feat(storage): add model metadata tables

[tanishqgandhi1908]

What changes were proposed in this PR?

Adds the database tables that track ML models as a first-class resource in Texera. The model* tables mirror the existing dataset* tables — their own primary key (mid), their own LakeFS repo namespace — and
add model-specific attributes (framework, format):

• model — the model asset (owner, name, visibility, description, cover image, framework, format)
• model_user_access — per-user privilege (reuses privilege_enum)
• model_version — versions of a model (mvid, version_hash)
• model_upload_session / model_upload_session_part — multipart upload bookkeeping
• model_user_likes / model_view_count — like/view counters

The change is pure schema, with no application code and no behavior change:

• Fresh installs: tables added to sql/texera_ddl.sql (with matching DROP TABLE entries and FK-safe ordering).
• Existing deployments: a new idempotent migration sql/updates/30.sql (all CREATE TABLE IF NOT EXISTS, no data backfill), registered as changeSet id="30" in sql/changelog.xml.
• The JOOQ data-access layer is git-ignored and regenerated from the live DB on build, so no generated code is committed.

Migration ordering note: this uses id=30 to sit after #6495's migration 29 (PR #6502, currently in review). These model tables do not depend on #6495, so this PR can be reviewed independently.

Any related issues, documentation, discussions?

Closes #6496.

How was this PR tested?

Validated against a throwaway groonga/pgroonga:4.0.1-debian-15 Postgres:

• sql/texera_ddl.sql runs end-to-end with no errors; all 7 model* tables are created with the expected primary keys, foreign keys (ON DELETE CASCADE), unique constraints, and CHECK constraints.
• sql/updates/30.sql applies cleanly on a DB that already has the dataset tables but no model tables, and is idempotent

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

Generated-by: Claude Code (Claude Opus 4.8)

[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
  You can notify them by mentioning @kunwp1 in a comment.

[github-actions]

⚠️ Benchmark changes need a look

🟢 0 better · 🔴 2 worse · ⚪ 13 noise (<±5%) · 0 without baseline

Compared against main a9f384e 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	419	0.256	23,401/31,031/31,031 us	⚪ within ±5% / 🔴 +92.5%
🔴	bs=100 sw=10 sl=64	927	0.566	105,383/155,687/155,687 us	🔴 +16.5% / 🔴 +44.9%
⚪	bs=1000 sw=10 sl=64	1,127	0.688	885,944/918,819/918,819 us	⚪ within ±5% / 🟢 -13.0%

Baseline details

Latest main a9f384e from same runner

config	metric	PR	latest main	7d avg	Δ latest	Δ 7d
bs=10 sw=10 sl=64	throughput	419 tuples/sec	439 tuples/sec	760.2 tuples/sec	-4.6%	-44.9%
bs=10 sw=10 sl=64	MB/s	0.256 MB/s	0.268 MB/s	0.464 MB/s	-4.5%	-44.8%
bs=10 sw=10 sl=64	p50	23,401 us	22,596 us	12,781 us	+3.6%	+83.1%
bs=10 sw=10 sl=64	p95	31,031 us	31,851 us	16,118 us	-2.6%	+92.5%
bs=10 sw=10 sl=64	p99	31,031 us	31,851 us	19,074 us	-2.6%	+62.7%
bs=100 sw=10 sl=64	throughput	927 tuples/sec	960 tuples/sec	988.68 tuples/sec	-3.4%	-6.2%
bs=100 sw=10 sl=64	MB/s	0.566 MB/s	0.586 MB/s	0.603 MB/s	-3.4%	-6.2%
bs=100 sw=10 sl=64	p50	105,383 us	100,768 us	101,448 us	+4.6%	+3.9%
bs=100 sw=10 sl=64	p95	155,687 us	133,606 us	107,410 us	+16.5%	+44.9%
bs=100 sw=10 sl=64	p99	155,687 us	133,606 us	120,258 us	+16.5%	+29.5%
bs=1000 sw=10 sl=64	throughput	1,127 tuples/sec	1,122 tuples/sec	1,023 tuples/sec	+0.4%	+10.2%
bs=1000 sw=10 sl=64	MB/s	0.688 MB/s	0.685 MB/s	0.624 MB/s	+0.4%	+10.2%
bs=1000 sw=10 sl=64	p50	885,944 us	888,810 us	983,888 us	-0.3%	-10.0%
bs=1000 sw=10 sl=64	p95	918,819 us	930,691 us	1,030,100 us	-1.3%	-10.8%
bs=1000 sw=10 sl=64	p99	918,819 us	930,691 us	1,056,513 us	-1.3%	-13.0%

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
0,10,10,64,20,476.80,200,128000,419,0.256,23401.03,31030.57,31030.57
1,100,10,64,20,2157.09,2000,1280000,927,0.566,105383.05,155687.17,155687.17
2,1000,10,64,20,17739.14,20000,12800000,1127,0.688,885944.17,918818.90,918818.90

[tanishqgandhi1908]

/request-review @aicam

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 75.20%. Comparing base (a9f384e) to head (de806ba).
⚠️ Report is 7 commits behind head on main.

Additional details and impacted files

@@             Coverage Diff              @@
##               main    #6799      +/-   ##
============================================
- Coverage     75.20%   75.20%   -0.01%     
+ Complexity     3447     3446       -1     
============================================
  Files          1160     1160              
  Lines         45807    45807              
  Branches       5071     5071              
============================================
- Hits          34451    34450       -1     
  Misses         9745     9745              
- Partials       1611     1612       +1     

Flag	Coverage Δ		*Carryforward flag
access-control-service	70.00% <ø> (ø)
agent-service	76.76% <ø> (ø)		Carriedforward from a9f384e
amber	67.17% <ø> (-0.01%)	⬇️
computing-unit-managing-service	20.49% <ø> (ø)
config-service	66.66% <ø> (ø)
file-service	67.21% <ø> (ø)
frontend	79.42% <ø> (ø)		Carriedforward from a9f384e
notebook-migration-service	78.94% <ø> (ø)
pyamber	92.15% <ø> (ø)		Carriedforward from a9f384e
workflow-compiling-service	55.14% <ø> (ø)

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

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