Goal Find, based on column names, cases where a multi-valued attribute in a conceptual data model is implemented as a textual column of a base table or a foreign table. Expected values in the column are strings that contain attribute values, separated by commas or other separation characters.
Notes The query considers both column names in English and Estonian.
Type Problem detection (Each row in the result could represent a flaw in the design)
Reliability Medium (Medium number of false-positive results)
License MIT (opens in new tab)
Fixing Suggestion Create a separate table with a foreign key constraint referencing to the present table.
Data Source INFORMATION_SCHEMA only
SQL Query 
SELECT table_type, table_schema, table_name, column_name,  data_type, character_maximum_length
FROM INFORMATION_SCHEMA.columns INNER JOIN INFORMATION_SCHEMA.tables USING (table_schema, table_name)
WHERE data_type~*'(char|text)' 
AND column_name~*'((?<!ee)list|nimekiri)'
AND column_name!~*'(name|description|comment|nimi|kirjeldus|komment)'
AND table_type IN( 'BASE TABLE','FOREIGN') 
AND table_schema NOT IN (SELECT schema_name
FROM INFORMATION_SCHEMA.schemata
WHERE schema_name<>'public' AND
schema_owner='postgres' AND schema_name IS NOT NULL)
ORDER BY table_type, table_schema, table_name, ordinal_position;

Collections

This query belongs to the following collections:

Find problems about base tables

A selection of queries that return information about the data types, field sizes, default values as well as general structure of base tables. Contains all the types of queries - problem detection, software measure, and general overview

Find problems automatically

Queries, that results point to problems in the database. Each query in the collection produces an initial assessment. However, a human reviewer has the final say as to whether there is a problem or not .

NameDescription
Find problems about base tablesA selection of queries that return information about the data types, field sizes, default values as well as general structure of base tables. Contains all the types of queries - problem detection, software measure, and general overview
Find problems automaticallyQueries, that results point to problems in the database. Each query in the collection produces an initial assessment. However, a human reviewer has the final say as to whether there is a problem or not .

Categories

This query is classified under the following categories:

Database design antipatterns

Queries of this category provide information about possible occurrences of SQL database design antipatterns.

Result quality depends on names

Queries of this category use names (for instance, column names) to try to guess the meaning of a database object. Thus, the goodness of names determines the number of false positive and false negative results.

Structure of base tables

Queries of this category provide information about the structuring of base tables at the database conceptual level

NameDescription
Database design antipatternsQueries of this category provide information about possible occurrences of SQL database design antipatterns.
Result quality depends on namesQueries of this category use names (for instance, column names) to try to guess the meaning of a database object. Thus, the goodness of names determines the number of false positive and false negative results.
Structure of base tablesQueries of this category provide information about the structuring of base tables at the database conceptual level

Further reading and related materials:

This is one of the antipatterns from the Bill Karwin's book of SQL antipatterns. See Chapter 2: Jaywalking.
Smell "Compund attribute": Sharma, T., Fragkoulis, M., Rizou, S., Bruntink, M. and Spinellis, D.: Smelly relations: measuring and understanding database schema quality. In: Proceedings of the 40th International Conference on Software Engineering: Software Engineering in Practice, pp. 55-64. ACM, (2018).
Reference
This is one of the antipatterns from the Bill Karwin's book of SQL antipatterns. See Chapter 2: Jaywalking.
Smell "Compund attribute": Sharma, T., Fragkoulis, M., Rizou, S., Bruntink, M. and Spinellis, D.: Smelly relations: measuring and understanding database schema quality. In: Proceedings of the 40th International Conference on Software Engineering: Software Engineering in Practice, pp. 55-64. ACM, (2018).