Hash functions are by definition and implementation generally regarded as
Pseudo Random Number Generators (PRNG). From this generalization it can
be assumed that the performance of hash functions and comparisons between
other hash functions can be determined by modeling the functions as PRNGs.
Analysis techniques such a Poisson distribution can be used to analyze
the collision rates of different hash functions for different groups
of data. In general there is a theoretical hash function known as the
Perfect Hash Function for any specific group of data. The perfect
hash function by definition states that no collisions will occur meaning
no repeating hash values will arise from different elements of the group.
In reality it is very difficult to find a perfect hash function for an
arbitrary set of data, and furthermore the practical applications of perfect
hashing and its variant minimal perfect hashing are quite limited. So instead
one may choose to pursue the concept of an Ideal Hash Function, which
is commonly defined as a function that produces the least amount of
collisions for a particular set of data.
One of the fundamental problems with hashing data or specifically mapping values from
one domain to another, is that there are soo many permutations of types of data, some
highly random, others containing high degrees of patterning or structure that it is
difficult to generalize a hash function for all data types or even for specific data
types. All one can do is via trial and error and utilization of formal hash function
construction techniques derive the hash function that best suites their needs.
Some factors to take into account when choosing hash functions are:
Data Distribution
This is the measure of how well the hash function distributes the hash
values of elements within a set of data. Analysis in this measure
requires knowing the number of collisions that occur with the data set
meaning non-unique hash values, If chaining is used for collision
resolution the average length of the chains (which would in theory be
the average of each bucket's collision count) analyzed, also the
amount of grouping of the hash values within ranges should be
analyzed.
In the following diagram there are five unique messages to be hashed
(M0,...,M4) and inserted into a hash-table. Each
message is hashed using two different hash functions H1
and H2. The diagram depicts how H1 generally
distributes the hash values for the messages evenly over the given bucket-space
(table), where as H2 returns the same value for each
message causing numerous collisions.
Hash Function Efficiency
This is the measure of how efficiently the hash function produces hash
values for elements within a set of data. When algorithms which contain
hash functions are analyzed it is generally assumed that hash functions
have a complexity of O(1), that is why look-ups for data in a hash-table
are said to be "on average of O(1) time complexity", where as
look-ups of data in other associative containers such as maps (typically
implemented as Red-Black trees) are said to be of O(logn) time complexity.
A hash function should in theory be a very quick, stable and deterministic
operation. A hash function may not always lend itself to being of O(1)
complexity, however in general the linear traversal through a string or
byte array of data that is to be hashed is so quick and the fact that
hash functions are generally used on primary keys which by definition
are supposed to be much smaller associative identifiers of larger blocks
of data implies that the whole operation should be quick, deterministic
and to a certain degree stable.
The hash functions in this essay are known as simple hash functions or General
Purpose Hash Functions. They are typically used for data hashing (string hashing).
They are used to create keys which are used in associative containers such as hash-tables.
These hash functions are not cryptographically safe, they can easily be reversed and
many different combinations of data can be easily found to produce identical hash values
for any combination of data.
When designing and implementing hash functions the common building blocks typically
used are bitwise operations, mathematical operations and look-up tables. These operations
are applied either to individual bytes or blocks of bytes (words etc), furthermore
they are fast, deterministic and readily available on most CPU architecture, making
them ideal for implementing hash functions. The following is a list of the commonly
used operations:
Bitwise Operations: Not (!), Or (|), And (&), Xor (^), Shift-Left/Right (<<, >>), Rotate-Left/Right (<<<, >>>)
Lookup Tables: List of prime numbers, List of magic numbers, S-Box, P-Box
A common method for constructing collision resistant cryptographic hash functions is known
as the Merkle–Damgard construction.
A simplified version of this method can be used to easily generate well performing general
purpose hash functions. In this method a piece of data (message) is transformed into a hash
value as follows:
Initialise an internal state
Consume the message N-bits at a time (aka block size typically 32-bits, 64-bits, 128-bits etc..)
