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Database Seeding

Point Misata at an existing database schema and fill a dev or staging environment with production-like data — FK ordering handled automatically.

Introspect and seed

from misata import schema_from_db, generate_from_schema, seed_database

# Read your live schema — no column definitions needed
schema = schema_from_db("postgresql://user:pass@localhost/myapp")
tables = generate_from_schema(schema)

report = seed_database(tables, "postgresql://user:pass@localhost/myapp_dev")
print(report)
# SeedReport: seeded 6 tables, 47,300 rows in 1.2s

CLI workflow

# Step 1 — introspect into a YAML file
misata init --db postgresql://user:pass@localhost/myapp

# Step 2 — review / edit misata.yaml, then seed
misata generate --db-url postgresql://user:pass@localhost/myapp_dev --db-create

SQLAlchemy models

from misata import seed_from_sqlalchemy_models
from myapp.models import Base

report = seed_from_sqlalchemy_models(
    Base,
    db_url="sqlite:///test.db",
    row_count=500,
    create_tables=True,
)

Read back from a database

from misata import load_tables_from_db

tables = load_tables_from_db("postgresql://user:pass@localhost/myapp_dev")
print(tables["orders"].head())

Supported databases

  • PostgreSQL — pip install "misata[db]" (psycopg3)
  • MySQL / MariaDB — via SQLAlchemy pip install "misata[orm]"
  • SQLite — built-in, no extras needed
  • Any SQLAlchemy-compatible engine

FK insert order

Misata performs a topological sort on the relationship graph before inserting, so parent rows always exist before child rows are inserted. Circular FK references raise a ConfigurationError.