feat(storage): add the user_warehouse table schema

[mengw15]

What changes were proposed in this PR?

Adds the user_warehouse table (umbrella #6870): one row per warehouse a user registers. The DDL below is the full shape — base columns only; the assume-role (BYO-S3) columns come in a later change.

CREATE TABLE IF NOT EXISTS user_warehouse
(
    whid                    SERIAL PRIMARY KEY,
    uid                     INT          NOT NULL,
    name                    VARCHAR(128) NOT NULL,
    warehouse_name          VARCHAR(255) NOT NULL UNIQUE,
    lakekeeper_warehouse_id UUID,
    flavor                  VARCHAR(32)  NOT NULL,
    s3_bucket               VARCHAR(255),
    s3_endpoint             VARCHAR(255),
    s3_region               VARCHAR(64),
    created_at              TIMESTAMPTZ  NOT NULL DEFAULT now(),
    UNIQUE (uid, name),
    FOREIGN KEY (uid) REFERENCES "user" (uid) ON DELETE CASCADE
);

Schema only — nothing reads or writes the table yet. The DDL, the incremental migration (sql/updates/32.sql), and the changelog registration ship together; jOOQ classes are generated from the live database at build time as usual.

Any related issues, documentation, discussions?

Closes #6931. Part of #6870 (design discussions #5293 and #6040).

How was this PR tested?

A new UserWarehouseSpec (MockTexeraDB, embedded Postgres) pins the DDL's structural properties against the generated jOOQ classes: insert/read-back of a registered warehouse, the per-user name uniqueness, and the ownership cascade. Verified locally with sbt "DAO/testOnly *UserWarehouseSpec" plus scalafmt/scalafix; the cascade case was deliberately broken once to confirm it fails red.

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

Generated-by: Claude Code (claude-fable-5)

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Automated Reviewer Suggestions

Based on the git blame history of the changed files, we recommend the following reviewers:

• Contributors with relevant context: @xuang7
  You can notify them by mentioning @xuang7 in a comment.

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 84.63%. Comparing base (d72b8be) to head (b8bbaef).
⚠️ Report is 1 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff            @@
##               main    #7386   +/-   ##
=========================================
  Coverage     84.62%   84.63%           
- Complexity     4150     4152    +2     
=========================================
  Files          1169     1169           
  Lines         46740    46740           
  Branches       5202     5202           
=========================================
+ Hits          39556    39557    +1     
- Misses         5469     5470    +1     
+ Partials       1715     1713    -2     

Flag	Coverage Δ		*Carryforward flag
access-control-service	70.00% <ø> (ø)
agent-service	85.50% <ø> (ø)		Carriedforward from d72b8be
amber	80.85% <ø> (+<0.01%)	⬆️
computing-unit-managing-service	50.72% <ø> (ø)
config-service	65.97% <ø> (ø)
file-service	69.05% <ø> (ø)
frontend	86.30% <ø> (ø)		Carriedforward from d72b8be
notebook-migration-service	78.89% <ø> (ø)
pyamber	97.55% <ø> (ø)		Carriedforward from d72b8be
workflow-compiling-service	26.31% <ø> (ø)

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⚠️ Benchmark changes need a look

🟢 0 better · 🔴 11 worse · ⚪ 4 noise (<±5%) · 0 without baseline

Compared against main ae17c8f 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	351	0.214	26,949/44,358/44,358 us	🔴 +26.2% / 🔴 +174.5%
🔴	bs=100 sw=10 sl=64	766	0.467	127,281/168,788/168,788 us	🔴 +21.9% / 🔴 +52.2%
🔴	bs=1000 sw=10 sl=64	897	0.547	1,107,351/1,200,586/1,200,586 us	🔴 +5.3% / 🔴 +13.3%

Baseline details

Latest main ae17c8f from same runner

config	metric	PR	latest main	7d avg	Δ latest	Δ 7d
bs=10 sw=10 sl=64	throughput	351 tuples/sec	406 tuples/sec	755.31 tuples/sec	-13.5%	-53.5%
bs=10 sw=10 sl=64	MB/s	0.214 MB/s	0.248 MB/s	0.461 MB/s	-13.7%	-53.6%
bs=10 sw=10 sl=64	p50	26,949 us	24,981 us	12,952 us	+7.9%	+108.1%
bs=10 sw=10 sl=64	p95	44,358 us	35,149 us	16,161 us	+26.2%	+174.5%
bs=10 sw=10 sl=64	p99	44,358 us	35,149 us	19,292 us	+26.2%	+129.9%
bs=100 sw=10 sl=64	throughput	766 tuples/sec	823 tuples/sec	957.58 tuples/sec	-6.9%	-20.0%
bs=100 sw=10 sl=64	MB/s	0.467 MB/s	0.503 MB/s	0.584 MB/s	-7.2%	-20.1%
bs=100 sw=10 sl=64	p50	127,281 us	121,811 us	104,473 us	+4.5%	+21.8%
bs=100 sw=10 sl=64	p95	168,788 us	138,443 us	110,867 us	+21.9%	+52.2%
bs=100 sw=10 sl=64	p99	168,788 us	138,443 us	120,336 us	+21.9%	+40.3%
bs=1000 sw=10 sl=64	throughput	897 tuples/sec	910 tuples/sec	986.93 tuples/sec	-1.4%	-9.1%
bs=1000 sw=10 sl=64	MB/s	0.547 MB/s	0.556 MB/s	0.602 MB/s	-1.6%	-9.2%
bs=1000 sw=10 sl=64	p50	1,107,351 us	1,101,394 us	1,021,008 us	+0.5%	+8.5%
bs=1000 sw=10 sl=64	p95	1,200,586 us	1,139,842 us	1,059,187 us	+5.3%	+13.3%
bs=1000 sw=10 sl=64	p99	1,200,586 us	1,139,842 us	1,093,309 us	+5.3%	+9.8%

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
0,10,10,64,20,570.01,200,128000,351,0.214,26948.52,44357.63,44357.63
1,100,10,64,20,2612.03,2000,1280000,766,0.467,127281.39,168787.52,168787.52
2,1000,10,64,20,22301.28,20000,12800000,897,0.547,1107350.94,1200586.09,1200586.09