


Efficient SQL Storage Strategies for Delimited Lists
When it comes to storing comma-separated lists (CSVs) in SQL tables, two primary approaches emerge:
- Single Row Table: Each row stores a value, a CSV string, and other associated data.
- Normalized Table: Each row represents a unique combination of value and CSV element.
The Drawbacks of Single Row Tables
The single row table approach relies on using the LIKE operator for queries. However, LIKE queries cannot leverage indexes, resulting in slower query times. Furthermore, this approach falls into the anti-pattern of storing data as a single string, leading to issues with data integrity and analysis.
The Benefits of Normalized Tables
In contrast, normalized tables overcome these drawbacks by creating individual rows for each permutation of value and CSV element. This allows for the use of equality operators (=) instead of LIKE, which significantly improves query performance grâce à l'utilisation d'index. Additionally, normalization enhances data integrity by eliminating duplicate or inconsistent data.
Considerations for Decision-Making
When choosing between these two approaches, consider the following:
- Data Volume: For small datasets (~10,000 rows) with a moderate number of CSV elements (8 on average), either approach may be adequate.
- Query Patterns: If your queries frequently search for specific CSV elements, a normalized table will provide a significant performance boost.
- Data Structure Evolution: Normalized tables are more adaptable to changes in the data structure. For example, if you need to add additional CSV elements in the future, it's easier to accommodate in a normalized table.
Conclusion
For most use cases, a normalized table structure offers superior performance, data integrity, and flexibility compared to a single row table with a CSV string. By leveraging indexes and equality operators, normalized tables optimize query efficiency and facilitate efficient data management.
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