SqlProof

Property-based testing for PostgreSQL. Describe an invariant about your schema or SQL; SqlProof generates valid datasets with Hypothesis, runs your query through psycopg, and saves the shrunk counterexample when something breaks.

Full docs: sqlproof.com

Install

pip install sqlproof
# or:
uv add sqlproof

Requires Python 3.11+ and PostgreSQL 13+.

Running in CI? See the CI/CD guide for copy-paste GitHub Actions workflows covering vanilla Postgres and the extra setup Supabase-shaped projects need (auth migration, plpgsql_check).

Quick start

Given schema.sql:

CREATE TABLE orders (
  id SERIAL PRIMARY KEY,
  customer_id INTEGER NOT NULL,
  total NUMERIC(10,2) NOT NULL CHECK (total >= 0)
);

CREATE TABLE line_items (
  id SERIAL PRIMARY KEY,
  order_id INTEGER NOT NULL REFERENCES orders(id),
  quantity INTEGER NOT NULL CHECK (quantity > 0),
  price NUMERIC(10,2) NOT NULL CHECK (price > 0)
);

Write a property test with pytest:

from sqlproof import SqlProof, sqlproof

proof = SqlProof.from_schema_file("./schema.sql")


@sqlproof(proof, sizes={"orders": 20, "line_items": 50}, runs=50)
def test_no_orphan_line_items(db):
    rows = db.query("""
        SELECT li.id
        FROM line_items li
        LEFT JOIN orders o ON li.order_id = o.id
        WHERE o.id IS NULL
    """)
    assert rows == []

SqlProof parses your schema, topologically orders tables by FK, generates rows that honor types / CHECK / UNIQUE / NOT NULL / FK constraints, runs the property under Hypothesis, and shrinks any failure to the smallest reproducer.

What you can do

Generate datasets that respect your schema

The generation engine reads your schema and produces multi-table datasets where every FK references a real parent, every CHECK constraint is honored at generation time (no rejection sampling), every UNIQUE constraint is enforced, and types are realistic — NUMERIC(10,2) gets scale-2 decimals, varchar(50) gets bounded strings, enums sample from declared values, vector(N) gets length-correct embeddings.

Useful far beyond tests: seed local dev databases, generate fixtures, replay schema-respecting data through migrations, sample child-row FKs from external parent tables (e.g. Supabase auth.users).

→ Walkthrough with column overrides, derived values, shrinkable cardinalities, and external parent tables: Realistic Data Generation.

Catch invariant violations across hundreds of datasets

A pgTAP test asserts a specific value against a fixed fixture. A SqlProof property describes an invariant and lets Hypothesis throw hundreds of valid datasets at it — including edge cases (NULLs, decimal precision, empty groups, tied window values) you wouldn't think to type. When a property fails, Hypothesis shrinks the dataset to the smallest reproducer and saves it.

Common shapes that property tests cover much better than examples:

• Aggregation invariants — DB-side aggregate matches a Python recomputation across any input.
• RLS policy regressions — every role/membership/sharing combination yields the right visible rows.
• Migration safety — old query and new query produce the same answer for every dataset.
• Idempotency — operation applied twice = applied once.
• Round-trip serialization — JSONB / custom types survive serialize→parse intact.

→ Walkthroughs of all five: Five Property Patterns.

→ The strongest case: testing SQL functions with stacked discounts, country-specific tax, and rounding edge cases — pgTAP version side-by-side with the SqlProof version, showing four realistic regressions where pgTAP silently passes and SqlProof catches: Testing SQL Functions — pgTAP vs SqlProof.

→ Honest comparison with pgTAP: SqlProof vs pgTAP.

Generate ad-hoc fixtures from the same schema

Property tests cover most cases, but a few residuals — RLS regression pins, HTTP-layer tests that need a fixture row to exist, pytest fixtures shared across many examples — still want one specific row inserted ahead of time. The reflex is to write a helper:

# Anti-pattern: hand-rolled INSERT in a test helper.
def insert_project(db, owner_id, *, name):
    db.execute(
        "INSERT INTO projects (id, user_id, name) VALUES (%s, %s, %s)",
        new_id(), owner_id, name,
    )

This compiles fine, looks fine in review, and silently breaks the next time a migration adds a NOT NULL column to projects. The failure surfaces commits later, in tests that aren't nominally about projects, as a cryptic NotNullViolation.

Use SqlProof.row_strategy instead — a thin, schema-aware wrapper over the same generator the property runner uses:

# Same fixture, schema-backed. When a migration adds `org_id NOT NULL`,
# this helper keeps working — the generator fills the new column.
def insert_project(db, owner_id, *, name):
    row = proof.row_strategy("projects", user_id=owner_id, name=name).example()
    db.execute(
        f"INSERT INTO projects ({', '.join(row)}) VALUES ({', '.join(['%s'] * len(row))})",
        *row.values(),
    )
    return row

Override kwargs accept Hypothesis strategies, callables, or bare values. Unknown column names raise immediately. Inside a @given-decorated test, draw from the strategy directly instead of calling .example().

