Splitting the Table and Performing Joining without Common Column

In this article, we’ll explore how to split a table into two derived tables based on a specific column, perform joining operations on these derived tables, and obtain the desired output. We’ll cover the SQL Server implementation using Azure Databricks.

Introduction

When working with datasets that require splitting and joining, it can be challenging to achieve the expected output without common columns between the tables. In this article, we’ll focus on solving this problem using SQL Server and Azure Databricks.

Problem Statement

Given a table with varying code values (01 and 04), we want to split the data into two derived tables based on these codes. Then, we need to perform a left join operation on these derived tables to obtain the desired output.

Sample Data

Here’s an example of the input table:

version value  code   type     year
PMS    0.00    01    HOURS     2006
000    312.00  01    HOURS     2006
000    0.00    04    HOURS     2006
PMS    0.00    01    NON STOCK 2006
000    835.00  01    NON STOCK 2006
000    835.00  04    NON STOCK 2006
000    0.00    04    HOURS     2007

Expected Output

The desired output should look like this:

version value  code   type      year   version value  code    type      year
  PMS    0.00    01    HOURS     2006   000      0.00    04    HOURS     2006
  000    312.00  01    HOURS     2006   000      835.00  04    NON STOCK 2006
  PMS    0.00    01    NON STOCK 2006   000      0.00    04    HOURS     2007
  000    835.00  01    NON STOCK 2006   null     null   null   null      null 

Solution Overview

To solve this problem, we’ll use the ROW_NUMBER() function in SQL Server to assign a unique number to each row within a partition of a result set. We’ll create two derived tables using this function and then perform a left join operation on these tables.

Here’s a step-by-step explanation:

Step 1: Create Derived Tables

First, we’ll create two derived tables that split the data based on the code values. We’ll use the ROW_NUMBER() function to assign a unique number to each row within a partition of the result set.

-- Derived table for code = '01'
select ROW_NUMBER() over(order by code) as id, *
from @input
where code = '01'

-- Derived table for code = '04'
select ROW_NUMBER() over(order by code) as id, *
from @input
where code = '04'

Step 2: Perform Left Join Operation

Next, we’ll perform a left join operation on the two derived tables. This will ensure that all rows from the first table are included in the output, even if there is no matching row in the second table.

left join 
(
    select ROW_NUMBER() over(order by code) as id, *
    from @input
    where code = '01'
) a
on a.id = b.id

left join 
(
    select ROW_NUMBER() over(order by code) as id, *
    from @input
    where code = '04'
) b
on a.id = b.id;

Step 3: Get the Desired Output

Finally, we’ll combine the results of the two derived tables using the UNION ALL operator. This will give us the desired output.

select *
from 
(
    select * from /*derived table for code = '01'*/
    union all
    select * from /*derived table for code = '04'*/
) as combined_table;

Azure Databricks Implementation

To implement this solution in Azure Databricks, we’ll use the SQL Server database connection and execute the T-SQL queries.

-- Create a new SparkSession
val spark = SparkSession.builder.appName("Split Table").getOrCreate()

// Create a DataFrame from the input data
val input_df = spark.read.format("csv").option("header", true).option("inferSchema", true).load("input.csv")

// Split the data into two derived tables
val derived_table_01 = input_df.filter(input_df.code === "01")
val derived_table_04 = input_df.filter(input_df.code === "04")

// Assign a unique number to each row within a partition of the result set
derived_table_01 = derived_table_01.withColumn("id", lit(1).alias("id"))
derived_table_04 = derived_table_04.withColumn("id", lit(2).alias("id"))

// Perform left join operation on the two derived tables
val joined_df = derived_table_01.join(derived_table_04, "id", "outer")

// Get the desired output using UNION ALL operator
val combined_df = joined_df.union(joined_df.select("*").withColumnRenamed("_c0", "_c1"))

// Display the results
combined_df.show()

Conclusion

In this article, we’ve explored how to split a table into two derived tables based on a specific column and perform joining operations without common columns between the tables. We’ve covered the SQL Server implementation using Azure Databricks and provided a step-by-step explanation of the solution.

By using the ROW_NUMBER() function and performing left join operations, we can achieve the desired output and solve real-world problems involving data splitting and joining.

Last modified on 2023-12-19