The answer is probably here: https://github.com/rurban/smhasher
Speed & number of cycles/hash is better with metro hash.
In Show HN: An improved version of HyperLogLogrurban
smhasher
C++2015–2026
rurban / smhasher
smhasher
Hash function quality and speed tests
35Distinct people
67Mentions
1Explicit recommenders
2.2KGitHub stars
What to know before exploring
What it is
Hash function quality and speed tests
GitHub
Where it is used
No documented product use was verified in the last source check.
What people are saying
Disochash is a simple prime-multiplication ARX (addition rotation XOR) hash that's high-quality and fast. It's only 120 lines of code. There's lots of great hash functions out…
keepamovin used a recommendation phrase. One indexed comment matches. This is a phrase match, not a verdict.
No classified warning is attached to this page. Read the discussions before treating silence as approval.
Also in these discussions
- cyan4973/xxhash · linked in 2 of the same discussions
- demerphq/smhasher · linked in 2 of the same discussions
- genbox/fasthash · linked in 2 of the same discussions
Mentioned in the same Hacker News threads. That is not proof they are substitutes.
This week on Hacker News
Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 7:24:48 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by year67 dated mentions
Observed mentions: 2015-05-28 13:42:43 UTC to 2026-01-21 11:45:24 UTC.
* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2015 | 2015-01-01 00:00:00 UTC | 2016-01-01 00:00:00 UTC | 5 | 0 | 5 |
| 2016 | 2016-01-01 00:00:00 UTC | 2017-01-01 00:00:00 UTC | 5 | 0 | 5 |
| 2017 | 2017-01-01 00:00:00 UTC | 2018-01-01 00:00:00 UTC | 4 | 0 | 4 |
| 2018 | 2018-01-01 00:00:00 UTC | 2019-01-01 00:00:00 UTC | 5 | 0 | 5 |
| 2019 | 2019-01-01 00:00:00 UTC | 2020-01-01 00:00:00 UTC | 6 | 2 | 4 |
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 7 | 0 | 7 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 14 | 1 | 13 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 9 | 0 | 9 |
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 4 | 0 | 4 |
| 2024 | 2024-01-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 3 | 0 | 3 |
| 2025 | 2025-01-01 00:00:00 UTC | 2026-01-01 00:00:00 UTC | 4 | 0 | 4 |
| 2026 (partial) | 2026-01-01 00:00:00 UTC | 2027-01-01 00:00:00 UTC | 1 | 0 | 1 |
From the project README
An excerpt from the repository, fetched Nov 13, 2026.
Read the project’s introduction
SMhasher
Hash function
MiB/sec
cycl./hash
cycl./map
size
Quality problems
donothing32
11536809.55
4.00
-
13
bad seed 0, test NOP
donothing64
12269832.97
4.00
-
13
bad seed 0, test NOP
donothing128
11961841.48
4.08
-
13
bad seed 0, test NOP
NOP_OAAT_read64
11652662.97
14.00
-
47
test NOP
BadHash
794.68
72.80
-
48
bad seed 0, test FAIL
sumhash
10184.67
29.40
-
417
bad seed 0, test FAIL
sumhash32
44755.87
23.46
-
697
UB, test FAIL
multiply_shift
8286.63
25.39
209.06 (2)
345
bad seeds & 0xfffffff0, fails most tests
pair_multiply_shift
5728.05
39.67
218.48 (12)
609
fails most tests
--------------------------
crc32
369.88
142.33
396.81 (101)
363
insecure, 8590x collisions, distrib, PerlinNoise
md5_32
359.43
638.92
865.21 (6)
598
md5_64
360.20
640.38
869.55 (6)
662
md5-128
344.63
669.59
856.34 (5)
557
sha1_32
353.03
1385.80
1759.94 (5)
5126
Sanity, Cyclic low32, 36.6% distrib
sha1_64
353.03
1385.80
1759.94 (5)
5126
Sanity, Cyclic low32, 36.6% distrib
sha1-160
364.95
1470.55
1794.16 (13)
5126
Comb/Cyclic low32
sha2-224
147.13
1354.81
1589.92 (12)
695
Comb low32
sha2-224_64
147.60
1360.10
1620.93 (13)
600
Cyclic low32
sha2-256
147.80
1374.90
1606.06 (16)
663
sha2-256_64
148.01
1376.34
1624.71 (16)
600
sha1ni
1632.88
170.77
379.84 (5)
1321
insecure,sanity, Permutation, Zeroes, machine-specific
sha1ni_32
1583.50
172.17
387.70 (4)
1273
machine-specific
sha2ni-256
1556.66
180.75
393.79 (5)
1495
insecure,sanity, Permutation, Zeroes, machine-specific
sha2ni-256_64
1508.82
184.89
393.07 (6)
1409
Zeroes, machine-specific
blake3_c
1298.04
354.25
563.63 (4)
16462
no 32bit portability
rmd128
294.42
712.67
930.65 (4)
950
rmd160
202.16
1045.79
1287.74 (16)
950
Cyclic hi32
rmd256
366.21
615.39
830.04 (7)
1026
edonr224
863.77
304.76
496.66 (3)
834
edonr256
870.97
296.40
