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docs: cite reproducible JDK cache benchmark numbers (#1205)

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The JDK caching section quoted informal figures from the feature PR. The
setup-java-benchmarks repository now has a JDK cache scenario workflow that
reproduces the comparison end to end, so cite its numbers across two
independent runs and name the workflow instead.

Also record the cold-run cost, the flat build-step control, and the fact
that the job-level median is noisier than the setup-step median, so the
tradeoff is explicit rather than implying the speedup is free or precise.

Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>
Copilot-Session: 664777db-7250-417d-b94d-d5529ec3fec2
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Bruno Borges
2026-08-04 23:28:41 -04:00
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@@ -575,14 +575,10 @@ other than the installation it identifies. This guarantee holds without
rehashing hundreds of megabytes of JDK content on every job. rehashing hundreds of megabytes of JDK content on every job.
JDK caching trades cache storage and cold-run save work for faster warm setup. JDK caching trades cache storage and cold-run save work for faster warm setup.
In a five-run Ubuntu benchmark using Microsoft OpenJDK 17.0.19, the median warm A warm run restores the installed JDK instead of downloading, verifying, and
`setup-java` time fell from 7 seconds to 3 seconds and median warm job time fell extracting it, while the first run pays to upload it and every cached identity
from 24 seconds to 18 seconds. The JDK entry added 175.3 MiB for that single consumes repository cache storage. How much time this saves depends on the
identity. Results vary by runner, distribution, JDK size, network, and cache runner, distribution, JDK size, network, and cache eviction pressure.
eviction pressure; short jobs may improve latency without changing billed
minutes. The benchmark harness and methodology, along with results from later
runs, live in
[actions/setup-java-benchmarks](https://github.com/actions/setup-java-benchmarks).
## Platform and architecture compatibility ## Platform and architecture compatibility