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jafeerr merged 1 commit into from
Jul 22, 2025
Merged

Code refactoring, Comments added. #3

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12 changes: 9 additions & 3 deletions spark_data_test/jobs/comparison_job.py
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Original file line number Diff line number Diff line change
Expand Up @@ -186,6 +186,8 @@ def __write_results(comparison_result, job_name, output_config):
def compare_dataframes(spark, source_df, target_df, params):
non_key_cols = list(set(source_df.columns) - set(params.primary_keys))
target_df_schema = target_df.select(*params.select_cols).drop(*params.drop_cols).schema

# Apply transformations (select, drop columns), add checksum & append _src/_target for all columns
source_df = __apply_source_target_transformations(
source_df,
SRC_COL_SUFFIX,
Expand All @@ -199,6 +201,7 @@ def compare_dataframes(spark, source_df, target_df, params):
params
).cache()

# Join Source and Target DF and match _chechsum of Source and Target
joined_df = source_df.join(
target_df, params.primary_keys, FULL_OUTER_JOIN
).cache()
Expand All @@ -210,26 +213,29 @@ def compare_dataframes(spark, source_df, target_df, params):
MATCHED_COL_SUFFIX.format(CHK_SUM_COL), ALL_ROWS_MATCHED_COL
).cache()

# For records with unmatched _checksum values, perform a column-by-column comparison. For numeric fields, apply a difference_tolerance to determine if values are acceptably close.
unmatched_rows_aftr_tolerance = __process_unmatched_records(
joined_df.filter(
(f.col(ALL_ROWS_MATCHED_COL) == False)
(~f.col(ALL_ROWS_MATCHED_COL))
& (f.col(SRC_COL_SUFFIX.format(CHK_SUM_COL)).isNotNull())
& (f.col(TGT_COL_SUFFIX.format(CHK_SUM_COL)).isNotNull())
),
target_df_schema,
params.primary_keys,
params.test_params
)
unmatched_records = unmatched_rows_aftr_tolerance.filter(f.col(ALL_ROWS_MATCHED_AFTR_TOL_COL) == False)


# Update the all_rows_matched column in joined_df for records that are considered matched after applying the difference_tolerance to numeric fields.
joined_df = joined_df.join(
unmatched_rows_aftr_tolerance.filter(f.col(ALL_ROWS_MATCHED_AFTR_TOL_COL)).select(*(params.primary_keys+[ALL_ROWS_MATCHED_AFTR_TOL_COL])),
params.primary_keys,
LEFT_JOIN
).withColumn(ALL_ROWS_MATCHED_COL, f.col(ALL_ROWS_MATCHED_COL) | f.coalesce(f.col(ALL_ROWS_MATCHED_AFTR_TOL_COL), f.lit(False).cast(BooleanType()))).cache()

unmatched_records = unmatched_rows_aftr_tolerance.filter(~f.col(ALL_ROWS_MATCHED_AFTR_TOL_COL))
matched_records = joined_df.filter(f.col(ALL_ROWS_MATCHED_COL) == True).dropDuplicates(params.primary_keys)

# Generate reports
col_lvl_report = __get_column_level_test_report(
params.dataset_name, unmatched_records, non_key_cols
)
Expand Down