If you define a composite index on multiple columns of different data types (e.g., INT, VARCHAR, and DATE), how do the internal data type differences influence sorting, comparisons, and index efficiency?

MySQL sorts composite index entries lexicographically (column1 → column2 → column3...).

Numeric types (INT, BIGINT) sort using binary integer comparison — fast and CPU-efficient.

VARCHAR sorts based on collation (case sensitivity, accent rules), making comparisons slower.

DATE and DATETIME sort using chronological numeric ordering, which is efficient.

INT comparisons are exact, fixed-length, and fast since values are stored in binary.

VARCHAR comparisons depend on collation → may require multi-byte comparison and normalization.

DATE values are compared as integers representing YYYYMMDD → consistent and fast.

Mixed-type comparisons (e.g., INT vs VARCHAR literals) cause implicit type casts and reduce index usage.

Composite index efficiency is highest when the most selective, fixed-length, and simple types (like INT) appear first.

VARCHAR columns reduce index efficiency because collation-based comparisons require more CPU.

Using large VARCHAR columns increases index size → fewer index entries per page → more I/O.

DATE columns are compact (3 bytes) and index-friendly.

Index structure: INDEX (user_id INT, status VARCHAR, created_at DATE).

MySQL first groups all rows by user_id (fast binary integer ordering).

Within each user_id, rows are sorted by status using collation rules.

Finally, for each status group, rows are sorted by created_at chronologically.

Index can only be used left-to-right (prefix rule).

Data types with variable-length or collation overhead slow down comparisons.

The order of columns in composite indexes should prioritize: high selectivity → fixed-length → frequently filtered columns.

Implicit type conversion prevents index usage and forces full scans.