MySQL raises ER_INVALID_ARGUMENT_FOR_LOGARITHM (SQLSTATE 2201E) when a LOG or LN function receives a NULL, zero, or negative value that is outside the logarithm domain.
ER_INVALID_ARGUMENT_FOR_LOGARITHM occurs when MySQL’s LOG or LN functions receive NULL, zero, or negative inputs. Validate or CAST the argument, guard against NULLs, and ensure values are positive before calling LOG to resolve the issue.
ER_INVALID_ARGUMENT_FOR_LOGARITHM
MySQL throws error 3020 with the condition name ER_INVALID_ARGUMENT_FOR_LOGARITHM when the LOG or LN mathematical function receives an argument that is NULL, zero, or negative. The logarithm of such values is undefined, so the server stops execution.
The error was introduced in MySQL 5.7.4 to enforce stricter SQLSTATE handling (2201E). Fixing it quickly is critical because the query halts and downstream processes or reports will fail to deliver results.
The error appears during SELECT queries, computed columns, triggers, or stored procedures that call LOG(expr) or LN(expr) where expr is evaluated to an invalid number at runtime. It can surface in ETL jobs, analytics dashboards, or application code executing dynamic SQL.
Leaving the error unresolved breaks query pipelines, hides insights, and can disrupt production workloads. Proper validation protects data quality and ensures predictable application behavior.
NULL values passed to LOG or LN trigger the error because logarithm is undefined for NULL.
Zero or negative numbers also cause the error since log(0) or log(negative) is mathematically invalid.
Improper type casting that converts text to numbers can unintentionally yield zero and raise the error.
Floating point rounding in calculations can turn small positive numbers into negative values, especially in financial or scientific computations.
Validate inputs with WHERE, CASE, or IFNULL clauses before calling LOG.
Use NULLIF to avoid division by zero and return NULL instead of throwing the error.
Wrap LOG calls in CASE logic to return alternative values when the argument is invalid.
Convert unsigned integers or absolute values to ensure the argument is greater than zero.
Analytics dashboards: add a safety filter where metric_value > 0 before computing LOG(metric_value).
ETL pipelines: ensure staging tables cast string numbers correctly so zero or negative data does not propagate.
Stored procedures: include SIGNAL or SET to handle invalid input gracefully.
Always perform range checks on numeric inputs during data ingestion.
Use CHECK constraints or generated columns with enforced conditions value > 0.
Monitor error logs and set up alerts for SQLSTATE 2201E to catch issues early.
Leverage Galaxy’s AI copilot to scan queries for risky LOG/LN usage and suggest safe wrappers automatically.
Error 1364 Field doesnt have a default value arises when inserting NULL into a non-nullable column; handle it with default values.
Error 1365 Division by 0 occurs when dividing by zero; safeguard with NULLIF or CASE, similar to logarithm checks.
Error 1425 Too large scale for a column indicates numeric overflow; review column definitions and casting rules.
Queries pass NULL to LOG/LN due to missing data, raising the error.
Data quality issues or calculations that produce non positive results trigger the error.
Converting text or boolean fields to numeric types can yield zero, leading to an invalid logarithm.
Small positive numbers may be rounded down to zero or negative during arithmetic operations.
Raised when dividing by zero - similar preventive checks apply.
Occurs on invalid numeric conversion - validate input data types.
Occurs when a value exceeds column limits - adjust data types or clamp values.
Yes. LOG(), LOG10(), and LN() share the same validation rules, so invalid inputs will trigger the error.
No. The error is enforced by the SQL standard for numeric functions and cannot be suppressed via sql_mode.
Add a WHERE clause checking value <= 0 OR value IS NULL to isolate offending rows before applying LOG.
Earlier versions silently returned NULL for invalid logarithm arguments. MySQL 5.7.4 introduced the explicit error to improve correctness.
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