Converting SQL Queries to Use Joins Instead of Nested Queries or Subqueries: An Oracle Example

Introduction

As data sets grow in complexity, the need for efficient querying techniques becomes increasingly important. One common technique used in many databases is joining tables to retrieve data from multiple sources. However, sometimes it’s necessary to convert existing queries that rely on nested queries or subqueries to use joins instead. In this article, we’ll explore how to convert a SQL query written in Oracle that uses a join to achieve the same result.

Understanding the Original Query

Let’s analyze the original query provided by the OP:

SELECT *
  FROM EXPORT_TABLE doe
 WHERE 1 = 1
   AND doe.last_update_date < SYSDATE - 1
   AND ID NOT IN (
                   SELECT ID
                     FROM EXPORT_TABLE
                    WHERE LAST_UPDATE_DATE IN (
                                                SELECT MAX(LAST_UPDATE_DATE)
                                                  FROM EXPORT_TABLE
                                                 GROUP BY DI_EXTRACT_ID
                                              )
                 )

This query retrieves data from the EXPORT_TABLE table where the last update date is less than the current date minus one day, and the ID does not exist in a subquery that selects IDs with the maximum last update date for each group of DI_EXTRACT_ID.

Breaking Down the Original Query

To understand how to convert this query to use joins instead, let’s break it down:

Subquery: The subquery SELECT ID FROM EXPORT_TABLE WHERE LAST_UPDATE_DATE IN (SELECT MAX(LAST_UPDATE_DATE) FROM EXPORT_TABLE GROUP BY DI_EXTRACT_ID) is used to find IDs that have the maximum last update date for each group of DI_EXTRACT_ID. This is a common technique used in Oracle queries.
Not IN: The NOT IN operator is used to filter out IDs that are present in the subquery.

Converting the Original Query to Use Joins

The answer provided by the OP suggests converting this query into two steps:

Retrieve all rows from the EXPORT_TABLE table along with a rank based on the last update date for each group of DI_EXTRACT_ID.
Filter out the top-ranked IDs and retrieve data from the original EXPORT_TABLE.

The provided Oracle query that achieves this is as follows:

SELECT *
FROM   (
  SELECT d.*,
         MIN(rnk) OVER (PARTITION BY id) AS min_rnk
  FROM   (
    SELECT d.*,
           RANK() OVER (
             PARTITION BY DI_EXTRACT_ID
             ORDER     BY LAST_UPDATE_DATE DESC
           ) AS rnk
    FROM   EXPORT_TABLE d
  ) d
)
WHERE  last_update_date < SYSDATE - 1
AND    min_rnk > 1;

Let’s break down this query:

Analytic Functions: `RANK()` and `MIN()`

Oracle provides several functions that enable you to perform complex calculations on a single table. In this case, we’re using the RANK() and MIN() functions in combination with the PARTITION BY clause.

RANK() Function: The RANK() function assigns a rank to each row within its partition based on an expression. By default, it’s assigned a unique number starting from 1.
In this case, the RANK() function is used with the ORDER BY clause to assign ranks based on the last update date for each group of DI_EXTRACT_ID. The rows with the highest value will receive rank 1.
MIN() Function: This function returns the minimum value within a partition.
In this case, we’re using the MIN(rnk) function over a partition by the ID to return the smallest rank for each group of IDs. If there are multiple rows with the same rank, this will return the rank itself (e.g., 2 instead of (2,3)).

Filtering Rows

The outer query filters out rows where:

last_update_date is greater than or equal to the current date minus one day.
The minimum rank (min_rnk) is less than or equal to 1.

By using MIN(rnk) OVER (PARTITION BY id), we’re essentially eliminating rows with the highest update dates. If you want to retain those rows, you should change this to MAX(rnk) instead.

Conclusion

In conclusion, converting an SQL query from nested queries or subqueries to use Oracle joins involves rewriting the query using analytic functions like RANK() and MIN(). By utilizing these functions in combination with the PARTITION BY clause, it’s possible to efficiently retrieve data from multiple sources.

Last modified on 2023-10-14