Effect of DataSet View Calculated Columns on Performance
I want confirmation that if I shift over a bunch of dataset view calculated columns to run in the dataflow instead of on the dataset view that I can potentially boost the performance of my dashboards.
Currently, the dashboards run okay, but filtering takes a while. The dashboards are heavily leveraging COUNT(DISTINCT()), which I know is bad for performance, and I hope to eventually develop a workaround that does rely on it quite as much, but for now, confirmation on the effects of a calculated column shift would be appreciated.
Best Answers
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As a view is processed when the data is accessed/visualized in a graph so it can affect the performance of the graphs loading on a dashboard. Moving the logic into a Dataflow will remove the processing of the logic when loading the graphs/dashboard. The caveat to this is that if your dashboard is utilizing filtering your logic won't take the filters into account.
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Beast modes allow you to add conditions to your aggregations dynamically based on what the user selected on the page as filters. Pre-aggregating your data in a Dataflow will set those aggregate values before you're able to do any dynamic filtering as the data is recorded directly into the dataset.
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Answers
-
As a view is processed when the data is accessed/visualized in a graph so it can affect the performance of the graphs loading on a dashboard. Moving the logic into a Dataflow will remove the processing of the logic when loading the graphs/dashboard. The caveat to this is that if your dashboard is utilizing filtering your logic won't take the filters into account.
**Was this post helpful? Click Agree or Like below**
**Did this solve your problem? Accept it as a solution!**1 -
Thanks @GrantSmith! Everything made sense to me up until that last sentence. Can you provide an example or rephrase it?
0 -
Beast modes allow you to add conditions to your aggregations dynamically based on what the user selected on the page as filters. Pre-aggregating your data in a Dataflow will set those aggregate values before you're able to do any dynamic filtering as the data is recorded directly into the dataset.
**Was this post helpful? Click Agree or Like below**
**Did this solve your problem? Accept it as a solution!**1
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