Perform a mixing operation with the current block and the internal state
Update the internal state with the the result of [3]
If there are any remaining bytes in the message proceed to [2]
Finalize the internal state and return the hash value
Note: The term Internal State (IS), depicted in the diagram as red circles, refers
to a piece of memory, that is to be at least as large as the number of bits expected in the hash
value. The IS at the start of the hash function will be initialised with a predetermined value -
this is typically known as the Initialisation Vector (IV). The IS is primarily used during
the mixing process and can generally be thought of as the final state of the hash function after
the previous round. Its use in the mixing process is to create a data dependency between the result
of the mix with the current message block and the results of the mixes from all the preceding
message blocks. This process is commonly referred to as: Chaining
Note: The term Finalize means to take the internal state and prepare the final hash
value that will be returned from the function. In the event the IS contains more bits than is required
for the hash value, the IS itself will undergo a mixing process that will result in a value that is
size compatible with the hash value type. Some implementations will also attempt to integrate side
information such as the total number of bits hashed, or mixes with magic numbers based on the final
value of the IS.
The "Mix" operation denoted above takes as input the internal state and the current
message block, performs some computation and returns the value of the internal state.
The following is a pseudo code example of a possible implementation of "Mix", that
operates on blocks and internal state of size 32-bits at a time:
The "Mix" operation denoted above, will compute the multiplication
between the current message block and the internal state, it will
then proceed to compute the sum of the internal state shifted by
three bits to the left and the message block shifted by two bits
to the right, then it will bitwise xor the first computation with
the second and return that value as the result of the Mix process.
Putting it all together requires looping over all of the message 32-bits
at-a-time, performing the mix operation upon each block, updating the internal
state and taking care of any remainder bits after the main loop has completed.
The resulting hash function will generally look like the following:
uint32_t hash(const char* message, size_t message_length)
{
uint32_t internal_state = 0xA5A5A5A5; // IV: A magic number
uint32_t message_block = 0;
// Loop over the message 32-bits at-a-time
while (message_length >= 4)
{
memcpy(message_block, message, sizeof(uint32_t));
internal_state = mix(message_block, internal_state);
message_length -= sizeof(uint32_t);
message += sizeof(uint32_t);
}
// Are there any remaining bytes?
if (message_length)
{
memcpy(message_block, message, message_length);
internal_state = mix(message_block, internal_state);
}
return internal_state;
}
Lookup Tables (LUTs)
Various hash function designs utilize look-up tables within their mixing
process. The reasons for using LUTs are varied but primarily seem to
center around the ability to increase the variance of "static values"
used within the fundamental mixing operation.
In the mix process defined above there are two static values 2 and 3, being
used - if one where to utilize a LUT of values, then perhaps as an example
a different value can be used on each round, rather than just the values
2 and 3 for each and every round.
When LUTs are employed in a hash function they are typically used in a combination
of one or more of the following ways:
Lookup per round index
Lookup per internal state
Lookup per current message
Given a LUT named P, the various ways P can be used to generate the next
state of the hash via the mixing process based on the above list of possible
operations are as follows:
Where: Hi, Hi+1 are the current and next
internal states respectively, roundi is the index of the
current mixing round, Blocki is the block of the message
in the current mixing round and |P| is the size of the lookup table.
As far as the composition of the LUTs are concerned, the types of values
(numbers) used are typically implementation defined. Sometimes they may
be a list of prime numbers, other times they may be a set of values that
possess specific bit patterns (eg: 0xA5A5A5A5 etc), or they may
simply just be a list of randomly selected numbers
(Nothing up my sleeve numbers)
.
In the design of cryptographic hash functions and ciphers, the construction
of S-Boxes and P-Boxes have become a well studied area. If one is looking
to implement a general purpose hash function based on utilizing a list of
values during the mixing process, a review of the literature associated
with S-Box and P-Box design techniques would be highly recommendable.
The following are some general recommendations to consider when implementing a hash function:
Recommendation 1: The "mix" operation does not need to be the same on
each round. There could be multiple mix operations which are selected based
on criteria such as the index of the current round, the value of the internal
state etc.
Recommendation 2: When hashing large messages, one could break the message up
into chunks and compute the hash of each chunk in parallel then aggregate the
hashes using a mix operation, this will dramatically increase the throughput of the
hash function when running upon architectures that support multiple cores. This method
of hashing is known as a Merkle-Tree
or a hash-tree.
