How To Fix KM829 - Large data volumes can lead to long runtimes; see long text

KM829 - Overview

• Message type: E = Error

• Message class: KM - Error messages for Profit Center Accounting

• Message number: 829

• Message text: Large data volumes can lead to long runtimes; see long text

What causes this issue?

There may be a large number of sales orders stored in the system. Large
numbers can cause long runtimes for the program if you make too few
restrictions in the selection screen.

System Response

The system issues an error message and will not allow you to continue with this transaction until the error is resolved.

How to fix this error?

If you do not expect any problems with the runtime for this program, you
can ignore this warning message.
If, however, the runtime of the program turns out to be very long,
restrict the selection of the materials in the selection screen, if
possible. Incorrect assignments from sales order items to profit centers
are often the result of incorrect material assignments. Data selection
can be accelerated by restricting the materials selected.
If this is not possible or if the runtime is still too long, you can set
the <LS>Execute in Background</> indicator.

Procedure for System Administrators

Error message extract from SAP system. Copyright SAP SE.

Smart SAP Assistant

• What is the cause and solution for SAP error message KM829 - Large data volumes can lead to long runtimes; see long text ?

  The SAP error message KM829 indicates that the system is experiencing performance issues due to large data volumes being processed, which can lead to long runtimes for queries or reports. This message is typically associated with the SAP Business Warehouse (BW) or SAP BusinessObjects environments, where large datasets can significantly impact performance.
  
  Cause:
  
  Large Data Volumes: The primary cause of this error is the processing of large datasets, which can overwhelm the system's resources and lead to slow performance.
  Inefficient Queries: Queries that are not optimized can exacerbate the problem, leading to longer execution times.
  Insufficient System Resources: Limited memory, CPU, or disk I/O can contribute to the performance issues when handling large datasets.
  Network Latency: If the data is being pulled from a remote source, network latency can also affect performance.
  
  
  Solution:
  
  
  Optimize Queries:
  
  Review and optimize the queries being executed. Use appropriate filters, aggregations, and limits to reduce the amount of data being processed at once.
  Consider