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Note: Ryan Lempa and Darren McGurran also contributed to this piece.
We here at Tableau are very proud of how easy it is to see and understand data with Tableau. Once you get started, it’s intuitive to dive deeper by adding more and more fields, formulae, and calculations to a simple visualization—until it becomes slower and slower to render. In a world where two-second response times can lose an audience, performance is crucial.
So how can you make your dashboards run faster? Your first step is to identify the problem spots by running and interpreting your performance recording. The performance recorder is every Tableau speed demon’s ticket to the fast lane. The performance recorder can pinpoint slow worksheets, slow queries, and long render-times on a dashboard. It even shows the query text, allowing you to work with your database team on optimizing at the database level.
Now that you know which views or data connections are slowing you down, below are six tips to make those dashboards more performant. For each tip, we’ve listed the most common causes of performance degradation as well as some quick solutions.
Extracts are typically much faster to work with than a live data source, and are especially great for prototyping. The key is to use domain-specific cuts of your data. The Data Engine is not intended to be a replacement for a data warehouse. Rather, it’s meant to be a supplement for fast prototyping and data discovery.
Since an extract is a columnar store, the wider the data set, the slower the query time.
Keep in mind: Extracts are not always the long-term solution. The typical extent of an extract is between 500 million to one billion rows; mileage will vary. When querying against constantly-refreshing data, a live connection often makes more sense when operationalizing the view.
For more information on data extracts, check out these additional resources:
When data is highly granular, Tableau must render and precisely place each element. Each mark represents a batch that Tableau must parse. More marks create more batches; drawing 1,000 points on a graph is more difficult than drawing three bars in a chart.
Large crosstabs with a bevy of quick filters can cause increased load times when you try to view all the rows and dimensions on a Tableau view.
Excessive marks (think: data points) on a view also reduce the visual analytics value. Large, slow, manual table scans can cause information overload and make it harder to see and understand your data.
Here’s how you can avoid this trap:
Filtering in Tableau is extremely powerful and expressive. However, inefficient and excessive filters are one of the most common causes of poorly performing workbooks and dashboards. 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.
For more details on these areas and many more, check out our whitepaper on designing efficient workbooks.