Using the Advanced Dimensional Caching Feature to Speed Up Data Studio Dashboards
Analytics Canvas has a proprietary caching system to speed up your Data Studio dashboards and reports. When this feature is enabled, if the data is already available in cache when a report is opened or refreshed, the cache tables will be used.
This has 2 main benefits: faster loading dashboards and reports, and a reduction in Online Bytes processed.
When dimensional caching is enabled, the first refresh will always take slightly longer as the cache is being built, but subsequent refreshes, even from other users, will be faster as they query the cache tables instead of the full data set.
How to enable Dimensional Caching to speed up Data Studio Reports
The controls for Dimensional Caching are found on the Data Studio block controls and within the Data Studio Data Set Manager in both Desktop and Online.
In both cases, when the option is selected, the cache will generate the next time a report using the data set is refreshed in Data Studio. You do not need to run the Canvas or write to the data set for this setting to apply.
If this option is grayed out, it means that the data set is too small to benefit from caching.
The second way to access this control is through the Data Studio Data Set Manager.
In the left navigation menu, click Data Studio to bring up the Data Studio Data Set Manager. Select a data set, then click "Edit". Under the Advanced tab, check the box that says "Enable Dimensional Caching in connector".
Additional Information about Dimensional Caching for Data Studio
- When a dashboard that uses a dataset with this feature enabled is refreshed, it will add the size of the cache tables to the storage in your account.
- When you update or overwrite a dataset that uses cache, the cache will be dropped and recreated on the first refresh of the Data Studio report.
- Small data sets will not use dimensional caching even if this box is checked. This is because there would be no performance improvement by using a cache.
- Data sets that use dimensional caching cannot use averages or ratios as metrics. This is because the cache table aggregates the data set by summing all the metrics. For example, if you have a data set with price, quantity and revenue, don't include price- just include revenue and quantity- price can be added in Data Studio using a calculated metric. This way, revenue and quantity will properly aggregate when the cache table is created.
If you have any questions or concerns about how this feature works, visit our support page and connect with us.