durable_rules

durable_rules is a polyglot micro-framework for real-time, consistent and scalable coordination of events. With durable_rules you can track and analyze information about things that happen (events) by combining data from multiple sources to infer more complicated circumstances.

A full forward chaining implementation (A.K.A. Rete) is used to evaluate facts and events in real time. A simple meta-linguistic abstraction lets you define simple and complex rulesets as well as control flow structures such as flowcharts, statecharts, nested statecharts and time driven flows.

The durable_rules core engine is implemented in C, which enables fast rule evaluation as well as muti-language support.

In durable_rules V2, less is more: The Rete tree is fully evaluated in C. Thus, the framework is 5x to 10x faster (depending on the scenario) and does not require Redis. The programming model for posting events, asserting and retracting facts is synchronous and does not prescribe any web framework.

Getting Started

durable_rules is simple: to define a rule, all you need to do is describe the event or fact pattern to match (antecedent) and the action to take (consequent).

To install the framework do: pip install durable_rules

from durable.lang import *

with ruleset('test'):
    # antecedent
    @when_all(m.subject == 'World')
    def say_hello(c):
        # consequent
        print ('Hello {0}'.format(c.m.subject))

post('test', { 'subject': 'World' })

Forward Inference

durable_rules super-power is the foward-chaining evaluation of rules. In other words, the repeated application of logical modus ponens(https://en.wikipedia.org/wiki/Modus_ponens) to a set of facts or observed events to derive a conclusion. The example below shows a set of rules applied to a small knowledge base (set of facts).

from durable.lang import *

with ruleset('animal'):
    @when_all(c.first << (m.predicate == 'eats') & (m.object == 'flies'),
              (m.predicate == 'lives') & (m.object == 'water') & (m.subject == c.first.subject))
    def frog(c):
        c.assert_fact({ 'subject': c.first.subject, 'predicate': 'is', 'object': 'frog' })

    @when_all(c.first << (m.predicate == 'eats') & (m.object == 'flies'),
              (m.predicate == 'lives') & (m.object == 'land') & (m.subject == c.first.subject))
    def chameleon(c):
        c.assert_fact({ 'subject': c.first.subject, 'predicate': 'is', 'object': 'chameleon' })

    @when_all((m.predicate == 'eats') & (m.object == 'worms'))
    def bird(c):
        c.assert_fact({ 'subject': c.m.subject, 'predicate': 'is', 'object': 'bird' })

    @when_all((m.predicate == 'is') & (m.object == 'frog'))
    def green(c):
        c.assert_fact({ 'subject': c.m.subject, 'predicate': 'is', 'object': 'green' })

    @when_all((m.predicate == 'is') & (m.object == 'chameleon'))
    def grey(c):
        c.assert_fact({ 'subject': c.m.subject, 'predicate': 'is', 'object': 'grey' })

    @when_all((m.predicate == 'is') & (m.object == 'bird'))
    def black(c):
        c.assert_fact({ 'subject': c.m.subject, 'predicate': 'is', 'object': 'black' })

    @when_all(+m.subject)
    def output(c):
        print('Fact: {0} {1} {2}'.format(c.m.subject, c.m.predicate, c.m.object))


assert_fact('animal', { 'subject': 'Kermit', 'predicate': 'eats', 'object': 'flies' })
assert_fact('animal', { 'subject': 'Kermit', 'predicate': 'lives', 'object': 'water' })
assert_fact('animal', { 'subject': 'Greedy', 'predicate': 'eats', 'object': 'flies' })
assert_fact('animal', { 'subject': 'Greedy', 'predicate': 'lives', 'object': 'land' })
assert_fact('animal', { 'subject': 'Tweety', 'predicate': 'eats', 'object': 'worms' })

Pattern Matching

durable_rules provides string pattern matching. Expressions are compiled down to a DFA, guaranteeing linear execution time in the order of single digit nano seconds per character (note: backtracking expressions are not supported).

from durable.lang import *

with ruleset('test'):
    @when_all(m.subject.matches('3[47][0-9]{13}'))
    def amex(c):
        print ('Amex detected {0}'.format(c.m.subject))

    @when_all(m.subject.matches('4[0-9]{12}([0-9]{3})?'))
    def visa(c):
        print ('Visa detected {0}'.format(c.m.subject))

    @when_all(m.subject.matches('(5[1-5][0-9]{2}|222[1-9]|22[3-9][0-9]|2[3-6][0-9]{2}|2720)[0-9]{12}'))
    def mastercard(c):
        print ('Mastercard detected {0}'.format(c.m.subject))

assert_fact('test', { 'subject': '375678956789765' })
assert_fact('test', { 'subject': '4345634566789888' })
assert_fact('test', { 'subject': '2228345634567898' })

Business Rules and Miss Manners

durable_rules can also be used to solve traditional Production Business Rules problems. This example is an industry benchmark. Miss Manners has decided to throw a party. She wants to seat her guests such that adjacent people are of opposite sex and share at least one hobby.

Note how the benchmark flow structure is defined using a statechart to improve code readability without sacrificing performance nor altering the combinatorics required by the benchmark. For 128 guests, 438 facts, the execution time is 600 ms.

https://github.com/jruizgit/rules/blob/master/testpy/manners.py

IMac, 4GHz i7, 32GB 1600MHz DDR3, 1.12 TB Fusion Drive

Image recognition and Waltzdb

Waltzdb is a constraint propagation problem for image recognition: given a set of lines in a 2D space, the system needs to interpret the 3D depth of the image. The first part of the algorithm consists of identifying four types of junctions, then labeling the junctions following Huffman-Clowes notation. Pairs of adjacent junctions constraint each other’s edge labeling. So, after choosing the labeling for an initial junction, the second part of the algorithm iterates through the graph, propagating the labeling constraints by removing inconsistent labels.

In this case too, the benchmark flow structure is defined using a statechart to improve code readability. The benchmark requirements are not altered. The execution time, for the case of 4 regions 654 ms.

https://github.com/jruizgit/rules/blob/master/testrb/waltzdb.rb

IMac, 4GHz i7, 32GB 1600MHz DDR3, 1.12 TB Fusion Drive

Reference Manual:

https://github.com/jruizgit/rules/blob/master/docs/py/reference.md

Metadata

Release files for durable-rules 2.0.12

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for durable-rules 2.0.12
File	Size	Uploaded
durable_rules-2.0.12.tar.gz	55.9 kB	Oct 28, 2019	Details

Release files / durable_rules-2.0.12.tar.gz

Download URL	durable_rules-2.0.12.tar.gz
Size	55.9 kB
Tags	Source
SHA-256 checksum How to use checksums	`5c94418324978409626e15175b38fb0c662603b48dddc433e52170c4debfdf6f`
BLAKE2b-256 checksum How to use checksums	`087f514984b8c8cff15666a7e200416cfe586bdec7cabd2cd0eec88ce0a72782`
Upload date	Oct 28, 2019
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/1.9.1 pkginfo/1.4.1 requests/2.18.4 setuptools/36.2.7 requests-toolbelt/0.8.0 tqdm/4.15.0 CPython/2.7.9`