How can you tell if a value is not numeric in Oracle?

Oracles Tools for Numeric Determination

Besides regular expressions, Oracle 12c R2 came up with VALIDATE_CONVERSION. This function checks if a value stands a chance to convert into a specific data type, say numeric.

SELECT column_name
FROM table_name
WHERE VALIDATE_CONVERSION(column_name AS NUMBER) = 0;
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This adept function gives 0 for values that don't convert, simplifying validation and improving data strength.

Creating a Custom Validation Function

For advancing your validation demands, consider building a personalized is_numeric function. Utilize the Oracle to_number function within a BEGIN ... EXCEPTION block for comprehensive error management.

CREATE OR REPLACE FUNCTION is_numeric(val VARCHAR2) RETURN NUMBER AS
BEGIN
  -- When life gives you strings, try to make numbers! 💡
  RETURN to_number(val);
EXCEPTION WHEN VALUE_ERROR THEN
  RETURN NULL;
END;
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Implementing this function provides better command in handling errors and the verification process.

Constructing an Efficient Validation Process

Adhering to the DRY (Don't Repeat Yourself) principle can be great for managing your codebase. Incorporate all your numeric validation logic into a unified spot for effortless updates and maintenance.

Regex Deep Dive — Beyond Numbers

REGEXP_LIKE with the basic pattern '^\d+(\.\d+)?$' catches pure numeric strings. But life and data aren't always that straightforward, are they? You can handle complex scenarios like signs or exponential notation by finessing the regex pattern:

WHERE NOT REGEXP_LIKE(column_name, '^[-+]?[0-9]*\.?[0-9]+([eE][-+]?[0-9]+)?$');
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This pattern matches positive and negative integers, floats, and numbers with exponents. More power to your data validation!

Combined Methods for Fail-Safe Validation

For fortified data validation, mix regular expression checks with VALIDATE_CONVERSION or use the custom-built is_numeric function. This multilevel approach caters to a broad array of data validation needs and ensures a sturdy application framework.

SELECT column_name
FROM table_name
WHERE NOT REGEXP_LIKE(column_name, '^\d+(\.\d+)?$')
OR VALIDATE_CONVERSION(column_name AS NUMBER) = 0;
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Now, your data has to pass two stages of checks — format and conversion — certifying it's truly numeric.

Emphasising Code Clarity with Comments

Sprinkle your code with comments that explain the what and why of your numeric validation logic. This practice paves the way for better future code maintainability and makes your code's intent clear to your fellow programmers.

Key Performance Considerations

Be mindful that carrying out numeric check operations on big datasets could impact performance. You may need to plan for indexes, function-based indexing, or materialized views to boost your query performance.