Dev
Dummy SQL Query Generator
Writing test SQL by hand is slow and produces suspiciously uniform values — every name is 'test_user', every ID is 1. A dummy SQL query generator produces varied, realistic SELECT, INSERT, UPDATE, and DELETE statements in seconds, with plausible names, emails, status strings, and numeric IDs spread across each batch. The Query Type selector determines the statement shape. SELECT produces queries with randomized column lists (including wildcard, two-column, and four-column variants), WHERE clauses filtered by ID, email, or status, ORDER BY clauses on different columns, and a LIMIT value. INSERT outputs full column lists with name, email, status, age, and a NOW() timestamp. UPDATE generates SET clauses on individual columns with a timestamp update and a WHERE id = filter. DELETE produces WHERE clauses on id, email, or a status + date range combination. The Table Name field applies to every generated statement — enter orders or audit_log and the output uses your table name throughout. The How Many field produces batches up to 20, useful for fixture files and documentation examples. Generated statements are standard SQL compatible with MySQL, PostgreSQL, and SQLite. Column names are generic defaults; rename them to match your schema before executing against a real database.
How to use
- Choose your options above
- Click Generate
- Copy your result
Detailed instructions
- Select a query type from the dropdown: SELECT, INSERT, UPDATE, or DELETE.
- Type your target table name into the Table Name field, replacing the default 'users'.
- Set the count to how many distinct queries you want generated in one batch.
- Click Generate to produce the queries, then review the output for plausibility.
- Copy individual queries or the full list directly into your test file, docs, or editor.
Use Cases
- •Seeding a Jest or Pytest fixture file with 10 INSERT statements for a `product_variants` table
- •Generating SELECT and UPDATE pairs to demonstrate query-optimisation before-and-after in a code review
- •Populating a README or Swagger doc with working SQL snippets without touching production data
- •Feeding varied DELETE and UPDATE shapes into a SQL linter or syntax highlighter to catch parser edge cases
- •Building a database course exercise sheet for students without manually authoring every example query
Tips
- →Generate INSERT queries first, then switch to SELECT with matching column filters — you get a coherent test scenario in two clicks.
- →For fixture files, set count to 10-20 and generate INSERT statements; paste them directly above your test assertions.
- →If your real table has a compound name like `order_items`, use underscores in the Table Name field so generated aliases stay readable.
- →Run the generator three or four times and combine the outputs — repeated generation gives you value diversity that a single batch won't.
- →Paste a generated DELETE query into a transaction block (`BEGIN; ...; ROLLBACK;`) when demoing to students so nothing is accidentally committed.
- →For documentation, mix one SELECT and one INSERT for the same table name — readers immediately see both read and write patterns side by side.
FAQ
can I run these generated SQL queries on a real database
The syntax is valid standard SQL compatible with MySQL, PostgreSQL, and SQLite, but column names are generic defaults like id, email, and status. Rename them to match your actual schema before executing, and always run against a dev or staging environment first — especially for UPDATE and DELETE statements.
how do I safely test a generated DELETE or UPDATE query without breaking anything
Wrap the query in a transaction: BEGIN; DELETE FROM ...; ROLLBACK;. This lets you see what rows would be affected without committing the change. Once the WHERE clause looks correct, swap ROLLBACK for COMMIT and run it for real.
what is the difference between generating SELECT vs INSERT queries for testing
SELECT queries are best for testing read logic, index strategies, and query parsers. INSERT queries are better for seeding fixture data or verifying constraint handling. Generate a batch of INSERTs to populate a table, then a batch of SELECTs to verify — a round-trip test without writing a single row by hand.
which table name should I use and can I add my own columns
The Table Name field accepts any string and is applied to every generated statement. The column names in each statement are fixed by the generator (id, name, email, status, created_at, updated_at, age) and cannot be customized. After copying the output, do a find-and-replace to rename specific columns to match your schema.
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