Split for Each in Pega
Split for Each in Pega is a mechanism used to iterate over a collection of items, such as a page list or page group, and perform actions on each item individually. This feature is particularly useful for batch processing and parallel execution of tasks within a business process. By using Split for Each in Pega, developers can ensure that each item in the collection is processed efficiently and simultaneously, which improves the overall performance and scalability of the application. This functionality is essential for handling large data sets, automating repetitive tasks, and streamlining complex workflows in Pega applications.
What is Split for Each in Pega?
Split for Each in Pega is a powerful feature designed to facilitate the parallel processing of multiple items within a collection. It allows for iterating over a collection of items, such as a page list or page group, and performing specific actions on each item individually. This mechanism is particularly beneficial for batch processing and enhancing the efficiency of workflows by allowing parallel task execution.
Why use split for each?
In complex business processes, there is often a need to process multiple records or items independently. Developers can design flows that effectively handle these collections, ensuring each item follows the defined business logic. This not only improves efficiency but also maintains consistency across the processing tasks.
Using a split for each flow shape, we can call one flow and pass a page list parameter. Now, the flow will get executed N number of times, where N is the number of pages available in the page list. The sub will be executed in a for loop. The subflow, which we call split for each flow shape, must be available in the class ‘for which page list is referring to’. The primary page of the subflow will be the page list.
Detailed Example of Split for Each in Pega
Scenario: Processing Customer Orders
Furthermore, imagine a scenario where an e-commerce application needs to process a batch of customer orders. Each order must be individually verified and updated, and then a confirmation email must be sent to the customer. Here’s how Split for Each can be utilized to achieve this.
Flow Design:
Begin by creating a flow that includes the split for each shape.
Position the split for each shape after populating the list of orders (OrderList)
Configuring Split for Each:
In the properties of the Split for Each shape, specify the OrderList as the Page List property to iterate over.
Define the subprocess or activity to execute for each item in OrderList. Let’s name this subprocess ProcessOrder.
Defining the subprocess:
Create a subprocess named ProcessOrder.
Moreover, within ProcessOrder, include steps to update the status of the order, verify order details, and send a confirmation email.
Executing the Flow:
When the flow runs, the Split for Each shape will loop through each order in the OrderList, executing the ProcessOrder.
subprocess for each item.
This parallel execution ensures that all orders are processed simultaneously, thereby enhancing processing efficiency.
Benefits of Using Splits for Each
1. Parallel Processing:
Split for Each allows multiple instances of a subprocess to run concurrently. This parallel execution significantly reduces the time required to process large collections of items.
2. Simplified Logic:
Handling each item individually simplifies the process. Developers can focus on the specific actions required for each item without worrying about managing the collection as a whole.
3. Improved Scalability:
The ability to process items in parallel makes the application scalable. It can efficiently handle increasing volumes of data without a significant drop in performance.
4. Enhanced Error Handling:
By processing items individually, Split for Each enables specific error handling for each item. If an error occurs while processing an item, it can be logged, retried, or handled without affecting the processing of other items in the collection.
Use cases for splitting each
1. Batch Processing:
Additionally, ideal for scenarios requiring bulk processing of a large number of items. For example, processing daily transactions, generating invoices, or updating inventory records.
2. Notifications and Alerts:
It is useful for sending emails or notifications to a list of recipients. Customize each email or notification based on the recipient’s details.
3. Data Updates:
Efficient for updating records in a database. For example, updating customer details, processing payroll, or managing employee records.
4. Reporting:
Generating reports for multiple data sets. Process each report individually and compile them into a final report.
Advanced Configuration and Considerations.
Dynamic Subprocess Selection:
Split for Each allows configuration of dynamic selection of a subprocess or activity based on specific conditions. This adds flexibility to handle different types of items within the same collection differently.
Performance Considerations:
While Split for Each improves processing efficiency, monitoring performance and ensuring adequate management of the system’s resources are essential. Overloading the system with too many parallel processes can lead to performance bottlenecks.
Error Handling Strategies:
Implement robust error handling within the subprocess to manage any issues that arise during processing. Consider logging errors, retry mechanisms, and notifying administrators of critical failures.
Testing and validation:
However, thoroughly test the split for each configuration to ensure that it correctly handles the collection of items. Validate that the process handles all items as expected and gracefully manages errors.
Example Configuration:
Here’s an example configuration of Split for Each in Pega:
Flow Setup:
Create a flow named Process Orders.
Add a split for each shape named ProcessEachOrder.
Property Configuration:
Set the Page List property to OrderList.
Subprocess Definition:
Create a subprocess named ProcessOrder.
Include steps to update order status, verify order details, and send a confirmation email.
Execution:
When the ProcessOrders flow runs, ProcessEachOrder will iterate over the OrderList. executing ProcessOrder for each order.
ConclusionÂ
In conclusion, by utilizing Split for Each in Pega, developers can design robust and efficient workflows to handle large collections of items effectively. Split for Each in Pega ensures parallel processing, significantly reducing processing time by allowing simultaneous processing of multiple items. Split for Each in Pega ensures parallel processing, significantly reducing processing time by allowing simultaneous processing of multiple items. Additionally, it enhances scalability, allowing the system to manage increased data volumes without performance issues. The Split for Each in Pega is particularly useful for batch processing, data updates, notifications, and reporting tasks, making it an essential tool for optimizing business processes.