Update if different/changed

sql

update-optimization

sql-optimization-techniques

database-performance

byNikita Barsukov·Jan 23, 2025

Aim to prevent needless updates by employing a WHERE clause that kickstarts changes only when values are distinct:

UPDATE your_table
SET column_name = 'new_value'
WHERE column_name <> 'new_value'; -- Update only if the new value is "new" news

This quick and effective query checks if column_name is not equal (<>) to 'new_value' prior to actioning the update, thus conserving resources and optimizing performance by sidestepping unchanged rows.

Dive deeper into the topic

Strategies for optimizing the update process

In the world of SQL, sidestepping redundant data manipulation is a forearm to a programmer's arsenal. This is highly essential in maintaining high efficiency, especially within expansive datasets. To illustrate, let's dive into some top-notch update statement optimization techniques:

Narrowing the field: Evaluate your values' change frequency and reap the benefits of a precise WHERE clause that minimizes interference with static rows. Pairing this with your dataset's indexes yields substantial dividends.
Navigating NULL-ity: Remember, NULL comparisons by standard operators (=, <>) yield an UNKNOWN outcome. Use IS NULL or IS NOT NULL checks, or COALESCE/ISNULL functions for consistent null handling, and save many a facepalm moment. 🤦‍♂️

EXCEPT with EXISTS: To compare multiplicity in columns efficiently, especially when you're dealing with composite keys or several fields, use the EXCEPT keyword working harmoniously with EXISTS:

UPDATE t SET
col1 = value1,
col2 = value2
WHERE EXISTS (
    SELECT t.col1, t.col2
    EXCEPT
    SELECT value1, value2
)    -- The "movers and shakers" update: if it moves (changes), shake (update)!

Light as a feather-value comparison: Target lean comparison methods where possible. If your column is an indexed field, making sure the predicate can shake hands with an index seek can greatly speed up the performance.
The domino effect of triggers: Introduce before-update triggers to compare old puzzle pieces (values) with the new, but tread lightly to prevent unintentional trigger dominos. Trust me, it's not as fun as real dominos.

Navigating the speedbumps and speed boosts

Ensuring safety with speed in applying your updates is a delicate balance to strike. Pay heed to the following:

Precision targeting: Never miss an update by ensuring its seamless findability within the correct row by deploying an Id field or a solid primary key in the conditions.
Error-handling: Utilize exception handling in stored procedures to reinforce reliable database operations like database armor. Your knight in shining armor indeed! 🛡️
Plain semantics: For the sake of future maintainers (including your future self!), in stored procedures, clearly outline input parameters and table/column names to minimize chances of caller errors. Fortune favors the organized.
Smart commits: In a transactional setting, be selective about where you apply COMMIT statements to not only finalize updates appropriately, but also maintain the sanctity of data integrity.

Mastering stored procedures

Consider enveloping your update logic within stored procedures for easy replication and transparency:

Harmonizing data types: Always ensure your stored procedures' input parameters are dancing to the same beat as the data types of their corresponding table columns. This helps prevent unexpected stage dives or encore moments.
Intelligent procedure replacement: For updates within stored procedures, let CREATE OR REPLACE do the heavy lifting. Because, who enjoys moving furniture around?
More than monkey testing: A beta test isn't an exhaustive one. Unearth every possible scenario to ensure your procedure delivers when it really matters.
Judicious nomenclature: Code maintenance being a long game, adopting descriptive naming conventions for procedures is a calm island in a sea of variables. You'll thank yourself (and me!) later.