Recommendation 3: The operations present in the Mix should be carefully chosen,
as more often than not the mixing process may result in lowering the entropy of the
internal state to the point where the internal state does not change.
As an example in the mix operation denoted above if the internal state
reaches the value ZERO it will typically end-up returning zero.
Recommendation 4: The mix operation should handle repeated values in the message
block, without causing a "flush effect" on the internal state. A typical scenario
might be a message comprised of bytes with the value of zero. Question: how many consecutive zero bytes will it take to get the internal
state of the hash function to become zero? The answer for a well designed hash
function should be: A-LOT.
Recommendation 5: When hashing a key derived-from or otherwise aliasing a pointer
to memory that has been obtained from an allocator that returns aligned addresses, one will
observe that the first 2 or 3 Least Significant Bits (LSBs) of the key will always
be zero, or in other words will have zero entropy. This is due to the fact that
the pointers will be storing addresses that are multiples of either 4 or 8 depending on
the machine's addressing granularity (eg: 32-bit, 64-bit aligned addresses).
The following are a couple of techniques to resolve the issue of low-entropy LSBs:
Fill in the LSBs with bits from their higher order neighbours: pointer |= (pointer >> 3) & 0x03
Fill in the LSBs with bits from a LUT: pointer |= lut[round_i % lut_size] & 0x03
Generally speaking there is a rich tapestry of fact and fiction when it comes
to the use of prime numbers in hash functions. Prime numbers are typically
used in two areas regarding hash function implementation, they are:
Hash value quantisation
Mixing and entropy boosting
Hash Value Quantisation Using Prime Numbers
This area is by far the most reasonable and contains mathematically
solid explanations for the use of prime-numbers under certain situations.
The problems that arise here are typically related to the
Pigeonhole Principle.
When using a hash function as part of a hash-table, one will want to
quantize or in other words reduce the hash value to be within the range
of the number of buckets in the hash-table. It is assumed that a good
hash functions will map the message m within the given range in a uniform
manner. The quantisation of the hash value is efficiently and simply
obtained by computing the hash value modulus the table size N, as follows:
The result of this operation will be a value H in the range R defined as [0, N).
Problem 1 - Non-Uniform Mappings
Because the size of the table N will typically be smaller than the
maximum value of the hash function Hmax(hence the
requirement for quantization) and Hmax will most
likely not be a multiple of N, nor will N likely be a factor or a
multiple of a factor of Hmax, the first (Hmax
mod N) values in the range will have a higher probability of
having values hashed to them when compared to the remaining values
in the range. So rather than having a value be mapped to any of those
buckets with equal probability of 1/N, they will instead be mapped
with a probability of 2/N, which is twice that of the other buckets.
This will result in a higher load factor (collision rate) for the
first (Hmax mod N) buckets in the table.
As an example assume we have a good hash function h(x) that uniformly maps
keys in the range [0,15] as H and a hash-table with only 10 buckets, we
may simply quantize the hash values as follows:
The problem, assuming uniformly distributed keys in the range [0,15], is
that the buckets [0,5] will have a higher probability of having values
mapped to them when compared to the other buckets in the range [6,9].
Simply put this particular problem can't be resolved by using prime numbers
as the quantisation value. That being said there are a couple of things that
can be done to resolve or mitigate this problem, they are as follows:
Set the hash table size to be a factor of Hmax (eg: A good basis would be 2n)
Increase the hash table size (where possible have the size tend closer to Hmax)
Note: This problem also appears when generating random numbers within a specified
range, where the underlying random number generator generates values in a range that is
larger than and is not a multiple of the desired range.
Problem 2 - Non-Uniformly Distributed Keys And Factors of N (Table Size)
N is the number of buckets in a hash table and as such is commonly used as the
quantisation value. There is an issue which can arise if the keys being hashed
are not uniformly distributed. Specifically when the keys result in values before
quantisation that are factors or multiples of factors of N. In this situation
buckets that aren't factors of N or multiples of factors of N will remain empty,
causing the load factor of the other buckets to increase disproportionately.
