⚡ Bolt: [performance improvement] Optimize PyArrow .as_py() calls

[SatoryKono]

💡 What: Optimize PyArrow scalar to Python object conversion in serialize_column_to_json by caching .as_py() result using walrus operator.
🎯 Why: PyArrow's .as_py() method is expensive. The previous implementation called it twice for each element (once for null-checking, once for serialization). This caching avoids redundant evaluations.
📊 Impact: Expected to significantly improve serialization performance for complex Arrow columns (lists, structs) during export-safe flattening.
🔬 Measurement: To verify, benchmark the flatten_arrow_table_for_export function on a large table with list or struct columns and compare execution times.

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PR created automatically by Jules for task 8558119282180853928 started by @SatoryKono

Summary by CodeRabbit

• Refactor

  • Optimized data serialization to eliminate redundant processing during exports, improving export performance and reducing memory overhead.

• Chores

  • Removed a stale test artifact from the repository to reduce clutter and avoid misleading test output during local inspection.

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[coderabbitai]

No actionable comments were generated in the recent review. 🎉

ℹ️ Recent review info

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📥 Commits

Reviewing files that changed from the base of the PR and between 499191c and 5f4cd0b.

📒 Files selected for processing (1)

• .pytest-tmp/infra-integ/collect-only.txt

💤 Files with no reviewable changes (1)

• .pytest-tmp/infra-integ/collect-only.txt

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📝 Walkthrough

Walkthrough

Cached v.as_py() via the walrus operator in serialize_column_to_json to avoid a redundant call; also removed a generated pytest collect-only artifact file containing test collection errors.

Changes

Cohort / File(s)	Summary
JSON Serialization Optimization src/bioetl/domain/serialization.py	Use the walrus operator to store v.as_py() in serialize_column_to_json, removing a duplicate call in the comprehension while preserving existing null handling and output construction.
Test Artifact Removal .pytest-tmp/infra-integ/collect-only.txt	Deleted a generated pytest collect-only output file that recorded multiple import errors during test collection; no code behavior changed.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

Poem

🐰 I munched a call, then cached it fast,

No more double-hops from present to past.
A tiny cleanup, a vanished test page,
My whiskers twitch — the code runs sage. 🥕

🚥 Pre-merge checks | ✅ 2 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Description check	⚠️ Warning	The description provides context (what, why, impact, measurement) but lacks the structured template sections (Summary, Changes, Type, Affected layers, Test plan, Checklist) required by the repository.	Use the repository's PR description template with all required sections: Summary, Changes, Type, Affected layers, Test plan, and Checklist. Mark applicable checkboxes and clearly list test verification steps.

✅ Passed checks (2 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly identifies the main change: optimizing PyArrow .as_py() calls for performance improvement, directly matching the core modification in serialize_column_to_json.
Docstring Coverage	✅ Passed	Docstring coverage is 100.00% which is sufficient. The required threshold is 80.00%.

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✨ Finishing Touches

🧪 Generate unit tests (beta)

• Create PR with unit tests
• Commit unit tests in branch bolt-optimize-as-py-8558119282180853928

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[coderabbitai]

🧹 Nitpick comments (1)

src/bioetl/domain/serialization.py (1)

266-273: Consider using to_pylist() for more efficient bulk conversion instead of per-element .as_py() calls.

The walrus operator approach works correctly, but using col.to_pylist() delegates the entire conversion to PyArrow's C++ layer, avoiding per-scalar Python calls:

♻️ Suggested refactor

 def serialize_column_to_json(col: pa.ChunkedArray) -> pa.Array:
     """Serialize a complex Arrow column into stringified JSON values."""
-    # Use walrus operator to cache expensive .as_py() call
     vals = [
-        serialize_to_json(val) if (val := v.as_py()) is not None else None
-        for v in col
+        serialize_to_json(val) if val is not None else None
+        for val in col.to_pylist()
     ]
     return pa.array(vals, type=pa.string())

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@src/bioetl/domain/serialization.py` around lines 266 - 273, The current
serialize_column_to_json uses per-element v.as_py() calls which are slow; change
it to call col.to_pylist() and iterate that Python list, passing each element to
serialize_to_json (preserving None handling) and then build the output
pa.array(..., type=pa.string()); update serialize_column_to_json to use
col.to_pylist() and keep serialize_to_json for element serialization to leverage
PyArrow's C++ bulk conversion.

🤖 Prompt for all review comments with AI agents

Verify each finding against the current code and only fix it if needed.

Nitpick comments:
In `@src/bioetl/domain/serialization.py`:
- Around line 266-273: The current serialize_column_to_json uses per-element
v.as_py() calls which are slow; change it to call col.to_pylist() and iterate
that Python list, passing each element to serialize_to_json (preserving None
handling) and then build the output pa.array(..., type=pa.string()); update
serialize_column_to_json to use col.to_pylist() and keep serialize_to_json for
element serialization to leverage PyArrow's C++ bulk conversion.

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ℹ️ Review info

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📥 Commits

Reviewing files that changed from the base of the PR and between 83d9d1b and 499191c.

📒 Files selected for processing (1)

• src/bioetl/domain/serialization.py