How can I analyze CloudFront CDN caching logs efficiently to identify which expensive dynamic assets are constantly hitting our origin servers?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How can I analyze CloudFront CDN caching logs efficiently to identify which expensive dynamic assets are constantly hitting our origin servers?
2 real searches · 1 real-search pass
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.
Classifying 2 queries…
Searches and sources
ChatGPT2 real searches · gpt-6
AWS CloudFront standard access logs x-edge-result-type Miss RefreshHit Athena query documentation Google ↗
Amazon CloudFront real-time logs fields cache behavior standard logs delivery Athena Google ↗
- Standard logging reference · docs.aws.amazon.com
- Query Amazon CloudFront logs · docs.aws.amazon.com
- Use real-time access logs · docs.aws.amazon.com
- Create a table for CloudFront real-time logs · docs.aws.amazon.com
- FAQs · aws.amazon.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
AWS CloudFront standard access logs x-edge-result-type Miss RefreshHit Athena query documentation OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
Amazon CloudFront real-time logs fields cache behavior standard logs delivery Athena OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 |