CA Live API Creatorexecutes your business logic in response to REST POST/PUT/DELETE requests. This article provides information about how API Server operates so that you can debug your API. Shown conceptually in the following Reactive Logic video, this article illustrates how to use rules, how rules work in
CA Live API Creator, and how event rules integrate with resource events.
For more information about resource events, see Manage Resource Events.
In this article:
Watch the Video
The Reactive Logic video describes the concepts and operation of reactive logic for the following:
- Execution: watch, react, chain
For more information about reactive logic, see Reactive Logic.
Verify the Prerequisites
Before reviewing the information in this article, ensure that you have completed the following prerequisites:
CA Live API Creatoruses your business rules (reactive logic) to process RESTful updates (POST, PUT, DELETE).
Connect and Declare Rules
When you create an API and you connect API Creator to your database,
For more information about the
rowobject, see Row Objects.
Validation: return row.CreditLimit >= row.BalanceDerive Balance asSum(Orders.AmountTotal where ShippedDate === null)
RESTful Update Processing
After you have created the rules, RESTful-update processing calls the rules, orders the rules, and reads/writes the data. The following subsections explain how
CA Live API Creatorprocesses update requests. The following diagram shows the workflow:
For more information about request events, see Event Handlers.
You can define multiple custom resources on the same underlying base table. The resources can represent projects and aliases of table columns. Integrity demands that the base table logic is enforced regardless of the resource used.
Resource/object mapping is required to map the resource objects onto their respective row objects. This includes column name de-aliasing and materialization of columns not projected (sometimes called "hydrated"). The declarative and event logic deals with a full row.
CA Live API Creatorshares that logic over all resources that you explicitly define in API Creator. To do this,
CA Live API Creatormust read the database, with concurrency control supplied by the DBMS.
So that you can define logic based on data changes,
CA Live API Creatorbuilds the following row objects:
- row. This object reflects client changes from JSON, and all logic processing.
- oldRow. This object reflects the row before the transaction started. This object is from the database read.
API Server ensures that updated rows do not overlay the updates of other users by performing optimistic locking checks. The check is on a time-stamp field, if provided. Otherwise it is the hash of all attributes in the resource row.
For more information:
Pre-Insert Event Rule
CA Live API Creatorprocesses the logic when resource rows are mapped to table rows. The logic occurs in advance of the other events. You can have
CA Live API Creatorgenerate an alphanumeric primary key using a pre-insert event rule that computes the keys.
You do not need to create pre-insert event rules for database-generated primary keys. The database handles these keys automatically.
Database Key Generation
Databases support system-generated primary keys. The requirements for processing JSON POSTs include child rows for these tables, such as the items for an order.
CA Live API Creatormust "stamp" the generated order# into each item before it executes logic and when it performs managed parent.
When using a resource with nested children (for example, Customer contains Orders, contains Items), you can POST (insert) a new Customer, Order, and Items in a single transaction. You can do this only when
CA Live API Creatorcan propagate the primary key of the parent (Customer or Orders) down to the child (for example, Orders or Items).
For more information:
Logic Phase: Row Logic Cycle
CA Live API Creatorprocesses each submitted row in the order it receives them, as follows:
- Calls early event rules, supplied with therow,oldRow, andlogicContextvariables. Your event rule can inspect/alter the row before rules fire. For example, you can compute network-unique primary keys.
- Executes (only) those rules whose dependent row data has changed (based on comparingrowwitholdRow). It computes the rule execution order based on rule dependencies, discovered by automatic rule parsing. The rule updates the row (state), so it is visible to ensuing rules.
- Executes event rules so that you can do whatever is not expressed in declarative logic. For example, send email, post data to other systems, and credit card checks. You are passedrow,oldRow, andlogicContext. The effects of rule changes are visible in your row objects.You can alter the row, but you must save your changes.For more information about event rules, see Event Rule Types.
