Best Practices for Improving Query Response Time in Large Cube Models
Hello everyone,
I’m currently working with a large AtScale cube that serves multiple dashboards and reports. While the model works well in general, some of the aggregation queries are slower than expected.
So far, I’ve reviewed the aggregate awareness configurations and ensured all dimensions are properly defined. However, I’m still noticing delays when running high-volume queries, particularly on derived measures.
Are there any specific strategies or advanced settings in AtScale that help improve response time for large datasets? I'm especially interested in tuning tips related to partitioning, aggregate table strategy, or query routing.
Any insights from those who have worked with complex models would be very helpful.
Thanks in advance!
calculadoradedias.com
Comments
1 comment
Are you working with the Install version of AtScale or the Container one, and also what is the underlying data platform? There are some different routes you can take based on what you are leveraging - let us know and I can send over some thoughts and ideas on how to best tune your environment.
AtScale Community Admin
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