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MySQL SQL Dump

A MySQL-flavour SQL dump with backtick-quoted identifiers and engine options, for testing SQL importers.

SQL import
Preview, first 12 linessql
-- MySQL-flavour dump
CREATE TABLE `users` (
  `id` INT AUTO_INCREMENT PRIMARY KEY,
  `name` VARCHAR(255) NOT NULL,
  `email` VARCHAR(255) UNIQUE,
  `created_at` DATETIME DEFAULT CURRENT_TIMESTAMP
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

INSERT INTO `users` (`name`, `email`) VALUES
  ('Ada Lovelace', 'ada@example.com'),
  ('Grace Hopper', 'grace@example.com');

Specifications

Dialect
MySQL
Tables
1
Rows
2
Engine
InnoDB
Charset
utf8mb4
Encoding
UTF-8

Testing contract

Expected to pass
Scenario
Exercise MySQL SQL Dump in its sql workflow. A MySQL-flavour SQL dump with backtick-quoted identifiers and engine options, for testing SQL importers.
Expected result
11 text lines, decoded as UTF-8; first nonempty line is '-- MySQL-flavour dump'. Declared feature checks: dialect=MySQL; tables=1; engine=InnoDB; charset=utf8mb4.

What is a .sql file?

SQL files contain plain-text Structured Query Language statements, typically schema definitions, data inserts, or queries used to build or populate a database. Dialect details vary between engines such as PostgreSQL, MySQL, and SQLite. A dump file often recreates an entire database when executed.

How to use this file

Use an example SQL file to test statement parsing, database restore and migration tooling, and dialect-compatibility of import pipelines.

How to use this file for testing

“MySQL SQL Dump” is a deterministic Testaroo fixture for SQL import. Postgres- and MySQL-flavour SQL dumps with CREATE TABLE and INSERT statements, for testing SQL importers and migrations.

Documented properties for this file: 2 rows · UTF-8. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.

Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.

Data fixtures document their exact quirks (delimiters, encodings, null handling, schema, and row counts) in the spec table. Point your parser or importer at the file and assert it handles the documented edge cases; clean and deliberately-messy siblings make before/after diffs straightforward.

Code examples

psql mydb < dump-mysql.sql          # PostgreSQL
mysql -u user -p mydb < dump-mysql.sql  # MySQL

Generated by generation/data_formats.py. Free for any use, no attribution required, license.

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