2018-09-02 11:02:08 +00:00
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human-friendly-id-gen
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2018-09-02 20:05:13 +00:00
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=====================
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2018-09-02 11:02:08 +00:00
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2018-09-02 20:05:13 +00:00
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New Haskell project to generate Human Friendly Ids.
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2018-09-02 11:02:08 +00:00
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2018-09-04 13:20:20 +00:00
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Those ids should be easier to read / write and remember than classical random
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base64 ids.
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The package provide both a lib and an executable `hfig` (for Human Friendly
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Identifier Generator).
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## Strategies
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There are different strategies depending on your preferences.
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### Short strategy
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We generate random phonemes that should be not too hard to pronounce but in the
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same time having sufficiently different phonemes to be able to have not too long
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words to prevent collision.
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~~~
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rupomdovi
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waziridro
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moplaloxo
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kankujochplu
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drubrusadka
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dripuxmopbi
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jotchibluzuv
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plotabrprabudr
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zopranblokplab
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tirbrozprakow
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~~~
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Here is the probability of collision if you generate a sample of n of those words:
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| n | % |
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|------|--------|
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| 1000 | 2.5e-8 |
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| 10k | 2.5e-6 |
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| 100k | 2.5e-4 |
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| 1M | 2.5e-2 |
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You can also ask to use more phonemes if you only use 2 phonemes which generate words like:
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~~~
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blilwa
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wirpa
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winupl
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tani
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ludu
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probrip
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pichprox
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joprux
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drudibl
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zibrku
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~~~
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The probility of collision become:
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| n | % |
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|-----|------|
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| 10 | 1e-5 |
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| 100 | 1e-3 |
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| 1k | 0.11 |
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| 10k | 1.0 |
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### Lovecraftian strategy
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My nickname isn't yogsototh for nothing so why not generate as if Lovecraft
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could have invented them.
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~~~
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ymhiovhotl
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zhaobritl
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v'odher
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neltha
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ucnouthlaxr
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kola
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adavhig
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ctuthrilbh
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yakthembru
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athoubr'murh
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~~~
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The probability collision table looks like:
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| n | % |
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|------|----------------------|
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| 10 | 6.669334400426838e-8 |
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| 100 | 6.669334400426838e-6 |
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| 1k | 6.669334400426837e-4 |
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| 10k | 6.669334400426838e-2 |
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| 100k | 1.0 |
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if you generate two names for an id, you should be safe.
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| n | % |
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|------|---------|
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| 10 | 8.8e-17 |
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| 100 | 8.8e-15 |
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| 1k | 8.8e-13 |
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| 10k | 8.8e-11 |
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| 100k | 8.8e-9 |
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| 1M | 8.8e-7 |
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### Dictionary Strategy
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You can read any file and each line will be considered as a word.
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We then take a few random words.
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You can gather some word list in this repository to use.
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There is a default english dictionary with approximatively 370k English words.
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Here is an example:
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~~~
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shuckins-digitinerved-microspectrophotometrical
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indeterminableness-getaways-sceloporus
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diverts-okayed-cast
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semirhythmically-thasian-thrawart
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smashups-phototherapeutics-swollenness
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bindingness-phoenicia-ringy
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execs-axes-barotaxis
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monimiaceous-presutural-submembers
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heterodyned-pourparley-zecchino
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fragmentate-contrude-taeniae
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~~~
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And here are the different table of collision probability.
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use 1 word to make the identifier:
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| n | % |
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|---------|--------|
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| 10 | 1.3e-4 |
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| 100 | 1.3e-2 |
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| 1k | 1.0 |
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combine 2 words to make the identifier:
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| n | % |
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|------|---------|
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| 10 | 3.6e-10 |
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| 100 | 3.6e-8 |
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| 1k | 3.6e-6 |
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| 10k | 3.6e-4 |
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| 100k | 3.6e-2 |
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| 1M | 1.0 |
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combine 3 words to make the identifier:
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| n | % |
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|------|---------|
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| 10 | 9.8e-16 |
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| 100 | 9.8e-14 |
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| 1k | 9.8e-12 |
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| 10k | 9.8e-10 |
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| 100k | 9.8e-8 |
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| 1M | 9.8e-6 |
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