Corticon's Advanced Data Callout (ADC) feature provides an alternative to Corticon’s Enterprise Data Connector (EDC) for accessing database data. It provides greater control over the query amd insert statements that are used. This is beneficial when you need finer control for performance or need to retrieve large amounts of data. Batch processing applications are a good example of where ADC is effective. With ADC you define a mapping of your vocabulary to a database, define queries, and control when queries are performed to retrieve data. With ADC you can quickly retrieve large amounts of data.
By contrast, with EDC you define a mapping of your vocabulary to a database and rely on Corticon to retrieve data as needed. EDC makes data access very simple and is a great option when small amounts of data are needed or performance is not paramount. EDC performs lazy loading of data in that it only loads data as needed. This is particularly evident when retrieving data for associations. When large amounts of data need to be retrieved or performance is paramount this can be inefficient.
Comparing ADC and EDC
Consider a database with these tables and relations:
‘Person’ Table has 1,000 rows
‘Job’ Table has 10,000 rows (10 associated Job records for each Person record)
‘Duty’ Table has 100,000 rows (10 associated Duty records for each Job)
In EDC, you create one SQL Statement to retrieve all Person Entities into Corticon, and then one SQL Statement is created for each Person to load that Person’s associated Job Entities – another 1,000 SQL executions. Then, with one SQL Statement for each Job to load that Job’s associated Duty Entities adds another 10,000 SQL executions to the process. A lot of time is dedicated to retrieving data. With ADC, this can be reduced to three queries; one for each table. This can greatly reduce the time spent accessing data.
How ADC Works
ADC functions as a service callout -- it accesses data as a step in a Ruleflow. To use ADC you do the following:
1. Map your vocabulary to a database. This is done in Corticon vocabulary editor the same as is done for EDC. The mapping tells ADC how to construct entities and associations for data retrieved from the database and how to save data when storing to the database.
2. Define parameterized SQL statements for the queries and inserts to be performed. You have full control over these queries. They are parameterized such that substitutions can be performed at runtime. To make these statements easy to manage, they are also stored in a database. This can be the same or separate database from the data to be queried.
3. Add the ADC callout to a Ruleflow. This is done in the Corticon Ruleflow editor. When you add ADC to a Ruleflow you will need to configure it to identify the query or insert operation to be performed by selecting one of the SQL statements you have defined. To make this easier, you can give the SQL statements logical names.
When all steps are completed you are ready to deploy your Ruleflow or test it in the Corticon tester. When ADC runs it will perform substitutions into the statement and use it to access data. For queries, ADC will construct entities, set attributes, and define associations using the vocabulary mapping. For inserts and updates, ADC will use the mapping data for storing to the database. You can use multiple instance of ADC in a Ruleflow. A typical use case would be to have an instance at the start of a Ruleflow to retrieve data and one later in the Ruleflow to save data.
‘Person’ Table has 1,000 rows