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fix(excel-parser): Fix memory issue in ExcelParser #729

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fix(excel-parser): Fix memory issue in ExcelParser #729

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1b4d1d3
Fix memory issue in excel_parser
tolik0 Aug 27, 2025
7424cc6
Delete unused code
tolik0 Aug 27, 2025
aa26ef9
Merge branch 'main' into tolik0/file-based/fix-memory-issue-in-excel-…
aaronsteers Aug 28, 2025
3c9cc6f
Merge branch 'main' into tolik0/file-based/fix-memory-issue-in-excel-…
aaronsteers Aug 29, 2025
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6 changes: 3 additions & 3 deletions airbyte_cdk/sources/file_based/file_types/excel_parser.py
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Original file line number Diff line number Diff line change
Expand Up @@ -118,9 +118,9 @@ def parse_records(
# DataFrame.to_dict() method returns datetime values in pandas.Timestamp values, which are not serializable by orjson
# DataFrame.to_json() returns string with datetime values serialized to iso8601 with microseconds to align with pydantic behavior
# see PR description: https://github.com/airbytehq/airbyte/pull/44444/
yield from orjson.loads(
df.to_json(orient="records", date_format="iso", date_unit="us")
)
for index, row in df.iterrows():
# Convert each row (as a Series) to a JSON string
yield orjson.loads(row.to_json(date_format="iso", date_unit="us"))
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Copilot AI Aug 27, 2025

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Using df.iterrows() is inefficient for large DataFrames as it returns copies of data and has significant overhead. Consider using df.to_dict('records') with manual datetime conversion, or df.itertuples() for better performance while maintaining the memory benefits.

Suggested change
for index, row in df.iterrows():
# Convert each row (as a Series) to a JSON string
yield orjson.loads(row.to_json(date_format="iso", date_unit="us"))
# Efficiently convert the DataFrame to a list of records with proper datetime serialization
records = orjson.loads(df.to_json(orient="records", date_format="iso", date_unit="us"))
for record in records:
yield record

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Based on this copilot recommendation, should we use itertuples instead of iterrows?


except Exception as exc:
# Raise a RecordParseError if any exception occurs during parsing
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