- If the logic has altered data that rules in related objects reference,CA Live API Creatorinstantiates rows for them and invokes their logic. For example, alteringOrderTotalupdates the related customer's balance, whose rules would verify theCreditLimit. This is an efficient one-row update, not an aggregate query.
CA Live API Creatoruses a transaction for all updates of a given request, both rows from the client, and chained updates (step 4). It buffers rows into the write-cache so that multiple updates to the same row (for example, many line items might adjust the
Commit Phase: Row Commit Cycle
For each submitted row,
CA Live API Creatorexecutes commit event rules and commit validation rules. These rules reflect the logic from the complete transaction. If it does not detect errors,
CA Live API Creatorflushes to disk the updates that have been buffered to cache and commits the transaction (it automatically transaction-brackets each request).
CA Live API Creatorflushes the write-cache to the database at the end of the transaction, after it processes all rows. Your logic specifications for validation rules and event rules can stipulate that they run during normal per-row execution or can be deferred until commit. If you elect your validation rules and event rules run during normal per-row execution,
CA Live API Creatorexecutes the logic only prior flushing the transaction.
CA Live API Creatorhas completed the Logic Phase, so the logic for the rows are visible in your row objects.
For example, you want to ensure Purchase Orders have at least one line item. You can define a
Purchaseorder.item_count, with a
Purchaseordervalidation rule that
item_count > 0.
While a good approach, this would fail. Why?
CA Live API Creatorprocesses the
Purchaseorderinsert first before line items. At this point, the count is zero (0), so the validation fails. Instead, you can create commit event rules. These validation rules and event rules must operate on the end-result of logic processing for all the rows in the entire transaction.
For more information:
Request Events (Response)
Request events provide and entry point after server processing is complete, just before returning to the client. You can reformat the response message, as illustrated in the
CA Live API Creatorraises request post events. You can alter the response message or you can perform other functions such as logging.
With forward chaining, if you change a referenced value,
CA Live API Creatorrecomputes the derived referencing attributes. The term chaining correctly infers that a derived attribute (for example,
Purchaseorder.amount_total) is itself referenced in another derivation (
CA Live API Creatortracks these references and performs the forward chaining, automatically.
For formulas (for example, price * quantity), forward chaining entails evaluating the expression (though see ordering, in the following sections). Forward chaining is more complicated for dependencies and multi-table derivations.
Columns dependent on changed columns can themselves have interdependencies. For example:
a = b + x
b = x + 2
It is clear that
b, so if
CA Live API Creatormust recompute
bbefore it recomputes
a. You can state these rules in any order. You can change the rules during maintenance, without concern for ordering.
Customer.balanceexample, imagine a simple update where a
Purchaseorderis marked paid. We need to recompute the new balance. A dreadful approach is to issue a SQL Sum query to add all the existing orders. In general, there could be thousands! And worse, this could be chained, where the summed attributes depend on further summed attributes. That is, in fact, just the case here: the
Purchaseorder.amount_totalis itself a sum of the
Lineitem.amount. This is a significant performance factor. ORM products are often blamed for poor performance due to excessive use of chained aggregate queries.
CA Live API Creatoradjusts the parent sum by the change in the child. The result is a one-row update (unless it was pruned).
Analogous considerations are where the client alters a parent attribute referred to in child logic (for example, a formula). When this occurs,
CA Live API Creatorvisits each related child to run the dependent logic. Running the dependent logic might update the child, and might trigger further adjustment / cascade processing to other related data.
For more information about formulas, see Formula Rule Type.
Adjustment and cascade-processing make updates to related data.
CA Live API Creatoroften issues SQL updates for data beyond that originally sent from the client. This is a natural consequence of your logic and exactly what business logic is supposed to do. These triggered updates are subjected to the full logic analysis/chaining process, so will often result in still other updates. For example, consider a simple update to a
- Lineitem.amountis derived as price*quantity, so is recomputed.
