This page summarizes Ontimize Logic, illustrated with the following video:
Background: the automation gap
The Automation Gap
While there is automation for important parts of an application, the Transaction Logic - 40% of the app - is manually coded. This is a problem due to:
- Time and cost: this gap affects business agility and application TCO
- Complexity: even the approach for manual logic is unclear: a service layer? DAO Objects? Hibernate events?
Existing Approaches fall short
Attempts to address the Automation Gap using existing tools appear promising, but fall short:
- Rete engines provide valuable Decision Logic, but have well-known sum/count performance issues, resulting in a geometric number of reads
- Process engines are excellent for work flows, but their inherently procedural nature does not enable a declarative approach
- Hibernate attribute validations fall far short of the typical requirements for multi-attribute validation, and multi-table derivations such as sums and counts
5 Simple Rules, 500 lines of code... why??
To illustrate the cost and complexity of Transaction Logic, consider an application consisting of Customer, their Purchase Orders, the related Line Items, and Parts.
5 Simple Rules
Let's consider the Add Order Use Case. The "Check Credit" requirement is succinctly captured in 5 simple rules:
- balance < limit
- balance = sum (unpaid order totals)
- total = sum (item amounts)
- amount = price * qtyOrdered
- qtyOrdered = copy(Part.price)
It seems so simple... why can't the computer just do this?
500 lines of Java code -- for related Use Cases
Like an iceberg, the screen enables a set of related Use Cases not initially visible, shown at right. So our simple screen is actually rather complex:
- Error Prone Analysis: the Developer must first analyze the system to produce the list of Use Cases. Errors here cause serious breaches in data integrity. Can you spot the missing case?
- Complex Design: next, the Developer determines the approach: service layer? DAOs? Hibernate Events?
- Large Development effort: handling all of these Use Cases, including their change detection, change propagation, and optimization requires 500 lines of code - examine them here.
Remarkably, the actual processing for each of the Use Cases can be easily inferred from a single set of rules. For example, the second rule dictates not only that Add Order should increase the balance, but also that the balance should be reduced by Delete Order or Pay Order.
Transaction Logic: 5 simple declarative rules
By contrast, the windows illustrate the complete logic required to implement Add Order and all the related Use Case;
- These parallel the Domain Objects (e.g., stored in parallel packages), later used by the Transaction Logic Engine described below. So, CustomerLogic is the business logic for the Customer domain object.
- Logic is specified in Java (or Groovy) classes, using an annotated method for each rule (see transparent blue boxes, below). For example, the CustomerLogic window contains the first 2 rules.
This sample problem illustrates the use and operation of business logic - examine it here.
Referring to the blue callout in the diagram, there are several important aspects worth noting, discussed below.
Automatic Invocation / Re-use - automates multiple Use Cases
Unlike procedural programming where you must explicitly call and order your logic, the rules above are automatically invoked by the Transaction Logic Engine (described below). The engine assumes the responsibility to monitor all of your transactions, re-using the logic whenever dependent data is changed.
Consequently, the 5 rules above address all of the Use Cases - automatically. This not only saves 500 lines of code, it eliminates an entire class of design errors such as neglecting logic elements of a Use Case.
Such automation is familiar. If you declare a spreadsheet cell as the sum of a column, the spreadsheet "knows" that it should recompute the cell when any column data is altered, or when a row is added or deleted. In ABL, the
balance = sum (unpaid ordertotals) rule automatically adjusts the balance for all Use cases that touch the dependent data - Add Order, Delete Order, Pay Order, etc.
Automatic Optimization - avoid geometric reads
In a declarative approach, you specify what you want rather than how to do it. This enables - obligates - the engine to optimize your logic.
balance = sum (unpaid ordertotals
) looks very much like a SQL sum. It is not.
Instead, the logic engine's optimizer utilizes adjustment logic, so altering an
amountTotal by X simply adds X to the
select sum logic for purchaseorders and items is replaced by a single row update.
Automatic Ordering - automates maintenance
Inherent in a declarative approach is that rule chaining is supported (one rule can reference the results of another), and that the system assumes the responsibility for detecting dependencies for correct ordering. So, the derivation rules for balance and ordertotal can be specified in any order.
This is most important during maintenance: in manual code, the principal time is spent in studying the existing code to understand its dependence-based ordering. ABL eliminates this costly (and unpleasant) "archaeology" by deducing dependencies to compute a correct order of operations. So, you need only alter rules to meet the business requirements - dependency management / ordering is automatic.
Plug-in Architecture: no re-coding, fits in
This diagram depicts the use and operation of business logic; note that:
- You do not directly invoke business logic or the Transaction Logic Engine
- Instead, the Transaction Logic Engine listens for Hibernate events (beforeUpdate, beforeCommit, etc). When these occur, the engine loads your logic classes, determines dependencies using byte code analysis, and executes the logic in a correct and optimized order.
Easy to get started - plug-in means no recoding
This "plug-in" approach means you don't have to alter your (or your framework's) existing code to utilize business logic. You simply change a configuration file, and introduce logic classes - at your own pace.
Automation often incurs costs of efficiency, or limitations. The Transaction Logic engine is designed for extensibility:
- In addition to using annotations to specify logic, your logic methods can use Java or Groovy logic with if/else, loops, etc as required
- Such Java/Groovy logic can invoke Java/Groovy methods, so you can build re-usable rule type extensions
ABL includes extensions that illustrate extensibility, and automate classically complex Use Cases such as a Bill of Materials explosion, a deep copy, or the allocation of a Payment to a set of outstanding orders.
Easy to Integrate into your runtime environment
The plug-in approach means that logic follows Hibernate/JPA support for different servers (Stand alone, Web Server, App Server), databases (including App Server distributed transaction support), etc. Anything that runs with Hibernate can use the ABL engine.
Easy to Integrate into your development environment
Logic is specified in Java or Groovy, so you can use your current IDE (e.g., Eclipse) and source control procedures. Logic provides extensive logging, and can be debugged using standard debuggers.
Secret Sauce: Declarative Encapsulation
ABL derives its benefits from the following key elements:
- Declarative: what not how means:
- Automatic Ordering, which automates maintenance
- Automated Optimizations such as pruning and adjustment help achieve and maintain enterprise class performance
- Domain Based: logic is supplied on Domain Objects (not specific services), so that the system can enforce them across all Use Cases
- Plug-in: the plug-in architecture makes it easy to get started (no recoding), and fits into your existing runtime / development environments
Strategic Business Advantage
ABL provides strategic business advantage:
- Agility: replacing 500 lines of Java code with 5 rules - for 40% of your system - means you can better respond to business change
- Quality: automated re-use can reduce the common (and painful) scenario often discovered in final tests, where logic elements are not coded into some Use Cases
- Performance: pruning and adjustment optimizations can reduce the common (and painful) scenario where systems ran fine in development for small database sizes, but require major rewrite to deal with production size data.