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.

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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 87.87%. Comparing base (e878df3) to head (83187c0).

Additional details and impacted files

@@             Coverage Diff              @@
##               main    #7567      +/-   ##
============================================
- Coverage     87.88%   87.87%   -0.02%     
+ Complexity     4276     4271       -5     
============================================
  Files          1176     1176              
  Lines         47018    47019       +1     
  Branches       5245     5245              
============================================
- Hits          41323    41317       -6     
- Misses         3972     3974       +2     
- Partials       1723     1728       +5     

Flag	Coverage Δ		*Carryforward flag
access-control-service	70.00% <ø> (ø)
agent-service	98.62% <ø> (ø)		Carriedforward from e878df3
amber	82.84% <100.00%> (-0.04%)	⬇️
computing-unit-managing-service	60.38% <ø> (ø)
config-service	65.97% <ø> (ø)
file-service	69.05% <ø> (ø)
frontend	89.94% <ø> (ø)		Carriedforward from e878df3
notebook-migration-service	78.89% <ø> (ø)
pyamber	97.52% <ø> (ø)		Carriedforward from e878df3
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

🟢 0 better · 🔴 5 worse · ⚪ 10 noise (<±5%) · 0 without baseline

Compared against main e878df3 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	355	0.217	26,901/39,622/39,622 us	🔴 +21.8% / 🔴 +147.8%
⚪	bs=100 sw=10 sl=64	788	0.481	124,178/149,766/149,766 us	⚪ within ±5% / 🔴 +38.6%
⚪	bs=1000 sw=10 sl=64	920	0.562	1,080,766/1,141,948/1,141,948 us	⚪ within ±5% / 🔴 -10.7%

Baseline details

Latest main e878df3 from same runner

config	metric	PR	latest main	7d avg	Δ latest	Δ 7d
bs=10 sw=10 sl=64	throughput	355 tuples/sec	426 tuples/sec	784.3 tuples/sec	-16.7%	-54.7%
bs=10 sw=10 sl=64	MB/s	0.217 MB/s	0.26 MB/s	0.479 MB/s	-16.5%	-54.7%
bs=10 sw=10 sl=64	p50	26,901 us	22,078 us	12,590 us	+21.8%	+113.7%
bs=10 sw=10 sl=64	p95	39,622 us	33,509 us	15,991 us	+18.2%	+147.8%
bs=10 sw=10 sl=64	p99	39,622 us	33,509 us	18,694 us	+18.2%	+112.0%
bs=100 sw=10 sl=64	throughput	788 tuples/sec	812 tuples/sec	1,002 tuples/sec	-3.0%	-21.4%
bs=100 sw=10 sl=64	MB/s	0.481 MB/s	0.495 MB/s	0.612 MB/s	-2.8%	-21.3%
bs=100 sw=10 sl=64	p50	124,178 us	119,080 us	101,285 us	+4.3%	+22.6%
bs=100 sw=10 sl=64	p95	149,766 us	153,632 us	108,068 us	-2.5%	+38.6%
bs=100 sw=10 sl=64	p99	149,766 us	153,632 us	118,235 us	-2.5%	+26.7%
bs=1000 sw=10 sl=64	throughput	920 tuples/sec	908 tuples/sec	1,030 tuples/sec	+1.3%	-10.7%
bs=1000 sw=10 sl=64	MB/s	0.562 MB/s	0.554 MB/s	0.629 MB/s	+1.4%	-10.6%
bs=1000 sw=10 sl=64	p50	1,080,766 us	1,093,407 us	991,882 us	-1.2%	+9.0%
bs=1000 sw=10 sl=64	p95	1,141,948 us	1,190,176 us	1,038,496 us	-4.1%	+10.0%
bs=1000 sw=10 sl=64	p99	1,141,948 us	1,190,176 us	1,068,265 us	-4.1%	+6.9%

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
0,10,10,64,20,562.59,200,128000,355,0.217,26901.03,39622.26,39622.26
1,100,10,64,20,2539.23,2000,1280000,788,0.481,124177.66,149766.13,149766.13
2,1000,10,64,20,21736.86,20000,12800000,920,0.562,1080766.46,1141947.78,1141947.78

[kz930]

@aglinxinyuan May you take a look at this?