Aggregate Data from Two Tables

In this article, we’ll explore how to aggregate data from two tables, where some records in one table are linked to multiple records in the other. We’ll delve into the challenges of dealing with duplicate IDs and how to handle them effectively.

Understanding the Problem

The problem presented involves combining data from two tables: table1 (let’s call it A) and table2 (let’s call it B). The records in table A have a single ID, but there are multiple corresponding records in table B, each with the same ID. We want to aggregate this data by summing up specific columns from both tables.

Table A

For clarity, let’s assume that table A has the following structure:

We’ll call this table our “source” or “primary” table. The records in table A are unique, and we want to link them to multiple records in table B.

Table B

Assuming that table B has the following structure:

We’ll call this table our “secondary” or “target” table. The records in table B are not unique, as there can be multiple rows with the same ID.

The Challenge: Handling Duplicate IDs

In SQL, when you join two tables, the resulting set of columns is limited by the columns present in both tables. However, when dealing with duplicate IDs between tables A and B, we need to decide how to handle these duplicates.

One common approach is to use a self-join or a subquery to identify unique combinations of IDs from both tables. This way, we can ensure that each record from table A only appears once in the aggregated result set.

Joining Tables A and B

Let’s revisit the original SQL query provided by the user:

select r.variable, s.variable_1  s.variable_2, sum(r.sum), 
from table1 r
join table2 s on r.variable = s.variable
where some_cirrcumstances
group by  r.variable ,s.variable_1
order by r.variable ,s.variable_1;

There are several issues with this query:

1. The sum(r.sum) expression is not valid SQL. It seems to be a typo, and we should use r.id instead.
2. The group by clause only includes two columns (r.variable and s.variable_1). We need to add the unique ID from table A to this list.

Correcting the Query

To fix these issues, let’s modify the query as follows:

select 
    r.id,  -- Add the primary key (ID) from table A
    r.variable,  
    s.variable_1  s.variable_2,  
    sum(s.other_variable_1) + r.sum  -- Sum up other variables from both tables
from table1 r
join table2 s on r.variable = s.variable
where some_cirrcumstances
group by 
    r.id,
    r.variable,
    s.variable_1
order by 
    r.id,  
    r.variable ,s.variable_1;

In this corrected query:

• We’ve added r.id to the GROUP BY clause and the result set.
• When aggregating values from both tables, we’re adding the sum of other_variable_1 from table B (s.other_variable_1) with the value stored in column sum from table A (r.sum). This will effectively combine duplicate IDs.

Another Approach: Using a Self-Join

As an alternative to the corrected query above, we can use a self-join to handle duplicate IDs. A self-join allows us to join each row of one table with all rows of the same table.

Here’s how you could implement this:

select 
    ta.id,
    ta.variable_1,
    ta.variable_2,
    tb.other_variable_1 + tb.our_variable  -- Sum up other variables from both tables
from tableA ta  -- Table A (self-join)
join tableB tb on ta.id = tb.id  -- Join table B to itself based on ID
group by 
    ta.id,
    ta.variable_1,
    ta.variable_2
order by 
    ta.id,  
    ta.variable_1 ,ta.variable_2;

This approach may be more efficient for smaller datasets, but it can result in slower performance as the dataset grows.

Using Subqueries

Another option is to use subqueries to identify unique records from table A and then join these records with table B.

Here’s an example of how you could implement this:

select 
    ta.id,
    ta.variable_1,
    ta.variable_2,
    tb.other_variable_1 + tb.our_variable  -- Sum up other variables from both tables
from (
    select id, variable_1, variable_2, row_number() OVER (PARTITION BY id ORDER BY variable_1) as rn
    from tableA
) ta  -- Table A subquery
join tableB tb on ta.id = tb.id and ta.rn = 1  -- Join table B to itself based on ID with a specific order
order by 
    ta.id,  
    ta.variable_1 ,ta.variable_2;

This approach uses the row_number() function to assign a unique row number (rn) for each record within each group of records with the same ID. The join then only includes records where the row number is 1.

Conclusion

Handling duplicate IDs between tables can be challenging, especially when aggregating data from these tables. By using different SQL techniques such as self-joins, subqueries, or modifications to the original query, we can effectively combine duplicate IDs and produce accurate aggregated results.

In conclusion, this article covered common challenges in handling aggregate data from two tables with duplicate IDs. We explored different approaches and provided examples of corrected queries that should meet your specific requirements.

Last modified on 2024-03-08