Jazzy Fish - Sufficiently-large, unique, human-friendly identifiers - Python implementation

⚠️ Warning: This library is in development and not yet ready for production. Its API is not yet stable and might (and probably will) still change. Follow repository issues for more information.

Jazzy Fish is a library that helps you generate a sufficient number of identifiers, with a human-friendly kick.

This is not a new idea and similar implementation can be seen in various places (i.e., GitHub new repository name suggestions.

Jazzy Fish is able to generate word sequences that can be mapped to unique integer values, which can be used as identifiers.

The implementation roughly works as follows:

• configure a Generator, more information below
• call generator.next_id(), which returns a unique integer
• call Encoder.encode(id), which returns a [word sequence]
• optionally, if a word sequence needs to be decoded into an integer, call Encoder.decode([word sequence])

Configuring a Generator

Integer IDs are constructed by combining 3 parts:

• a timestamp: can be relative to the UNIX epoch, or a custom epoch - to maximize the possible solution size; the timestamp can be chosen between seconds and milliseconds, in increments of 1/10ms (1s, 1/10s, 1/100s, 1ms)
• a machine id: since it may be necessary to run multiple generators (i.e., in distributed systems), the solution domain can be partitioned by multiple 'machines'
• a sequence id: representing a number of identifiers that can be generated, all things being equal (e.g., same time, same machine)

Thus, the algorithm is configurable enough to split a solution domain (e.g., N potential word combinations, where N is a large integer) into smaller partitions, that can be reasoned about in terms of: For how many years can IDs/word sequences be generated before the implementation needs to be changed?

The idea behind this implementation is also inspired from Bitcoin's Improvement Proposal 39.

Note: The BIP39 implementation uses a single word list to convert 12 or 24 unique words out of a total of 2048 words into a very large integer that can be used to derive secret keys.

Jazzy Fish different from BIP39 in that it uses multiple word lists (specifically, adverbs, verbs, adjectives, and nouns) to generate word sequences that are similar to natural (English) language, with the assumption that sequences such as yellow cat, hectic fish, dreadful elephant (while somewhat nonsensical) are easy to memorize by humans used to combining word parts. So, the aim of this library is to choose sufficiently-large word lists that can generate sufficiently-large unique word sequences, for a reasonable duration (i.e., several years or more).

Another relevant detail of this algorithm, is its ability to map chosen word sequences to smaller prefixes that can be used to form constant-length identifiers. While each sequence maps to an integer, remembering integers is hard for most humans. Thus, based on this implementation's assumption that humans can remember structured sentences, it selects the input wordlists in such a way that, for a given and pre-configured prefix length, there exists a single word that corresponds to that prefix.

For example, given a prefix length of size=1, yellow cat can be encoded to yc and then decoded back to the same two words. In this example, yellow is an adjective, and cat is a noun. There do not exist any other adjectives that start with y, nor nouns that start with c in our input word lists.

The reference implementation of the algorithm comes with a default wordlist of prefix 3, containing adverbs, verbs, adjectives, and nouns.

It can map the following solution domains:

• 2,178,560 unique combinations of adjective noun
• 2,740,628,480 unique combinations of verb adjective noun
• 1,205,876,531,200 unique combinations of adverb verb adjective noun

Two-word sequences may be impractical for sustained identifier generation, however, three word and four word sequences can sustain 87 and 38,238 years respectively at a rate of 1 identifier generated per second, using a single machine.

However, the default implementation can be changed to using longer prefixes and you can also bring your own wordlists, if desired.

The preprocessor package contains code that can process input wordlists and generate input word list combinations, which can be inspected to help users infer the best choice depending on use-case.

Contents

This directory contains the following resources:

• src/encoder: Python code that generates unique identifiers and can encode/decode them to word sequences; this is the main package a client implementation needs to generate jazzy-fish word sequences
• src/preprocessor: Utility that works with wordlists, cleaning up input words (removing invalid or inappropriate words) and generating various combinations that allows a user to create new input wordlists with different criteria than the default one.

Preprocessor

Once you install the library you can generate all word combinations given an input wordlist by executing the following command:

generate_words $PATH_TO_REPO/wordlists/5

You may replace the wordlist, with a directory of your choosing, as long as it contains the same file structure - four files named after the relevant four parts in the English language:

• adverb.txt
• adjective.txt
• verb.txt
• noun.txt

After running this command, you can examine all outputs in the out/processed directory and you can see a high-level comparison by running make show-stats.

Developers

Unless you are interested in contributing to this code (or are curious about this library's development processes), you can stop reading here.

Publishing

GitHub-based version publishing

The simplest way to publish a new version (if you have committer rights) is to tag a commit and push it to the repo:

# At a certain commit, ideally after merging a PR to main
git tag v0.1.x
git push origin v0.1.x

A GitHub Action will run, build the library and publish it to the PyPI repositories.

Manual

These steps can also be performed locally. For these commands to work, you will need to export two environment variables:

export TESTPYPI_PASSWORD=... # token for https://test.pypi.org/legacy/
export PYPI_PASSWORD=... # token for https://upload.pypi.org/legacy/

First, publish to the test repo and inspect the package:

make publish-test

If correct, distribute the wheel to the PyPI index:

make publish

Verify the distributed code

make publish-verify