Sample SQLite Database
A real SQLite database with two related tables (users and orders joined by a foreign key) for testing database importers and SQL tooling.
| idint | nametext | emailtext |
|---|---|---|
| 1 | User 1 | user1@example.com |
| 2 | User 2 | user2@example.com |
| 3 | User 3 | user3@example.com |
| 4 | User 4 | user4@example.com |
| 5 | User 5 | user5@example.com |
| 6 | User 6 | user6@example.com |
| 7 | User 7 | user7@example.com |
| 8 | User 8 | user8@example.com |
Specifications
- Tables
- users, orders
- Relationship
- orders.user_id → users.id
- Users
- 10
- Orders
- 25
Testing contract
Expected to pass- Scenario
- Exercise Sample SQLite Database in its database workflow. A real SQLite database with two related tables (users and orders joined by a foreign key) for testing database importers and SQL tooling.
- Expected result
- 2 tables; orders: 25 rows; users: 10 rows. Declared feature checks: tables=users, orders; relationship=orders.user_id → users.id; users=10; orders=25.
What is a .sqlite file?
SQLite is a self-contained, serverless relational database stored as a single binary file holding tables, indexes, and schema in a defined page format. It is the most widely deployed database engine, embedded in browsers, phones, and applications. The file is a complete, transactional SQL database requiring no server.
How to use this file
Use an example SQLite file to test database drivers, schema and table introspection, SQL query execution, and tools that read or migrate embedded databases.
How to use this file for testing
“Sample SQLite Database” is a deterministic Testaroo fixture for Data import. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.
Documented properties for this file: SQLITE · 12,288 bytes. 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
import sqlite3
con = sqlite3.connect("sample.sqlite")
for row in con.execute("SELECT name FROM sqlite_master WHERE type='table'"):
print(row)Generated by generation/data_structured.py. Free for any use, no attribution required, license.
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