What are the exact steps to optimize slow-loading Looker dashboards when queries involve millions of rows
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What are the exact steps to optimize slow-loading Looker dashboards when queries involve millions of rows
1 real search · 1 real-search pass
site:cloud.google.com/looker/docs performance optimization dashboards aggregate awareness datagroups persistent derived tables query caching SQL RunnerGoogle ↗
OBSERVEDOther / navigational
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (1)
Classes come from the query-classifier (a local Laya model). 1 of 1 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT1 real searches · gpt-6
site:cloud.google.com/looker/docs performance optimization dashboards aggregate awareness datagroups persistent derived tables query caching SQL Runner Google ↗
- Optimize Looker performance | Google Cloud Documentation · docs.cloud.google.com
- Derived tables in Looker | Google Cloud Documentation · docs.cloud.google.com
- Considerations when building performant Looker dashboards | Google Cloud Documentation · docs.cloud.google.com
Queries
1 queries · click a column to sort