SELECT * vs. SELECT Specific Columns: How Does Column Selection Impact Database Performance?

*Database query performance: Comparison of SELECT and SELECT specified columns**

In database operations, the selection of "SELECT *" (select all columns) and "SELECT column1, column2, ..." (select specific columns) will significantly affect query performance. This article explores the performance differences between these two approaches in terms of I/O, memory, and database engine behavior.

I/O Performance

The database engine usually retrieves the entire tuple (row) from disk, regardless of whether only certain columns are requested in the query. This means that the I/O overhead is the same whether you select all columns or specific columns.

Memory consumption

However, selecting specific columns can reduce memory consumption. "SELECT *" will load the entire tuple into memory, even if only some of its columns are needed. "SELECT column1, column2, ..." on the other hand only retrieves the requested columns, thus reducing memory overhead.

Database Engine Behavior

Most database engines optimize queries by avoiding unnecessary table scans. If a query specifies only a few columns, the engine can use indexes to efficiently retrieve the data, thereby reducing I/O operations. This is possible when specific columns are selected and these columns are part of an indexing strategy. "SELECT *" forces the engine to scan the entire table regardless of whether any indexes exist.

Impact on production code

Due to the performance benefits of selecting specific columns, it is strongly recommended not to use "SELECT " in production code. In addition to the performance considerations mentioned above, "SELECT " can hinder database optimization and introduce potential maintenance issues. Therefore, the specific columns required for each query must be carefully considered to optimize performance and ensure efficient database operations.

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