fix(workflow-operator): validate base64 in HF binary-column fallback …

[PG1204]

What changes were proposed in this PR?

In the HuggingFace operator's _read_binary_value, the final fallback decoded a column value with base64.b64decode(val). Without validate=True, b64decode silently ignores characters outside the base64 alphabet, so ordinary text "successfully" decodes into garbage bytes and the intended val.encode("utf-8") fallback rarely fires and a plain-text cell could be turned into garbage and sent as image/audio bytes.

Adding validate=True makes b64decode reject non-base64 input (spaces, punctuation, newlines), so real text now correctly falls through to UTF-8. Only this fallback call changes; the data:-URL decodes are left as-is (genuine base64 that can legitimately contain whitespace).

Any related issues, documentation, discussions?

Closes #7200.

How was this PR tested?

sbt "WorkflowOperator/testOnly org.apache.texera.amber.operator.huggingFace.* org.apache.texera.amber.util.PythonCodeRawInvalidTextSpec": passes (125 tests). Added a test asserting the fallback uses validate=True; PythonCodeRawInvalidTextSpec py-compiles the generated Python. scalafmt clean.

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

This PP was co-authored with Claude Opus 4.8 in compliance with ASF policy.

[github-actions]

Backport auto-label report

This fix: PR was checked against each actively-supported release branch. release/* labels drive the post-merge backport, so add or remove one to change where this fix lands.

Release branch

Analysis

⚠️ release/v1.2

Not labeled automatically — none of the files this PR modifies exist on this branch (common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/huggingFace/codegen/HuggingFaceCodegenBase.scala, common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/huggingFace/HuggingFaceInferenceOpDescSpec.scala). The fix may target code that isn't on this release, or the files were moved/renamed after the branch was cut. Please check and add release/v1.2 by hand if this fix should be backported here.

Auto-label run.

[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 82.63%. Comparing base (33cc5d1) to head (e8ee1fa).

Additional details and impacted files

@@             Coverage Diff              @@
##               main    #7243      +/-   ##
============================================
- Coverage     82.63%   82.63%   -0.01%     
+ Complexity     4079     4076       -3     
============================================
  Files          1162     1162              
  Lines         46275    46275              
  Branches       5160     5160              
============================================
- Hits          38241    38238       -3     
  Misses         6343     6343              
- Partials       1691     1694       +3     

Flag	Coverage Δ		*Carryforward flag
access-control-service	70.00% <ø> (ø)
agent-service	83.65% <ø> (ø)		Carriedforward from 33cc5d1
amber	79.29% <ø> (-0.02%)	⬇️
computing-unit-managing-service	43.60% <ø> (ø)
config-service	65.97% <ø> (ø)
file-service	66.80% <ø> (ø)
frontend	83.53% <ø> (ø)		Carriedforward from 33cc5d1
notebook-migration-service	78.89% <ø> (ø)
pyamber	97.36% <ø> (ø)		Carriedforward from 33cc5d1
workflow-compiling-service	26.31% <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 · 🔴 6 worse · ⚪ 7 noise (<±5%) · 0 without baseline

Compared against main 33cc5d1 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	648	0.396	15,487/18,990/18,990 us	🟢 -15.8% / 🔴 +24.2%
🔴	bs=100 sw=10 sl=64	1,192	0.728	82,819/107,204/107,204 us	🔴 +21.6% / 🟢 +19.6%
🔴	bs=1000 sw=10 sl=64	1,509	0.921	665,845/730,229/730,229 us	🔴 +6.2% / 🟢 +47.4%

Baseline details

Latest main 33cc5d1 from same runner

config	metric	PR	latest main	7d avg	Δ latest	Δ 7d
bs=10 sw=10 sl=64	throughput	648 tuples/sec	627 tuples/sec	782.98 tuples/sec	+3.3%	-17.2%
bs=10 sw=10 sl=64	MB/s	0.396 MB/s	0.383 MB/s	0.478 MB/s	+3.4%	-17.1%
bs=10 sw=10 sl=64	p50	15,487 us	15,900 us	12,467 us	-2.6%	+24.2%
bs=10 sw=10 sl=64	p95	18,990 us	22,553 us	15,695 us	-15.8%	+21.0%
bs=10 sw=10 sl=64	p99	18,990 us	22,553 us	18,950 us	-15.8%	+0.2%
bs=100 sw=10 sl=64	throughput	1,192 tuples/sec	1,384 tuples/sec	996.92 tuples/sec	-13.9%	+19.6%
bs=100 sw=10 sl=64	MB/s	0.728 MB/s	0.844 MB/s	0.608 MB/s	-13.7%	+19.6%
bs=100 sw=10 sl=64	p50	82,819 us	71,384 us	100,899 us	+16.0%	-17.9%
bs=100 sw=10 sl=64	p95	107,204 us	88,139 us	107,385 us	+21.6%	-0.2%
bs=100 sw=10 sl=64	p99	107,204 us	88,139 us	115,226 us	+21.6%	-7.0%
bs=1000 sw=10 sl=64	throughput	1,509 tuples/sec	1,588 tuples/sec	1,024 tuples/sec	-5.0%	+47.4%
bs=1000 sw=10 sl=64	MB/s	0.921 MB/s	0.969 MB/s	0.625 MB/s	-5.0%	+47.4%
bs=1000 sw=10 sl=64	p50	665,845 us	627,160 us	989,300 us	+6.2%	-32.7%
bs=1000 sw=10 sl=64	p95	730,229 us	697,069 us	1,031,689 us	+4.8%	-29.2%
bs=1000 sw=10 sl=64	p99	730,229 us	697,069 us	1,058,394 us	+4.8%	-31.0%

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
0,10,10,64,20,308.57,200,128000,648,0.396,15487.04,18989.69,18989.69
1,100,10,64,20,1677.48,2000,1280000,1192,0.728,82819.19,107204.45,107204.45
2,1000,10,64,20,13250.38,20000,12800000,1509,0.921,665844.80,730229.45,730229.45