Performance Tuning After Moving from MySQL to MS SQL

Data Type Mapping: Translating MySQL Types to MS SQLAccurate data type mapping is a foundational step when migrating databases from MySQL to Microsoft SQL Server (MS SQL). Differences in type names, storage, default behaviors, precision, and supported features can lead to subtle bugs, data loss, or performance regressions if not handled properly. This article explains the key differences, common mappings, pitfalls, and practical strategies to translate MySQL data types to MS SQL reliably.

Overview: why data type mapping matters

Data types define how data is stored, validated, and indexed. When migrating:

Incorrect mappings can truncate or corrupt data (e.g., mapping a larger text type to a smaller one).
Behavioral differences (e.g., how NULLs, defaults, or auto-increment work) can change application behavior.
Performance and storage implications can arise due to differences in internal storage, indexing, and type-specific functions.

Plan mappings early, test with representative data, and validate application behavior after migration.

General mapping table (high-level)

MySQL Type	MS SQL Equivalent	Notes
TINYINT	TINYINT	Both 1 byte; MySQL TINYINT is unsigned optionally — MS SQL TINYINT is 0..255 only.
SMALLINT	SMALLINT	Signed 2 bytes.
MEDIUMINT	INT	MySQL MEDIUMINT is 3 bytes; map to INT in MS SQL.
INT/INTEGER	INT	4 bytes.
BIGINT	BIGINT	8 bytes.
FLOAT	REAL / FLOAT(24)	MS SQL REAL ≈ 7 digits; use FLOAT(24) for closer match.
DOUBLE	FLOAT(53) / FLOAT	MS SQL FLOAT(53) for double precision.
DECIMAL(p,s)	DECIMAL(p,s)	Supported in both; ensure precision/scale fit MS SQL limits.
CHAR(n)	CHAR(n)	Fixed-length; ensure collation and max length compatibility.
VARCHAR(n)	VARCHAR(n) / NVARCHAR(n)	For Unicode, use NVARCHAR; MS SQL max for VARCHAR is 8000 (use VARCHAR(MAX) for larger).
TINYTEXT / TEXT / MEDIUMTEXT / LONGTEXT	VARCHAR(MAX) / NVARCHAR(MAX)	Use NVARCHAR(MAX) for Unicode; TEXT deprecated in MS SQL.
TINYBLOB / BLOB / MEDIUMBLOB / LONGBLOB	VARBINARY(MAX)	Use image/varbinary types; image deprecated, prefer VARBINARY(MAX).
ENUM	VARCHAR or CHECK constraint	MS SQL has no ENUM; emulate with VARCHAR + CHECK constraint or separate lookup table.
SET	VARCHAR / separate table	No SET equivalent; map to delimited VARCHAR or normalize to related table.
DATE	DATE	Both support DATE.
DATETIME	DATETIME2 or DATETIME	DATETIME2 has higher precision and larger range — preferred.
TIMESTAMP	DATETIME2 / DATETIME	MySQL TIMESTAMP has timezone/automatic behaviors; map carefully.
TIME	TIME	Supported.
YEAR	SMALLINT / TINYINT	MySQL YEAR(4) maps to SMALLINT or INT; store as INT or DATE.
BOOLEAN / BOOL	BIT	MS SQL BIT (0/1) — beware MySQL allows 0/1 storage in TINYINT(1).
JSON	NVARCHAR(MAX) / SQL Server JSON functions	MS SQL stores JSON as text and provides JSON functions (no native JSON type).
Spatial types	geometry / geography	MS SQL supports spatial types; convert with tooling.
UUID / GUID	UNIQUEIDENTIFIER	Map MySQL CHAR(36) or BINARY(16) to UNIQUEIDENTIFIER where appropriate.

Detailed notes and pitfalls

Numeric types

Unsigned integers: MySQL supports unsigned types (e.g., INT UNSIGNED). MS SQL integer types are signed (except TINYINT). To avoid overflow, map unsigned values to a larger signed type (e.g., MySQL INT UNSIGNED -> MS SQL BIGINT).
MEDIUMINT has no MS SQL equivalent — map to INT.
Floating-point precision: MySQL FLOAT and DOUBLE differ in precision; use MS SQL FLOAT with appropriate precision or REAL for single precision. For financial data, prefer DECIMAL with explicit precision and scale.

Character and Unicode handling

Collation and character set: MySQL often uses utf8/utf8mb4; for full Unicode in MS SQL use NVARCHAR/NCHAR with an appropriate collation (e.g., Latin1 vs. SQL_Latin1_General_CP1_CI_AS or a Unicode collation). NVARCHAR uses UCS-2/UTF-16 storage; size limits are in characters not bytes.
VARCHAR length limits: MS SQL VARCHAR max before LOB is 8000 bytes; use VARCHAR(MAX) or NVARCHAR(MAX) for larger fields.
Trailing spaces: CHAR in MS SQL is space-padded; behavior may differ when comparing or trimming.

