Real ChatGPT answer · index seedib8UyQV8reIcompleteB2B software · data and analytics platformscommercial
We need to optimize our slow-loading Looker dashboards powered by BigQuery; what caching and derived table strategies do top data teams recommend?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
We need to optimize our slow-loading Looker dashboards powered by BigQuery; what caching and derived table strategies do top data teams recommend?
2 real searches · 1 real-search pass
Looker documentation caching datagroups persist_with max_cache_age persistent derived tables incremental PDT aggregate awareness BigQuery performanceGoogle ↗
OBSERVEDReason
Google Cloud BigQuery performance best practices partitioning clustering materialized views BI Engine Looker dashboardsGoogle ↗
OBSERVEDComparison
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 (2)
Classes come from the query-classifier (a local Laya model). 2 of 2 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT2 real searches · gpt-6
Looker documentation caching datagroups persist_with max_cache_age persistent derived tables incremental PDT aggregate awareness BigQuery performance Google ↗
Google Cloud BigQuery performance best practices partitioning clustering materialized views BI Engine Looker dashboards Google ↗
- Looker cookbooks: Getting the most out of derived tables in Looker | Google Cloud Documentation · docs.cloud.google.com
- Derived tables in Looker | Google Cloud Documentation · docs.cloud.google.com
- datagroup | Looker | Google Cloud Documentation · docs.cloud.google.com
- Caching queries | Looker | Google Cloud Documentation · ccn.web.tr
- Optimize Looker performance | Google Cloud Documentation · docs.cloud.google.com
- Optimizing Performance of LookML Queries · skills.google
- Optimize query computation | BigQuery | Google Cloud Documentation · docs.cloud.google.com
- Introduction to BI Engine | BigQuery | Google Cloud Documentation · docs.cloud.google.com
- What is BI Engine? | BigQuery | Google Cloud Documentation · docs.cloud.google.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
Looker documentation caching datagroups persist_with max_cache_age persistent derived tables incremental PDT aggregate awareness BigQuery performance OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 | |
Google Cloud BigQuery performance best practices partitioning clustering materialized views BI Engine Looker dashboards OBSERVED | Google ↗ AI Mode ↗ | — | Comparison | — | — | 1x of 1 |