Posts tagged adf bc
Last week – just when I was at the far end of a narrow internet connection – Oracle released JDeveloper 12c (12.1.2) along with ADF 12c and WebLogic 12c (12.1.2). Hot on the heels of Oracle Database 12c (12.1.2), which was released on June 25th – about two weeks earlier. The next figure gives an overview of recent new releases. It is clear that we are in a turbulent period right now – which also includes Java EE 7 (about a month ago) and the upcoming Java SE 8 release (next month). All in all there will be plenty to talk about at JavaOne and Oracle OpenWorld in September.
What is the significance of this ADF and JDeveloper release? What are the important themes and key features? Wow, that is a big question to ask and even more so to answer.
By: Robert van Mölken and Tim Askamp
An end-to-end application usually consists of multiple components that are one-way or another decoupled. A component can be an ADF frontend, OSB proxy, SOA Suite Composite, Database package or JAXWS web service. For maintaining all the components of an end-to-end application the components need to be checked individually to know if everything is in working order. So if there is for example a performance issue with the application there is no simple task to checked which component in the chain is responsible for this.
For one of our clients we’ve developed a ‘Probe’ application. The reason was that maintenance needed a simple way to determine if the SOA and Database environment: was running, configured as required and the basics where functioning. So basically we needed to mimic service operations that use the following components: OSB, SOA Suite, JAX-WS and ADF Business Components. Executing this functionality should lead to a traceable path of the used components. In this blog we will explain the solution we developed.
ADF DVT Speed Date: Present Metrics per Year, Quarter and Month using a zoom-enabled ADF DVT Resource Utilization Gantt and ADF BC0
The challenge I will address in this article is the following: I would like to provide a nice presentation of data aggregated by time period. For example: an overview of the number of employees that was hired in each year in each department (example is drawn from table EMP). The presentation could look like this:
To extend the challenge a little bit: I would like to be able to drill down. From the year level shown in this picture, to the Quarter level and even to the Month level. The Quarter level would look similar – but more fine grained:
This article shows how this challenge can be addressed using ADF DVT – Data Visualization components, more specially the Resource Utilization Gantt Chart. It will describe how ADF BC is used in conjunction with the SQL TRUNC function and a smart bind parameter to allow for dynamic zooming to different time aggregation levels. And the approach demonstrated in this article can easily be reused for other time based presentations.
The ADF framework strongly suggests if not dictates a certain application architecture. Through ADF BC (Business Components) – the predominant business service implementation with ADF – applications will typically interact directly with the database, over JDBC Database Connections from a shared connection pool. Developers who create the ADF BC Entity Objects and View Objects will be quite aware of the data model and the database implementation. They will usually write SQL. And the result of their work is substantially coupled with the database. Transactions across multiple data source are very hard to implement in that typical ADF BC scenario because ADF BC talks to a single database and typically controls its own transaction.
When ADF applications are developed in an environment where an enterprise architecture has been laid down, and decoupling is an important objective and service orientation is mandated – then this typical implementation of the business service using ADF BC connecting directly to the database may not be desirable or even allowed.
On one of my projects, we are currently in the situation where we try to determine the guidelines for the implementation of the More >
In a recent article – Advanced SQL to find valid periods – juggling with outer joins, running totals and analytical functions – I discussed how to use Analytical Functions in SQL to cleverly (!) derive the valid periods from a database table that contains periods of inclusion and exclusion. A valid period is a period for which there is at least one inclusion and for which there is no exclusion. I used several powerpoint based graphics to illustrate the business case. For example:
to depict the periods of inclusion and exclusion and this figure to demonstrate how to derive the valid periods (the blue bars):
After completing this article – and fiddling around in Powerpoint quite a bit – I realized that for visualizing data in a table, I have a perfect tool at my fingertips: the Data Visualization Tags (DVT) in ADF 11g are created for this very purpose: turning data into information through visualization. And this rich library of DVTs components contains – in addition to fairly straightforward visualizations such as bar charts, pie charts and line graphs – also more complex visualization components such as the Bubble Chart, Thematic Map and Gantt Chart. The Gantt Chart has three More >