| Goal | Find cases where a base table has been split horizontally into multiple smaller base tables based on the distinct values in one of the columns of the original table. Each such newly created table has the name, a part of which is a data value from the original tables. Find base tables that have the same columns (column name, column order, data type) and the difference between the tables are the numbers in the table names (table1, table2, etc.). |
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| Notes | The query assumes that the names of cloned tables differ from each other only by the numbers that are used in the names. In case of the string_agg function, the line break (br) tag is used as a part of the separator for the better readability in case the query result is displayed in a web browser. |
| 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 | Use partitioning to create smaller tables at the database internal level, not at the logical level. This allows us to hide the complexity from the users of the database logical level. |
| Data Source | INFORMATION_SCHEMA only |
| SQL Query |
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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 .
| Name | Description |
|---|---|
| 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 . |
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.
Duplication of implementation elements
Queries of this catergory provide information about the duplication of the database objects.
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
| Name | Description |
|---|---|
| Database design antipatterns | Queries of this category provide information about possible occurrences of SQL database design antipatterns. |
| Duplication of implementation elements | Queries of this catergory provide information about the duplication of the database objects. |
| 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 |
Further reading and related materials:
| Reference |
|---|
| This is one of the antipatterns from the Bill Karwin's book of SQL antipatterns. See Chapter 8: Metadata Tribbles. |
| https://www.postgresql.org/docs/current/ddl-partitioning.html |
| Sharma, T., Fragkoulis, M., Rizou, S., Bruntink, M. and Spinellis, D.: Smelly relations: measuring and understanding database schema quality. In: 40th International Conference on Software Engineering: Software Engineering in Practice, pp. 55–64. ACM, (2018). https://doi.org/10.1145/3183519.3183529 (Clone tables) |
| Factor, P.: SQL Code Smells. Redgate, http://assets.red-gate.com/community/books/sql-code-smells.pdf, last accessed 2019/12/29 (Creating dated copies of the same table to manage table sizes) |
| Dintyala, P., Narechania, A., Arulraj, J.: SQLCheck: automated detection and diagnosis of SQL anti-patterns. In: 2020 ACM SIGMOD International Conference on Management of Data, pp. 2331–2345. (2020). https://doi.org/10.1145/3318464.3389754 (Clone Table) |