This situation seems to be the only valid reason to use a prime number
as a quantisation value. Given that a prime number has only itself
and one as factors, using a prime number to resolve this problem means
even if the keys are not uniformly distributed and instead possess some
of kind structure (specifically multiples of a value etc), the likelihood
that those values will arbitrarily hash to either the value one or N
(the prime number) will be vanishingly small.
Example: Lets assume we have a set of 30 messages M0, M1, ..., M29,
where we proceed to hash each message using a well behaved hash function h(x).
This gives us the values H0, H1, ..., H29, which
so happened to be in the form: 30, 33, 36, 39, 42, 45, ... ,111, 114, 117 - essentially
all are multiples of three.
Furthermore we have a hash table with 21 (3x7) buckets or in other words a
hash-space in the range [0,20]. We decide to quantise the values by computing their
modulus by 21 like so: Hi = h(Mi) mod 21. By doing this
we discover that the only buckets used to map the values denoted above are: 0, 3,
6, 9, 12, 15 and 18 - That is only 7 out of a total of 21 buckets or roughly
33% utilisation of the bucket-space and as a result incurred 21 collisions over the 30
values hashed.
Now if we had instead chosen a prime number, for example 23, we would have utilized
each and every one of the buckets in the table and would have only had 7 collisions - which
would also be the theoretical minimum number of possible collisions given the table size
(bucket-space).
One further thing to consider would be that instead of a prime number we could have
chosen a composite number that does not have three as one of its factors. But we would
then also have to guarantee that none of the multiples of three, which we
intend to quantise, are also not one of its factors - in short the problem becomes very
hairy very quickly for little to no gain - Q.E.D
That being said, this particular solution has a slight overhead of its own - that is
once a maximum table load factor has been reached the table will need to be re-sized.
This is typically achieved by simply doubling the size of the current table, but one
must also remember to snap to the next largest prime number as the new size, rather
than simply doubling the current size.
In conclusion it would be far more productive and effective to mix the keys more
thoroughly and rigorously than it would be to faff around with the ever changing
quantisation parameter. Using something as simple as a power of two (2n)
will generally suffice, given a satisfactory mixing process and relatively well
distributed keys [Mitzenmacher et al. 2011].
Mixing And Entropy Boosting Using Prime Numbers
This area is where most of the mythology surrounding the use
of prime numbers in hash functions reside. In implementing the
mixing operation, one tries to define a process where by all
the bits of the message block, equally affect all the bits of the
internal state.
As an example using a well designed hash function, given two messages
that differ by only one bit, the expectation is that the hash values
will be starkly different (aka having a large hamming distance).
This property is derived from the Bit Independence Criterion (BIC) -
cryptographic hash functions typically posses this trait, whereas general
purpose hash functions don't necessarily need to have this trait (as it's
largely an unrequired overhead), but instead attempt to attain something
similar or close to BIC.
The use of prime numbers in the mixing function, is due to an
assumption that fundamental mathematical operations (such as addition
and multiplication) with prime numbers "generally" result in
numbers who's bit biases are close to random.
In simple terms the "theory" states that when you multiply
(or add) a set of random numbers (aka keys) by a prime number
the resulting numbers when as a group statistically analyzed at their
bit levels should show no (or very little) bias towards being
one state or another ie: Pr(Bi = 1) ~= 0.5
There is no concrete proof or other forms of evidence to support
that this is the case or that it only (or more often) happens with prime
numbers as opposed to composite numbers. It just seems to be an ongoing
self-proclaimed intuition that some professionals in the field seem
obliged to follow and preach.
Bit sequence generators, be they purely random or in some way deterministic,
will generate bits with a particular probability of either being one state
or another - this probability is known as the Bit Bias. In the case
of purely random generators the bit bias of any generated bit being high or
low is always 50% (Pr = 0.5).
However in the case of pseudo random number generators (PRNG), the algorithm
generating the bits will define the bit bias of the bits generated in the
minimal output block of the generator.
Example: Let's assume a PRNG that produces 8-bit blocks as its
output. For some reason the MSB is always set to high, the bit bias
then for the MSB will be a probability of 100% being set high. From
this one concludes that even though there are 256 possible values that
can be produced with this PRNG, values less than 128 will never be generated.
