Performance issues with WS_DELIVERY_UPDATE – how to reduce performance time?

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Summary

The performance issue with WS_DELIVERY_UPDATE is causing significant delays in processing large outbound deliveries with multiple Handling Units (HUs). The function module’s consistency checks against all existing database entries and assigned HUs are leading to degraded performance as the number of HUs increases.

Root Cause

The root cause of the performance issue is:

  • Excessive database queries: WS_DELIVERY_UPDATE performs consistency checks against all existing database entries and assigned HUs on every call.
  • Increasing Handling Units: As more Transfer Orders (TOs) are completed, the delivery contains an increasing number of HUs, leading to more database queries and slower performance.
  • Inefficient processing: The function module’s design leads to repeated checks and inefficient processing of large deliveries.

Why This Happens in Real Systems

This issue occurs in real systems due to:

  • Large datasets: Processing large outbound deliveries with multiple TOs and HUs.
  • Concurrent processing: Multiple warehouse workers processing TOs in parallel or sequentially.
  • Complex business logic: The need for consistency checks and updates to ensure data integrity.

Real-World Impact

The performance issue has significant real-world impacts, including:

  • Delayed processing: Total runtime exceeding 30 minutes or even several hours.
  • Inefficient use of resources: Warehouse workers waiting for the system to process deliveries.
  • Increased costs: Potential losses due to delayed deliveries and inefficient use of resources.

Example or Code

DATA: lt_hus TYPE TABLE OF ty_hu.

" Assuming lt_hus contains the Handling Units to be packed
CALL FUNCTION 'WS_DELIVERY_UPDATE'
  EXPORTING
    i_hus      = lt_hus
  EXCEPTIONS
    OTHERS     = 0.

How Senior Engineers Fix It

Senior engineers can fix this issue by:

  • Optimizing database queries: Reducing the number of database queries and using more efficient query methods.
  • Implementing caching mechanisms: Storing frequently accessed data in memory to reduce database queries.
  • Using alternative approaches: Exploring alternative function modules or custom solutions that can handle large deliveries more efficiently.

Why Juniors Miss It

Junior engineers may miss this issue due to:

  • Lack of experience: Limited exposure to large-scale systems and complex business logic.
  • Insufficient understanding: Not fully comprehending the performance implications of excessive database queries and inefficient processing.
  • Overreliance on standard solutions: Not considering alternative approaches or custom solutions to optimize performance.