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⚡️ Speed up function format_numeric_fields by 8% #35

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⚡️ Speed up function format_numeric_fields by 8% #35

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29 changes: 15 additions & 14 deletions datacompy/spark/helper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,8 @@

import logging

import pyspark.sql

from datacompy.spark.sql import SparkSQLCompare

LOG = logging.getLogger(__name__)
Expand Down Expand Up @@ -98,10 +100,10 @@ def compare_by_row(
sorted_base_df, sorted_compare_df = sort_rows(base_dataframe, compare_dataframe)
column_to_join = ["row"]

LOG.info("Compared by column(s): ", column_to_join)
LOG.info("Compared by column(s): %s", column_to_join)
if string2double_cols:
LOG.info(
"String column(s) cast to doubles for numeric comparison: ",
"String column(s) cast to doubles for numeric comparison: %s",
string2double_cols,
)
return SparkSQLCompare(
Expand Down Expand Up @@ -222,25 +224,24 @@ def format_numeric_fields(df: "pyspark.sql.DataFrame") -> "pyspark.sql.DataFrame
-------
pyspark.sql.DataFrame
"""
fixed_cols = []
numeric_types = [
# Set-based lookup is faster than list-based for membership tests
numeric_types = {
"tinyint",
"smallint",
"int",
"bigint",
"float",
"double",
"decimal",
]

for c in df.dtypes:
# do not change non-numeric fields
if c[1] not in numeric_types:
fixed_cols.append(col(c[0]))
# round & truncate numeric fields
else:
new_val = format_number(col(c[0]), 5).alias(c[0])
fixed_cols.append(new_val)
}

# Precompute the column transform functions with a generator expression
fixed_cols = (
format_number(col(c[0]), 5).alias(c[0]) if c[1] in numeric_types else col(c[0])
for c in df.dtypes
)

# No need to unpack a list; select accepts * generator expression directly

formatted_df = df.select(*fixed_cols)
return formatted_df