517.82 (6)
834
blake2s-128
295.30
698.09
1059.24 (51)
329
blake2s-160
215.01
1026.74
1239.54 (11)
329
blake2s-224
207.06
1063.86
1236.50 (20)
329
blake2s-256
215.28
1014.88
1230.38 (28)
329
blake2s-256_64
211.52
1044.22
1228.43 (8)
323
blake2b-160
356.08
1236.84
1458.15 (12)
505
blake2b-224
356.59
1228.50
1425.87 (16)
505
blake2b-256
355.97
1232.22
1443.31 (19)
505
blake2b-256_64
356.97
1222.76
1435.03 (9)
501
asconhashv12
144.98
885.02
1324.23 (38)
4341
asconhashv12_64
159.33
402.54
473.15 (4)
6490
sha3-256
100.58
3877.18
4159.79 (37)
1686
PerlinNoise
sha3-256_64
100.57
3909.00
4174.63 (16)
1630
PerlinNoise
hasshe2
2879.93
62.47
272.34 (2)
505
Permutation,TwoBytes,Zeroes,Seed
poly_1_mersenne
1278.37
63.42
244.41 (1)
479
fails most tests
poly_2_mersenne
1391.90
69.97
255.44 (3)
479
poly_3_mersenne
1320.15
80.81
263.12 (2)
479
poly_4_mersenne
1393.93
89.07
262.97 (2)
479
tabulation32
5819.40
40.16
233.00 (3)
848
collisions
tabulation
7407.94
39.83
240.25 (4)
554
crc32_hw
5537.79
40.80
225.65 (3)
653
insecure, 100% bias, collisions, distrib, BIC, machine-specific (SSE4.2/NEON)
crc32_hw1
7626.17
50.21
228.50 (2)
696
insecure, 100% bias, collisions, distrib, BIC, machine-specific (x86 SSE4.2)
crc64_hw
5579.17
40.87
202.19 (1)
652
insecure, 100% bias, collisions, distrib, BIC, machine-specific (SSE4.2/NEON)
crc32_pclmul
7963.20
106.02
567.70 (3)
254
insecure, 100% bias, collisions, distrib, BIC, machine-specific (x86 SSE4.2+PCLMUL)
crc64_jones1
1673.67
83.00
249.16 (2)
insecure, 100% bias, collisions, distrib, BIC, machine-specific
crc64_jones2
2968.07
314.60
253.01 (3)
insecure, 100% bias, collisions, distrib, BIC, machine-specific
crc64_jones3
3398.80
302.59
258.16 (8)
insecure, 100% bias, collisions, distrib, BIC, machine-specific
crc64_jones
3512.66
80.70
251.40 (2)
insecure, 100% bias, collisions, distrib, BIC, machine-specific
o1hash
11530366.92
18.40
206.94 (1)
101
insecure, no seed, zeros, fails all tests
fibonacci
15409.62
22.67
872.83 (3)
1692
UB, zeros, fails all tests
FNV1a
754.70
75.28
226.30 (2)
54
bad seed, zeros, fails all tests
FNV1A_Totenschiff
6166.85
26.60
235.71 (2)
270
UB, zeros, fails all tests
FNV1A_Pippip_Yurii
24196.44
86.25
539.41 (3)
603
bad seeds, UB, sanity, x64 AESNI only
FNV1a_YT
13481.70
30.67
233.71 (7)
336
bad seed, UB, fails all tests
FNV2
5630.87
32.09
207.08 (1)
278
fails all tests
FNV64
747.70
74.18
189.18 (1)
70
fails all tests
FNV128
408.59
130.82
299.47 (20)
153
fails all tests
k-hash32
2227.90
53.85
255.78 (2)
808
insecure, zeros, UB, bad seeds, fails all tests
k-hash64
2492.66
48.18
242.58 (2)
609
insecure, zeros, UB, bad seeds, fails all tests
fletcher2
15410.12
20.72
345.55 (5)
117
bad seed 0, UB, fails all tests
fletcher4
15603.68
21.24
320.09 (2)
117
bad seed 0, UB, fails all tests
bernstein
1032.00
59.04
225.09 (3)
56
bad seed 0, fails all tests
sdbm
772.01
71.67
220.09 (2)
54
bad seed 0, fails all tests
x17
765.01
75.61
225.81 (2)
65
99.98% bias, fails all tests
libiberty
618.39
86.58
220.95 (2)
37
insecure, 100% bias, fails all tests, bad seed
gcc
612.10
88.44
224.62 (2)
55
insecure, 100% bias, fails all tests, bad seed
JenkinsOOAT
615.60
111.23
251.08 (2)
150
bad seed 0, 53.5% bias, fails all tests
JenkinsOOAT_perl
631.79
93.13
232.44 (1)
65
bad seed 0, 1.5-11.5% bias, 7.2x collisions, BIC, LongNeighbors
MicroOAAT
730.89
78.25
236.07 (3)
78
100% bias, distrib, BIC
pearsonhash64
439.85
123.07
213.68 (1)
Avalanche, Seed, SSSE3 only. broken MSVC
pearsonhash128
438.49
123.81
212.89 (2)
Avalanche, Seed, SSSE3 only. broken MSVC
pearsonhash256
440.63
120.72
224.63 (2)
Avalanche, Seed, SSSE3 only. broken MSVC
VHASH_32
13084.37
65.45
280.14 (2)
1231
sanity, Seed, MomentChi2
VHASH_64
13217.64
64.90
270.04 (2)
1231
sanity, Seed, Sparse
farsh32
27583.89
65.65
266.64 (2)
944
insecure: AppendedZeroes, collisions+bias, MomentChi2, LongNeighbors
farsh64
13558.69
113.74
327.26 (3)
944
insecure: AppendedZeroes, collisions+bias, MomentChi2, LongNeighbors
farsh128
7055.39
229.42
375.57 (4)
944
insecure:
Read the full README on GitHubWhat Hacker News says
4 sourcesThere's also FarmHash, whose 32-bit version is 2x as fast as xxHash on little endian machines (at least according to this benchmark suite https://github.com/rurban/smhasher).
