Initializing balances in a payroll system is a critical process that ensures accurate calculations and reporting. However, before loading any balance initialization data into a new environment where this process has not yet been performed, it is essential to establish a structured approach.
By following a best-practice framework, organizations can ensure efficiency, minimize errors, and optimize system performance. Below, we outline a step-by-step methodology to streamline the balance initialization load process.
1. Preparing for Balance Initialization
The first step in any balance initialization process is to ensure that the required elements and balance feeds are in place. To do this effectively:
- Identify the balances you plan to initialize.
- Rank them based on the volume of occurrences, from highest to lowest.
- Group them into sets of 20 to manage them efficiently.
- Create balance initialization element sets for each group to generate the required elements and associated balance feeds.
- Leverage a single employee record and use a consistent initialization value (e.g., 1) to streamline the creation process.
- Load the balance initialization element set and execute the ‘Load Initial Balance’ process in Validate Mode. This step ensures that the elements are created without generating balance records for specific employees.
Example Execution Plan
Let’s assume you need to initialize 50 balances. Here’s how you can break it down:
- Step 1: List all balances and record the number of occurrences.
- Step 2: Rank them from highest to lowest (e.g., Balance 1 appears 1,000 times, Balance 2 appears 999 times, etc.).
- Step 3: Divide them into three groups:
- Group 1: Balances 1–20
- Group 2: Balances 21–40
- Group 3: Balances 41–50
- Step 4: Create three separate balance initialization files, one per group.
- Step 5: Use HDL (HCM Data Loader) to import and load the files.
- Step 6: Run the ‘Load Initial Balance’ process in Validate Mode for each group to confirm the creation of elements, input values, and balance feeds.
- Step 7: Verify that all elements, input values, and balance feeds have been successfully created.
2. Managing Initial Balance Loads for Optimal Performance
Start Small: Initial Batch Size Considerations
When beginning the balance conversion process, best practice suggests starting with a small data file, ideally no more than 2,000 records. This allows for controlled table growth and performance monitoring.
For larger-scale implementations, batch file sizes should be capped at 200,000 records to balance efficiency and system stability.
3. Optimizing Load Performance with HDL and Parallel Processing
To accelerate the balance initialization process, organizations can leverage parallel processing within HDL. Specifically:
- Set the ‘Transfer Group Size’ parameter to 200,000. This enforces parallel processing across 10 threads to optimize data throughput.
******(Should we also mention that the number of threads can be increased via configuration?)
Why Parallel Processing is Critical
While the HDL file loading process can be multi-threaded, the ‘Load Initial Balance’ validation step is single-threaded. This means that only one process can validate the records at a time, making it a potential bottleneck.
To mitigate this, it is recommended to load batches in parallel so that the validation is performed in each batch separately. This approach significantly reduces total processing time and ensures efficient use of system resources.
Recommended Approach for Parallel Processing
- Baseline Cleanup: Purge all prior data sets to create a clean starting point.
- Phase 1: Load 10 files (Files 1–10) in parallel.
- Phase 2: Once Phase 1 completes, trigger 10 ‘Load Initial Balance’ processes, running sequentially.
- Phase 3 (Concurrent Processing): While the first batch is being validated, begin loading the next set of 10 files (Files 11–20).
- Phase 4: As Phase 2 completes, purge the initial set (Files 1–10) to free up system resources.
- Phase 5: Load the next batch (Files 21–30).
- Repeat the cycle. Purging older files while loading new ones ensures continuous processing without overwhelming the system.
4. Parallel Purge and Loading: Maintaining Stage Tables
To maintain performance and ensure efficient data processing, purging stage table data periodically is essential. The stage tables can grow rapidly as large volumes of data are loaded into Oracle HCM Cloud, making periodic purging a critical step in the balance initialization process.
Best Practices for Purging Stage Table Data
- Use the “Delete Stage Table Data” Task: This process removes all staging data that is no longer needed. It should be run periodically, such as after loading every 10 HDL files.
- Follow a Scheduled Deletion Approach: Establish a routine for deleting processed data sets. The frequency should align with the volume and frequency of data loads.
- During Data Migration: Delete each large data set after processing to prevent unnecessary data accumulation in staging tables.
- Extended Retention Data Sets: Certain data sets, such as those for element entries, may be held in secondary stage tables. These should be managed carefully to avoid purging prematurely.
How Oracle HCM Cloud Handles Stage Table Data
When data is loaded via HCM Data Loader (HDL) or HCM Spreadsheet Data Loader, it first enters stage tables for validation. Data that supports rollback, such as element entry, may be transferred to secondary stage tables to prevent premature deletion.
The Import and Load Data process automatically schedules a nightly deletion of processed data sets unless configured otherwise. Users can manually delete data or override the retention period for specific data sets.
5. Key Considerations for Large-Scale Balance Initialization
When executing large-scale balance initialization, the following best practices can help maintain efficiency and system stability:
- Parallel Purging & Loading: Data sets should be purged in parallel with subsequent data loads to prevent bottlenecks.
- Continuous Processing: As each batch of ‘Load Initial Balance’ processes completes, a new batch should be initiated immediately.
- Mitigating Single-Threaded Validation Delays: Since validation is single-threaded, it is essential to run multiple batches in parallel to optimize overall processing time.
- Periodic Table Statistics Gathering: To optimize performance, table statistics should be updated periodically. This is a critical step, as large-scale balance initialization can significantly impact database performance.
- Oracle Support for Performance Optimization: Customers can request Oracle to perform table statistics gathering by raising a Service Request during the balance initialization cycle.
Conclusion
Balance initialization is a foundational process that assumes even greater importance when doing a Mid-Year Go Live implementation. By structuring the approach, starting with element and balance feed creation, leveraging manageable batch sizes, optimizing parallel processing, and maintaining stage tables, organizations can execute a smooth and efficient balance conversion.
By following these best practices, payroll teams can avoid performance bottlenecks, reduce errors, and ensure a seamless transition to a fully initialized system.