Assuming for simplicity that the other bits being generated are purely random,
then there is an equal chance that any value between 128 and 255 will be
generated, however at the same time, there is 0% chance that a value less
than 128 will be produced.
All PRNGs, be they the likes of hash functions, ciphers, m-sequences
or anything else that produces a bit sequence will all exhibit some
form of bit bias. Most PRNGs will attempt to converge their bit biases
to an equality, stream ciphers are one example, whereas others will work
best with a known yet unstable bit bias.
Mixing or scrambling of a bit sequence is one way of producing a common
equality in the bit bias of a stream. Though one must be careful to ensure
that by mixing they do not further diverge the bit biases. A form of mixing
used in cryptography is known as avalanching, this is where a block of bits
are mixed together sometimes using a substitution or permutation box (S-Box,
P-Box), with another block to produce an output that will be used to mix
with yet another block.
As displayed in the figure below the avalanching process begins with one
or more pieces of binary data. Bits in the data are taken and operated upon
(usually some form of input sensitive bit reducing bitwise logic)
producing an ith-tier piece data. The process is then repeated on the
ith-tier data to produce an i+1'th tier data where the number of bits in
the current tier will be less than or equal to the number of bits in the
previous tier.
The culmination of this repeated process will result in one bit whose value
is said to be dependent upon all the bits from the original piece(s) of data.
It should be noted that the figure below is a mere generalisation of the
avalanching process and need not necessarily be the only form of the process.
In data communications that use block code based error correcting codes, it has been
seen that to overcome burst errors, that is when there is a large amount of noise for
a very short period of time in the carrier channel, if one were to bit-scramble whole
code blocks with each other, then have the scrambled form transmitted and then descrambled
at the other end that burst errors would then most likely be distributed almost evenly
over the entire sequence of blocks transmitted allowing for a much higher chance of
fully detecting and correcting all the incurred errors. This type of deterministic scrambling
and descrambling without the need for a common key is known as interleaving and deinterleaving.
Hashing as a tool to associate one set or bulk of data with an identifier has many different
forms of application in the real-world. Below are some of the more common uses of
hash functions.
Used for data/user verification and authentication. A strong cryptographic hash
function has the property of being very difficult to reverse the result of the
hash and hence reproduce the original piece of data. Cryptographic hash functions
are used to hash user's passwords and have the hash of the passwords stored on a
system rather than having the password itself stored. Cryptographic hash functions
are also seen as irreversible compression functions, being able to represent large
quantities of data with a signal ID, they are useful in seeing whether or not the
data has been tampered with, and can also be used as data one signs in order to
prove authenticity of a document via other cryptographic means.
This form of hashing is used in the field of computer vision for the
detection of classified objects in arbitrary scenes.
The process involves initially selecting a region or object of interest.
From there using affine invariant feature detection algorithms such
as the Harris corner detector (HCD), Scale-Invariant Feature Transform
(SIFT) or Speeded-Up Robust Features (SURF), a set of affine features are
extracted which are deemed to represent said object or region. This set is
sometimes called a macro-feature or a constellation of features. Depending
on the nature of the features detected and the type of object or region
being classified it may still be possible to match two constellations of
features even though there may be minor disparities (such as missing or
outlier features) between the two sets. The constellations are then said
to be the classified set of features.
A hash value is computed from the constellation of features. This is
typically done by initially defining a space where the hash values are
intended to reside - the hash value in this case is a multidimensional
value normalized for the defined space. Coupled with the process for
computing the hash value another process that determines the distance
between two hash values is needed - A distance measure is required rather
than a deterministic equality operator due to the issue of possible disparities
of the constellations that went into calculating the hash value. Also owing
to the non-linear nature of such spaces the simple Euclidean distance metric
is essentially ineffective, as a result the process of automatically determining
a distance metric for a particular space has become an active field of research
in academia.
Typical examples of geometric hashing include the classification of various
kinds of automobiles, for the purpose of re-detection in arbitrary scenes. The
level of detection can be varied from just detecting a vehicle, to a particular
model of vehicle, to a specific vehicle.
A Bloom filter
allows for the "state of existence" of a large set of possible
values to be represented with a much smaller piece of memory than the sum
size of the values. In computer science this is known as a membership
query and is a core concept in associative containers.
