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 56.63%. Comparing base (24b587f) to head (47d49e4).
✅ All tests successful. No failed tests found.

Additional details and impacted files

@@             Coverage Diff              @@
##               main    #5989      +/-   ##
============================================
- Coverage     56.89%   56.63%   -0.27%     
+ Complexity     3058     3040      -18     
============================================
  Files          1129     1129              
  Lines         43802    43805       +3     
  Branches       4743     4743              
============================================
- Hits          24922    24807     -115     
- Misses        17449    17548      +99     
- Partials       1431     1450      +19     

Flag	Coverage Δ		*Carryforward flag
access-control-service	70.00% <ø> (ø)		Carriedforward from 3d7486b
agent-service	44.59% <ø> (ø)		Carriedforward from 3d7486b
amber	58.00% <ø> (-0.66%)	⬇️	Carriedforward from 3d7486b
computing-unit-managing-service	0.00% <ø> (ø)		Carriedforward from 3d7486b
config-service	52.30% <ø> (ø)		Carriedforward from 3d7486b
file-service	62.81% <ø> (ø)		Carriedforward from 3d7486b
frontend	50.09% <ø> (-0.04%)	⬇️	Carriedforward from 3d7486b
notebook-migration-service	78.57% <ø> (ø)		Carriedforward from 3d7486b
pyamber	91.15% <100.00%> (+<0.01%)	⬆️
python	90.68% <ø> (-0.01%)	⬇️	Carriedforward from 3d7486b
workflow-compiling-service	55.14% <ø> (ø)		Carriedforward from 3d7486b

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[github-actions]

⚠️ Benchmark changes need a look

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

Compared against main 24b587f 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	362	0.221	27,805/34,012/34,012 us	🔴 +20.4% / 🔴 +122.5%
🟢	bs=100 sw=10 sl=64	922	0.563	108,233/124,803/124,803 us	🟢 -6.3% / 🔴 +15.2%
⚪	bs=1000 sw=10 sl=64	1,092	0.667	914,027/956,549/956,549 us	⚪ within ±5% / 🟢 -10.5%

Baseline details

Latest main 24b587f from same runner

config	metric	PR	latest main	7d avg	Δ latest	Δ 7d
bs=10 sw=10 sl=64	throughput	362 tuples/sec	435 tuples/sec	770.95 tuples/sec	-16.8%	-53.0%
bs=10 sw=10 sl=64	MB/s	0.221 MB/s	0.266 MB/s	0.471 MB/s	-16.9%	-53.0%
bs=10 sw=10 sl=64	p50	27,805 us	23,090 us	12,775 us	+20.4%	+117.7%
bs=10 sw=10 sl=64	p95	34,012 us	31,655 us	15,286 us	+7.4%	+122.5%
bs=10 sw=10 sl=64	p99	34,012 us	31,655 us	18,795 us	+7.4%	+81.0%
bs=100 sw=10 sl=64	throughput	922 tuples/sec	934 tuples/sec	976.93 tuples/sec	-1.3%	-5.6%
bs=100 sw=10 sl=64	MB/s	0.563 MB/s	0.57 MB/s	0.596 MB/s	-1.2%	-5.6%
bs=100 sw=10 sl=64	p50	108,233 us	103,288 us	102,557 us	+4.8%	+5.5%
bs=100 sw=10 sl=64	p95	124,803 us	133,170 us	108,383 us	-6.3%	+15.2%
bs=100 sw=10 sl=64	p99	124,803 us	133,170 us	115,249 us	-6.3%	+8.3%
bs=1000 sw=10 sl=64	throughput	1,092 tuples/sec	1,114 tuples/sec	1,009 tuples/sec	-2.0%	+8.2%
bs=1000 sw=10 sl=64	MB/s	0.667 MB/s	0.68 MB/s	0.616 MB/s	-1.9%	+8.3%
bs=1000 sw=10 sl=64	p50	914,027 us	899,979 us	997,695 us	+1.6%	-8.4%
bs=1000 sw=10 sl=64	p95	956,549 us	943,196 us	1,036,731 us	+1.4%	-7.7%
bs=1000 sw=10 sl=64	p99	956,549 us	943,196 us	1,069,334 us	+1.4%	-10.5%

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
0,10,10,64,20,552.39,200,128000,362,0.221,27805.38,34012.31,34012.31
1,100,10,64,20,2169.00,2000,1280000,922,0.563,108233.06,124803.36,124803.36
2,1000,10,64,20,18308.77,20000,12800000,1092,0.667,914027.29,956548.87,956548.87