84 lines
4.4 KiB
Plaintext
84 lines
4.4 KiB
Plaintext
2013/11/20 - How hashing works internally in haproxy - maddalab@gmail.com
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This document describes how Haproxy implements hashing both map-based and
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consistent hashing, both prior to versions 1.5 and the motivation and tests
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that were done when providing additional options starting in version 2.2
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A note on hashing in general, hash functions strive to have little
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correlation between input and output. The heart of a hash function is its
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mixing step. The behavior of the mixing step largely determines whether the
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hash function is collision-resistant. Hash functions that are collision
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resistant are more likely to have an even distribution of load.
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The purpose of the mixing function is to spread the effect of each message
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bit throughout all the bits of the internal state. Ideally every bit in the
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hash state is affected by every bit in the message. And we want to do that
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as quickly as possible simply for the sake of program performance. A
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function is said to satisfy the strict avalanche criterion if, whenever a
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single input bit is complemented (toggled between 0 and 1), each of the
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output bits should change with a probability of one half for an arbitrary
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selection of the remaining input bits.
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To guard against a combination of hash function and input that results in
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high rate of collisions, haproxy implements an avalanche algorithm on the
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result of the hashing function. In all versions 1.4 and prior avalanche is
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always applied when using the consistent hashing directive. It is intended
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to provide quite a good distribution for little input variations. The result
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is quite suited to fit over a 32-bit space with enough variations so that
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a randomly picked number falls equally before any server position, which is
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ideal for consistently hashed backends, a common use case for caches.
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In all versions 1.4 and prior Haproxy implements the SDBM hashing function.
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However tests show that alternatives to SDBM have a better cache
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distribution on different hashing criteria. Additional tests involving
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alternatives for hash input and an option to trigger avalanche, we found
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different algorithms perform better on different criteria. DJB2 performs
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well when hashing ascii text and is a good choice when hashing on host
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header. Other alternatives perform better on numbers and are a good choice
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when using source ip. The results also vary by use of the avalanche flag.
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The results of the testing can be found under the tests folder. Here is
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a summary of the discussion on the results on 1 input criteria and the
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methodology used to generate the results.
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A note of the setup when validating the results independently, one
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would want to avoid backend server counts that may skew the results. As
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an example with DJB2 avoid 33 servers. Please see the implementations of
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the hashing function, which can be found in the links under references.
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The following was the set up used
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(a) hash-type consistent/map-based
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(b) avalanche on/off
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(c) balanche host(hdr)
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(d) 3 criteria for inputs
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- ~ 10K requests, including duplicates
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- ~ 46K requests, unique requests from 1 MM requests were obtained
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- ~ 250K requests, including duplicates
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(e) 17 servers in backend, all servers were assigned the same weight
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Result of the hashing were obtained across the server via monitoring log
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files for haproxy. Population Standard deviation was used to evaluate the
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efficacy of the hashing algorithm. Lower standard deviation, indicates
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a better distribution of load across the backends.
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On 10K requests, when using consistent hashing with avalanche on host
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headers, DJB2 significantly out performs SDBM. Std dev on SDBM was 48.95
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and DJB2 was 26.29. This relationship is inverted with avalanche disabled,
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however DJB2 with avalanche enabled out performs SDBM with avalanche
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disabled.
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On map-based hashing SDBM out performs DJB2 irrespective of the avalanche
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option. SDBM without avalanche is marginally better than with avalanche.
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DJB2 performs significantly worse with avalanche enabled.
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Summary: The results of the testing indicate that there isn't a hashing
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algorithm that can be applied across all input criteria. It is necessary
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to support alternatives to SDBM, which is generally the best option, with
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algorithms that are better for different inputs. Avalanche is not always
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applicable and may result in less smooth distribution.
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References:
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Mixing Functions/Avalanche: https://papa.bretmulvey.com/post/124027987928/hash-functions
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Hash Functions: http://www.cse.yorku.ca/~oz/hash.html
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