feat(pyamber): match Java Unicode hashes

[carloea2]

What changes were proposed in this PR?

Python tuple hashing now processes strings as UTF-16 code units, matching Java String hashing for characters outside the basic multilingual plane. Existing string hashes remain unchanged.

Any related issues, documentation, discussions?

Closes #8247

How was this PR tested?

python -c "import sys,pytest; sys.path[:0]=[r'C:\Users\carlo\texera\texera-worktrees\investigate-bug60\amber\src\main\python',r'C:\Users\carlo\texera\texera\amber\src\main\python']; raise SystemExit(pytest.main(['amber/src/test/python/core/models/test_tuple.py::TestTuple::test_hash_non_bmp_string_matches_java','amber/src/test/python/core/architecture/sendsemantics/test_partitioners.py','-q','-p','no:cacheprovider']))"
36 passed

ruff check amber/src/main/python amber/src/test/python
All checks passed

ruff format --check amber/src/main/python amber/src/test/python
213 files already formatted

Direct reproduction after the fix:

1772930

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

Generated-by: Codex

[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/tuple.py, amber/src/test/python/core/models/test_tuple.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	Already labeled — this fix is queued to backport here.

Auto-label run.

[github-actions]

Automated Reviewer Suggestions

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

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

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 93.90%. Comparing base (50321e4) to head (ce76c6e).
⚠️ Report is 11 commits behind head on main.

Additional details and impacted files

@@             Coverage Diff              @@
##               main    #8248      +/-   ##
============================================
- Coverage     93.93%   93.90%   -0.04%     
+ Complexity     4762     4761       -1     
============================================
  Files          1191     1191              
  Lines         48308    49055     +747     
  Branches       5377     5377              
============================================
+ Hits          45380    46063     +683     
- Misses         1494     1557      +63     
- Partials       1434     1435       +1     

Flag	Coverage Δ		*Carryforward flag
access-control-service	81.00% <ø> (ø)		Carriedforward from 50321e4
agent-service	99.32% <ø> (ø)		Carriedforward from 50321e4
amber	90.02% <ø> (-0.02%)	⬇️	Carriedforward from 50321e4
computing-unit-managing-service	73.67% <ø> (ø)		Carriedforward from 50321e4
config-service	86.86% <ø> (ø)		Carriedforward from 50321e4
file-service	87.91% <ø> (ø)		Carriedforward from 50321e4
frontend	96.41% <ø> (ø)		Carriedforward from 50321e4
notebook-migration-service	79.31% <ø> (ø)		Carriedforward from 50321e4
pyamber	97.87% <100.00%> (-1.00%)	⬇️
workflow-compiling-service	77.19% <ø> (ø)		Carriedforward from 50321e4

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

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[github-actions]

✅ No material benchmark regressions detected

🟢 4 better · 🔴 0 worse · ⚪ 11 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	418	0.255	22,771/28,956/28,956 us	🟢 -8.1% / 🔴 +85.9%
🟢	bs=100 sw=10 sl=64	806	0.492	121,668/155,316/155,316 us	🟢 -19.7% / 🔴 +44.8%
⚪	bs=1000 sw=10 sl=64	893	0.545	1,117,298/1,212,202/1,212,202 us	⚪ within ±5% / 🔴 +18.5%

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	418 tuples/sec	405 tuples/sec	787.98 tuples/sec	+3.2%	-47.0%
bs=10 sw=10 sl=64	MB/s	0.255 MB/s	0.247 MB/s	0.481 MB/s	+3.2%	-47.0%
bs=10 sw=10 sl=64	p50	22,771 us	22,004 us	12,593 us	+3.5%	+80.8%
bs=10 sw=10 sl=64	p95	28,956 us	31,504 us	15,579 us	-8.1%	+85.9%
bs=10 sw=10 sl=64	p99	28,956 us	31,504 us	18,786 us	-8.1%	+54.1%
bs=100 sw=10 sl=64	throughput	806 tuples/sec	798 tuples/sec	1,008 tuples/sec	+1.0%	-20.0%
bs=100 sw=10 sl=64	MB/s	0.492 MB/s	0.487 MB/s	0.615 MB/s	+1.0%	-20.0%
bs=100 sw=10 sl=64	p50	121,668 us	119,843 us	100,701 us	+1.5%	+20.8%
bs=100 sw=10 sl=64	p95	155,316 us	193,521 us	107,244 us	-19.7%	+44.8%
bs=100 sw=10 sl=64	p99	155,316 us	193,521 us	116,122 us	-19.7%	+33.8%
bs=1000 sw=10 sl=64	throughput	893 tuples/sec	916 tuples/sec	1,042 tuples/sec	-2.5%	-14.3%
bs=1000 sw=10 sl=64	MB/s	0.545 MB/s	0.559 MB/s	0.636 MB/s	-2.5%	-14.3%
bs=1000 sw=10 sl=64	p50	1,117,298 us	1,097,163 us	981,959 us	+1.8%	+13.8%
bs=1000 sw=10 sl=64	p95	1,212,202 us	1,159,457 us	1,023,080 us	+4.5%	+18.5%
bs=1000 sw=10 sl=64	p99	1,212,202 us	1,159,457 us	1,051,697 us	+4.5%	+15.3%

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
0,10,10,64,20,478.79,200,128000,418,0.255,22770.84,28956.43,28956.43
1,100,10,64,20,2481.17,2000,1280000,806,0.492,121667.89,155315.76,155315.76
2,1000,10,64,20,22389.44,20000,12800000,893,0.545,1117297.88,1212202.03,1212202.03

[Yicong-Huang]

I am converting this PR to a draft: please follow our PR template and mark it ready for review.

[carloea2]

The description now follows the current template, and this is ready for review.