[PG1204]

What changes were proposed in this PR?

The four legacy Hugging Face operators (Spam Detection, Sentiment Analysis, Text Summarization and Iris Logistic Regression) now validate their configuration the same way in getOutputSchemas, through one shared helper, HuggingFaceSchemaValidation:

• They throw and never return null, as @Ma77Ball recommended on Align schema validation across the four legacy Hugging Face operators #8728. Spam and Sentiment used to return null for an empty result column. The compiler runs getOutputSchemas inside a Try and stores a thrown error on the output port. A returned null counted as success, then failed one line later in PhysicalOp.propagateSchema (schemaMapping.foldLeft) with a NullPointerException, so the user saw an NPE instead of a message. Summarization and Iris already threw, but with generic messages. All four now name the field, e.g. Spam result attribute cannot be empty.
• Input columns are checked at build time. The column each operator reads (Attribute for the three text operators, the two petal columns for Iris) must be set and present in the input table. The match is exact-case: Schema lookups ignore case, but the generated Python does not. The error lists the available columns, as fix(workflow-operator): report a clear error when a configured HF image/audio column is missing #7299's does. Iris's petal columns must also be numeric (integer, long or double), matching its existing frontend type rule.
• Summarization reads its input schema by port id (inputSchemas(operatorInfo.inputPorts.head.id)), as the other three do, instead of inputSchemas.values.head.

Two corrections to the text of #8728. Iris reads two input columns (petal length and petal width), not four. And #7299's check runs in the generated Python at run time, so it is the precedent for the message wording, not for where the check lives.

Any related issues?

Closes #8728

How was this PR tested?

The tests were written first. 22 new or rewritten tests across the four specs failed against main for the expected reasons; Spam's and Sentiment's propagation tests failed with the NullPointerException itself. All 22 pass with the change, and reverting the four operators makes exactly those 22 fail again. The six tests that asserted null now assert the thrown message. The Spam and Iris happy-path inputs gained the columns those operators read, and their assertions are unchanged.

The full WorkflowOperator suite passes: 2756 tests, 0 failures, with 1 cancelled and 2 pending, none of them in the Hugging Face suites. The generated Python of all 117 operators compiles, and scalafmt and scalafix are clean.

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

Yes, this PR was co-authored with Claude in compliance with ASF policy.

[PG1204]

/request-review @Ma77Ball

[github-actions]

Backport auto-label report

This fix: PR was checked against each actively-supported release branch. A release/* label nominates a backport target; the branch's release manager approving this PR is what sends the fix there. The required Backport Approvals check stays red until every label below is approved, so each manager either approves or removes their own label — which is why the labels left on a merged PR are exactly the branches it reached.

Release branch	Analysis
✅ release/v1.3	Change detected on this branch — label added; this fix is queued to backport here. @mengw15 decides: approving sends the fix here, removing this label declines it. The merge waits on one or the other. Review requested.
✅ release/v1.2	Change detected on this branch — label added; this fix is queued to backport here. @xuang7 decides: approving sends the fix here, removing this label declines it. The merge waits on one or the other. Review requested.

Auto-label run.

[github-actions]

Automated Reviewer Suggestions

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

• Contributors with relevant context: @kz930
  You can notify them by mentioning @kz930 in a comment.

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 92.06%. Comparing base (f673554) to head (9a959b4).

Additional details and impacted files

@@            Coverage Diff            @@
##               main    #9209   +/-   ##
=========================================
  Coverage     92.05%   92.06%           
+ Complexity     5041     5029   -12     
=========================================
  Files          1286     1287    +1     
  Lines         55911    55928   +17     
  Branches       7137     7135    -2     
=========================================
+ Hits          51469    51488   +19     
- Misses         2692     2693    +1     
+ Partials       1750     1747    -3     

Flag	Coverage Δ		*Carryforward flag
access-control-service	77.44% <ø> (ø)
agent-service	99.16% <ø> (ø)		Carriedforward from f673554
amber	86.59% <100.00%> (+0.02%)	⬆️
computing-unit-managing-service	64.02% <ø> (ø)
config-service	87.61% <ø> (ø)
file-service	82.29% <ø> (ø)
frontend	96.48% <ø> (ø)		Carriedforward from f673554
notebook-migration-service	83.77% <ø> (ø)
pyamber	98.54% <ø> (ø)		Carriedforward from f673554
workflow-compiling-service	74.25% <ø> (ø)

*This pull request uses carry forward flags. Click here to find out more.

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[github-actions]

⚠️ Benchmark changes need a look

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

Compared against main f673554 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	670	0.409	14,422/20,065/20,065 us	🔴 -15.4% / 🔴 +43.0%
🔴	bs=100 sw=10 sl=64	1,646	1.004	59,313/67,701/67,701 us	🟢 -14.0% / 🟢 -39.2%
⚪	bs=1000 sw=10 sl=64	1,966	1.2	498,797/593,621/593,621 us	⚪ within ±5% / 🟢 +58.7%

Baseline details

Latest main f673554 from same runner

config	metric	PR	latest main	7d avg	Δ latest	Δ 7d
bs=10 sw=10 sl=64	throughput	670 tuples/sec	792 tuples/sec	905.73 tuples/sec	-15.4%	-26.0%
bs=10 sw=10 sl=64	MB/s	0.409 MB/s	0.483 MB/s	0.553 MB/s	-15.3%	-26.0%
bs=10 sw=10 sl=64	p50	14,422 us	12,600 us	11,331 us	+14.5%	+27.3%
bs=10 sw=10 sl=64	p95	20,065 us	18,652 us	14,032 us	+7.6%	+43.0%
bs=10 sw=10 sl=64	p99	20,065 us	18,652 us	17,338 us	+7.6%	+15.7%
bs=100 sw=10 sl=64	throughput	1,646 tuples/sec	1,754 tuples/sec	1,205 tuples/sec	-6.2%	+36.6%
bs=100 sw=10 sl=64	MB/s	1.004 MB/s	1.07 MB/s	0.735 MB/s	-6.2%	+36.5%
bs=100 sw=10 sl=64	p50	59,313 us	55,455 us	89,237 us	+7.0%	-33.5%
bs=100 sw=10 sl=64	p95	67,701 us	78,685 us	96,280 us	-14.0%	-29.7%
bs=100 sw=10 sl=64	p99	67,701 us	78,685 us	111,344 us	-14.0%	-39.2%
bs=1000 sw=10 sl=64	throughput	1,966 tuples/sec	2,003 tuples/sec	1,239 tuples/sec	-1.8%	+58.7%
bs=1000 sw=10 sl=64	MB/s	1.2 MB/s	1.223 MB/s	0.756 MB/s	-1.9%	+58.7%
bs=1000 sw=10 sl=64	p50	498,797 us	493,785 us	877,826 us	+1.0%	-43.2%
bs=1000 sw=10 sl=64	p95	593,621 us	586,484 us	919,412 us	+1.2%	-35.4%
bs=1000 sw=10 sl=64	p99	593,621 us	586,484 us	963,000 us	+1.2%	-38.4%

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
0,10,10,64,20,298.44,200,128000,670,0.409,14422.42,20064.93,20064.93
1,100,10,64,20,1215.28,2000,1280000,1646,1.004,59312.91,67701.35,67701.35
2,1000,10,64,20,10172.52,20000,12800000,1966,1.200,498797.08,593621.49,593621.49