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Provide actual threshold data for an object with "No Baseline Window" or "No Comparison Window" statuses

It has been my experience that by far the most common reason for seeing an object with a status of ‘No Baseline Window’ or ‘No Comparison Window’ in the Reorg Recommendation Monitor screen of Query Monitor is because one or more of the user specified CQM_REORG_METRICS absolute thresholds was not met.   However, the user currently has no clue as to what the actual metrics for an object are, because there is no way to display this data. 

Since QM must evaluate these thresholds for each object any way, it would be very helpful if QM could simply externalize this info upon request by the user.  This would allow the user to be able to see the actual calculated values for each metrics so as to better understand the reason why an object is listed as ‘No Baseline Window’ or ‘No Comparison Window’.  This way the user can see how far away or how close to the threshold an object is.  Once the user has this info, they can make adjustments to the thresholds as needed in order to get the best results from Smarter Reorg. 

  • Avatar32.5fb70cce7410889e661286fd7f1897de Guest
  • Jun 21 2019
  • Future consideration
Who would benefit from this IDEA? DBA
How should it work?

My suggestion would be to allow the user to click on the line for an object, and then a detail pane on the right-hand side of the screen would be populated with the actual metrics that were evaluated for that object.  This detail pane would be similar to how the Activity Browser window works today.

Idea Priority Medium
Priority Justification The ability to see this data would make Smarter Reorg much more usable to the DBA. It would also allow the DBA to adjust the thresholds based on actual data within a performance window. This will make Smarter Reorg much more usable.
Customer Name PennDOT
  • Attach files
  • Avatar40.8f183f721a2c86cd98fddbbe6dc46ec9
    Guest commented
    24 Sep, 2019 03:33pm

    We are thinking of removing the requirment for baselines and using continuous learning to detect degrading performance.

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