Question: convert below SQL into spark code select distinct a.branch_number, b.post_code, case when a.location_format is NULL and a.location_type like "Petrol-Filling-Station" then "Petrol" when a.location_format is NULL and a.legacy_format_code like "PH" then "Pharmacy" else a.location_format end as store_format_description from
convert below SQL into spark code select distinct a
.
branch
_
number,
b
.
post
_
code,
case when a
.
location
_
format is NULL and a
.
location
_
type like "Petrol
-
Filling
-
Station" then "Petrol"
when a
.
location
_
format is NULL and a
.
legacy
_
format
_
code like
"
PH
"
then "Pharmacy"
else a
.
location
_
format end as store
_
format
_
description
from channels
_
reporting.vw
_
channels
_
locations a
inner join

(
select distinct location
_
id
,
post
_
code from location.location
LATERAL VIEW INLINE
(
array
(
address
)
)
post
_
code
where lifecycle
_
status like "Trading"

)
b
-
-
limit is for testing
on a
.
location
_
id
=
b
.
location
_
id
where
a
.
lifecycle
_
status like "Trading"
and a
.
country
_
code like
"
GB
"
;

Question

Question: convert below SQL into spark code select distinct a.branch_number, ﻿ b.post_code, ﻿ case when a.location_format is NULL and a.location_type like "Petrol-Filling-Station" then "Petrol" ﻿ when a.location_format is NULL and a.legacy_format_code like "PH" ﻿then "Pharmacy" ﻿ else a.location_format end as store_format_description ﻿ from
convert below SQL into spark code select distinct a
.
branch
_
number,
﻿ b
.
post
_
code,
﻿ case when a
.
location
_
format is NULL and a
.
location
_
type like "Petrol
-
Filling
-
Station" then "Petrol"
﻿ when a
.
location
_
format is NULL and a
.
legacy
_
format
_
code like 
"
PH
"
 ﻿then "Pharmacy"
﻿ else a
.
location
_
format end as store
_
format
_
description
﻿ from channels
_
reporting.vw
_
channels
_
locations a
inner join
﻿ 
(
select distinct location
_
id
,
 ﻿post
_
code from location.location
﻿ LATERAL VIEW INLINE 
(
array
(
address
)
)
 ﻿post
_
code
﻿ where lifecycle
_
status like "Trading"
﻿ 
)
 ﻿b 
-
-
 ﻿limit is for testing
on a
.
location
_
id
=
b
.
location
_
id
where
a
.
lifecycle
_
status like "Trading"
and a
.
country
_
code like 
"
GB
"
;

Accepted Answer

```python from pyspark.sql import SparkSession from pyspark.sql.functions import col, when, lit # Initialize a Spark session spark = SparkSession.builder.appName("SQL to Spark").getOrCreate() # Load the data into DataFrames df_channels_reporting = spark.table("channels_reporting.vw_channels_locations") df_location = spark.table("location.location") # Create a DataFrame with distinct location_id and post_code where lifecycle_status is "Trading" df_location_filtered = df_location.select("location_id", "post_code").distinct() \ .where(col("lifecycle_status") == "Trading") # Join the DataFrames on location_id df_joined = df_channels_reporting.alias("a") \ .join(df_location_filtered.alias("b"), col("a.location_id") == col("b.location_id")) # Select the required columns with the case statement logic df_result = df_joined.select( col("a.branch_number").alias("branch_number"), col("b.post_code").alias("post_code"), when((col("a.location_format").isNull()) & (col("a.location_type") == "Petrol-Filling-Station"), lit("Petrol")) .when((col("a.location_format").isNull()) & (col("a.legacy_format_code") == "PH"), lit("Pharmacy")) .otherwise(col("a.location_format")).alias("store_format_description") ).distinct() # Show the result for testing (remove limit in production) df_result.limit(10).show() ``` ```python # main function that tests the SQL to Spark conversion def main_test_sql_to_spark(): # test the SQL to Spark conversion function # use case 1: check if the DataFrame is not empty assert df_result.count() > 0, "The DataFrame should not be empty" # use case 2: check if the DataFrame has the expected columns expected_columns = ['branch_number', 'post_code', 'store_format_description'] assert df_result.columns == expected_columns, "The DataFrame should have the expected columns" # Call the main test function main_test_sql_to_spark() ``` ∻Explanation∻ ⚹The provided Spark code performs the same operations as the given SQL query. It filters, joins, and selects distinct values with a case statement to create a new column. The main function tests the conversion by checking if the DataFrame is not empty and has the expected columns.⚹ ∻Step-by-step Instruction∻ ‖ ▪ ⋮ Initialize a Spark session to allow for data processing. ‖ ‖ ▪ ⋮ Load the data into DataFrames from the specified tables. ‖ ‖ ▪ ⋮ Filter the location DataFrame to get distinct rows where lifecycle_status is "Trading". ‖ ‖ ▪ ⋮ Perform an inner join on the DataFrames using the location_id column. ‖ ‖ ▪ ⋮ Select the required columns and apply the case statement logic using when(). ‖ ‖ ▪ ⋮ Use distinct() to ensure that only unique rows are selected. ‖ ‖ ▪ ⋮ Define concrete use cases and testing cases for the function: check if the DataFrame is not empty and has the expected columns. ‖ ∻Time Complexity∻ ⚹The time complexity depends on the size of the data and the complexity of the operations, particularly the join and distinct operations.⚹ ∻Space Complexity∻ ⚹The space complexity is related to the amount of data being processed and stored in the resulting DataFrame.⚹

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