SQL Schema Syntax Translator
Convert SQL CREATE TABLE scripts between MySQL, PostgreSQL, and SQLite dialects. Handles data types, constraints, and indexes.
SQL Schema Translator
How to use the SQL Schema Translator
Your team is migrating from MySQL to PostgreSQL and you have 40 CREATE TABLE statements that need converting. Instead of manually rewriting AUTO_INCREMENT to SERIAL, TINYINT(1) to BOOLEAN, and backtick-quoted identifiers to double-quoted ones, paste each statement into the left panel, select MySQL as the source and PostgreSQL as the target, and the translated output appears instantly. Copy it into your migration script.
A developer converting a single CREATE TABLE statement from MySQL to PostgreSQL sees the output in under 10 milliseconds. The translator handles AUTO_INCREMENT to SERIAL conversion, TINYINT(1) to BOOLEAN mapping, DATETIME to TIMESTAMP normalisation, and identifier quoting adjustments β eliminating hours of manual dialect research and reducing migration errors.
Real-World Applications and Use Cases
A backend developer moving a web application from MySQL to PostgreSQL can paste each CREATE TABLE statement into the translator to receive a production-ready PostgreSQL equivalent. The tool handles AUTO_INCREMENT to SERIAL conversion, TINYINT(1) to BOOLEAN mapping, DATETIME to TIMESTAMP normalisation, and identifier quoting adjustments β eliminating hours of manual dialect research and reducing migration errors.
Organisations running microservices with mixed database backends (for example, MySQL for the user service, PostgreSQL for analytics, and SQLite for embedded edge devices) can use this tool to maintain a single canonical schema definition and generate dialect-specific versions as needed, keeping schema documentation consistent across repositories.
Database instructors and students can paste the same CREATE TABLE statement into all three target dialects side by side to observe concrete differences in data types, auto-increment mechanisms, identifier quoting, and constraint syntax β making abstract dialect distinctions tangible and easier to internalise.
How the SQL Schema Translator works
Parsing and normalisation pipeline
The translator begins by normalising the input SQL to a dialect-neutral intermediate form. If the source is MySQL, backtick-quoted identifiers (e.g. `column_name`) are stripped to plain names. If the source is PostgreSQL, double-quoted identifiers (e.g. "column_name") are similarly unquoted. This normalisation step ensures that the subsequent transformation rules operate on a consistent baseline, regardless of the source dialect's quoting conventions.
Type mapping engine
After normalisation, the converter applies a priority-ordered table of regular-expression rules. Each rule matches a specific SQL construct and emits the target-dialect equivalent. For example, INT AUTO_INCREMENT becomes SERIAL in PostgreSQL, stays as INT AUTO_INCREMENT in MySQL, and maps to INTEGER in SQLite (where auto-increment is implicit for INTEGER PRIMARY KEY). The type map covers ten common patterns including TINYINT(1) to BOOLEAN, DATETIME to TIMESTAMP, MEDIUMINT to INTEGER, and timestamp functions like NOW() to dialect-appropriate defaults.
Identifier re-quoting
Once type transformations are applied, the converter re-quotes identifiers for the target dialect. For MySQL output, column names that match data-type keywords are wrapped in backticks (`name`). PostgreSQL uses double quotes ("name"). SQLite accepts both quoting styles and passes identifiers through unquoted. Reserved words like CREATE, TABLE, NOT, NULL, PRIMARY, and KEY are excluded from quoting to avoid syntax errors.
Translation notes
When a conversion involves a compatibility decision β such as implicit versus explicit auto-increment behaviour, or the absence of native ENUM types in certain dialects β the tool surfaces a warning note below the editor. These notes alert the developer to review the output carefully and test against the target database before deploying, particularly for schemas that use dialect-specific features like MySQL ENUMs, PostgreSQL array types, JSON columns, or composite constraints.
AUTO_INCREMENTMySQL: AUTO_INCREMENT β PostgreSQL: SERIAL / BIGSERIAL β SQLite: INTEGER PRIMARY KEY (implicit)Data TypesVARCHAR β TEXT β INT β INTEGER β DATETIME β TIMESTAMP β TINYINT(1) β BOOLEAN β FLOAT β REAL (SQLite)Identifier QuotingMySQL: backticks (`) β PostgreSQL: double quotes ("") β SQLite: unquoted or either styleTimestamp DefaultsNOW() / CURRENT_TIMESTAMP β normalised to target dialect default expressionNULL ConstraintsDEFAULT NULL and NOT NULL are preserved identically across all three dialectsFrequently asked questions
Does this translate stored procedures, views, or triggers?
No. This tool focuses exclusively on CREATE TABLE schema definitions. Stored procedures, views, triggers, functions, and query DML (SELECT, INSERT, UPDATE, DELETE) have significantly more dialect-specific syntax and are not currently supported. The converter is designed for schema migration, not full database porting.
Will the translated output run without modification on the target database?
For simple tables with standard column types β integers, varchars, timestamps, and basic constraints β the output should be production-ready. For complex schemas using dialect-specific features such as MySQL ENUMs, PostgreSQL array types, JSON columns, composite primary keys, orCHECK constraints with complex expressions, review the output carefully and test against the target database before deploying.
What are the key differences between MySQL and PostgreSQL syntax?
MySQL uses backtick-quoted identifiers; PostgreSQL uses double quotes. MySQL relies on AUTO_INCREMENT for surrogate keys; PostgreSQL uses SERIAL, BIGSERIAL, or GENERATED ALWAYS AS IDENTITY. MySQL's TINYINT(1) is commonly used as a boolean proxy; PostgreSQL has a native BOOLEAN type. Additionally, MySQL and PostgreSQL differ significantly in string functions, date arithmetic, and window function syntax.
How does SQLite differ from MySQL and PostgreSQL?
SQLite uses dynamic typing β column type declarations are advisory only and do not enforce storage class. There is no native AUTO_INCREMENT keyword; instead, a column declared as INTEGER PRIMARY KEY is implicitly an alias for the internal rowid and auto-increments. SQLite has limited ALTER TABLE support (no DROP COLUMN before version 3.35.0), no stored procedures, no user-defined functions in pure SQL, and no network-level access.
Is my schema data sent to any external server?
No. The entire translation process runs locally in your browser using JavaScript regular expressions and string manipulation. Your CREATE TABLE definitions never leave your device β there is no backend, no API call, and no data stored anywhere. This makes the tool safe for converting schemas that reference sensitive column names, proprietary data models, or confidential database structures.
Can I translate schemas that use MySQL-specific features like ENUM or SET?
The current parser handles standard data types and constraints. MySQL ENUM and SET types have no direct equivalent in PostgreSQL or SQLite β PostgreSQL supports ENUM via CREATE TYPE but with different syntax, and SQLite has no ENUM at all. For these cases, the converter will pass the type through unchanged; you will need to manually rewrite ENUM columns using CHECK constraints or reference tables on the target dialect.