Daniel Rothig
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153 votesAdam responded
We’ve made some improvements in this area but will leave it open so you can continue to give us your suggestions
An error occurred while saving the comment An error occurred while saving the comment Daniel Rothig commentedHi Barry,
Thanks for your suggestion. We are thinking a lot about data retention (for example, we've recently done some research to pinpoint recommended purge windows), and data aggregation is the holy grail in our considerations. However, different types of data require different aggregation tactics, and making this work across the app is a significant chunk of work. We are interested in doing more on this, but since most users are more interested in their recent data rather than their historic data, we often prioritise other work over this problem.
It is possible to extract pruned data (ie. an arbirary subset of representative data points for a given duration) for graphable metrics relatively easily from our data repository. For example, the query...
SELECT * FROM [Cluster_Machine_LogicalDisk_Capacity_UnstableSamples_DateRange]([utils].[DateTimeToTicks]('2013-03-25'), [utils].[DateTimeToTicks]('2013-03-26'), 500)
(object names quoted from memory, please double check)
...would retrieve 500 data points each for disk utilization of every disk on every machine you are monitoring for the specified time range. You could set up a weekly job to extract a week's worth of data from data sources you are interested in and put them into corresponding tables in a different database and derive SSRS reports from this. If you are looking for a workaround, this is probably the best route for you.
Daniel
Daniel Rothig shared this idea ·
The underlying problem here seems to be that we run into the 10GB limit in order to not having to provision a non-express SQL Server version. I'll merge this suggestion into the corresponding idea.