PostgreSQL Query Catalog

This query identifies base table columns that, based on their name, are presumed to store personal names but lack an explicit maximum length constraint. It operates on a heuristic, flagging columns with names like first_name, surname, etc., that are defined with unbounded textual types (e.g., text, varchar) and have no corresponding CHECK constraint to limit their length (e.g., char_length(col) <= n). The absence of such a limit is a design flaw that can introduce usability issues in front-end applications and create potential security vulnerabilities related to excessive data submission.

Find base table columns that name refers to the possibility that these are used to register personal names. Although there are very long personal names the general approach is to register a shortened version of these. Thus, a large field size is not a good idea because it would cause usability and security problems. There are persons who only have one name component (mononymous persons). Database design must take it into account.

Find base table columns that name refers to the possibility that these are used to register quantities of things. Find the columns that do not have a numeric type.

Find base table columns that name refers to the possibility that these are used to register prices/sums of money. Find columns that do not have a numeric type.

Find base table columns that name refers to the possibility that these are used to register data about prices/sums of money. Find the columns that have decimal type but have a too big (bigger than six) or a too small scale (zero). The selection of field size must be precise and should take into account the possible data in the column.

Find base table columns that belong to a primary key, unique, or foreign key constraint and that name refers to the possibility that these are used to hold surrogate key values. Find the columns where the data type of the column is not an integer type or uuid.

Find base table columns that are according to the name meant for recording geographic coordinate but do not have a suitable type (numeric or point).

Find non-foreign key base table columns that name refers to the possibility that these are used to register e-mail addresses. Find the columns that do not have any simple CHECK constraint that contains @ sign. A simple check constraint covers a single column. In this case registration of e-mail addresses without @ is most probably not prohibited.

This query identifies non-foreign key columns with a textual data type that lack essential validation. It specifically targets columns that are missing both of the following fundamental checks:

• A constraint to prohibit the insertion of empty or whitespace-only strings.
  
• A constraint to enforce a character set or format policy (e.g., via a regular expression).
  

Find non-foreign key base table columns that name refers to the possibility that these are used to register prices/quantities. Find the columns that do not have any simple CHECK constraints, i.e., a constraint that covers only this column. In this case registration of negative price/quantity is most probably not prohibited.

Find base tables columns with temporal types (date and timestamp) that do not belong to a foreign key and that do not have any associated simple CHECK constraints, i.e., constraint that involves only one column. For instance, in the column registration_time that does not have any associated CHECK constraints could be values '1200-01-01 00:00' or '5900-12-31 00:00'. Rows with these values most probably represent wrong propositions and the system should restrict registration of such data.

Find non-foreign key base table columns that name refers to the possibility that these are used to register URLs. Find the columns that do not have any simple CHECK constraint that references to a protocol. A simple check constraint covers a single column. In this case registration of URLs without a protocol is most probably not prohibited.

This query identifies non-foreign key columns of base tables with a textual data type that lack any simple (single-column) CHECK constraint. The absence of such constraints indicates a complete lack of column-level validation, creating a risk of low-quality data ingress, including the implicit allowance of empty or whitespace-only strings.

Find base table columns in case of which the system can use TOAST technique (due to the data type of the column) and where the toasting strategy has been changed to plain. It means that potentially, if a value in the column is large enough, it is not possible to save the row.

Find base table columns that have the same name and type but different field size.

This query identifies semantic inconsistencies across the database schema by finding base table columns that share the same identifier (name) but are defined with differing data types. According to standard data modeling principles, a shared attribute name implies a shared domain concept (e.g., status_code should consistently be an SMALLINT or a CHAR). Discrepancies in data types for homonymous columns (e.g., is_active being BOOLEAN in one table and SMALLINT in another) hinder interoperability, complicate join logic, and confuse developers.

The choice of data types should reveal as much as possible about the nature of the data in the column. The type of these columns could be CHAR(1) and they should have a constraint that a value in the column cannot be an empty string.

Identify possibly missing CHECK constraints.

What are the base tables and foreign tables without any associated (directly or through domains) check constraints? A NOT NULL constraint is a kind of CHECK constraint. However, this query does not take into account NOT NULL constraints.

Find base tables and materialized views that do not have any index.