refactor(pyamber): share receiver-batch construction in partitioners

[Ma77Ball]

What changes were proposed in this PR?

• Added Partitioner.build_receiver_batches(channels) to the base class, the single home for the ordered (receiver, batch) list built via dict.fromkeys (which preserves channel order where a set literal would not).
• Replaced the three identical inline copies of that construction in RoundRobinPartitioner, HashBasedShufflePartitioner, and RangeBasedShufflePartitioner with a call to the shared helper, dropping the drifted copy-pasted comments.
• No behavior change: each partitioner still builds the same ordered, deduplicated receiver list.

Any related issues, documentation, discussions?

Closes: #5988

How was this PR tested?

• Run cd amber && PYTHONPATH=src/main/python python -m pytest src/test/python/core/architecture/sendsemantics/test_partitioners.py -q, expect all 35 tests to pass.
• The order/dedup behavior owned by the new helper is covered by the existing TestRoundRobinPartitioner::test_init_preserves_channel_order and test_init_dedupes_duplicate_channels_preserving_first_seen_order; confirm they still pass against the refactored construction.

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

Co-authored with Claude Opus 4.8 in compliance with ASF

[github-actions]

Automated Reviewer Suggestions

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

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

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 56.23%. Comparing base (0e0ec11) to head (8f9db91).

Additional details and impacted files

@@            Coverage Diff            @@
##               main    #5989   +/-   ##
=========================================
  Coverage     56.23%   56.23%           
  Complexity     2987     2987           
=========================================
  Files          1120     1120           
  Lines         43167    43170    +3     
  Branches       4658     4658           
=========================================
+ Hits          24274    24277    +3     
  Misses        17472    17472           
  Partials       1421     1421           

Flag	Coverage Δ		*Carryforward flag
access-control-service	70.00% <ø> (ø)		Carriedforward from 0e0ec11
agent-service	44.59% <ø> (ø)		Carriedforward from 0e0ec11
amber	57.77% <ø> (ø)		Carriedforward from 0e0ec11
computing-unit-managing-service	0.00% <ø> (ø)		Carriedforward from 0e0ec11
config-service	51.56% <ø> (ø)		Carriedforward from 0e0ec11
file-service	59.02% <ø> (ø)		Carriedforward from 0e0ec11
frontend	49.27% <ø> (ø)		Carriedforward from 0e0ec11
notebook-migration-service	78.57% <ø> (ø)		Carriedforward from 0e0ec11
pyamber	90.16% <100.00%> (-0.05%)	⬇️
python	90.75% <ø> (-0.01%)	⬇️	Carriedforward from 0e0ec11
workflow-compiling-service	55.14% <ø> (ø)		Carriedforward from 0e0ec11

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

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[github-actions]

✅ No material benchmark regressions detected

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

Compared against main 0e0ec11 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	399	0.244	24,549/34,741/34,741 us	🟢 -9.0% / 🔴 +126.6%
⚪	bs=100 sw=10 sl=64	783	0.478	125,764/150,060/150,060 us	⚪ within ±5% / 🔴 +35.7%
⚪	bs=1000 sw=10 sl=64	917	0.56	1,092,138/1,151,254/1,151,254 us	⚪ within ±5% / 🔴 +8.2%

Baseline details

Latest main 0e0ec11 from same runner

config	metric	PR	latest main	7d avg	Δ latest	Δ 7d
bs=10 sw=10 sl=64	throughput	399 tuples/sec	409 tuples/sec	757.16 tuples/sec	-2.4%	-47.3%
bs=10 sw=10 sl=64	MB/s	0.244 MB/s	0.25 MB/s	0.462 MB/s	-2.4%	-47.2%
bs=10 sw=10 sl=64	p50	24,549 us	24,286 us	12,971 us	+1.1%	+89.3%
bs=10 sw=10 sl=64	p95	34,741 us	38,189 us	15,333 us	-9.0%	+126.6%
bs=10 sw=10 sl=64	p99	34,741 us	38,189 us	18,732 us	-9.0%	+85.5%
bs=100 sw=10 sl=64	throughput	783 tuples/sec	795 tuples/sec	957.66 tuples/sec	-1.5%	-18.2%
bs=100 sw=10 sl=64	MB/s	0.478 MB/s	0.485 MB/s	0.585 MB/s	-1.4%	-18.2%
bs=100 sw=10 sl=64	p50	125,764 us	124,983 us	103,982 us	+0.6%	+20.9%
bs=100 sw=10 sl=64	p95	150,060 us	154,087 us	110,583 us	-2.6%	+35.7%
bs=100 sw=10 sl=64	p99	150,060 us	154,087 us	118,369 us	-2.6%	+26.8%
bs=1000 sw=10 sl=64	throughput	917 tuples/sec	906 tuples/sec	979.6 tuples/sec	+1.2%	-6.4%
bs=1000 sw=10 sl=64	MB/s	0.56 MB/s	0.553 MB/s	0.598 MB/s	+1.3%	-6.3%
bs=1000 sw=10 sl=64	p50	1,092,138 us	1,103,034 us	1,024,553 us	-1.0%	+6.6%
bs=1000 sw=10 sl=64	p95	1,151,254 us	1,147,957 us	1,063,789 us	+0.3%	+8.2%
bs=1000 sw=10 sl=64	p99	1,151,254 us	1,147,957 us	1,096,239 us	+0.3%	+5.0%

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
0,10,10,64,20,500.72,200,128000,399,0.244,24549.34,34741.23,34741.23
1,100,10,64,20,2553.10,2000,1280000,783,0.478,125763.58,150059.92,150059.92
2,1000,10,64,20,21805.97,20000,12800000,917,0.560,1092137.56,1151253.80,1151253.80