If you have millions of records in a straight table, it will likely render slowly because the table contains many dimensions, and associating each value can take time. Below are some tips to improve performance:
Limit the data in the straight table:
You can either reduce the dataset size (e.g., load only 2 years of data instead of 5) or remove key fields from the straight table, as these can significantly slow down rendering.
Apply a calculation condition on the straight table:
This forces the user to make a selection before the data is displayed. By filtering the data first, the table will render more efficiently.
Ensure key fields are optimized:
If the fields in the table come from different tables, make sure the key fields between them are correctly associated and not stored as text. If a key field is text, consider converting it to a number using theautonumber or autonumber# function.
Remove calculated dimensions:
If you’re using calculated dimensions in the table, move those calculations to the script instead. This will reduce the load during rendering.
Increase server RAM:
As a last resort, you can increase server RAM to improve performance, but this option may require extensive checks and approvals before implementation.