Reach for property tests (check(), dataset_strategy) first — they're stronger. row_strategy is the smaller hammer for the residual case.

Built for Supabase projects shipping with AI agents

If you're building on Supabase and most of your tests are written by Claude or Cursor, SqlProof was made for that workflow:

• AGENTS.md — primes your AI coding agent on the exact patterns for RLS policies, RPC functions, and stateful tests on a Supabase schema.
• sqlproof-skills — a Claude Code / Cursor plugin that teaches the agent how to drive SqlProof end-to-end.
• sqlproof-mcp — an MCP server exposing schema introspection and property generation as agent tools.
• Inbox sample — a multi-tenant Supabase app (organizations, tickets, agents, messages, pgvector embeddings) with 10 intentional bugs across RLS, RPCs, and triggers, each paired with a property test that catches it and a walkthrough recipe.

60-second path: Test your Supabase project in 60 seconds.

API at a glance

proof = SqlProof.from_schema_file("./schema.sql")
proof = SqlProof.from_connection_string("postgresql://localhost/postgres")

# Property runner (decorator shown above, or method form):
proof.check("name", sizes={"orders": 10}, property=lambda db: ...)

# Shorthand for "this query must return no rows":
proof.invariant(
    "no bad rows",
    sizes={"orders": 10},
    query="SELECT id FROM orders WHERE total < 0",
    expect_empty=True,
)

# Ad-hoc fixture row (see above):
order = proof.row_strategy("orders", customer_id=42).example()

proof.disconnect()

The property function receives a SqlProofClient:

rows = db.query("SELECT id, total FROM orders WHERE total >= %s", 0)
total = db.scalar("SELECT count(*) FROM orders")
typed = db.query_typed("SELECT id, total FROM orders", OrderRow)
data = db.get_generated_data()

Full reference at sqlproof.com.

When a property fails

Property failed: order totals match sum of line items
Failure: AssertionError: expected totals to match
Row context: {'order_id': 1}
Dataset shape: {'orders': {'rows': 1}, 'line_items': {'rows': 2}}

Counterexamples are written under .sqlproof/failures/ and can be inspected with:

sqlproof report .sqlproof/failures/test_name.json
sqlproof report .sqlproof/failures/test_name.json --format json
sqlproof replay .sqlproof/failures/test_name.json

Supported PostgreSQL types

integer, smallint, bigint, serial, bigserial, numeric(p,s), real, double precision, boolean, text, varchar(n), char(n), uuid, timestamp, timestamptz, date, time, json, jsonb, bytea, vector(n) (pgvector), custom ENUM types, custom domains (with CHECK inheritance), built-in range types, and composite types.

Schema features parsed and respected: foreign keys, CHECK / UNIQUE / EXCLUSION constraints, partial unique indexes, GENERATED ALWAYS AS columns, and enums.

How it works

1. Schema parsing — reads your SQL file (or introspects a live DB) to extract tables, columns, types, foreign keys, CHECK/UNIQUE/EXCLUSION constraints, partial unique indexes, generated columns, and enums.
2. Dependency ordering — topologically sorts tables by foreign key so parents are inserted first.
3. Data generation — maps PostgreSQL types to Hypothesis strategies and applies constraint-aware generation for CHECK, UNIQUE, NOT NULL, and FK.
4. Isolated execution — schema-file proofs run against an in-memory client for fast local feedback. DSN-backed proofs insert generated data inside savepoints, run the property, then roll back the run.
5. Shrinking — when a property fails, Hypothesis shrinks the dataset to find the simplest counterexample.

Development

git clone https://github.com/alialavia/sqlproof.git
cd sqlproof
uv sync --extra dev

uv run pytest
uv run ruff check src tests
uv run pyright src/sqlproof
uv run mypy src/sqlproof

Postgres-backed tests

Integration tests are optional and read SQLPROOF_TEST_DATABASE_URL:

SQLPROOF_TEST_DATABASE_URL='postgresql://postgres:postgres@127.0.0.1:5432/postgres' uv run pytest tests/integration
uv run pytest tests/benchmarks

The integration tests create a temporary schema named sqlproof_it_* and drop it at the end.

Why SqlProof tests itself with properties

SqlProof uses Hypothesis internally, and its own tests use properties for schema fingerprinting, dependency ordering, FK validity, constraint generation, shrinking, parser idempotence, and counterexample replay. This keeps the library honest about the same invariants it asks users to write.

License

MIT