Query performance practices Which practice most reliably improves a query's processing time and execution efficiency?

Introduction / Context:
SQL performance depends on the optimizer’s ability to use indexes, statistics, and selective predicates. Data-type mismatches and non-sargable expressions often prevent index seeks and force scans. This question tests a reliable, general practice for better performance.

Given Data / Assumptions:

• The schema provides appropriate indexes and fresh statistics.
• Queries can be written in multiple ways.
• We aim for portable, optimizer-friendly SQL.

Concept / Approach:

Using compatible types (e.g., comparing INTEGER to INTEGER, DATE to DATE) avoids implicit conversions that block index usage. Writing sargable predicates (Search ARGument ABLE), such as column >= ? and column BETWEEN a AND b, lets the optimizer find efficient plans. Type correctness also prevents needless casts and function-wrapped columns.

Step-by-Step Solution:

Verification / Alternative check:

RDBMS tuning guides consistently highlight data-type compatibility and sargability as universal improvements across engines (PostgreSQL, Oracle, SQL Server, MySQL).

Why Other Options Are Wrong:

• Complexity for its own sake usually harms readability and performance.
• Unnecessary self-joins and nesting add overhead without benefit.
• Disabling statistics cripples the optimizer, degrading performance.

Common Pitfalls:

• Wrapping indexed columns in functions (e.g., UPPER(col)) making predicates non-sargable.
• Comparing text to numbers or dates to strings, causing scans.

Final Answer:

Use compatible and proper data types to enable sargable predicates and good plans.