How To Fix CNV_10020416 - Division into blocks was not carried out for &1 &2

CNV_10020416 - Overview

• Message type: E = Error

• Message class: CNV_10020 - Message class for package 10020

• Message number: 416

• Message text: Division into blocks was not carried out for &1 &2

What causes this issue?

For object type &V3& and target determination object &V4&, &V1& &V2&
has been identified as the determination path. The relationship data
for &V1& &V2& is meant to be partially loaded from the object tables.
This is only possible if the data has been divided into blocks
beforehand. This has not yet been done.

System Response

Partial determination is not possible.

How to fix this error?

Divide the data for &V1& &V2& into blocks by calling transaction
CNV_10020_SCAN.
&HINTS&
With regard to the division, consider also the documentation for
transaction CNV_10020_SCAN as well as the documentation for the path
parameters (transaction CNV_10020_PARAMP).

Error message extract from SAP system. Copyright SAP SE.

CNV_10020416 - Details

• The SAP error message CNV_10020416, which states "Division into blocks was not carried out for &1 &2," typically occurs during data migration or conversion processes, particularly when using the SAP S/4HANA Migration Cockpit or other data migration tools. This error indicates that the system was unable to divide the data into manageable blocks for processing.
  Causes:
  
  Data Volume: The volume of data being processed may be too large, leading to issues with memory allocation or processing limits.
  
  Configuration Issues: Incorrect configuration settings in the migration tool or the source/target systems can prevent proper data handling.
  Data Integrity Issues: There may be inconsistencies or errors in the data that prevent it from being processed correctly.
  System Performance: Insufficient system resources (CPU, memory) can lead to failures in processing large datasets.
  Missing Authorizations: The user executing the migration may lack the necessary authorizations to perform the operation.
  
  Solutions:
  
  Check Data Volume: If the dataset is too large, consider breaking it