- Purchaseorder.amount_totalis derived as Sum (Lineitem.amount), so it is recomputed (adjusted).
- Customer.balance is derived as Sum (Purchaseorder.amount_totalwhere Paid = false), so is is adjusted.
The customer logic re-evaluates the credit limit check - if not passed, the entire transaction is rolled back, and an exception is returned to the client.
Chaining means that
CA Live API Creatorcan execute your logic more than once on the same row multiple times within a transaction. Consider a transaction comprised of a purchase order with multiple Line Items. The purchase order logic is clearly executed on insertion. Now consider that each Line Item would adjust the Purchase Order's amount_total. This re-executes the purchase order logic, now as an update. Your logic can determine
initialVerbby way of the
For more information about the LogicContext object, see The logicContext Object.
Reactive Programming vs Conventional Procedural Programming
The following are key observations about some fundamental characteristics that distinguish reactive programming from conventional procedural (imperative) programming:
- No Control flow.CA Live API Creatorinvokes the rules and only in reaction to actual changes. You do not order their execution. Rules are bound to the data, not a specific use case, so they apply to all in-coming transactions. In other words, the logic automatically processes the following transactions:
- Order inserted - balance increased
- Order deleted - balance decreased (if not paid)
- Order unshipped - balance decreased
- Order shipped - balance decreased
- Order amountTotal changed - balance adjusted
- Order reassigned to different customer - balance increased for new customer, decreased for old
- OrderDetail inserted - obtain price, adjust Order and Customer (and check credit)
- OrderDetail Deleted - reduce Order and customer totals
- OrderDetail Quantity increased - adjust Order and Customer (and check credit)
- OrderDetail Product Changed - obtain price, adjust Order and Customer (and check credit)
- OrderDetail Quantity and Product Changed - obtain price, adjust Order and Customer (and check credit)
- Customer CreditLimit changed - check credit
- Elimination of Boilerplate code.Reactive programming automates detection, change propagation, and persistence handling (SQL commands). The logic is executable:
- Change detection.Most of the alternative code is determining when to propagate updates by detecting changes. This is eliminated in the declarative-reactive approach.
- SQL (caching).SQL handling is tedious. Rules automate the SQL, including the underlying services for caching.
Simple Example: Check Credit
In the example, a solution of Check Credit is devised. Building on the previous two rules:
This represents the complete, executable solution, that includes:
- Ordering.You can alter the previous rules in any order because they are automatically ordered per dependency management.
- Re-use.CA Live API Creatorapplies the rules to all incoming transactions, automatically invoking the previous (relevant) logic.
- Automatic Persistence.CA Live API Creatorprocesses incoming transactions by providing the SQL. Adjusting a quantity automatically reads/adjusts the Orders and Customer rows. It does so efficiently (a one-row update, not an expensive select sum query).
Common Logic Patterns
This simple "
CreditLimit" example illustrates one of the most common logic patterns, validating a rollup/constraint-derived result. Other examples of the pattern include:
- Rollup employee salaries to department, constrain to budget.
- Rollup departments, constrain to budget.
- Rollup Student Course Credit, constrain to max for student, max for course.
- Compute Product Price from the sum of the Component Parts (nested).
- Compute EventGiftsAmountfrom sum ofEventGifts.
A similar pattern is Existence Checks: validation rules on [qualified] counts, such as an Order must have items and Department must have employees.
For more information about the common logic patterns, including validate a sum (rollup) and existence checks, see Learning Rules.
Business Perspective: Agility, Transparency, and Quality
Declarative logic is more expressive than imperative code. The previous five lines of logic equate to over 200 lines of triggers, or 500 lines of Java. It is also far more readable, in fact understandable, to business users. In an industry where we walk over hot coals for a 30% gain, this is a 40X improvement in expression factor. You can deliver update business logic 10X faster using rules. Removing boilerplate code and automatic re-use drives this compression factor.