Definition

Unpacking this definition:

• block of data: the input for a hash function is always, in concept, an ordered byte array. This hashing API accepts an arbitrary sequence of byte and multibyte values (via Hasher), but this is merely a convenience; these are always translated into raw byte sequences under the covers.
• hash code: each hash function always yields hash codes of the same fixed bit length (given by bits()). For example, Hashing.sha1() produces a 160-bit number, while Hashing.murmur3_32() yields only 32 bits. Because a long value is clearly insufficient to hold all hash code values, this API represents a hash code as an instance of HashCode.
• pure function: the value produced must depend only on the input bytes, in the order they appear. Input data is never modified. HashFunction instances should always be stateless, and therefore thread-safe.
• collision-averse: while it can't be helped that a hash function will sometimes produce the same hash code for distinct inputs (a "collision"), every hash function strives to some degree to make this unlikely. (Without this condition, a function that always returns zero could be called a hash function. It is not.)

Summarizing the last two points: "equal yield equal always; unequal yield unequal often." This is the most important characteristic of all hash functions.

Desirable properties

A high-quality hash function strives for some subset of the following virtues:

• collision-resistant: while the definition above requires making at least some token attempt, one measure of the quality of a hash function is how well it succeeds at this goal. Important note: it may be easy to achieve the theoretical minimum collision rate when using completely random sample input. The true test of a hash function is how it performs on representative real-world data, which tends to contain many hidden patterns and clumps. The goal of a good hash function is to stamp these patterns out as thoroughly as possible.
• bit-dispersing: masking out any single bit from a hash code should yield only the expected twofold increase to all collision rates. Informally, the "information" in the hash code should be as evenly "spread out" through the hash code's bits as possible. The result is that, for example, when choosing a bucket in a hash table of size 2^8, any eight bits could be consistently used.
• cryptographic: certain hash functions such as Hashing.sha512() are designed to make it as infeasible as possible to reverse-engineer the input that produced a given hash code, or even to discover any two distinct inputs that yield the same result. These are called cryptographic hash functions. But, whenever it is learned that either of these feats has become computationally feasible, the function is deemed "broken" and should no longer be used for secure purposes. (This is the likely eventual fate of all cryptographic hashes.)
• fast: perhaps self-explanatory, but often the most important consideration. We have published microbenchmark results for many common hash functions.

Providing input to a hash function

The primary way to provide the data that your hash function should act on is via a Hasher. Obtain a new hasher from the hash function using newHasher(), "push" the relevant data into it using methods like Hasher.putBytes(byte[]), and finally ask for the HashCode when finished using Hasher.hash(). (See an example of this.)

If all you want to hash is a single byte array, string or long value, there are convenient shortcut methods defined directly on HashFunction to make this easier.

Hasher accepts primitive data types, but can also accept any Object of type T provided that you implement a Funnel to specify how to "feed" data from that object into the function. (See an example of this.)

Compatibility note: Throughout this API, multibyte values are always interpreted in little-endian order. That is, hashing the byte array {0x01, 0x02, 0x03, 0x04} is equivalent to hashing the int value 0x04030201. If this isn't what you need, methods such as Integer.reverseBytes(int) and Ints.toByteArray(int) will help.

Relationship to Object.hashCode()

Java's baked-in concept of hash codes is constrained to 32 bits, and provides no separation between hash algorithms and the data they act on, so alternate hash algorithms can't be easily substituted. Also, implementations of hashCode tend to be poor-quality, in part because they end up depending on other existing poor-quality hashCode implementations, including those in many JDK classes.

Object.hashCode implementations tend to be very fast, but have weak collision prevention and no expectation of bit dispersion. This leaves them perfectly suitable for use in hash tables, because extra collisions cause only a slight performance hit, while poor bit dispersion is easily corrected using a secondary hash function (which all reasonable hash table implementations in Java use). For the many uses of hash functions beyond data structures, however, Object.hashCode almost always falls short -- hence this library.