You will find it helpful to explore these in the context of running examples. These are provided in the Logic Tutorial
. They are also provided with your account, so you can experiment with real transactions, and study the logs etc.
Suggestion: The Table of Contents (below) serves as an important "checklist", so you can be sure you are aware of the key patterns.
Key Concept: Think Spreadsheet
The key to getting the value out of API Creator is to use events in conjunction with spreadsheet-like Reactive Logic. We call it Think Spreadsheet, and a good way to understand it is via the examples below. The basic idea is that derivations are like spreadsheet cell formulas, in that they set up reactive logic:
Watch, React and Chain: the system watches for changes to referenced data, and adjusts the derived data accordingly. This may chain when the adjusted data is itself referenced by other derivations.
Going a bit further, these examples illustrate several of the Key Logic Patterns. We recommend you review these concepts, so you have them at your disposal when addressing complex logic. You'll find the rules to be deceptively simple - they are quite powerful.
You might want to keep a list handy of the rules.
Formulas enable you to access other values in the same row. Often useful, but most interesting transactions involve data from a related table. Consider the example shown below:
Multi-table derivations are automated by rules, based on relationships (either Foreign Keys defined in databases, or Virtual Foreign Keys):
- Parent Access to Child Data - when you define aggregates (sum, count, min, max), the child watches for changes to referenced child values, and, when necessary, adjusts the parent
- Child Access to Parent Data - when a child formula or validation references parent data (e.g., parent.attributeName), the parent watches for changes to attributeName and propagates changes to all the child rows. Such propagation does not occur for Parent Copy derivations.
Forward Chaining: constraining chained derivations
Probably the most common pattern is defining a validation that requires a series dependent derivations to produce the data used in the validation. The most familiar example is Check Credit
, where the Customer's balance is derived by a series of derivations over 3 Domain Objects.
Business Logic services enable you to access data from related
- Sum and Count rules enable Parent objects to aggregate related child data
Automatic adjustment processing occurs when child rows are changed in such a manner
Automatic Cascade processing occurs when the parent row's reference data is changed
Counts for Existence Checks
derivation can produce a count of related child rows, optionally qualified by a condition. In many cases, the objective is to know whether the count is 0 or non-zero - that is, are there any [qualified] children. For example:
- the No Empty Orders requirement is implemented by a countItems, which is then checked in a validation
- the Bill of Materials Explosion (see rules 1 and 2) requires identifying whether a Product is a kit - has any components
You introduce Junction Tables
for many-to-many relationships between 2 end-point objects. It is often a requirement that an end-point object needs to sum/count an attribute from the other end-point. Since sums/count operate on 1:n relationships - and not n:m relationships, you will need to replicated the summed attribute into the Junction.
The replicate can take two forms, depending on whether the sum should reflect changes in the summed attribute. The Tutorial
illustrates both, as discussed in the sub-sections below.
Do reflect summed changes into sum: use Parent Reference
The Bill of Materials Price Rollup example illustrates is a many to many relationship between Product objects, informally referred to as Kits and Components.
ProductBillofmaterials is the Junction Entity implementing the many to many relationship. A Kit needs to sum its components price to determine its own price.
Since the Business Requirement is that Component price changes be reflected in the Kit Price. We define
kitNumberRequired * product.price. We can then sum this result into the Kit.
This Formula contains a Parent Reference to the
product.price. Changes to parent references are cascaded to child rows, unlike @copy logic as shown in the next example.
Do not reflect summed changes into sum: use @ParentCopy
The Place Order
example illustrates a many to many relationship between Orders and Products, where Lineitem is the Junction entity.
Our business requirement is that we do not wish to change Purchaseorder amountTotals for subsequent Product price changes. We therefore define Lineitem.amountTotal as an @Copy
The classic request pattern is to invoke behavior from the construction of Command Objects. Such objects can be maintained in a list and used for undo, for example.
The same pattern can be used in data processing applications. The request object is inserted, representing a transaction requesting some behavior such as a Credit Request, or an Employee raise. Instead of code, you declare logic to implement the desired behavior.
Form Flow based on Derivation Results
It is a very common requirement to drive Form flow based on derivation results. For example, you might define a Wizard for Document Processing, with tab sheets enabled/activated on the basis of whether restricted Products are ordered, or the customers credit history. Derivations are excellent mechanisms to compute the data used to drive such conditional transitions.
Recall that derivations fire only when a transaction is saved. We therefore recommend using the Ready Pattern, as described below.
