feat(pyamber): canonicalize NaN hashes

[carloea2]

What changes were proposed in this PR?

Python tuple hashing now canonicalizes every NaN bit pattern before applying the Java double hash. Finite values, infinities, and signed zero remain unchanged.

Any related issues, documentation, discussions?

Closes #8251

How was this PR tested?

python -c "import sys,pytest; sys.path[:0]=[r'C:\Users\carlo\texera\texera-worktrees\investigate-bug62\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_noncanonical_nan_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:

2146959391

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 (c441477).
⚠️ Report is 11 commits behind head on main.

Additional details and impacted files

@@             Coverage Diff              @@
##               main    #8252      +/-   ##
============================================
- Coverage     93.93%   93.90%   -0.04%     
+ Complexity     4762     4761       -1     
============================================
  Files          1191     1191              
  Lines         48308    49053     +745     
  Branches       5377     5377              
============================================
+ Hits          45380    46061     +681     
- 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]

⚠️ Benchmark changes need a look

🟢 0 better · 🔴 10 worse · ⚪ 5 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	397	0.242	23,640/33,120/33,120 us	🔴 +12.2% / 🔴 +112.6%
🔴	bs=100 sw=10 sl=64	888	0.542	111,188/140,412/140,412 us	🔴 +11.9% / 🔴 +30.9%
🔴	bs=1000 sw=10 sl=64	1,063	0.649	932,746/1,061,157/1,061,157 us	🔴 +5.2% / 🟢 -5.0%

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	397 tuples/sec	449 tuples/sec	787.98 tuples/sec	-11.6%	-49.6%
bs=10 sw=10 sl=64	MB/s	0.242 MB/s	0.274 MB/s	0.481 MB/s	-11.7%	-49.7%
bs=10 sw=10 sl=64	p50	23,640 us	21,063 us	12,593 us	+12.2%	+87.7%
bs=10 sw=10 sl=64	p95	33,120 us	33,405 us	15,579 us	-0.9%	+112.6%
bs=10 sw=10 sl=64	p99	33,120 us	33,405 us	18,786 us	-0.9%	+76.3%
bs=100 sw=10 sl=64	throughput	888 tuples/sec	956 tuples/sec	1,008 tuples/sec	-7.1%	-11.9%
bs=100 sw=10 sl=64	MB/s	0.542 MB/s	0.583 MB/s	0.615 MB/s	-7.0%	-11.9%
bs=100 sw=10 sl=64	p50	111,188 us	104,099 us	100,701 us	+6.8%	+10.4%
bs=100 sw=10 sl=64	p95	140,412 us	125,455 us	107,244 us	+11.9%	+30.9%
bs=100 sw=10 sl=64	p99	140,412 us	125,455 us	116,122 us	+11.9%	+20.9%
bs=1000 sw=10 sl=64	throughput	1,063 tuples/sec	1,082 tuples/sec	1,042 tuples/sec	-1.8%	+2.0%
bs=1000 sw=10 sl=64	MB/s	0.649 MB/s	0.661 MB/s	0.636 MB/s	-1.8%	+2.0%
bs=1000 sw=10 sl=64	p50	932,746 us	920,869 us	981,959 us	+1.3%	-5.0%
bs=1000 sw=10 sl=64	p95	1,061,157 us	1,008,231 us	1,023,080 us	+5.2%	+3.7%
bs=1000 sw=10 sl=64	p99	1,061,157 us	1,008,231 us	1,051,697 us	+5.2%	+0.9%

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
0,10,10,64,20,503.94,200,128000,397,0.242,23639.65,33119.76,33119.76
1,100,10,64,20,2252.77,2000,1280000,888,0.542,111187.57,140411.64,140411.64
2,1000,10,64,20,18819.39,20000,12800000,1063,0.649,932746.13,1061157.07,1061157.07

[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.