feat(visualization): drop rows with missing values before laying out a Dumbbell Plot

[kz930]

What changes were proposed in this PR?

Dumbbell Plot took the distinct values of the compared column and sorted them. An empty cell reaches the operator as None, so unique() returned a list with a None in it and sorted compared None against a string, ending the run with a TypeError.

An empty value is ordinary input here. A blank CSV cell arrives as null, since univocity returns null for an empty field and AttributeTypeUtils.parseField passes it through by design.

It now drops rows missing any of the three columns it reads, which is what the other visualization operators do: twenty-four of them open their generated Python with dropna(subset=[...]) #remove missing values, and Dumbbell Plot was the one that did not. A table left empty by the drop renders the operator's own error rather than an exception, matching how it already reports an empty input.

Any related issues, documentation, discussions?

Closes #7562

How was this PR tested?

DumbbellPlotOpDescSpec gains a case asserting the generated Python drops on all three configured columns before it sorts. It fails on the previous behavior, 7 passed / 1 failed before the change and 8 / 0 after.

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

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:

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

[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	Change detected on this branch — label added; this fix is queued to backport here. Requested review from @xuang7.

Auto-label run.

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 90.13%. Comparing base (324278e) to head (150e471).
✅ All tests successful. No failed tests found.

Additional details and impacted files

@@             Coverage Diff              @@
##               main    #7567      +/-   ##
============================================
- Coverage     90.14%   90.13%   -0.02%     
+ Complexity     4417     4413       -4     
============================================
  Files          1174     1174              
  Lines         46964    46965       +1     
  Branches       5262     5262              
============================================
- Hits          42334    42330       -4     
- Misses         2876     2877       +1     
- Partials       1754     1758       +4     

Flag	Coverage Δ		*Carryforward flag
access-control-service	70.00% <ø> (ø)
agent-service	98.62% <ø> (ø)		Carriedforward from d420e20
amber	86.71% <100.00%> (-0.03%)	⬇️
computing-unit-managing-service	72.46% <ø> (ø)
config-service	77.31% <ø> (ø)
file-service	68.90% <ø> (ø)
frontend	91.44% <ø> (ø)		Carriedforward from d420e20
notebook-migration-service	78.89% <ø> (ø)
pyamber	97.57% <ø> (ø)		Carriedforward from d420e20
workflow-compiling-service	57.89% <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

🟢 4 better · 🔴 5 worse · ⚪ 6 noise (<±5%) · 0 without baseline

Compared against main 324278e 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	365	0.222	26,829/34,546/34,546 us	🔴 -9.8% / 🔴 +111.3%
🔴	bs=100 sw=10 sl=64	786	0.48	124,246/156,514/156,514 us	🟢 -21.7% / 🔴 +45.5%
🟢	bs=1000 sw=10 sl=64	923	0.563	1,080,012/1,155,567/1,155,567 us	🟢 -5.9% / 🔴 +11.5%

Baseline details

Latest main 324278e from same runner

config	metric	PR	latest main	7d avg	Δ latest	Δ 7d
bs=10 sw=10 sl=64	throughput	365 tuples/sec	402 tuples/sec	774.89 tuples/sec	-9.2%	-52.9%
bs=10 sw=10 sl=64	MB/s	0.222 MB/s	0.246 MB/s	0.473 MB/s	-9.8%	-53.1%
bs=10 sw=10 sl=64	p50	26,829 us	25,554 us	12,738 us	+5.0%	+110.6%
bs=10 sw=10 sl=64	p95	34,546 us	32,476 us	16,348 us	+6.4%	+111.3%
bs=10 sw=10 sl=64	p99	34,546 us	32,476 us	18,848 us	+6.4%	+83.3%
bs=100 sw=10 sl=64	throughput	786 tuples/sec	795 tuples/sec	1,005 tuples/sec	-1.1%	-21.8%
bs=100 sw=10 sl=64	MB/s	0.48 MB/s	0.485 MB/s	0.613 MB/s	-1.0%	-21.7%
bs=100 sw=10 sl=64	p50	124,246 us	116,484 us	100,970 us	+6.7%	+23.1%
bs=100 sw=10 sl=64	p95	156,514 us	199,891 us	107,605 us	-21.7%	+45.5%
bs=100 sw=10 sl=64	p99	156,514 us	199,891 us	116,429 us	-21.7%	+34.4%
bs=1000 sw=10 sl=64	throughput	923 tuples/sec	932 tuples/sec	1,030 tuples/sec	-1.0%	-10.4%
bs=1000 sw=10 sl=64	MB/s	0.563 MB/s	0.569 MB/s	0.629 MB/s	-1.1%	-10.5%
bs=1000 sw=10 sl=64	p50	1,080,012 us	1,069,957 us	991,433 us	+0.9%	+8.9%
bs=1000 sw=10 sl=64	p95	1,155,567 us	1,228,108 us	1,036,668 us	-5.9%	+11.5%
bs=1000 sw=10 sl=64	p99	1,155,567 us	1,228,108 us	1,070,470 us	-5.9%	+7.9%

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
0,10,10,64,20,548.65,200,128000,365,0.222,26829.22,34546.15,34546.15
1,100,10,64,20,2544.64,2000,1280000,786,0.480,124246.23,156514.33,156514.33
2,1000,10,64,20,21667.06,20000,12800000,923,0.563,1080011.53,1155566.63,1155566.63

[kz930]

@aglinxinyuan May you take a look at this?