Imagine an interactive transaction where the End User proceeds through a set of screens. When screen-1 is complete:
- Business Logic derives various data
- These derivations are then used by the Presentation Layer:
- For display
- For conditional processing (eg, a conditional transition, or to cause hiding of not-relevant portions of the screen)
Of course, it is highly undesirable to hold an open database transaction (and blocking locks) over screen transitions. Given that you need the Business Logic derivation results as noted above, this suggests that you should submit - and commit - changes at the completion of screen-1.
But this presents a problem: it might not be appropriate to "post" this incomplete transaction (e.g., adjust General Ledger, the Customer Balance, Product stock) until all the screens are complete.
- We have all seen such an example in on-line shopping. We can fill our cart, and see how much things cost (a derivation), as we shop. It is only when we checkout that our account is charged.
The Best Practice approach is to:
- define a
isReady attribute (e.g., in Purchaseorder)
- its value is false until the checkout screen, at which point it is set to true
- when defining rules, predicate the rules on
isReady == true as in Place Order
Derive Customer.balance as Sum (purchaseorders.amountUnPaid where isReady=true)
This of course requires that you provide a Make Ready
(aka "checkout") transaction to activate the remaining logic.
Commit Time Logic: Validations, Actions
Be aware that logic processing occurs in two phases
: logic phase (initial row processing), and commit phase (after all rows are processed). Counts of inserted child rows are always 0 during the logic phase, so requirements such as No Empty Order
(which depend on the count value) need to use Commit Validations
It is often necessary to enable Business Users to alter data used in formulas. For example, you might apply a
discountAmount to a Purchase Order.
You can achieve this easily as follows:
1 - Create a 1 row Domain Object (e.g.,
Create a "scalar" Domain Object (e.g.,
Parameters), with attributes you want authorized Business Users to change, such as
discountAmount. Provide access to this object in your applications' user interface.
2 - Provide
getParameters() in superclass of Logic Classes
Define your Logic Classes to extend
3 - Use LogicBase in formulas
LogicBase (see BusLogicIntro), where
LogicBase implements the method to return the single
getParameters() can be used in formulas (just as Parent references), e.g.,
Resources to define Partner object mappings
MetaData tags for Matching
You can match incoming data to target data using these services
, for example to locate parent data, or insert/update master data.
You can store subtotals that are incrementally maintained as updates occur, using the Group By Pattern. For example, you might wish to maintain total sales for each sales rep for each month, as explained here
The following sections describe patterns implemented by the extension package
provided with the product. These are critically important, both for use (they are very common patterns), and as illustrations of how to provide logic extensions.
The allocation pattern can be summarized as follows:
Put differently, the system receives a quantity of things (money, goods), and allocates them to a set of recipients. Some examples:
- Allocate a Payment to set of Purchase Orders
provider Payment.amount to designated
recipient Purchase Orders
allocation Payment Purchaseorder Allocation objects (a Provider/Recipient Junction) for each such allocation.
- Allocate a Department Bonus to a set of Employees
- Allocate an expenditure to a set of General Ledger accounts
- Allocate a sum to a set of organizations (who, via chained allocations, allocate their allotment to designated General Ledger accounts)
Business Logic supplies allocation as an extension.
It is a very common requirement to copy one or more source rows to target rows. A corollary requirement is that the created target rows are usually children of the source rows, and require proper initialization of their attributes. The general concept is loosely analogous to the SQL
Insert Into Select From command.
This is simply a copy of the current row, under the proper conditions. We illustrate a common example in Salary Auditing.
It is a common requirement to "clone" a Business Object, meaning a "master" and its related objects. For example, a customer might like to create a new order by copying an old one
- Bill of Materials explosion
This is widely regarded as a particularly tough pattern. We shall see it is, in fact, a variant of a deep copy, and can be solved with just a few rules.
Click the title link for further information.
Inclusion / Exclusion logic - Find Where (search collection)
It is common for logic to require searching lists to verify whether or not objects are present, for example:
- Inclusion - assure a row is included
For example, an Employee might have 2 child collections of languages: spokenLanguage and translatesLanguage. It is reasonable to ensure that each spoken language has a row for that user / language in spokenLanguage.
- Exclusion - assure a row is excluded
We therefore provide a set of logic services for FindWhere, that enable you to search through a list to find rows that match a specified criteria. You can search for the first row that matches, or the only row (which throws an exception if there are multiple matches).
For example, in a Bill of Materials
definition, it is desirable to ensure that a component is not also the containing part.