![]() Typically, this is as follows:īoolean > Int > Float > String > Date > DatetimeĪ quick solution here is to make sure that numbers are either integers or floats, that dates are dates, and that datetime is only used in cases when the time is also needed. Using incorrect data types can have an impact on performance as some comparisons are quicker than others. This can be multiple different issues: numbers stored as strings, dates as datetime, or something which is essentially a Boolean value stored as a string. ![]() While context filters do have other uses, they should only be used for performance improvements if they can reduce the amount of data to at least one tenth of the original size, as the added computation time will outweigh the performance increase in other situations. However, this is not good practice as it takes time to compute and create a new dataset. Knowing this, it can be tempting to apply context filters to everything. Applying a context filter can therefore have a great impact on performance, as it limits the amount of data that will need to be queried. This happens before other filters are applied or calculations are performed ( see order of operations). This keeps the fields but excludes them from being a part of the extract that is used by Tableau – doing this has an impact on both query speed and workbook size.Īny hidden fields can later be shown by clicking “Show Hidden Fields” in the same dropdown.Ī new, temporary dataset is created when context filters are applied. Simply click “Hide All Unused Fields” in the dropdown menu in the data pane. ![]() This is an easy thing to fix without having to resort to building new SQL views or creating custom SQL queries. If retained, these unnecessary fields will take up space and be a part of the extract, adding to the amount of data Tableau will have to go through each time a query is performed. You might have found that most data sources contain more fields than what is actually required when building a Tableau dashboard. Therefore, removing unused columns can significantly speed up performance – especially if the dataset is very wide but only a few columns are needed. The number of columns in a dataset has a larger impact on performance than the number of rows. An example of a bad practice of this could be importing transaction level data to create monthly sales reports.Ĭlearly, in instances like this, it is important to make sure you have carried out an effective requirements analysis with your stakeholders so that you are clear on the level of granularity that is going to be needed for your project (amongst other things).Ĭreating an aggregate can be done either in a database view or by using the built-in aggregation function in Tableau – to do this, visit the extract settings menu. ![]() Performance will be improved, and far noticeably for larger datasets, if you aggregate the data to the level required for the analysis or dashboard required. If your data is unnecessarily granular and you are ultimately going to be using a summary level, rather than the detail, you will be slowing down performance with the extra data being processed. Make sure the data is only at the required level of granularity. These filters make it possible to remove data from the final dataset, and in that way, limit the data Tableau has to query. This is where the extract and data source filters are useful. Having this data in the extract will only slow down queries as Tableau has to look through irrelevant data every time something needs to be computed. This can be data older than what is in scope or sales data for products not in your department. Most datasets contain a certain amount of data that is completely irrelevant for the analysis. There are however a few ways of minimising the amount of data used in Tableau. The background for this tip is fairly straightforward: The more data Tableau has to go through, the longer it takes.
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