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Refactor decimal conversion in PyArrow tables to use direct casting #544

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Refactor decimal conversion in PyArrow tables to use direct casting #544

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29 changes: 20 additions & 9 deletions src/databricks/sql/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -611,21 +611,32 @@ def convert_arrow_based_set_to_arrow_table(arrow_batches, lz4_compressed, schema


def convert_decimals_in_arrow_table(table, description) -> "pyarrow.Table":
new_columns = []
new_fields = []

for i, col in enumerate(table.itercolumns()):
field = table.field(i)

if description[i][1] == "decimal":
decimal_col = col.to_pandas().apply(
lambda v: v if v is None else Decimal(v)
)
precision, scale = description[i][4], description[i][5]
assert scale is not None
assert precision is not None
# Spark limits decimal to a maximum scale of 38,
# so 128 is guaranteed to be big enough
# create the target decimal type
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nit: Feel this comment is unnecessary

dtype = pyarrow.decimal128(precision, scale)
col_data = pyarrow.array(decimal_col, type=dtype)
field = table.field(i).with_type(dtype)
table = table.set_column(i, field, col_data)
return table

# convert the column directly using PyArrow's cast operation
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nit: Same don't think this comment is needed as code is descriptive enough

new_col = col.cast(dtype)
new_field = field.with_type(dtype)

new_columns.append(new_col)
new_fields.append(new_field)
else:
new_columns.append(col)
new_fields.append(field)

new_schema = pyarrow.schema(new_fields)

return pyarrow.Table.from_arrays(new_columns, schema=new_schema)


def convert_to_assigned_datatypes_in_column_table(column_table, description):
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