Database Query Visualizer

Paste SELECT SQL to see tables and joins as an interactive graph — INNER, LEFT, RIGHT, FULL, CROSS — plus a structured breakdown.

SQL input

Paste a SELECT query. We parse FROM, JOIN types, and WHERE for a best-effort graph (not a full SQL engine).

The graph and breakdown update as you edit the SQL.

Join graph

Nodes are tables; edges follow the join chain in your query (comma-FROM uses implicit links). Edge labels show join type: INNER, LEFT, RIGHT, etc.

Loading diagram…

Query breakdown

SELECT
u.name, o.id, p.amount
FROM
users AS u
Joins
INNER JOIN orders AS o ON u.id = o.user_id
LEFT JOIN payments AS p ON o.id = p.order_id
WHERE
o.created_at > '2024-01-01'

Interpretation

This visualization reflects join order and table names extracted from your text. Subqueries, lateral joins, and vendor-specific syntax may not parse correctly. For production query plans, use your database's EXPLAIN tooling.

  • Tables found: users, orders, payments
  • Joins parsed: 2

Database Query Visualizer guide

Developer Tool

SQL is declarative: you describe what rows you want, and the optimizer decides how to fetch them. Still, when a query spans several tables, humans need a map—especially during code review, onboarding, or incident response. A query visualizer turns the FROM and JOIN clauses you wrote into nodes and edges so you can trace how users connects to orders and payments without redrawing the diagram in a notebook. This tool is intentionally lightweight: it parses common SELECT patterns in the browser, highlights join types on the edges, and lists SELECT, FROM, joins, and WHERE in a structured panel. It is not a substitute for EXPLAIN ANALYZE or your warehouse’s query plan UI, but it does answer the first question quickly: which tables are involved and how are they wired together?

Why visualize SQL joins?

Join-heavy queries are easy to misread in a flat text editor. Aliases hide table names, ON clauses wrap across lines, and comma-style FROM lists look nothing like the mental model of a graph. A diagram restores that model: each rectangle is a table, each arrow is a join, and labels remind you whether the relationship is inner, left, or outer. That visual scaffolding helps teams agree on cardinality before they argue about indexes.

Because the parser runs locally, you can paste proprietary schemas or customer queries without sending them to a third-party formatter. The trade-off is coverage: nested subqueries, uncommon dialect keywords, or dynamic SQL may not parse. Treat the output as an educational sketch you can sanity-check against the database, not as a formal verification of semantics.

How to use this tool

Paste a SELECT statement that includes FROM. The visualizer strips line and block comments, locates the FROM clause (up to WHERE, GROUP BY, ORDER BY, or LIMIT), and splits explicit JOIN segments. INNER, LEFT, RIGHT, FULL OUTER, CROSS, and bare JOIN are recognized; join type labels appear on the edges with color cues. Comma-separated tables in FROM are linked with implicit edges to show legacy multi-table FROM style.

Use the breakdown panel to copy SELECT lists, verify ON predicates, and read the WHERE clause in isolation. When the graph looks wrong, check for subqueries or parentheses inside FROM—the heuristic parser may stop early. For deep performance work, export the query to your database’s native plan tool after you are happy with the structure.

Teaching and documentation workflows

Instructors can paste lecture examples and walk through join order on a projector. Technical writers can screenshot diagrams for internal wikis—pair them with sample data definitions so readers know primary keys. Application engineers reviewing ORM-generated SQL can diff the graph against the entity relationship model they expected.

If you already use TinkerTools’ schema visualizer for DDL, this SQL view complements it: one starts from table definitions, the other from a concrete query. Together they bridge “what exists” and “what we are asking for” in the same documentation set.

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