feat(pyamber): validate large binary URI

[carloea2]

What changes were proposed in this PR?

Validate that a large-binary S3 reference contains both a bucket and an object key during construction, before any stream reaches S3.

Any related issues, documentation, discussions?

Closes #8279

How was this PR tested?

Added negative coverage for missing buckets and keys. Existing valid URI, generated URI, equality, hashing, and stream tests remain green.

C:\Users\carlo\texera\texera\.venv312\Scripts\python.exe -c "import sys,pytest; sys.path[:0]=[r'C:\Users\carlo\texera\texera-worktrees\investigate-bug75\amber\src\main\python',r'C:\Users\carlo\texera\texera\amber\src\main\python']; raise SystemExit(pytest.main([r'amber\src\test\python\core\models\type\test_large_binary.py',r'amber\src\test\python\pytexera\storage\test_large_binary_input_stream.py','-p','no:cacheprovider','-q']))"

C:\Users\carlo\texera\texera\.venv312\Scripts\ruff.exe check amber/src/main/python/core/models/type/large_binary.py amber/src/test/python/core/models/type/test_large_binary.py

C:\Users\carlo\texera\texera\.venv312\Scripts\ruff.exe format --check amber/src/main/python/core/models/type/large_binary.py amber/src/test/python/core/models/type/test_large_binary.py

All 23 tests passed. Ruff checks passed.

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

Generated-by: Codex

[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]

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/v1.3	Not labeled automatically — none of the files this PR modifies exist on this branch (amber/src/main/python/core/models/type/large_binary.py, amber/src/test/python/core/models/type/test_large_binary.py). The fix may target code that isn't on this release, or the files were moved/renamed after the branch was cut. Please check and add release/v1.3 by hand if this fix should be backported here.
🚫 release/v1.2	Label was removed earlier (opt-out); not re-added. Re-add it by hand if this fix should be backported here after all.

Auto-label run.

[github-actions]

⚠️ Benchmark changes need a look

🟢 0 better · 🔴 7 worse · ⚪ 8 noise (<±5%) · 0 without baseline

Compared against main 50321e4 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	382	0.233	25,083/35,429/35,429 us	🔴 +15.3% / 🔴 +127.4%
🔴	bs=100 sw=10 sl=64	799	0.488	120,929/166,604/166,604 us	🔴 +6.8% / 🔴 +55.4%
⚪	bs=1000 sw=10 sl=64	921	0.562	1,082,977/1,137,292/1,137,292 us	⚪ within ±5% / 🔴 -11.6%

Baseline details

Latest main 50321e4 from same runner

config	metric	PR	latest main	7d avg	Δ latest	Δ 7d
bs=10 sw=10 sl=64	throughput	382 tuples/sec	429 tuples/sec	787.98 tuples/sec	-11.0%	-51.5%
bs=10 sw=10 sl=64	MB/s	0.233 MB/s	0.262 MB/s	0.481 MB/s	-11.1%	-51.6%
bs=10 sw=10 sl=64	p50	25,083 us	23,686 us	12,593 us	+5.9%	+99.2%
bs=10 sw=10 sl=64	p95	35,429 us	30,720 us	15,579 us	+15.3%	+127.4%
bs=10 sw=10 sl=64	p99	35,429 us	30,720 us	18,786 us	+15.3%	+88.6%
bs=100 sw=10 sl=64	throughput	799 tuples/sec	797 tuples/sec	1,008 tuples/sec	+0.3%	-20.7%
bs=100 sw=10 sl=64	MB/s	0.488 MB/s	0.486 MB/s	0.615 MB/s	+0.4%	-20.7%
bs=100 sw=10 sl=64	p50	120,929 us	121,346 us	100,701 us	-0.3%	+20.1%
bs=100 sw=10 sl=64	p95	166,604 us	155,977 us	107,244 us	+6.8%	+55.4%
bs=100 sw=10 sl=64	p99	166,604 us	155,977 us	116,122 us	+6.8%	+43.5%
bs=1000 sw=10 sl=64	throughput	921 tuples/sec	931 tuples/sec	1,042 tuples/sec	-1.1%	-11.6%
bs=1000 sw=10 sl=64	MB/s	0.562 MB/s	0.568 MB/s	0.636 MB/s	-1.1%	-11.6%
bs=1000 sw=10 sl=64	p50	1,082,977 us	1,067,878 us	981,959 us	+1.4%	+10.3%
bs=1000 sw=10 sl=64	p95	1,137,292 us	1,127,918 us	1,023,080 us	+0.8%	+11.2%
bs=1000 sw=10 sl=64	p99	1,137,292 us	1,127,918 us	1,051,697 us	+0.8%	+8.1%

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
0,10,10,64,20,523.24,200,128000,382,0.233,25082.83,35428.80,35428.80
1,100,10,64,20,2502.04,2000,1280000,799,0.488,120929.18,166603.95,166603.95
2,1000,10,64,20,21707.88,20000,12800000,921,0.562,1082977.01,1137292.30,1137292.30