spark dataframe filter operation - apache-spark

Question

I have a spark dataframe and then filter string to apply, filter only selects the some rows but I would like to know the reason for the rows not selected.
Example:
DataFrame columns: customer_id|col_a|col_b|col_c|col_d
Filter string: col_a > 0 & col_b > 4 & col_c < 0 & col_d=0
etc...
reason_for_exclusion can be any string or letter as long as it says why particular row excluded.
I could split the filter string and apply each filter but I have huge filter string and it would be inefficient so just checking is there any better way to this operation?
Thanks

Answer 1

You'll have to check for each condition within the filter expression which can be expensive regarding the simple operation of filtering.
I would suggest displaying the same reason for all filtred rows since it satisfies at least one condition in that expression. It's not pretty but I'd prefer this as it's efficient especially when you have to handle very large DataFrames.
data = [(1, 1, 5, -3, 0),(2, 0, 10, -1, 0), (3, 0, 10, -4, 1),]
df = spark.createDataFrame(data, ["customer_id", "col_a", "col_b", "col_c", "col_d"])
filter_expr = "col_a > 0 AND col_b > 4 AND col_c < 0 AND col_d=0"
filtered_df = df.withColumn("reason_for_exclusion",
when(~expr(filter_expr),lit(filter_expr)
).otherwise(lit(None))
)
filtered_df.show(truncate=False)
Output:
+-----------+-----+-----+-----+-----+-------------------------------------------------+
|customer_id|col_a|col_b|col_c|col_d|reason_for_exclusion |
+-----------+-----+-----+-----+-----+-------------------------------------------------+
|1 |1 |5 |-3 |0 |null |
|2 |0 |10 |-1 |0 |col_a > 0 AND col_b > 4 AND col_c < 0 AND col_d=0|
|3 |0 |10 |-4 |1 |col_a > 0 AND col_b > 4 AND col_c < 0 AND col_d=0|
+-----------+-----+-----+-----+-----+-------------------------------------------------+
EDIT:
Now, if you really want to display only the conditions which failed you can turn each condition to separated columns and use DataFrame select to do the calculation. Then you'll have to check columns evaluated to False to know which condition has failed.
You could name these columns by <PREFIX>_<condition> so that you could identify them easily later. Here is a complete example:
filter_expr = "col_a > 0 AND col_b > 4 AND col_c < 0 AND col_d=0"
COLUMN_FILTER_PREFIX = "filter_validation_"
original_columns = [col(c) for c in df.columns]
# create column for each condition in filter expression
condition_columns = [expr(f).alias(COLUMN_FILTER_PREFIX + f) for f in filter_expr.split("AND")]
# evaluate condition to True/False and persist the DF with calculated columns
filtered_df = df.select(original_columns + condition_columns)
filtered_df = filtered_df.persist(StorageLevel.MEMORY_AND_DISK)
# get back columns we calculated for filter
filter_col_names = [c for c in filtered_df.columns if COLUMN_FILTER_PREFIX in c]
filter_columns = list()
for c in filter_col_names:
filter_columns.append(
when(~col(f"`{c}`"),
lit(f"{c.replace(COLUMN_FILTER_PREFIX, '')}")
)
)
array_reason_filter = array_except(array(*filter_columns), array(lit(None)))
df_with_filter_reason = filtered_df.withColumn("reason_for_exclusion", array_reason_filter)
df_with_filter_reason.select(*original_columns, col("reason_for_exclusion")).show(truncate=False)
# output
+-----------+-----+-----+-----+-----+----------------------+
|customer_id|col_a|col_b|col_c|col_d|reason_for_exclusion |
+-----------+-----+-----+-----+-----+----------------------+
|1 |1 |5 |-3 |0 |[] |
|2 |0 |10 |-1 |0 |[col_a > 0 ] |
|3 |0 |10 |-4 |1 |[col_a > 0 , col_d=0]|
+-----------+-----+-----+-----+-----+----------------------+