doc/internals/hashing.txt - haproxy - Gitiles

 2013/11/20 - How hashing works internally in haproxy - maddalab@gmail.com

 This document describes how Haproxy implements hashing both map-based and
 consistent hashing, both prior to versions 1.5 and the motivation and tests
 that were done when providing additional options starting in version 2.0

 A note on hashing in general, hash functions strive to have little
 correlation between input and output. The heart of a hash function is its
 mixing step. The behavior of the mixing step largely determines whether the
 hash function is collision-resistant. Hash functions that are collision
 resistant are more likely to have an even distribution of load.

 The purpose of the mixing function is to spread the effect of each message
 bit throughout all the bits of the internal state. Ideally every bit in the
 hash state is affected by every bit in the message. And we want to do that
 as quickly as possible simply for the sake of program performance. A
 function is said to satisfy the strict avalanche criterion if, whenever a
 single input bit is complemented (toggled between 0 and 1), each of the
 output bits should change with a probability of one half for an arbitrary
 selection of the remaining input bits.

 To guard against a combination of hash function and input that results in
 high rate of collisions, haproxy implements an avalanche algorithm on the
 result of the hashing function. In all versions 1.4 and prior avalanche is
 always applied when using the consistent hashing directive. It is intended
 to provide quite a good distribution for little input variations. The result
 is quite suited to fit over a 32-bit space with enough variations so that
 a randomly picked number falls equally before any server position, which is
 ideal for consistently hashed backends, a common use case for caches.

 In all versions 1.4 and prior Haproxy implements the SDBM hashing function.
 However tests show that alternatives to SDBM have a better cache
 distribution on different hashing criteria. Additional tests involving
 alternatives for hash input and an option to trigger avalanche, we found
 different algorithms perform better on different criteria. DJB2 performs
 well when hashing ascii text and is a good choice when hashing on host
 header. Other alternatives perform better on numbers and are a good choice
 when using source ip. The results also vary by use of the avalanche flag.

 The results of the testing can be found under the tests folder. Here is
 a summary of the discussion on the results on 1 input criteria and the
 methodology used to generate the results.

 A note of the setup when validating the results independently, one
 would want to avoid backend server counts that may skew the results. As
 an example with DJB2 avoid 33 servers. Please see the implementations of
 the hashing function, which can be found in the links under references.

 The following was the set up used

 (a) hash-type consistent/map-based
 (b) avalanche on/off
 (c) balanche host(hdr)
 (d) 3 criteria for inputs
     - ~ 10K requests, including duplicates
     - ~ 46K requests, unique requests from 1 MM requests were obtained
     - ~ 250K requests, including duplicates
 (e) 17 servers in backend, all servers were assigned the same weight

 Result of the hashing were obtained across the server via monitoring log
 files for haproxy. Population Standard deviation was used to evaluate the
 efficacy of the hashing algorithm. Lower standard deviation, indicates
 a better distribution of load across the backends.

 On 10K requests, when using consistent hashing with avalanche on host
 headers, DJB2 significantly out performs SDBM. Std dev on SDBM was 48.95
 and DJB2 was 26.29. This relationship is inverted with avalanche disabled,
 however DJB2 with avalanche enabled out performs SDBM with avalanche
 disabled.

 On map-based hashing SDBM out performs DJB2 irrespective of the avalanche
 option. SDBM without avalanche is marginally better than with avalanche.
 DJB2 performs significantly worse with avalanche enabled.

 Summary: The results of the testing indicate that there isn't a hashing
 algorithm that can be applied across all input criteria. It is necessary
 to support alternatives to SDBM, which is generally the best option, with
 algorithms that are better for different inputs. Avalanche is not always
 applicable and may result in less smooth distribution.

