feat(dataset): add contributor metadata storage and API

[xuang7]

What changes were proposed in this PR?

This PR adds contributor metadata to datasets so that dataset authors and other contributors can be properly acknowledged. Each dataset can record a list of contributors, where each contributor has a name, a creator flag, an email address, an affiliation, and a free-text comments field.

Changes:

• Adds a new dataset_contributor table with a foreign key to dataset and cascade deletion in texera_ddl.sql, along with the incremental migration script sql/updates/29.sql and changelog entry 29.
• Updates file-service so that contributors are:
  • returned with the dataset detail response,
  • accepted during dataset creation, and
  • replaceable through a new POST /dataset/{did}/update/contributors endpoint protected by the existing WRITE-access check.
• Validates contributor names as required fields with a maximum length of 256 characters.

Currently, contributors are stored independently for each dataset. Cross-dataset contributor identity is left as a future extension. The UI will be added in a follow-up PR.

Any related issues, documentation, discussions?

Related to #6926

How was this PR tested?

7 ScalaTest cases were added.

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

Generated-by: Claude Code (Claude Fable 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: @kunwp1, @Neilk1021
  You can notify them by mentioning @kunwp1, @Neilk1021 in a comment.

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 78.93%. Comparing base (8c7089b) to head (e3c36cb).
⚠️ Report is 2 commits behind head on main.

Additional details and impacted files

@@             Coverage Diff              @@
##               main    #6952      +/-   ##
============================================
- Coverage     78.93%   78.93%   -0.01%     
  Complexity     3784     3784              
============================================
  Files          1160     1160              
  Lines         46049    46047       -2     
  Branches       5110     5110              
============================================
- Hits          36350    36348       -2     
  Misses         8078     8078              
  Partials       1621     1621              

Flag	Coverage Δ		*Carryforward flag
access-control-service	70.00% <ø> (ø)
agent-service	76.76% <ø> (ø)		Carriedforward from 8c7089b
amber	72.17% <ø> (ø)		Carriedforward from 8c7089b
computing-unit-managing-service	20.49% <ø> (ø)
config-service	66.66% <ø> (ø)
file-service	67.07% <ø> (-0.14%)	⬇️	Carriedforward from 8c7089b
frontend	82.91% <ø> (ø)		Carriedforward from 8c7089b
notebook-migration-service	78.94% <ø> (ø)
pyamber	95.38% <ø> (ø)		Carriedforward from 8c7089b
workflow-compiling-service	26.31% <ø> (ø)

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

☔ View full report in Codecov by Harness.
📢 Have feedback on the report? Share it here.

🚀 New features to boost your workflow:

• 📦 JS Bundle Analysis: Save yourself from yourself by tracking and limiting bundle sizes in JS merges.

[github-actions]

⚠️ Benchmark changes need a look

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

Compared against main 31f1e7b 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	383	0.234	25,567/33,728/33,728 us	🔴 +21.5% / 🔴 +107.8%
🟢	bs=100 sw=10 sl=64	833	0.508	118,235/133,709/133,709 us	🟢 -11.5% / 🔴 +23.1%
⚪	bs=1000 sw=10 sl=64	953	0.582	1,050,028/1,088,475/1,088,475 us	⚪ within ±5% / 🔴 -5.1%

Baseline details

Latest main 31f1e7b from same runner

config	metric	PR	latest main	7d avg	Δ latest	Δ 7d
bs=10 sw=10 sl=64	throughput	383 tuples/sec	457 tuples/sec	767.9 tuples/sec	-16.2%	-50.1%
bs=10 sw=10 sl=64	MB/s	0.234 MB/s	0.279 MB/s	0.469 MB/s	-16.1%	-50.1%
bs=10 sw=10 sl=64	p50	25,567 us	21,330 us	12,502 us	+19.9%	+104.5%
bs=10 sw=10 sl=64	p95	33,728 us	27,764 us	16,234 us	+21.5%	+107.8%
bs=10 sw=10 sl=64	p99	33,728 us	27,764 us	18,919 us	+21.5%	+78.3%
bs=100 sw=10 sl=64	throughput	833 tuples/sec	836 tuples/sec	974.8 tuples/sec	-0.4%	-14.5%
bs=100 sw=10 sl=64	MB/s	0.508 MB/s	0.51 MB/s	0.595 MB/s	-0.4%	-14.6%
bs=100 sw=10 sl=64	p50	118,235 us	116,762 us	102,449 us	+1.3%	+15.4%
bs=100 sw=10 sl=64	p95	133,709 us	151,101 us	108,652 us	-11.5%	+23.1%
bs=100 sw=10 sl=64	p99	133,709 us	151,101 us	116,310 us	-11.5%	+15.0%
bs=1000 sw=10 sl=64	throughput	953 tuples/sec	956 tuples/sec	1,004 tuples/sec	-0.3%	-5.1%
bs=1000 sw=10 sl=64	MB/s	0.582 MB/s	0.583 MB/s	0.613 MB/s	-0.2%	-5.0%
bs=1000 sw=10 sl=64	p50	1,050,028 us	1,044,502 us	999,606 us	+0.5%	+5.0%
bs=1000 sw=10 sl=64	p95	1,088,475 us	1,095,818 us	1,046,770 us	-0.7%	+4.0%
bs=1000 sw=10 sl=64	p99	1,088,475 us	1,095,818 us	1,076,937 us	-0.7%	+1.1%

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
0,10,10,64,20,522.35,200,128000,383,0.234,25566.56,33727.71,33727.71
1,100,10,64,20,2402.14,2000,1280000,833,0.508,118234.85,133709.45,133709.45
2,1000,10,64,20,20979.01,20000,12800000,953,0.582,1050028.12,1088474.63,1088474.63