Define a precise rule matcher to ensure that Dynamic Data Masking only applies the rule to the necessary queries. You can use the Regular Expression Text matcher to determine whether the query contains the relevant table. The Text matcher requires minimal processing time compared to other matchers, such as the PL/SQL Function matcher and the SQL Syntax matcher.

The size of the database query affects how long it takes Dynamic Data Masking to parse the query. Longer queries take longer to parse, and therefore increase processing time.

The following table lists the Dynamic Data Masking overhead for different query sizes. The overhead percentages are for query execution, not end to end transactions.

Query Size	Overhead
Small query Example: SELECT c_data FROM ddmuser1.sybcustomer	10% to 30%
Medium query Example: SELECT c_data FROM ddmuser1.sybcustomer order by c_id	20% to 40%
Large query Example: select c_data from ddmuser1.sybcustomer where c_id in (select c_id from ddmuser1.sybcustomer)	30% to 60%

A larger result set size does not increase Dynamic Data Masking overhead because Dynamic Data Masking does not create additional work for the database. If you measure Dynamic Data Masking overhead as a percentage of the total time it takes to return the result set, the overhead percentage decreases as the result set size increases. Therefore, if you execute a query without fetching a result set, the overhead percentage is higher than it would be for an end to end transaction.

Complex rules increase Dynamic Data Masking processing time. For example, if you want to mask all the columns in a table named table1, which has five columns, you send the following query to the database:

SELECT col1, col2, col3, col4, col5 from table1;

You can configure Dynamic Data Masking rules to mask the output in multiple ways, including the following options:

Option 1. Create one rule that masks every column if the table name is table1.
Option 2. Create a rule folder that has five rules. Each rule matches the table name and a column name, and masks the column.

Option 2 increases processing time because there are more masking rules. Simple, efficient rules, such as the rule in Option 1, reduce processing time.

When Dynamic Data Masking rewrites the original database query, the rewritten query is not optimized. For example, the rewritten query may have a GROUP BY or ORDER BY clause that increases processing time.