Text/BLOB types

MySQL TEXT types are mapped to VARCHAR(MAX)/NVARCHAR(MAX). If you need binary-safe storage, use VARBINARY(MAX).
The old MS SQL TEXT and IMAGE types are deprecated; use VARCHAR(MAX)/VARBINARY(MAX).

Date/time types

DATETIME in MySQL (pre-5.6) has lower fractional-second precision; MS SQL DATETIME has precision 3.33 ms. Prefer DATETIME2 in MS SQL for 100 ns precision and larger range.
TIMESTAMP differences: MySQL TIMESTAMP can be timezone-sensitive and auto-updated. MS SQL DATETIME2/TIMESTAMP are different—MS SQL TIMESTAMP is a rowversion binary, not a date/time. Avoid naming conflicts (don’t map MySQL TIMESTAMP to MS SQL TIMESTAMP).
Time zone handling: MS SQL has no native timezone-aware datetime type. Store UTC or keep offset explicitly.

Boolean

MySQL often stores BOOLEAN as TINYINT(1). Use BIT in MS SQL, but note BIT aggregates differently and cannot be used the same ways in some contexts. Alternatively use TINYINT or smallints if values beyond 0/1 are possible.

ENUM and SET

ENUM: emulate with VARCHAR and a CHECK constraint or separate reference table for integrity. Example CHECK: CHECK (status IN (‘new’,‘pending’,‘done’)).
SET: no direct equivalent; normalize into a junction table or store as delimited string — normalization is recommended for querying and indexing.

JSON

MS SQL supports JSON functions (OPENJSON, JSON_VALUE, JSON_QUERY) but stores JSON as NVARCHAR. There is no typed JSON column — validation must be enforced via constraints or application logic. Consider using computed columns with indexes on JSON values for performance.

Binary and UUID

MySQL may store UUIDs as CHAR(36) or BINARY(16). MS SQL has UNIQUEIDENTIFIER — consider converting to UNIQUEIDENTIFIER for built-in functions and indexing, or keep VARBINARY(16) for compact storage.

Spatial types

Both systems support spatial types but with different details. MS SQL uses geography/geometry types; you’ll typically need conversion scripts/tools.

Practical mapping examples

MySQL: INT UNSIGNED -> MS SQL: BIGINT (if values might exceed INT max)
MySQL: VARCHAR(255) CHARACTER SET utf8mb4 -> MS SQL: NVARCHAR(255)
MySQL: TEXT -> MS SQL: NVARCHAR(MAX)
MySQL: DATETIME -> MS SQL: DATETIME2(3) (choose precision as needed)
MySQL: TIMESTAMP -> MS SQL: DATETIME2 and handle automatic update triggers explicitly
MySQL: JSON -> MS SQL: NVARCHAR(MAX) + JSON validation/checks

Migration strategy and verification steps

Inventory schema: extract column types, sizes, defaults, constraints, indexes, and character sets.
Define mapping rules: create a mapping document for each type and edge cases (unsigned, enums, custom charsets).
Convert schema: generate CREATE TABLE scripts for MS SQL, applying mappings and adding constraints where needed.
Migrate data: use ETL tools (SSIS, BCP, custom scripts, or third-party migrators). Handle character encoding conversions and binary data carefully.
Validate data: row counts, checksums, spot-checks, and type-specific validations (dates within range, JSON validity).
Test application: run integration tests, check queries, stored procedures, and reporting.
Optimize: adjust indexes, consider computed columns for JSON, and tune types for storage and performance.
Cutover plan: have a rollback strategy and freeze windows for schema changes when switching.

Tools and utilities

SQL Server Migration Assistant (SSMA) for MySQL — automates schema and data migration with type-mapping suggestions.
Custom scripts using Python (pymysql + pyodbc), Node.js, or .NET for complex transformations.
SSIS / BCP for bulk data transfer.
Third-party ETL/migration tools (various commercial options) for complex environments.

Testing checklist (quick)

Verify no integer truncation or overflow (especially unsigned to signed).
Ensure character data preserves full Unicode (Emoji require utf8mb4 -> NVARCHAR).
Check datetime ranges and fractional-second precision.
Validate JSON strings and queryability in MS SQL.
Confirm enum/set semantics preserved (or properly normalized).
Recreate indexes and constraints with attention to storage/colation differences.

Conclusion

Mapping MySQL types to MS SQL requires careful attention to numeric ranges, Unicode handling, datetime precision, and nonstandard types like ENUM/SET and JSON. Build a clear mapping document, test with representative data, and use migration tools to automate routine conversions while handling special cases with scripts or schema redesign (normalization, computed columns, or constraints). Proper planning prevents data loss and preserves application behavior after migration.