An include filter runs much faster than an exclude filter, especially for dimensions with many members. Exclude filters load the entire domain of a dimension, while include filters do not. Double-check your filters and remove any that aren’t necessary. Excessive filters on a view will create a more complex query, which takes longer to return results.
Note: Showing the filter dialog requires Tableau to load its members and may create extra queries, especially if the filtered dimension is not in the view. However, inefficient and excessive filters are one of the most common causes of poorly performing workbooks and dashboards. Understanding Tableau Data Extracts (three-part series)įiltering in Tableau is extremely powerful and expressive. When querying against constantly-refreshing data, a live connection often makes more sense when operationalizing the view.įor more information on data extracts, check out these additional resources: The typical extent of an extract is between 500 million to one billion rows mileage will vary. Keep in mind: Extracts are not always the long-term solution. Optimize extracts to speed up future queries by materializing calculations, removing columns and the use of accelerated views.Use extract filters to keep only the data you need. Use the hide all unused fields option to remove unused columns from a data source. Minimize the number of fields based on the analysis being performed.Since an extract is a columnar store, the wider the data set, the slower the query time. Rather, it’s meant to be a supplement for fast prototyping and data discovery. The Data Engine is not intended to be a replacement for a data warehouse. The key is to use domain-specific cuts of your data. Reference Materials Toggle sub-navigationĮxtracts are typically much faster to work with than a live data source, and are especially great for prototyping.Teams and Organizations Toggle sub-navigation.