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⚡ Bolt: [performance improvement] Replace df.iterrows with df.to_dict('records') #81

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⚡ Bolt: [performance improvement] Replace df.iterrows with df.to_dict('records') #81

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4 changes: 4 additions & 0 deletions .jules/bolt.md
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Original file line number Diff line number Diff line change
Expand Up @@ -8,3 +8,7 @@
## 2024-03-29 - ASE Custom JSON encoding vs standard JSON
**Learning:** ASE's custom JSON encoder (`ase.io.jsonio.encode`) will generate dicts with special keys like `__ndarray__` or `__complex__` (e.g. `{"__ndarray__": [[5], "int64", ...]}`). When optimizing JSON deserialization using faster alternatives like `orjson`, it's critical to realize that a normal `json.loads` or `orjson.loads` will deserialize this into a Python dictionary, while ASE's custom `decode` will properly reconstruct the underlying numpy array. Bypassing ASE's decoder without checking for these keys leads to downstream type errors (e.g. `KeyError: '__ndarray__'`).
**Action:** When replacing or wrapping ASE's jsonio with `orjson`, always fall back to ASE's `decode` if the payload string contains `__ndarray__` or `__complex__` markers, to ensure custom objects are correctly reconstructed.

## 2025-07-05 - DataFrame iteration bottleneck in data validation
**Learning:** Iterating over large Pandas DataFrames using `df.iterrows()` wraps every single row in a Pandas Series object. This boxing/unboxing overhead creates massive bottlenecks (often >10x slower) during dictionary comprehensions and lookups.
**Action:** Always convert the DataFrame into a native list of dictionaries first using `df.to_dict('records')`. Then iterate over this list. Downstream code should be updated to treat the row as a standard Python dictionary instead of a Series object (e.g., replace `row.to_dict()` with `dict(row)`).
8 changes: 5 additions & 3 deletions src/lavello_mlips/verify_processed_omol25.py
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Expand Up @@ -77,8 +77,10 @@ def main() -> None:
)

logger.info(f"Loaded {len(df)} records from Parquet.")
parquet_by_sha = {row["geom_sha1"]: row for _, row in df.iterrows()}
parquet_by_argone_rel = {row["argonne_rel"]: row for _, row in df.iterrows()}

df_records = df.to_dict("records")
parquet_by_sha = {row["geom_sha1"]: row for row in df_records}
parquet_by_argone_rel = {row["argonne_rel"]: row for row in df_records}
logger.info(f"Loading ExtXYZ file from {args.extxyz} (this may take a moment)...")
all_atoms = read(str(args.extxyz), index=":")
if not isinstance(all_atoms, list):
Expand Down Expand Up @@ -108,7 +110,7 @@ def get_dump_entry(at):
info = dict(at.info)
rel = info.get("argonne_rel")
pq_row = parquet_by_argone_rel.get(rel)
pq_data = pq_row.to_dict() if pq_row is not None else None
pq_data = dict(pq_row) if pq_row is not None else None
return {"xyz": info, "parquet": pq_data}

duplicates = {
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