patrickhulce / patrickhulce/third-party-web
Execution time vs long tasks
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- Dominant language
- JavaScript
- Stars
- 1.1k
- Forks
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- Merged PRs (30d)
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Description
It would be super interesting to dig deeper into execution time if possible and highlight any blocking CPU (long tasks > 50ms) caused by a third party. Are long tasks captured by Lighthouse somewhere in the bootup time audit?
We're doing a bunch of third party analysis and just because a third party has a lot of CPU usage doesn't mean it's a bad actor. Only long tasks impact user experience and push out metrics like TTI.
In this example of CPU usage that splits out blocking vs non-blocking CPU, should Drift have the higher impact or Google Tag Manager? Long tasks can dramatically change how execution time is interpreted.
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First steps
- Read the whole issue, then the project's contributing guide.
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Research direction
Start by checking whether Lighthouse's bootup time audit captures long tasks over 50 ms and how its CPU data distinguishes blocking from non-blocking work. Determine how third-party attribution should account for long tasks, then define completion as reporting that impact clearly alongside execution time; the issue names no repository files or tests.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- javascript
- Domain
- performance, web-dev
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100