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Logic Operation

Logic Architecture

Logic Bank operates as shown above:

  1. Automatic Configuration

    a. Declare logic in logic/declare_logic.py. Here is a summary of the rule types

    b. The Basic Web App and JSON:API are already configured to load and execute this logic

  2. Basic Web App and JSON:API operate as usual: makes calls on SQLAlchemy for inserts, updates and deletes and issues session.commit()

  3. The Logic Bank engine handles SQLAlchemy before_flush events on Mapped Tables, so executes on this session.commit()

  4. The logic engine operates much like a spreadsheet:

    • watch for changes - at the attribute level
    • react by running rules that referenced changed attributes, which can
    • chain to still other attributes that refer to those changes. Note these might be in different tables, providing automation for multi-table logic

Logic does not apply to updates outside SQLAlchemy, nor to SQLAlchemy batch updates or unmapped sql updates.

Basic Idea - Like a Spreadsheet

Rules are spreadsheet-like expressions for multi-table derivations and constraints. For example (not actual syntax):

The Customer Balance is the sum of the unshipped Order AmountTotals

You can imagine that the spreadsheet watches for changes to referenced cells, reacts by recomputing the cell, which may chain to other cells.

 

Let's see how logic operates on a typical, multi-table transaction.

Watch, React, Chain

Let's consider a typical multi-table transaction. Here is the 5 rule solution for check credit:

As Order Details are inserted, the rule flow is shown below.

The add_order example illustrates how Watch / React / Chain operates to check the Credit Limit as each Order Detail is inserted:

  1. The OrderDetail.UnitPrice (copy, line 78) references Product, so inserts cause it to be copied

  2. Amount (formula, line 75) watches UnitPrice, so its new value recomputes Amount

  3. AmountTotal (sum, line 72) watches Amount, so AmountTotal is adjusted (more on adjustment, below)

  4. Balance (sum, line 68) watches AmountTotal, so it is adjusted

  5. And the Credit Limit constraint (line 64) is checked (exceptions are raised if constraints are violated, and the transaction is rolled back)

All of the dependency management to see which attributes have changed, logic ordering, the SQL commands to read and adjust rows, and the chaining are fully automated by the engine, based solely on the rules above.

Creating New Rule Types

Not only can you define Python events, but you can add new rule types. This is an advanced topic, described here