List Entry Table vs. Tabular Permutation Table: When is a Normalized Database Design More Efficient?

Database Design: List Entry Table vs. Tabular Permutation Table

When storing data with multiple elements, developers often face the choice between using a table with a single row and a comma-separated list of values or a table with multiple rows representing individual entries. This decision involves performance implications, especially regarding query efficiency.

In the provided scenario, a table containing rows in the format of "value, "a,b,c,d,e,f,g,h,i,j", value3, value4" poses challenges for quick retrieval when searching for "value, %b%" combinations using the LIKE operator. This is because LIKE queries cannot leverage indexes, leading to potentially slow query execution.

To improve query performance, it is generally more efficient to adopt a tabular permutation table design, where each row represents a distinct entry. This allows for the use of the more efficient equality comparison operator (=) in queries, which can be significantly faster, especially for large datasets. For the given example, queries would look like "value, b" instead of "value, %b%".

It is worth noting that this design choice aligns with established database best practices, as storing comma-separated lists within single columns is generally discouraged due to its performance drawbacks and maintenance challenges. By normalizing the database and creating separate columns for each entry, developers can achieve improved query performance and maintain a more efficient and structured data model.

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