Update a column in PySpark while doing multiple inner joins?

Question

I have a SQL query which I am trying to convert into PySpark. In SQL query, we are joining three tables and updating a column where there's a match. The SQL query looks like this:

UPDATE [DEPARTMENT_DATA]
INNER JOIN ([COLLEGE_DATA]
            INNER JOIN [STUDENT_TABLE]
            ON COLLEGE_DATA.UNIQUEID = STUDENT_TABLE.PROFESSIONALID)
ON DEPARTMENT_DATA.PUBLICID = COLLEGE_DATA.COLLEGEID
SET STUDENT_TABLE.PRIVACY = "PRIVATE"

The logic I have tried:

df_STUDENT_TABLE = (
    df_STUDENT_TABLE.alias('a')
    .join(
        df_COLLEGE_DATA('b'),
        on=F.col('a.PROFESSIONALID') == F.col('b.UNIQUEID'),
        how='left',
    )
    .join(
        df_DEPARTMENT_DATA.alias('c'),
        on=F.col('b.COLLEGEID') == F.col('c.PUBLICID'),
        how='left',
    )
    .select(
        *[F.col(f'a.{c}') for c in df_STUDENT_TABLE.columns],
        F.when(
            F.col('b.UNIQUEID').isNotNull() & F.col('c.PUBLICID').isNotNull()
            F.lit('PRIVATE')
        ).alias('PRIVACY')
    )
)

This code is adding a new column "PRIVACY", but giving null values after running.

Answer 1

• I have taken some sample data and when I apply the join using conditions, the following is the result I get (requirement is that the following record's privacy needs to be set to PRIVATE)

%sql

select student.*,college.*,department.* from department INNER JOIN college INNER JOIN student
ON college.unique_id = student.professional_id and department.public_id = college.college_id

• When I used your code (same logic), I got the same output i.e., an additional column being added to the dataframe with required values and the actual privacy column has nulls.

from pyspark.sql.functions import col,when,lit

df_s = df_s.alias('a').join(df_c.alias('b'), col('a.professional_id') == col('b.unique_id'),'left').join(df_d.alias('c'), col('b.college_id') == col('c.public_id'),'left').select(*[col(f'a.{c}') for c in df_s.columns],when(col('b.unique_id').isNotNull() & col('c.public_id').isNotNull(), 'PRIVATE').otherwise(col('a.privacy')).alias('req_value'))
df_s.show()

• Since, from the above, req_value is the column with required values and these values need to be reflected in privacy, you can use the following code directly.

final = df_s.withColumn('privacy',col('req_value')).select([column for column in df_s.columns if column!='req_value'])
final.show()

UPDATE:

You can also use the following code where I have updated the column using withColumn instead of select.

df_s = df_s.alias('a').join(df_c.alias('b'), col('a.professional_id') == col('b.unique_id'),'left').join(df_d.alias('c'), col('b.college_id') == col('c.public_id'),'left').withColumn('privacy',when(col('b.unique_id').isNotNull() & col('c.public_id').isNotNull(), 'PRIVATE').otherwise(col('privacy'))).select(*df_s.columns)

#or you can use this as well, without using alias.
#df_s = df_s.join(df_c, df_s['professional_id'] == df_c['unique_id'],'left').join(df_d, df_c['college_id'] == df_d['public_id'],'left').withColumn('privacy',when(df_c['unique_id'].isNotNull() & df_d['public_id'].isNotNull(), 'PRIVATE').otherwise(df_s['privacy'])).select(*df_s.columns)


df_s.show()