The Bloom filter achieves the storage efficiency through the use of multiple distinct
hash functions and by also allowing the result of a membership query for the existence
of a particular value to have a certain probability of error. The guarantee a Bloom filter
provides is that for any membership query there will never be any false negatives, however
there may be false positives. The false positive probability can be controlled by varying
the size of the table used for the Bloom filter and also by varying the number of hash
functions.
Subsequent research done in the area of hash functions and their use in bloom filters by
Mitzenmacher et al. suggest that for most practical uses of such constructs, the entropy
in the data being hashed contributes to the entropy of the hash functions, this further
leads onto theoretical results that conclude an optimal bloom filter (one which provides
the lowest false positive probability for a given table size or vice versa) providing a
user defined false positive probability can be constructed with at most two distinct hash
functions also known as pairwise independent hash functions, greatly increasing the efficiency
of membership queries.
Bloom filters are commonly found in applications such as spell-checkers, string matching algorithms,
network packet analysis tools and network/internet caches.
In consistent hashing,
the hash space is broken into address ranges of roughly equal length. Each range is
associated with one or more nodes, which hold values for keys that hash into that
address range. By using a Global Hash Function, one can query the address space
to determine the node(s) that will be holding the data corresponding to the key and
then subsequently query the node directly for the required data.
Typically nodes in adjacent addresses will replicate the closer half of keys from their
neighbours on both sides, and in some situations nodes may also replicate a randomly
selected node in the ring (random graph approach). This replication is done to provide
resilience against node failures and allow for load balancing and overall latency
reductions when data that hashes to a particular node is highly sought after or queried
by many clients. This data structure is the principle component of
Distributed Hash Tables (DHT).
In the diagram below, the blue segmented ring represents the address space for the range
of values the hash function (GH) can produce. Each segment, which denotes an address
range, will have one or more nodes associated with it. When a client with a key wants to
look up the corresponding data, they first invoke the GH which will give them the address
range, they then look up the node(s) handling that address range using a directory service
(obtain node's IP address or routing ID etc), then proceed to directly query them using the
key.
Cryptographic hash functions that are pre-image resistant, can be used in specific ways
to verify that a certain amount or type of work has been carried out, this is commonly
known as Proof Of Work, and makes up the fundamental basis of such things as
crypto-currencies,
challenge response hand-shakes
etc.
The way it typically works, is a known value K is given (some number of bits), then either
an expected hash value or a hash value with a specific trait (eg: certain number of leading
zero bits) is proposed.
The applicant (aka entity performing the work) will then go off and perform a computation
to determine another piece data D, such that H(K | D) (the hash of the concatenation of K and D)
will either be equal to the expected hash value or will posses the properties required to prove the
work has been done. Furthermore because the verification of the hash value can be performed cheaply
and efficiently and the work in computing the value D itself must be done in full (as currently
there are no known shortcuts - theoretic or otherwise), makes the use of such hash functions for
POW style protocols an optimal choice.
The General Hash Functions Library has the following mix of additive and
rotative general purpose string hashing algorithms. The following algorithms
vary in usefulness and functionality and are mainly intended as an example
for learning how hash functions operate and what they basically look like in
code form.
A simple hash function from Robert Sedgwicks Algorithms in C book.
I've added some simple optimizations to the algorithm in order to
speed up its hashing process.
unsigned int RSHash(const char* str, unsigned int length)
{
unsigned int b = 378551;
unsigned int a = 63689;
unsigned int hash = 0;
unsigned int i = 0;
for (i = 0; i < length; ++str, ++i)
{
hash = hash * a + (*str);
a = a * b;
}
return hash;
}
This hash algorithm is based on work by Peter J. Weinberger of Renaissance Technologies.
