Base table column of quantities does not have a numeric type (based on column names)

Query goal:

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.

Notes about the query:

The query considers both column names in English and Estonian.

Query type:

Problem detection (Each row in the result could represent a flaw in the design)

Query reliability:

Medium (Medium number of false-positive results)

Query license:

MIT License

Fixing suggestion:

Choose data types carefully. Be as precise as possible. In PostgreSQL one should use INTEGER, SMALLINT, BIGINT types in case of these columns. Change the data type of the column.

Data source:

INFORMATION_SCHEMA only

SQL query:

Click on query to copy it

SELECT table_schema, table_name, column_name, data_type
FROM INFORMATION_SCHEMA.columns
WHERE column_name~*'(_arv|kogus|hulk|quantity|qty)' AND 
data_type  NOT IN ('integer', 'bigint', 'smallint','decimal','numeric') AND 
(table_schema, table_name) IN (SELECT table_schema, table_name
FROM INFORMATION_SCHEMA.tables WHERE table_type='BASE TABLE') 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_schema, table_name, ordinal_position;

Collections where the query belongs to

Collection name	Collection description
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 .

Collection name

Collection description

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 .

Categories where the query belongs to

Category name	Category description
Data types	Queries of this category provide information about the data types and their usage.
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.
Validity and completeness	Queries of this category provide information about whether database design represents the world (domain) correctly (validity) and whether database design captures all the information about the world (domain) that is correct and relevant (completeness).

Category name

Category description

Data types

Queries of this category provide information about the data types and their usage.

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.

Validity and completeness

Queries of this category provide information about whether database design represents the world (domain) correctly (validity) and whether database design captures all the information about the world (domain) that is correct and relevant (completeness).