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⚡️ Speed up function sort_rows by 20% #34

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⚡️ Speed up function sort_rows by 20% #34

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20 changes: 14 additions & 6 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 @@ -162,22 +164,28 @@ def sort_rows(
compare_cols = compare_df.columns

# Ensure both DataFrames have the same columns

# Ensure both DataFrames have the same columns (optimized: use set for faster membership tests)
compare_cols_set = set(compare_cols)
for x in base_cols:
if x not in compare_cols:
if x not in compare_cols_set:
raise Exception(
f"{x} is present in base_df but does not exist in compare_df"
)

if set(base_cols) != set(compare_cols):
if set(base_cols) != compare_cols_set:
LOG.warning(
"WARNING: There are columns present in Compare df that do not exist in Base df. "
"The Base df columns will be used for row-wise sorting and may produce unanticipated "
"report output if the extra fields are not null."
)

w = Window.orderBy(*base_cols)
sorted_base_df = base_df.select("*", row_number().over(w).alias("row"))
sorted_compare_df = compare_df.select("*", row_number().over(w).alias("row"))

# Use withColumn instead of select("*", ...) to reduce column metadata copying overhead
sorted_base_df = base_df.withColumn("row", row_number().over(w))
sorted_compare_df = compare_df.withColumn("row", row_number().over(w))

return sorted_base_df, sorted_compare_df


Expand Down