How does the hashing algorithm work?

Hashing algorithms are one of the most important and fundamental types of algorithms in computer science. They were originally developed as a way to store information about very large files in a fast and efficient manner, but they have since been used for much more. Hash functions take an input value (or set of values) and create smaller representations called hashes which are used in many important areas such as cryptography, data integrity validation, database storage, distributed computing, data storage schemes and file systems.

Understanding the technology

The main way of making hashes is by substituting characters into some hash algorithm producing a digest or hash. This is done by hashing the input value through a one-way mapping function (such as a hash function) that maps the entire data set to produce a fixed-length output. The actual size of the output depends on the size of the data set, and hence it takes longer to compute hashes for larger sets.

A hash function works by taking an input value, usually called message , and converting it into an output value called digest . There are many hashing functions available, which vary in the speed and accuracy of the resulting hash value. Some common hash functions include:

Because all hashes are ultimately based on a function that maps some underlying unit of measure to another, it is useful to know how hash functions work. The idea with any one-way function is to take something and divide it into parts, and then map those parts through some transformation into a fixed-length value. The same operation is achieved by dividing the original into a set of parts, taking one part and mapping it through the function. The result is a single number according to the size of the input.

The hash function is not a specific algorithm that can be used for any data type (assuming only small inputs). It will work for strings, integers and floating point numbers. The large inputs make hash-based lookups more expensive than combinations of other types, however. Also, since it cannot determine whether two expected inputs are equal or not, only one output can be produced per given pair of inputs; two different values are produced if an input has multiple entries with the same value in its key field. Hash functions are also unable to tell the difference between an input that has no output, and one with an invalid key value. Even if one could, there is nothing stopping the input from not having a key field altogether.

The hashing algorithm  function works by computing the hash of all inputs first and then comparing them to find a match. The following example shows the use of a hash function for searching for items in a list. The hash is computed by concatenating all the values in the list and then computing the sum of the values divided by the number of items. The comparison function finds a match if two elements have identical hashes. If no matches are found, it yields “not found”.

If we want to find an item in a list, we index it using a key-value (the key field) that has been converted to a string with an associated hash value of zero. This is the same hash function used to add and remove items from the list.

The following example shows an implementation of a hash function for a generic data type, where we convert values into its string representation using strip_underscores () (the result of this operation will be unique since all values are converted to a string with the same length) and then adding the constant 20 to convert it into an integer. It then uses bit operations (left shift and multiply) to make it a 32-bit unsigned integer by taking the high-order 20 bits of this value.

The algorithm shown above performs identically on all computers, so no matter what computer is used to run this program, the same result will be produced. In theory, to improve security, the value is sent over a network in a form that is unreadable to anyone but the intended recipient, who will then compute its hash function. Since this relies on computation, the process can be made more secure by encrypting it. Decryption can be carried out using simple private key algorithms and public key encryption (for which one uses both private and public keys) or with advanced public key cryptography.

There are also many other hashing algorithms that make use of different methods to structure a hash output:

Types of Hashtag algorithm

The first two versions of the MD4 and MD5 algorithms, as well as the first two versions of SHA hash, are actually examples of a family or “families” of message digest functions defined using an algorithm called the Message Digest Algorithm (MD), which is based on the cyclic redundancy check (CRC) algorithm. CRC involves chopping any bit string into random size chunks, computing a checksum for each chunk and reassembling the bits in order. Each time all the chunks have been reassembled, a CRC-32 is computed with it. This allows an attacker to use certain known strings to generate data that will always produce the same CRC-32 value.

A version of MD5 is known as ANSI X9.31, and it is used in the Digital Signature Standard (DSS) for signing messages. Its output is 128 bits long—it uses 128-bits of input.

Message Digest 5 (MD5) algorithm is an algorithm developed by Professor Ronald Rivest of MIT, along with teammates Ray Whitmer, Adi Shamir, and Len Adleman. It is used in a wide variety of applications today and includes many versions that have been implemented in various systems over the years. The most recent version of MD5 was published in RFC 1321. A major feature of this RFC is that it includes information on how to extend the MD5 functions to support larger blocks of data than just 512 bits (which was originally defined).

The MD5 algorithm is based on the concept of message digest. A message digest value is computed from some input data, and then it can be used to verify that some other data was generated by the same source. Running a message digest against another message without knowing the initial input transforms it into a totally different value, and therefore, no meaningful hash value is produced.

MD5: When computing an MD5 hash value of an arbitrary-length input, the international standard recommends using an extra 128 bits of input. The MD5 function uses 128 bits of data with any bit string that has at least 16 distinct bits (that is, our original data).


Also known as the hashtag hash, hashtags are used on social media platforms to categorize comments. Posts with a particular tag denote that they are hashtag associated. A hashtag also creates an isolated unit within a larger corpus of words, making them easily searchable. One popular use of hashtags is for finding trending topics on Twitter. For example: If I say “I’m tweeting about football today” then the result I get is mostly likely to be the most searched-for hashtag relating to football, which may not be ideal information and certainly not all that helpful!

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