Rules Machine

Rules Against The Machine 🤘

• One Rules Engine to rule them all.
• With great Rules, comes great observability.

What is Rules Machine?

It's a fast, general purpose JSON Rules Engine 🚀

Key goals

• Share business logic - move logic around the I/O layer, just like data.
  • Shared validation logic (same logic from the web form to the backend)
  • Push rules where they are needed: Cloud functions, CloudFlare Workers, Lambda@Edge, etc.)
• Organize complexity - isolate complex Business Rules from App Logic and state.
  • Name, group and chain rules.
  • Don't repeat yourself: reference common rule(s) by name. (applySalesTax)
• Modeling workflows - model your business logic as a series of readable steps.
  • Help non-dev stakeholders (QA, Product) understand critical logic.
  • Simply formatting JSON Rules sheds light on both hierarchy & steps.

Key Terms

App Logic != Business Rules

• App Logic - applies more broadly and changes less frequently than Business Rules.
  • "Throw Error if ShoppingCart total is less than zero."
  • "Only one discount code can be applied at a time."
• Business Rules - targeted & detailed, tends to change frequently. Supports business goals & objectives, accumulated from Product, A/B Tuning, Legal, Finance, etc.
  • "Premium customers can apply 3 discounts, up to 25% off."
  • "Add covid19 discount for existing customers."
  • "If State is NY, add NY tax."
  • "If State is AZ and during Daylight Savings, offset an hour."

App Logic is close to Core component behavior. For example, adding a locale={countryCode} to the <Calendar> component will change it's App Logic.

Whereas "Prevent meeting requests on Weekends." is more of a Business Rule, because it's specific to a scheduling application, and its current context.

Using this as a mental model greatly accelerates identifying specific places to utilize a Rules Engine.

(I know there are other ways to describe this concept. I'm choosing to avoid CS jargon stuffing.)

Why Rules Engine?

Typically App Logic & Business Rules are woven together throughout the project. This co-location of logic is usually helpful, keeping things readable in small and even mid-sized projects.

This works great, until you run into one of the following challenges:

1. Storing Rules

• A note taking app could let users create custom shortcuts, where typing "TODO" could load a template.
• These "shortcuts" (JSON Rules) can be stored in a local file, synced to a database, or even broadcast over a mesh network.

2. Unavoidable Complexity

• In many industries like healthcare, insurance, finance, etc. it's common to find 100's or 1,000s of rules run on every transaction.
• Over time, "Hand-coded Rules" can distract & obscure from core App Logic.
  • Example: Adding a feature to a DepositTransaction controller shouldn't require careful reading of 2,000 lines of custom rules around currency hackery & country-code checks.
• Without a strategy, code eventually sprawls as logic gets duplicated & placed arbitrarily. Projects become harder to understand, risky to modify, and adding new rules become high-stakes exercises.

3. Tracing Errors or Miscalculations

• Complex pricing, taxes & discount policies can be fully "covered" by unit tests, yet still fail in surprising ways.
• Determine out how a customer's subtotal is calculated can be a pain.
  • Example: Sales tax rates and rules are defined by several layers of local government. (Mainly City, County, and State.)
    • Depending on the State rules, you'll need to calculate based on the Billing Address or Shipping Address.
  • Scenario: A California customer has expanded into Canada. Their new shipping destination seems to cause double taxation!?!
    • In this situation, a trace of the computations can save hours of dev work, boost Customer Support' confidence issuing a partial refund, and the data team can use the raw data to understand the scope of the issue.
  • Scenario: "Why did we approve a $10,000,000 loan for 'The Joker'?"
  • Scenario: "How did an Ultra Sports Car ($1M+) qualify for fiscal hardship rates?"

Pros

• Uses a subset of JavaScript and structured JSON object(s).
• Easy to start using & experimenting with, larger implementations require more planning.
• Provides a trace, with details on each step, what happened, and the time taken.

Cons

• Sizable projects require up-front planning & design work to properly adapt this pattern. (1,000s rules, for example.)
• Possible early optimization or premature architecture decision.
• Not as easy to write compared to a native language.

Examples

Example Rule: Apply Either $5 or $10 Discount

[
  {"if": {"and": ["price >= 25", "price <= 50"]}, "then": "discount = 5"},
  {"if": "price > 50", "then": "discount = 10"},
  {"return": "discount"}
]

- if: {and: [price >= 25, price <= 50]}
  then: discount = 5
- if: price > 50
  then: discount = 10
- return: discount

Example Rule: Apply $15 Discount if Employee, or Premium Customer

[
  {
    "if": "user.plan == \"premium\"",
    "then": "discount = 15"
  },
  {
    "if": "user.employee == true",
    "then": "discount = 15"
  },
  {
    "return": "discount"
  }
]

Example Rule: Multiple Conditional, Nested Rules

[
  {
    "if": "price <= 100",
    "then": "discount = 5"
  },
  {
    "if": {
      "or": [
        "price >= 100",
        "user.isAdmin == true"
      ]
    },
    "then": "discount = 20"
  },
  {
    "return": "discount"
  }
]

- if: price <= 100
  then: discount = 5
- if:
    or: [price >= 100, user.isAdmin == true]
  then: discount = 20
- return: discount

Example Rule: Use variable between rules

[
  {
    "if": "price <= 100",
    "then": [
      "discount = 5",
      "user.discountApplied = true"
    ]
  },
  {
    "if": {
      "and": [
        "price >= 90",
        "user.discountApplied != true"
      ]
    },
    "then": "discount = 20"
  },
  {
    "return": "discount"
  }
]

- if: price <= 100
  then:
  - discount = 5
  - user.discountApplied = true
- if:
    and:
    - price >= 90
    - user.discountApplied != true
  then: discount = 20
- return: discount

More Reading & Related Projects

• Should I use a Rules Engine?
• JSON Rules Engine.
• GitHub Actions YAML conditional syntax.

TODO

• [ ] Publish modules for CJS, ESM, AMD, UMD. (Implement parceljs, rollup, etc.)
• [ ] rule type: {"runRules": "ruleSetName"}
• [ ] rule type: {"throw": "error message"}
• [ ] rule type: {"log": "rule/value expression"}
• [ ] rule type: {"set": "newVar = value"}
• [ ] misc: Structured Type validation.
• [x] security: NEVER use eval/Function('...') parsing.
• [x] misc: Simplify TS, making Rule[] the sole recursive type.
• [x] misc: Use reduced JS syntax, scope.
• [x] misc: Use single object for input and output. (Doesn't mutate input.)
• [x] misc: Add support for multiple boolean expressions. (see: {"and": []} {"or": []}).
• [x] misc: Rules are serializable, and can be shared.