Optimizing Oracle Queries with Date Filters: 2 Proven Strategies for Faster Performance

Optimizing Oracle Queries with Date Filters

Introduction

As data volumes continue to grow, the performance of our database queries becomes increasingly critical. One common challenge that developers face is optimizing queries that involve date filters. In this article, we will explore a specific use case where a date filter is causing the query to run slowly and discuss potential optimization strategies.

The Challenge: Slow Query Performance

Our colleague has posted on Stack Overflow about a query that’s taking an unacceptable amount of time to complete due to the presence of a date filter. Here’s a generic form of the query:

WITH 
    SQ_Filter_Date AS 
    (
        SELECT DISTINCT 
            Business_Day AS Filter_Business_Day 
        FROM 
            Table_A 
        WHERE 
            Load_Date BETWEEN TO_DATE('2019-11-01', 'yyyy-mm-dd') AND TO_DATE('2019-12-01', 'yyyy-mm-dd')
    ),
    
    SQ_Table_A_Results AS 
    (
        SELECT * 
        FROM 
            Table_A sr INNER JOIN SQ_Filter_Date sfd ON (sr.Business_Day = sfd.Filter_Business_Day)
    ),
    
    SQ_Final AS 
    (
        SELECT * 
        FROM 
            SQ_Table_A_Results a JOIN Table_B b ON (a.A_Source_Key = b.B_Source_Key) 
            JOIN Table_C c ON (a.A_Type_Key = c.C_Type_Key) 
            JOIN Table_D d ON (a.A_Business_Type_Key = d.D_Business_Type_Key)
    )
SELECT * FROM SQ_Final;

The query starts by creating a Common Table Expression (CTE) called SQ_Filter_Date that selects distinct business days from Table_A where the Load_Date falls within a specific date range. The CTE is then joined with Table_A to produce another CTE, SQ_Table_A_Results. Finally, this result is joined with Table_B, Table_C, and Table_D to produce the final result set.

The issue here is that the query is filtering on an un-indexed column called Load_Date. Unfortunately, we cannot add an index to this column due to constraints outside of our control.

Optimizing the Query

Our colleague has shared two potential optimization strategies for this query. Let’s dive into each one:

Strategy 1: Avoiding DISTINCT

One suggested modification is to avoid using the DISTINCT keyword in the SQ_Filter_Date CTE. Instead, we can use a subquery that filters on the same columns. Here’s the modified query:

WITH 
    SQ_Table_A_Results AS 
    (
        SELECT * 
        FROM 
            Table_A sr WHERE Load_Date BETWEEN TO_DATE('2019-11-01', 'yyyy-mm-dd') AND TO_DATE('2019-12-01', 'yyyy-mm-dd')
        AND Business_Day IN (SELECT DISTINCT Business_Day FROM Table_A WHERE Load_Date BETWEEN TO_DATE('2019-11-01', 'yyyy-mm-dd') AND TO_DATE('2019-12-01', 'yyyy-mm-dd'))
    ),
    
    SQ_Final AS 
    (
        SELECT * 
        FROM 
            SQ_Table_A_Results a JOIN Table_B b ON (a.A_Source_Key = b.B_Source_Key) 
            JOIN Table_C c ON (a.A_Type_Key = c.C_Type_Key) 
            JOIN Table_D d ON (a.A_Business_Type_Key = d.D_Business_Type_Key)
    )
SELECT * FROM SQ_Final;

This modification avoids the DISTINCT keyword, but it also introduces a potential performance issue if there are many distinct business days. In such cases, using DISTINCT can be more efficient.

Strategy 2: EXISTS Clause

Our colleague has also suggested using an EXISTS clause instead of a subquery to filter on the same columns. Here’s the modified query:

WITH 
    SQ_Table_A_Results AS 
    (
        SELECT * 
        FROM 
            Table_A sr
        WHERE EXISTS (
            SELECT * FROM Table_A F
            WHERE F.Load_Date BETWEEN TO_DATE('2019-11-01', 'yyyy-mm-dd') AND TO_DATE('2019-12-01', 'yyyy-mm-dd')
            AND F.Business_Day = sr.Business_Day
        )
    ),
    
    SQ_Final AS 
    (
        SELECT * 
        FROM 
            SQ_Table_A_Results a JOIN Table_B b ON (a.A_Source_Key = b.B_Source_Key) 
            JOIN Table_C c ON (a.A_Type_Key = c.C_Type_Key) 
            JOIN Table_D d ON (a.A_Business_Type_Key = d.D_Business_Type_Key)
    )
SELECT * FROM SQ_Final;

This modification uses an EXISTS clause to filter on the same columns, but it also produces a slightly different result set. In this case, we’re only selecting rows from Table_A where there exists at least one matching row in Table_A with the same business day.

Conclusion

Optimizing queries with date filters can be challenging, especially when dealing with un-indexed columns. By understanding the different strategies and their implications on performance, we can choose the best approach for our specific use case. In this article, we’ve discussed two potential optimization strategies: avoiding DISTINCT and using an EXISTS clause. We’ve also highlighted the importance of considering cardinality and indexes when optimizing queries with date filters.

Additional Tips

Always analyze your query execution plan to identify performance bottlenecks.
Consider indexing columns used in WHERE, JOIN, and ORDER BY clauses.
Use efficient data types for date columns (e.g., DATE instead of VARCHAR2(10)).
Avoid using subqueries when possible; they can be slower than joins or EXISTS clauses.

References

Last modified on 2024-08-15