ci: check every GitHub-read YAML file for duplicate keys

[mengw15]

What changes were proposed in this PR?

A duplicate mapping key is invalid YAML, but PyYAML keeps the last one and says nothing:

>>> yaml.safe_load("jobs:\n  a: {}\njobs:\n  b: {}")
{'jobs': {'b': {}}}

A local parse therefore cannot see the mistake, while GitHub's loader rejects the file. Both halves of that gap are silent: the change looks fine before it merges, and afterwards the workflow simply stops starting rather than failing.

.github/scripts/test_ci_yaml.sh loads .asf.yaml and every .yml/.yaml under .github/ (30 files today) with a duplicate-key-strict loader. Beyond the workflows, the files worth naming are .github/release-branches.yml, where a duplicated key drops a release branch and with it every backport that branch would have been nominated for, and .github/labeler.yml, where it stops a rule matching without anyone noticing.

No change to build.yml: its infra job runs every .github/scripts/test_*.sh it finds, after installing amber/dev-requirements.txt, which already pins PyYAML.

Any related issues, documentation, discussions?

#8379 added this check as one of two invariants in .github/scripts/test_asf_rulesets.sh. The other one described a ruleset split GitHub refuses to create and goes away with #8624's revert; this keeps the half that still has a subject, widened beyond workflows and .asf.yaml.

How was this PR tested?

Green on the current tree (30 files). The failure path was verified on one file of each kind — a workflow, release-branches.yml, labeler.yml and .asf.yaml — by appending a duplicated key to each in turn: every one reports the file, the key and the line, and exits 1. The tree is unchanged afterwards.

Was this PR authored or co-authored using generative AI tooling?

Yes. Generated-by: Claude Code (claude-opus-5)

[github-actions]

Automated Reviewer Suggestions

Based on the git blame history of the changed files, we recommend the following reviewers:

• No candidates found from git blame history.

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 92.80%. Comparing base (279e12b) to head (46866d9).

Additional details and impacted files

@@            Coverage Diff            @@
##               main    #8625   +/-   ##
=========================================
  Coverage     92.80%   92.80%           
  Complexity     4898     4898           
=========================================
  Files          1239     1239           
  Lines         52252    52252           
  Branches       6425     6425           
=========================================
  Hits          48492    48492           
- Misses         2183     2184    +1     
+ Partials       1577     1576    -1     

Flag	Coverage Δ
access-control-service	71.78% <ø> (ø)
agent-service	99.16% <ø> (ø)
amber	88.59% <ø> (-0.01%)	⬇️
computing-unit-managing-service	55.20% <ø> (ø)
config-service	87.37% <ø> (ø)
file-service	81.53% <ø> (ø)
frontend	96.64% <ø> (+<0.01%)	⬆️
notebook-migration-service	83.73% <ø> (ø)
pyamber	98.48% <ø> (ø)
workflow-compiling-service	74.09% <ø> (ø)

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[github-actions]

⚠️ Benchmark changes need a look

🟢 0 better · 🔴 7 worse · ⚪ 8 noise (<±5%) · 0 without baseline

Compared against main 279e12b benchmarked on this same runner, so the delta is largely free of cross-runner hardware noise. The "7d avg" column still reflects the gh-pages dashboard. Treat <±5% as noise unless repeated.

Dashboard · Run

	config	throughput	MB/s	latency	max Δ latest / 7d
🔴	bs=10 sw=10 sl=64	441	0.269	21,157/38,281/38,281 us	🔴 +23.6% / 🔴 +142.3%
🔴	bs=100 sw=10 sl=64	945	0.577	100,668/141,752/141,752 us	🔴 +14.4% / 🔴 +30.4%
⚪	bs=1000 sw=10 sl=64	1,153	0.704	862,524/921,807/921,807 us	⚪ within ±5% / 🟢 -13.7%

Baseline details

Latest main 279e12b from same runner

config	metric	PR	latest main	7d avg	Δ latest	Δ 7d
bs=10 sw=10 sl=64	throughput	441 tuples/sec	482 tuples/sec	767.43 tuples/sec	-8.5%	-42.5%
bs=10 sw=10 sl=64	MB/s	0.269 MB/s	0.294 MB/s	0.468 MB/s	-8.5%	-42.6%
bs=10 sw=10 sl=64	p50	21,157 us	20,058 us	12,880 us	+5.5%	+64.3%
bs=10 sw=10 sl=64	p95	38,281 us	30,962 us	15,801 us	+23.6%	+142.3%
bs=10 sw=10 sl=64	p99	38,281 us	30,962 us	19,767 us	+23.6%	+93.7%
bs=100 sw=10 sl=64	throughput	945 tuples/sec	983 tuples/sec	988.73 tuples/sec	-3.9%	-4.4%
bs=100 sw=10 sl=64	MB/s	0.577 MB/s	0.6 MB/s	0.603 MB/s	-3.8%	-4.4%
bs=100 sw=10 sl=64	p50	100,668 us	98,806 us	102,684 us	+1.9%	-2.0%
bs=100 sw=10 sl=64	p95	141,752 us	123,878 us	108,712 us	+14.4%	+30.4%
bs=100 sw=10 sl=64	p99	141,752 us	123,878 us	118,731 us	+14.4%	+19.4%
bs=1000 sw=10 sl=64	throughput	1,153 tuples/sec	1,145 tuples/sec	1,022 tuples/sec	+0.7%	+12.8%
bs=1000 sw=10 sl=64	MB/s	0.704 MB/s	0.699 MB/s	0.624 MB/s	+0.7%	+12.8%
bs=1000 sw=10 sl=64	p50	862,524 us	871,466 us	999,086 us	-1.0%	-13.7%
bs=1000 sw=10 sl=64	p95	921,807 us	913,093 us	1,037,033 us	+1.0%	-11.1%
bs=1000 sw=10 sl=64	p99	921,807 us	913,093 us	1,066,123 us	+1.0%	-13.5%

Raw CSV

config_idx,batch_size,schema_width,string_len,num_batches,total_ms,total_tuples,total_bytes,tuples_per_sec,mb_per_sec,lat_p50_us,lat_p95_us,lat_p99_us
0,10,10,64,20,453.21,200,128000,441,0.269,21156.91,38281.32,38281.32
1,100,10,64,20,2116.38,2000,1280000,945,0.577,100667.87,141752.04,141752.04
2,1000,10,64,20,17343.67,20000,12800000,1153,0.704,862524.21,921807.10,921807.10