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toni_lajsner
Contributor III
Contributor III

ODQData Table Growth

We have recently reviewed the SAP Hana database growth and we looked at the historical growth of a number of tables. We have noticed that there is a ODQData Table which has been growing rapidly. We need to understand if we can apply some purging against this table as it has grown from 1.5 GB to 46.7 GB in last 1 year. When we looked at the daily growth on this table then in last 30 days it has grown from 37.6 GB to ~47 GB which is approx. 25% growth and Previous month that it did grow by 22%. If it continues to grow like this then it will require a significant investment to keep this table and it could have a significant impact on the overall system performance.

Can we purge this oDQData table ?

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1 Reply
Rahul_Kale
Support
Support

Hello toni_lajsner,

 

Thank you for reaching out to the Qlik community,

 

Yes, you can reduce/purge ODQDATA — but NOT by direct table deletion.
You must use SAP standard ODQ cleanup mechanisms (ODQMON + program ODQ_CLEANUP) and ensure subscriptions are properly maintained.

 

 Key points you will need to consider.

  • ODQDATA stores delta queue data (ODP framework) used by subscribers like BW, Qlik Replicate, etc. 
  • Data is retained intentionally for a retention period (for recovery/reload scenarios). document.
  • Therefore, direct DELETE/TRUNCATE is not allowed (will break delta replication).

 

 Supported way to purge:-

  1. Run standard cleanup job ODQ_CLEANUP
    • Deletes old/unused delta data from ODQ tables (including ODQDATA) document.
  2. Adjust retention period
    • Reduce retention hours/days to control growth
  3. Clean up subscriptions in ODQMON
    • Remove obsolete subscribers / unused queues
  4. Run cleanup in phases (month-by-month) for the large tables document.

Important precautions:-

  • Ensure all consumers (Qlik, BW, etc.) have already fetched data before cleanup
  • If you purge too aggressively → delta loss → reinitialization required
  • Large growth often means:
    • unused subscriptions
    • failed/paused replication
    • overly long retention