I have a MS Access SQL query which I want to convert into PySpark. The query looks like this (we have two tables Employee and Department):

UPDATE EMPLOYEE INNER JOIN [DEPARTMENT] ON
EMPLOYEE.STATEPROVINCE = [DEPARTMENT].[STATE_LEVEL] 
SET EMPLOYEE.STATEPROVINCE = [DEPARTMENT]![STATE_ABBREVIATION];

CodePudding user response：

Test dataframes:

from pyspark.sql import functions as F

df_emp = spark.createDataFrame([(1, 'a'), (2, 'bb')], ['EMPLOYEE', 'STATEPROVINCE'])
df_emp.show()
#  -------- ------------- 
# |EMPLOYEE|STATEPROVINCE|
#  -------- ------------- 
# |       1|            a|
# |       2|           bb|
#  -------- ------------- 

df_dept = spark.createDataFrame([('bb', 'b')], ['STATE_LEVEL', 'STATE_ABBREVIATION'])
df_dept.show()
#  ----------- ------------------ 
# |STATE_LEVEL|STATE_ABBREVIATION|
#  ----------- ------------------ 
# |         bb|                 b|
#  ----------- ------------------ 

Running your SQL query in Microsoft Access does the following:

In PySpark, you can get it like this:

df = (df_emp.alias('a')
    .join(df_dept.alias('b'), df_emp.STATEPROVINCE == df_dept.STATE_LEVEL, 'left')
    .select(
        *[c for c in df_emp.columns if c != 'STATEPROVINCE'],
        F.coalesce('b.STATE_ABBREVIATION', 'a.STATEPROVINCE').alias('STATEPROVINCE')
    )
)
df.show()
#  -------- ------------- 
# |EMPLOYEE|STATEPROVINCE|
#  -------- ------------- 
# |       1|            a|
# |       2|            b|
#  -------- ------------- 

First you do a left join. Then, select.

The select has 2 parts.

• First, you select everything from df_emp except for "STATEPROVINCE".
• Then, for the new "STATEPROVINCE", you select "STATE_ABBREVIATION" from df_dept, but in case it's null (i.e. not existent in df_dept), you take "STATEPROVINCE" from df_emp.