The book Compilers (Principles, Techniques and Tools) by Aho, Sethi and Ulman,
recommends the use of hash functions that employ the hashing methodology found
in this particular algorithm.
unsigned int PJWHash(const char* str, unsigned int length)
{
const unsigned int BitsInUnsignedInt = (unsigned int)(sizeof(unsigned int) * 8);
const unsigned int ThreeQuarters = (unsigned int)((BitsInUnsignedInt * 3) / 4);
const unsigned int OneEighth = (unsigned int)(BitsInUnsignedInt / 8);
const unsigned int HighBits =
(unsigned int)(0xFFFFFFFF) << (BitsInUnsignedInt - OneEighth);
unsigned int hash = 0;
unsigned int test = 0;
unsigned int i = 0;
for (i = 0; i < length; ++str, ++i)
{
hash = (hash << OneEighth) + (*str);
if ((test = hash & HighBits) != 0)
{
hash = (( hash ^ (test >> ThreeQuarters)) & (~HighBits));
}
}
return hash;
}
This hash function comes from Brian Kernighan and Dennis Ritchie's book "The C
Programming Language". It is a simple hash function using a strange set of possible
seeds which all constitute a pattern of 31....31...31 etc, it seems to be very similar
to the DJB hash function.
unsigned int BKDRHash(const char* str, unsigned int length)
{
unsigned int seed = 131; /* 31 131 1313 13131 131313 etc.. */
unsigned int hash = 0;
unsigned int i = 0;
for (i = 0; i < length; ++str, ++i)
{
hash = (hash * seed) + (*str);
}
return hash;
}
This is the algorithm of choice which is used in the open source SDBM
project. The hash function seems to have a good over-all distribution
for many different data sets. It seems to work well in situations
where there is a high variance in the MSBs of the elements in a data
set.
unsigned int SDBMHash(const char* str, unsigned int length)
{
unsigned int hash = 0;
unsigned int i = 0;
for (i = 0; i < length; ++str, ++i)
{
hash = (*str) + (hash << 6) + (hash << 16) - hash;
}
return hash;
}
An algorithm produced by Professor Daniel J. Bernstein and shown first to the
world on the usenet newsgroup comp.lang.c. It is one of the most efficient
hash functions ever published.
unsigned int DJBHash(const char* str, unsigned int length)
{
unsigned int hash = 5381;
unsigned int i = 0;
for (i = 0; i < length; ++str, ++i)
{
hash = ((hash << 5) + hash) + (*str);
}
return hash;
}
An algorithm produced by me Arash Partow. I took ideas from all of the above hash
functions making a hybrid rotative and additive hash function algorithm. There isn't
any real mathematical analysis explaining why one should use this hash function instead
of the others described above other than the fact that I tired to resemble the design
as close as possible to a simple LFSR. An empirical result which demonstrated the
distributive abilities of the hash algorithm was obtained using a hash-table with
100003 buckets, hashing The Project Gutenberg Etext of Webster's Unabridged Dictionary,
the longest encountered chain length was 7, the average chain length was 2, the number
of empty buckets was 4579.
unsigned int APHash(const char* str, unsigned int length)
{
unsigned int hash = 0xAAAAAAAA;
unsigned int i = 0;
for (i = 0; i < length; ++str, ++i)
{
hash ^= ((i & 1) == 0) ? ( (hash << 7) ^ (*str) * (hash >> 3)) :
(~((hash << 11) + ((*str) ^ (hash >> 5))));
}
return hash;
}
Note: For uses where high throughput is a requirement for computing hashes using
the algorithms described above, one should consider unrolling the internal loops and
adjusting the hash value memory foot-print to be appropriate for the targeted architecture(s).
Free use of the General Hash Functions Algorithm Library available on this site is permitted
under the guidelines and in accordance with the
MIT License.
The General Hash Functions Algorithm Library C and C++ implementation is compatible with
the following C & C++ compilers:
GNU Compiler Collection (3.3.1-x+)
Intel® C++ Compiler (8.x+)
Clang/LLVM (1.x+)
Microsoft Visual C++ Compiler (8.x+)
The General Hash Functions Algorithm Library Object Pascal and Pascal implementations
are compatible with the following Object Pascal and Pascal compilers:
Borland Delphi (1,2,3,4,5,6,7,8,2005,2006)
Free Pascal Compiler (1.9.x)
Borland Kylix (1,2,3)
Borland Turbo Pascal (5,6,7)
The General Hash Functions Algorithm Library Java implementation is compatible with the
following Java compilers:
