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New build for demoAugust 22, 2011We just upgraded the self-contained demo to the latest ABL version.
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These are the most obvious elements of business logic - multi-attribute, multi-table expressions that prevent bad data from being committed into the database. The most challenging element is derivations, since these are typically chained (dependencies require detection and properly ordered change propagation), and multi-table (so performance-sensitive).
Such logic should be architected so that it encapsulates domain access, automatically shared over all User Interfaces and Message-based transactions. Automated Business Logic is a "logic engine" that plugs into Hibernate, and executes declarative (spreadsheet-like) logic you supply in companion classes that correspond to your POJOs. This sample problem (Customers, Orders and Parts - shipped in the download) is a simple way to illustrate the use and operation of business logic. Logicdoc provides a mechanism to capture the Use Case Requirements, with traceability to the Business Logic that implements them. In particular, watch out for extended pseudocode which simply expands on Business Logic automation services. Be aware that Automated Business Logic services eliminate many key elements of design, such as delineating the individual Use Cases for adjustment.
Extension services (such as allocation) introduce concepts that can significantly increase the level of abstraction in team communications. Unlike procedural approaches where there are very complex design decisions (re-use, ordering, partitioning, and optimization, etc), Automated Business Logic automates these elements of design. The sub-sections below briefly examine the specific cases the Business Logic Engine must address to automate multi-table transactions (observe: all such Forward Chaining in executed in the context of a Hibernate transaction). The Dependency Analyzer of the Business Logic Engine examines every Formula, and orders their execution with respect to their dependencies. As described in Business Logic Execution triggered by Child changes, changes to child attributes are analyzed. If (and only if) Parent Referenced attributes are changed, the Business Logic Engine will Forward Chain to each referencing Child instance so that it can re-evaluate its Business Logic in light of the new data.
Referring again to the Design Process table above, we emphasize that the right hand column is fully executable. The Check Credit example above illustrates how just 7 Business Logic Rules can replace 10 pages of code. Your Business Logic is fully declarative: the Business Logic Engine is responsible for Ordering, Re-use and Optimization. Complexity ManagementThe underlying concept is Forward Chaining, a fundamental tool in complexity management. In addition to logic extensibility, the Business Logic Engine is architected for systems extensibility. Unlike Rete Business Rule Engines which require explicit invocation, this means that integrity is assured. Automation invocation also means Business Logic easy to adopt: you can introduce Automated Business Logic with minimal impact on the rest of your system (even if portions have already been coded). In addition, the engine provides services to eliminate migration of existing logic, so you can use Automated Business Logic for new logic, even in systems which previously used procedural business logic. It is sadly true that far too much procedural business logic winds up in client event handlers. Unlike manual approaches, Business Logic guarantees that the logic is partitioned for re-use and performance.
Unlike Rete Business Rule Engines that load all the data into memory, or issue aggregate Select sum queries, this assures optimal performance.
Maintenance is a process that is reduces business agility, is expensive, and hated by developers.
Re-use is a core development objective, requiring substantial design effort in procedural approaches to maintain critical database integrity. Business Logic, however, operates as an Event Handler, so active enforcement of business rules and re-use is automatic.
Object oriented languages provide this re-use, by requiring all accessing code to use an objects' method. Automated Business Logic employs these to assure that all Hibernate-based updates are subjected to the Business Logic you specify. The Customer.Balance rule, perhaps declared for Place Order, simply states that its value is the sum of the unpaid orders. Transaction Business Logic is an important issue, requiring substantial cost and time that can compromise Business Agility.
Rete engines are a well-accepted technology, and provide many of the features required for Transaction Logic. This article illustrates that ORM integration is the key enabling technology that makes Transaction Logic engines a better match for transaction processing. Transaction Business Logic is the logic required in processing database transactions to enforce business policies. Actions: the system might take various actions, such as sending email if the balance is close to the Credit Limit, starting a Business Process for order approval, or auditing.
The well-known adage "Garbage in, garbage out" well describes the need for Transaction Logic.
So, Transaction Logic is a large and important systems element - an ideal candidate for automation. The Business need for reliable enforcement is obvious: all changes must be subjected to our organizations rules. Poor design might bind logic to screen functions, requiring replication for message processing. Experience shows that it might be easy to build insert and delete logic, but the myriad of changes that might occur can make achieving re-use difficult. Programming errors become more and more common as the dependencies grow, so maintenance becomes slow and error-prone. Transition Logic is a very frequent requirement, where the logic depends on comparing the prior database values to the current ones. Many business rules explicitly refer to old values: "salary changes must lie within a given range", "invoke verification processes if balance changes exceed a threshold". As noted above, an extremely common requirement is rollups, where logic requires the derivation of a sum or count of related data. With the nondistributed architecture, presentation, business, and data access code are logically separated but are physically located in a single Web server process on the Web server. With nondistributed architecture, it is difficult to share business logic with other applications. With the distributed architecture, presentation logic communicates remotely to business logic located on a middle-tier application server as shown in following Figure.
Distributed architecture has the ability to scale out and load balance business logic independently.
Distributed architecture has separate server resources that are available for separate layers. Distributed architecture has additional serialization and network latency overheads due to remote calls. Distributed architecture is potentially more complex and more expensive in terms of total cost of ownership.


Identify any constraints and assumptions early in the design phase to avoid surprises later.
Target environments are often rigid, and your application design needs to accommodate the imposed restrictions.
You can host your application's business logic on your Web server along with the presentation layer or on a physically separate application server. Note This deployment architecture still assumes logical layers: presentation, business, and data access.
Do not physically separate your business logic layer unless you need to and you have evaluated the tradeoffs.
The security policy of your organization might prohibit you from installing business logic on your front-end Web servers.
You might want to offload the processing to a separate server because your business logic might be computationally intensive.
If you do need a remote application layer, use design patterns that help minimize the performance overhead. Scaling up with additional processor power and increased memory can be a cost-effective solution, It also avoids introducing the additional management cost associated with scaling out and using Web farms and clustering technology. You need to consider aspects of scalability that may vary by application layer, tier, or type of data. The ability to restrict queries to a single partition, thereby limiting the resource usage to only a fraction of the data.
The ability to engage multiple partitions, thereby getting more parallelism and superior performance because you can have more disks working to retrieve your data. Be aware that in some situations, multiple partitions may not be appropriate and could have a negative impact. Complex business logic can be implemented either with custom business rules or with the help of SQL action. Click Browse on the Project Designer toolbar, navigate to the Orders page, select an existing order and choose Standard Discount option in the context menu of the order data row. Confirm the action and the standard discount of 1% will be added to each line item that have a discount less than 10%.
You can affect multiple rows with the same definition of SQL action if you enable multiple selection in the Orders data view. The JavaScript expression in the When Client Script property is evaluated at runtime in the web browser to enable the Discount option on the action bar.
Note that the SQL script is identical to the script of action placed in the group with Grid scope.
Click Discount option on the action bar to apply a discount to the details of selected orders. Remember that application end users always have an option to go over each line item to rapidly change the Discount in the Data Sheet view by hand. Web applications created with Code On Time have a built-in support for Insert, Update, and Delete actions. Click Browse button on the Designer toolbar to start the application in your default web browser. The client library receives notification about the action result from the server and performs an iteration of the data controller action state machine in the web browser.
Finding the perfect balance between business and casual can be tough, especially during the summer (Hint: sandals and shorts, are a no-go).
It does so by applying the metaphor of a spreadsheet to business logic, as described in the sub-sections below.
As you can see, Business Logic solution closely matches the detailed statement of the problem: an Executable Design.
As illustrated in the table below, Automated Business Logic uses the same declarative metaphor, and provides analogous execution automation for ordering and change propagation. This is not a simple process, since the spreadsheet must determine a dependency-based execution order to get the correct answer. The example above automates a conventional multi-table transaction involving Customer, Purchaseorder, Lineitem and Product.
The Business Rule Engine's Dependency Analyzer detects all Parent References from child Domain Objects.
Even if you think you can code and debug 1 page per day, the agility of automation is overwhelming. You can declare your rules in either Java, or Groovy which provides a more natural syntax and automatic type.
As your local Computer Scientist will tell you,  program size depends on the number of interactions**2. This can interfere with re-use between screens, and makes the logic unavailable for message processing. The term "Compliance Certification" underscores that this is both important, and not automatic. Both approaches have different pros and cons in terms of performance, scalability, ease of development, administration, and operations. This can be good or bad — layers may work well together and result in optimized usage because one of them is always busy.
Sometimes design tradeoffs are required because of considerations such as protocol restrictions, firewalls, and specific deployment topologies. Server affinity occurs when all requests from a particular client must be handled by the same server.
A good layered design exhibits high degrees of cohesion by keeping frequently interacting components within a single layer, close to each other. Remote calls across physical boundaries (process and machine) are costly due to serialization and network latency. You achieve optimum performance by locating your business logic on the Web server in your Web application process.
You should make logical layers a design goal, regardless of your physical deployment architecture. You should look at scale-up options first and conduct performance tests to see whether scaling up your solution meets your defined scalability criteria and supports the necessary number of concurrent users at an acceptable level of performance. A loosely coupled, layered design with clean, remotable interfaces is more easily scaled out than tightly-coupled layers with "chatty" interactions. If you have stateless components (for example, a Web front end with no in-process state and no stateful business components), this aspect of your design supports scaling up and out. For this type of data, you can easily have many replicas in the right places if this helps your performance and scalability.
This is data that is relevant to a particular user or session (and if subsequent requests can come to different Web or application servers, they all need to access it), but the data for user A is not related in any way to the data for user B. Moving to a partitioned database later usually results in a significant amount of costly rework and often a complete database redesign. For example, some operations that use multiple disks could be performed more efficiently with concentrated data. If you keep discounting the order details, then the discount will revert to 1% after reaching 10%.
These actions are invoked when the end user is saving or deleting the existing record, or saving the new one. The purpose of this action is to select the newly created record in the read-only mode using form view editForm1.


Application framework will locate the value of the action property Data specified in the data controller definition. In fact, all values are new and there is no need for explicit entering of the “_NewValue” suffix.
The SQL scripts entered in the Data property of SQL action can make use of any facilities provided by the database server including transactions, stored procedures, cursors, etc. The first thing that comes to mind is an action that automatically deletes the supplier and all linked products. Child data views linked to the list of suppliers will become hidden and the selected record summary will disappear from the sidebar. But striking a cool balance between business wear and laidback personal style isn’t impossible.
So, automatic dependency management results in a profound reduction of code - arithmetic, not geometric. The latter are shipped with the product, both for your use, and as illustrations of how to build Extensible logic.
It can also lead to performance issues, since network traffic can be increased by not centralizing the logic in the server.
In our simple example, it is clearly unwise to accept Orders that customers cannot afford, or for which the Products are discontinued. However, if one layer requires disproportionately more resources, you starve resources from another layer. Study any available network diagrams, in addition to your security policy and operation requirements. Likewise, your design may rely on assumptions such as the amount of memory or CPU capacity or may not even consider them. It is most often introduced by using locally updatable caches or in-process or local session state stores. A multilayered approach with separate presentation, business, and data access logic helps you build a more scalable and more maintainable application.
If you avoid or exploit server affinity in your application design, this approach supports scaling up and scaling out. Performance overhead results from an increased number of round trips over the network with associated network latency and serialization costs. However, having a class of servers that have affinity with one resource type could be beneficial, but they must then be independently scaled.
A layered design will have natural clutch points, making it ideal for scaling out at the layer boundaries.
Typically, you optimize the price and performance within the boundaries of the other constraints you may have.
This has minimal impact on design and can be largely driven by optimization considerations. The framework will bind the field values passed from the client web browser to the matching parameters detected in the SQL script. The suffix can be useful when you are implementing an SQL action that must manipulate the field values of an existing data record. The child data view Employees is not displayed when an employee other than Fuller or Buchanan is selected. If your design causes server affinity, scaling out at a later point forces you to re-engineer or develop complex synchronization solutions to synchronize data across multiple servers.
You can add more hardware to the existing servers or add more servers to the Web layer as shown in following Figure. For example, although you can have an 8-processor server in this role, economics would probably drive you to a set of smaller servers instead of a few big ones.
For example, 2-processor Web or application servers may be optimal when you evaluate price and performance compared with 4-processor servers; that is, four 2-processor servers may be better than two 4-processor servers. Consolidating several logically separate and independent databases on one database server may or may not be appropriate even if you can do it in terms of capacity. This data is slightly more complicated to handle than static, read-only data, but you can still optimize and distribute quite easily.
Generally, you do not want to hold this type of data in many places due to the complexity of keeping it synchronized.
The client library invokes the action a13, which causes the new record to be selected in editForm1 view.
Any name following after “Master.” placed in square brackets is considered to be the value of the field in the master data view, whatever that master data view happens to be. And, mechanisms are provided to accomodate your Hibernate extensions, such as configuration. In this case, when the server becomes a bottleneck, you can scale out and use multiple copies.
For example, business logic may be more easily relocated to a load-balanced, middle-tier application server farm. On the other hand, scaling up and then out may be the right approach for your database servers, depending on the role of the data and how the data is used.
You also need to consider other constraints, such as the maximum number of servers you can have behind a particular load-balancing infrastructure. This is the classic case in which you would typically want to scale up as far as you can (ideally, remaining a single logical instance, with proper clustering), and only when this is not enough, consider partitioning and distribution scale-out. Three products with generic names are available for modification in the child data view linked to the Products data controller. It should not be an applications choice to invoke the Rete logic, since it might forget, specify the wrong Rule Set, etc.No Transition LogicPersistence engines (such as JPA, Hibernate, TopLink, etc) typically provide access to the old values of the submitted rows. If you do not need to support scaling out, consider the performance benefits that affinity to a resource may bring.
Creating an affinity between the data in the directory and the SQL Servers that serve the data allows you to specialize those servers and does not cause scaling problems later, so in this case affinity is a good idea. Apart from technical and performance considerations, you also need to take into account operational and management implications and related total cost of ownership costs. However, be aware that whenever you replicate, you will have a loosely synchronized system.
Advances in database technology (such as distributed partitioned views) have made partitioning much easier, although you should do so only if you need to. This is rarely because the database is too big, but more often it is driven by other considerations such as who owns the data, geographic distribution, proximity to the consumers and availability. For example, you ask for the contents of user A's shopping cart but do not ask to show all carts that contain a particular item. As such, it cannot analyze changes to the value (eg, new Line Items for an Order), and determine adjustments to the aggregate. It makes sense to create a condition that will make the detail data view visible if the master employee’s Title is “Vice President, Sales” or “Sales Manager”.



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