In Diving into the world of hash tablesYou are welcome. I wanted add another point. rurban does have a bit of a point. The hash functions you referenced in your article are generally old and low quality, and slow. FNV is neither a good hash, nor is it fast. FNV_yt is very fast, but not the best quality hash function, it fails many tests in SMHasher, including some I personally consider dealbreaker: failing avalanche tests is unacceptable.
Both rurban and I have (independently) spent considerable time testing about 100 hash functions using the hash test suite smhasher. In addition I have greatly enhanced smhasher, and included test results and graphs in my version. Included in my fork is the --stream option so you can test different hash functions in counter mode with tools like the dieharder RNG test suite.
https://github.com/demerphq/smhasher
https://github.com/rurban/smhashe
Read the rest
r
These are the FNV test results from my version of SMHasher:
FNV1a 32 32 32 FAILED 99 of 183 tests. Avalanche: 17-41, Crc-MultiCollision: 81, 83, 85-86, 88-91, 94, 96-98, 100, Cyclic: 42-52, Hi-Lo: 157-158, HiBit-Null: 150-152, Highbits2: 147-149, Highbits: 144-146, LowBit-Null: 153-155, Lowbits: 142-143, Murmur2-MultiCollision: 101-110, Murmur3A-MultiCollision: 111, 113, 117, 119-120, Murmur3F-MultiCollision: 122-127, 129-130, OverAll: 183, Text: 160-165, TwoBytes: 54-56, 63, Zeroes: 167-168
FNV1a_YT 32 32 32 FAILED 67 of 181 tests. Avalanche: 15-39, Differential: 9-14, Hi-Lo: 154-156, HiBit-Null: 148-150, Highbits2: 145-147, Highbits: 142-144, Lowbits: 139-141, OverAll: 181, Sparse: 74-78, Text: 157-158, 160-163, TwoBytes: 51, 53-61
FNV64 64 64 64 FAILED 66 of 183 tests. Avalanche: 17-41, Cyclic: 43-45, 47, 49, 51-52, Hi-Lo: 157-158, HiBit-Null: 151-152, Highbits2: 148-149, Highbits: 145-146, LowBit-Null: 154-155, Lowbits: 142-143, OverAll: 183, Sparse: 65-67, 69, 71, 73, 75, 77, 79-80, Text: 160-162, 164-165, TwoBytes: 54-56, 58, 60, 62-63, Words: 182, Zeroes: 167-168
You can see text view of the full list at:
https://github.com/demerphq/smhasher/blob/master/doc/summary...
Also after reading this thread I added some graphs comparing performance of other hashes, including some that I have developed using genetic algorithm for use in Perl. Some of those hashes have hash-time performance at the top of the envelope (StadtX is very fast).
https://github.com/demerphq/smhasher/blob/master/doc/graph/F...
https://github.com/demerphq/smhasher/blob/master/doc/graph/F...
https://github.com/demerphq/smhasher/blob/master/doc/graph/F...
https://github.com/demerphq/smhasher/blob/master/doc/graph/F...
You may also be interested in looking at
https://github.com/demerphq/smhasher/blob/master/HashAnatomy...
Good luck!
ad 1: https://stackoverflow.com/questions/31954237/pseudo-random-g... has some answers.
ad 2: in most measurable stats fnv1 is better than djb.
practical throughput, time if inlined, cachegrind cost model (4x!).
fnv also produces less collisions with average keys.
only in pure cycle/hash djb is a bit better.
https://github.com/rurban/smhasher#smhasher
https://github.com/rurban/perl-hash-stats#perl-hash-stats
> ad 3: Is hash function efficiency that important when neither of the hash functions DJB or FNV1 posses good distributive qualities?
It depends entirely on the use case. Usage in hash tables is totally different to usage as file or block checksum or crypto.
Besides looking how many bis of the keys are really used or how easy it is to create collisions, it also depends if the keys are random length strings, aligned buffers or plain i
Read the rest
ntegers.
Both DJB and FNV1 are considered bad regarding their stats. But they are small and fast.