Understanding the Different Row Counts in SQL Subqueries Using IN

Dive into the nuances of SQL subqueries with our guide to understanding why different row counts can occur when using `IN`, including an effective solution to optimize your queries.
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Understanding the Different Row Counts in SQL Subqueries Using IN

When working with SQL queries, especially in the context of complex databases, developers often encounter peculiar situations that can cause confusion. One such scenario arises when using the IN clause within a subquery. You might wonder, why does using IN as part of a subquery yield different row counts compared to performing an explicit IN with individual values?

The SQL Query Breakdown

Let's start by examining a sample SQL query that involves multiple JOINs and a subquery within the WHERE clause:

[[See Video to Reveal this Text or Code Snippet]]

In this query, the WHERE clause uses a subquery that is meant to filter records based on specific cpt_codes and insurance_group_name values. However, when run independently, replacing the subquery with explicit IN statements yields a noticeably higher row count.

The Problem

You might be surprised to find that when the subquery returns, say, 64 unique codes and 320 unique insurance group names, the output of the overall query is drastically different than fetching those distinct values directly through two separate IN clauses.

Why the Difference?

The differing row counts can arise from several factors:

NULL Values:

If any of the results from the subquery return rows with NULL in either cpt_codes.code or insurance_group_name, they will not match with any explicitly defined values in your second IN statements.

Combination vs Individual Matching:

Using a combination of values like (cpt_codes.code, ii.insurance_group_name) may inadvertently filter out combinations that exist within your data but were excluded by how unique values were treated in the subquery.

Joins and Row Multiplication:

Multiple JOINs can cause the output to explode in size. This explosion can fleece valuable outcomes if combinations are not taken into account distinctly in the filtering phase.

The Solution

To get the expected results while still harnessing the potential of the subquery, it may be beneficial to separate conditions more clearly:

Here is an optimized solution that returns the correct number of rows effectively:

[[See Video to Reveal this Text or Code Snippet]]

Key Points

Use DISTINCT to refine results in subqueries.

Ensure you’re considering NULL checks where appropriate.

Keep each filtering criterion clear and grouped logically to avoid unintended exclusions.

By revising your SQL structure and understanding the nuances of how IN clause functions with nested subqueries, you'll ensure that your queries return the data you expect effectively and efficiently.



In conclusion, SQL practices take time to master, and understanding why discrepancies occur in your row results is key. Happy querying!