How To Fix RSRCACHE022 - Cluster table for large data

RSRCACHE022 - Overview

• Message type: E = Error

• Message class: RSRCACHE - Messages for OLAP Cache

• Message number: 022

• Message text: Cluster table for large data

What causes this issue?

If this option is selected, cache entries with query data that exceed
the predefined size limit when initially created are written to the
cluster table (also known as index table). It is not beneficial to use
cluster tables when transporting larger data volumes between the
database and application server because this requires multiple implicit
SELECTS. However, it is possible to save in internal tables; this means
that the data export can take place in an internal table instead of an
XSTRING, which offers better performance under some circumstances.

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?

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 RSRCACHE022 - Cluster table for large data ?

  The SAP error message RSRCACHE022 typically indicates an issue related to the resource cache for cluster tables when dealing with large datasets. This error can occur in various scenarios, particularly when the system is trying to read or process large amounts of data from cluster tables, which can lead to performance issues or memory constraints.
  
  Cause:
  
  Large Data Volume: The error often arises when the system attempts to process a large volume of data from cluster tables, which can exceed the memory limits or cache size.
  Insufficient Memory: The SAP system may not have enough memory allocated to handle the data being processed, leading to cache overflow.
  
  Configuration Issues: Incorrect configuration settings for the resource cache or the database can also contribute to this error.
  Database Performance: Slow database performance or locks on the database can exacerbate the issue, leading to timeouts or failures in data retrieval.
  
  
  Solution:
  
  Increase Memory Allocation: Review and increase the memory allocation