #collisions of #indices when the hashing algorithm is applied. ??That’s the #goal, anyway! ??The algorithm needs to be #repeatable also. This means doing an in-depth ??Data Science project in the #dataset first, as prep for developing the hashing algorithm. ??Take care to account for #collisions; the goal should be to #minimize collisions ??If collisions persist, and hashing #performance suffers, adjust the hashing algorithm until the #performance nears O(1) execution time. Thanks ?? for reading ??! #hashing #softwaredevelopment #engineering #coding
Consider a library system that uses hashing to locate books. Each book is assigned a hash key, equivalent to its specific location on the shelves. When a library-goer wants a particular book, the system doesn't scan every shelf sequentially. Instead, it uses the book's hash key to point to its exact location, dramatically reducing search time. This is particularly beneficial in large libraries where manual or sequential searching would be time-consuming and inefficient.
A collision occurs when two different data items have the same hash value, which can cause conflicts and errors in the hash table. There are different ways to handle collisions, such as chaining, linear probing, quadratic probing, or double hashing. Chaining involves creating a linked list of data items that share the same hash value at each index of the hash table. Linear probing involves finding the next available index in the hash table for a data item that collides with an existing one. Quadratic probing involves finding the next available index by using a quadratic formula based on the original hash value. Double hashing involves using a second hash function to find the next available index for a colliding data item.
Take a classroom scenario where students' names are hashed to a numerical value representing their assigned lockers. If two students, say, "John" and "Jane", both hash to the same locker number (a collision), different techniques can be applied. With chaining, both students would share the locker, like a linked list. If linear probing is used, Jane would get the next available locker. For quadratic probing, Jane might get a locker further down, calculated through a quadratic function. In double hashing, a second hash function would provide Jane a different locker.
Hashing can also help you implement various data structures that can optimize data access and storage, such as sets, maps, caches, or bloom filters. A set is a collection of unique data items that supports fast membership testing and union, intersection, and difference operations. A map is a collection of key-value pairs that supports fast lookup and insertion of values by keys. A cache is a temporary storage of frequently accessed data that reduces the need to access slower or remote sources. A bloom filter is a probabilistic data structure that can test whether a data item is in a set or not, with a small possibility of false positives.
Consider a social network, like Threads - it uses hashing for friend recommendations, where a set data structure of users' friend lists allows quick membership testing, making the feature efficient. In a Google search, a map data structure hashes search queries to corresponding results for quick access. A browser cache uses hashing to store frequently visited web pages, minimizing the need to retrieve them from the server. Lastly, URL shortening services like Bitly may use Bloom filters. When you shorten a URL, the original URL gets hashed and checked against the Bloom filter to avoid duplicates.
Another benefit of hashing is that it can help you secure data by encrypting or verifying it. Encryption is the process of transforming data into a form that only authorized parties can read or modify. Verification is the process of checking whether data has been altered or corrupted. You can use hashing to encrypt or verify data by using cryptographic hash functions, which are designed to be irreversible, unique, and unpredictable. For example, you can use hashing to create passwords, digital signatures, checksums, or message digests.
Consider password storage: Instead of storing user passwords in plain text, systems usually store hashed versions. When a user enters their password, it's hashed and compared to the stored hash, verifying the user without exposing their actual password. In digital transactions, a cryptographic hash of the data is used as a digital signature, ensuring the transaction hasn't been altered in transit. Similarly, file download servers often provide a checksum - a hashed value that users can use to verify the integrity of the downloaded file.
Finally, hashing can help you improve performance by reducing the amount of data that needs to be stored or transferred. You can use hashing to compress data by removing redundant or unnecessary information and replacing it with shorter hashes. You can also use hashing to deduplicate data by identifying and eliminating duplicate data items and storing only one copy of them. This can save space and bandwidth, as well as improve speed and reliability.
Hashing is a powerful technique that can optimize data access and storage in many ways. However, hashing also has some limitations and challenges, such as choosing a suitable hash function, avoiding collisions, ensuring security, and handling dynamic data. Therefore, you should always consider the trade-offs and best practices of hashing before applying it to your data.