 References:
 Mixing Functions/Avalanche: https://papa.bretmulvey.com/post/124027987928/hash-functions
 Hash Functions: http://www.cse.yorku.ca/~oz/hash.html
	2013/11/20 - How hashing works internally in haproxy - maddalab@gmail.com

	This document describes how Haproxy implements hashing both map-based and
	consistent hashing, both prior to versions 1.5 and the motivation and tests
	that were done when providing additional options starting in version 2.0

	A note on hashing in general, hash functions strive to have little
	correlation between input and output. The heart of a hash function is its
	mixing step. The behavior of the mixing step largely determines whether the
	hash function is collision-resistant. Hash functions that are collision
	resistant are more likely to have an even distribution of load.

	The purpose of the mixing function is to spread the effect of each message
	bit throughout all the bits of the internal state. Ideally every bit in the
	hash state is affected by every bit in the message. And we want to do that
	as quickly as possible simply for the sake of program performance. A
	function is said to satisfy the strict avalanche criterion if, whenever a
	single input bit is complemented (toggled between 0 and 1), each of the
	output bits should change with a probability of one half for an arbitrary
	selection of the remaining input bits.

	To guard against a combination of hash function and input that results in
	high rate of collisions, haproxy implements an avalanche algorithm on the
	result of the hashing function. In all versions 1.4 and prior avalanche is
	always applied when using the consistent hashing directive. It is intended
	to provide quite a good distribution for little input variations. The result
	is quite suited to fit over a 32-bit space with enough variations so that
	a randomly picked number falls equally before any server position, which is
	ideal for consistently hashed backends, a common use case for caches.

	In all versions 1.4 and prior Haproxy implements the SDBM hashing function.
	However tests show that alternatives to SDBM have a better cache
	distribution on different hashing criteria. Additional tests involving
	alternatives for hash input and an option to trigger avalanche, we found
	different algorithms perform better on different criteria. DJB2 performs
	well when hashing ascii text and is a good choice when hashing on host
	header. Other alternatives perform better on numbers and are a good choice
	when using source ip. The results also vary by use of the avalanche flag.

	The results of the testing can be found under the tests folder. Here is
	a summary of the discussion on the results on 1 input criteria and the
	methodology used to generate the results.

	A note of the setup when validating the results independently, one
	would want to avoid backend server counts that may skew the results. As
	an example with DJB2 avoid 33 servers. Please see the implementations of
	the hashing function, which can be found in the links under references.

	The following was the set up used

	(a) hash-type consistent/map-based
	(b) avalanche on/off
	(c) balanche host(hdr)
	(d) 3 criteria for inputs
	- ~ 10K requests, including duplicates
	- ~ 46K requests, unique requests from 1 MM requests were obtained
	- ~ 250K requests, including duplicates
	(e) 17 servers in backend, all servers were assigned the same weight

	Result of the hashing were obtained across the server via monitoring log
	files for haproxy. Population Standard deviation was used to evaluate the
	efficacy of the hashing algorithm. Lower standard deviation, indicates
	a better distribution of load across the backends.

	On 10K requests, when using consistent hashing with avalanche on host
	headers, DJB2 significantly out performs SDBM. Std dev on SDBM was 48.95
	and DJB2 was 26.29. This relationship is inverted with avalanche disabled,
	however DJB2 with avalanche enabled out performs SDBM with avalanche
	disabled.

	On map-based hashing SDBM out performs DJB2 irrespective of the avalanche
	option. SDBM without avalanche is marginally better than with avalanche.
	DJB2 performs significantly worse with avalanche enabled.

	Summary: The results of the testing indicate that there isn't a hashing
	algorithm that can be applied across all input criteria. It is necessary
	to support alternatives to SDBM, which is generally the best option, with
	algorithms that are better for different inputs. Avalanche is not always
	applicable and may result in less smooth distribution.

	References:
	Mixing Functions/Avalanche: https://papa.bretmulvey.com/post/124027987928/hash-functions
	Hash Functions: http://www.cse.yorku.ca/~oz/hash.html