feat(pyamber): add workflow core runtime

[carloea2]

What changes were proposed in this PR?

Add the core runtime for generated Python workflow drivers: a local variable heap, explicit input/output boundaries, field ownership checks, and a fresh global-binding namespace per execution. Invalid boundary IDs and incomplete output contracts fail explicitly.

The tuple-operator adapter and native binding helpers follow in PR 3B.

Any related issues, documentation, discussions?

Related to #8319 (PR 3A). Depends on #8433; keep this PR in draft until the codec prerequisite merges and this branch is rebased.

The runtime slice adds 611 lines across two files (303 implementation, 308 tests). The current diff also includes the codec prerequisite: 938 additions across five files.

How was this PR tested?

From amber/, Python 3.12:

python -m pytest src/test/python/pytexera/workflow -m "not integration" -q -p no:cacheprovider --cov=pytexera.workflow.runtime --cov-branch --cov-report=term-missing
python -m ruff check src/main/python/pytexera/workflow src/test/python/pytexera/workflow
python -m ruff format --check src/main/python/pytexera/workflow src/test/python/pytexera/workflow

54 tests passed; core runtime statement and branch coverage is 100%. Lint and formatting passed. Regression tests reproduced acceptance of non-string boundary IDs before the validation fix. Coverage includes missing/duplicate boundaries, ownership conflicts, absent bindings, malformed payload recovery, and execution namespace isolation.

Local verification used an existing generated-protobuf directory on a secondary Python path; the runtime and codec were loaded from this release checkout. The coverage run emitted a NumPy reload warning. Full integration and cross-version results will come from CI.

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

Generated-by: OpenAI Codex (GPT-6)

[github-actions]

Automated Reviewer Suggestions

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

• No candidates found from git blame history.

[codecov-commenter]

Codecov Report

❌ Patch coverage is 95.04950% with 10 lines in your changes missing coverage. Please review.
✅ Project coverage is 92.77%. Comparing base (378a3b5) to head (10d2c9e).
✅ All tests successful. No failed tests found.

Files with missing lines	Patch %	Lines
amber/src/main/python/pytexera/workflow/codec.py	86.30%	5 Missing and 5 partials ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##               main    #8583    +/-   ##
==========================================
  Coverage     92.76%   92.77%            
  Complexity     4898     4898            
==========================================
  Files          1236     1238     +2     
  Lines         51962    52164   +202     
  Branches       6381     6420    +39     
==========================================
+ Hits          48205    48394   +189     
- Misses         2185     2192     +7     
- Partials       1572     1578     +6     

Flag	Coverage Δ		*Carryforward flag
access-control-service	71.78% <ø> (ø)		Carriedforward from 378a3b5
agent-service	99.32% <ø> (ø)		Carriedforward from 378a3b5
amber	88.60% <ø> (ø)		Carriedforward from 378a3b5
computing-unit-managing-service	54.61% <ø> (ø)		Carriedforward from 378a3b5
config-service	87.37% <ø> (ø)		Carriedforward from 378a3b5
file-service	81.53% <ø> (ø)		Carriedforward from 378a3b5
frontend	96.60% <ø> (ø)		Carriedforward from 378a3b5
notebook-migration-service	83.73% <ø> (ø)		Carriedforward from 378a3b5
pyamber	98.27% <95.04%> (-0.21%)	⬇️
workflow-compiling-service	74.09% <ø> (ø)		Carriedforward from 378a3b5

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

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[github-actions]

⚠️ Benchmark changes need a look

🟢 2 better · 🔴 6 worse · ⚪ 7 noise (<±5%) · 0 without baseline

Compared against main 378a3b5 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	430	0.263	22,682/27,114/27,114 us	🟢 -22.4% / 🔴 +90.0%
🔴	bs=100 sw=10 sl=64	922	0.563	108,388/136,982/136,982 us	🔴 +7.4% / 🔴 +37.1%
🔴	bs=1000 sw=10 sl=64	1,095	0.669	906,955/1,052,465/1,052,465 us	🔴 +8.5% / 🔴 +9.7%

Baseline details

Latest main 378a3b5 from same runner

config	metric	PR	latest main	7d avg	Δ latest	Δ 7d
bs=10 sw=10 sl=64	throughput	430 tuples/sec	454 tuples/sec	839.58 tuples/sec	-5.3%	-48.8%
bs=10 sw=10 sl=64	MB/s	0.263 MB/s	0.277 MB/s	0.512 MB/s	-5.1%	-48.7%
bs=10 sw=10 sl=64	p50	22,682 us	20,664 us	11,937 us	+9.8%	+90.0%
bs=10 sw=10 sl=64	p95	27,114 us	34,955 us	14,976 us	-22.4%	+81.1%
bs=10 sw=10 sl=64	p99	27,114 us	34,955 us	19,204 us	-22.4%	+41.2%
bs=100 sw=10 sl=64	throughput	922 tuples/sec	968 tuples/sec	1,096 tuples/sec	-4.8%	-15.9%
bs=100 sw=10 sl=64	MB/s	0.563 MB/s	0.591 MB/s	0.669 MB/s	-4.7%	-15.8%
bs=100 sw=10 sl=64	p50	108,388 us	100,892 us	94,033 us	+7.4%	+15.3%
bs=100 sw=10 sl=64	p95	136,982 us	132,616 us	99,939 us	+3.3%	+37.1%
bs=100 sw=10 sl=64	p99	136,982 us	132,616 us	110,148 us	+3.3%	+24.4%
bs=1000 sw=10 sl=64	throughput	1,095 tuples/sec	1,114 tuples/sec	1,128 tuples/sec	-1.7%	-2.9%
bs=1000 sw=10 sl=64	MB/s	0.669 MB/s	0.68 MB/s	0.688 MB/s	-1.6%	-2.8%
bs=1000 sw=10 sl=64	p50	906,955 us	896,300 us	921,191 us	+1.2%	-1.5%
bs=1000 sw=10 sl=64	p95	1,052,465 us	970,150 us	959,735 us	+8.5%	+9.7%
bs=1000 sw=10 sl=64	p99	1,052,465 us	970,150 us	988,604 us	+8.5%	+6.5%

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
0,10,10,64,20,464.79,200,128000,430,0.263,22681.57,27113.90,27113.90
1,100,10,64,20,2169.81,2000,1280000,922,0.563,108388.12,136981.72,136981.72
2,1000,10,64,20,18259.62,20000,12800000,1095,0.669,906955.49,1052465.39,1052465.39