Sustained.py

Sustained is a Python query builder, lightweight ORM, and schema migration tool, originally inspired by Objection.js.

You describe your tables in one set of model classes, and Sustained uses those classes both to build and run queries and to keep the schema in step.

The syntax will look familiar if you have worked with Objection, Kysely, or even Knex before:

adults = User.query().where(User.c.age >= 18).orderBy('name').run()

Managing queries through Sustained

With Sustained, you can:

• Build SQL programmatically. Selects, aggregates, window functions, CASE expressions, every join type, CTEs (including recursive), unions, INTERSECT and EXCEPT, and subqueries in SELECT, FROM, WHERE, and JOIN clauses.
• Target seven dialects. ANSI (default), PostgreSQL, MySQL and MariaDB, MSSQL, Presto, AWS Athena, and DuckDB. Quoting, placeholders, upsert syntax, LIMIT/OFFSET spelling, and function names all follow the dialect. Unsupported features raise DialectError at build time instead of failing in the database. Migrating queries between dialects is a one-line change.
• Execute queries safely. Every statement runs parameterized against any DB-API 2.0 connection or a ConnectionPool. Transactions nest through savepoints, and update() and delete() refuse to run without a WHERE clause.
• Write data. insert(), update(), delete(), upserts through onConflict(), INSERT ... SELECT, CREATE TABLE AS, and RETURNING.
• Hydrate results. Rows become model instances, plain dicts, pandas DataFrames, or pyarrow Tables. Relations eager load with withGraphFetched(). A type checker reads Show.query().run() as List[Show].
• Run queries async. The same queries run through driver adapters, including asyncpg and aiosqlite, with await query.arun(). AsyncConnectionPool pools those adapters, so concurrent queries do not queue behind one connection.

Schema management with Sustained

Sustained also manages schema changes. It generates migrations from your models, tests each change before it runs, and rolls a migration back when you ask. Schema and Migrations describes these features in detail.

With Sustained, schema migrations are:

• Generated from your models. Migrator.up(models=[...]) diffs the live database against your models, generates the migration, records it, and applies it. If you run it again after a model change, it applies only the difference. down() rolls it back.
• Rehearsed before they land. sustained rehearse applies every pending migration, runs the downgrade steps to test the revert plan, and rolls the whole thing back. If a migration fails to run or fails to reverse, the rehearsal reports it before the migration reaches the real schema. A config module can send the rehearsal to a scratch database instead.
• Planned in one screen. sustained plan shows your pending migrations, outstanding problems that validate would report, and any gap between your models and the database's current state.
• Verified before every run. Sustained keeps a per-database tracking table that records a sequence number, a SHA-256 checksum, an apply timestamp, execution time, and a success flag per migration. validate refuses a run when a migration was edited after it ran, arrives out of order, or left a failed attempt behind. After manual corrections, repair deletes failed runs from the tracking table and updates script checksums.
• Gated by custom safeguards. A guard is a built-in rule such as no_drops(), index_must_be_concurrent(), or max_statements(n), or a function you write. Guards read every statement a run would apply and block the deployment when a rule fails.
• Safe by default. Drops need an explicit allow_drops=True, renames need explicit hints, and NOT NULL changes need a default or backfill, so destructive changes never run by default.
• Written your way. Migrations can be Python Migration objects, <id>.up.sql and <id>.down.sql files with ${placeholders}, or <id>.repeat.sql files for views and seed data, which re-run whenever their contents change.
• Ready for deploys. The sustained console script runs plan, status, rehearse, migrate, down, validate, repair, script, and baseline, with exit codes for pipelines and before_migrate, after_migrate, and on_error callbacks around a run. Concurrent deploys queue on an advisory lock. baseline adopts a database whose schema already matches the migrations. script('up') renders the SQL for a DBA instead of running it. AsyncMigrator does all of this on an async adapter.

Installation

python3 -m pip install sustained

Usage

from sustained import Model, RelationType

class Person(Model):
    tableName = 'persons'

class Animal(Model):
    tableName = 'animals'
    relationMappings = {
        'owner': {
            'relation': RelationType.BelongsToOneRelation,
            'modelClass': Person,
            'join': {
                'from': 'animals.ownerId',
                'to': 'persons.id'
            }
        }
    }

# Build a query
query = Animal.query().select('animals.name', 'persons.name').leftOuterJoinRelated('owner')

print(query)
# SELECT animals.name, persons.name
# FROM animals
# LEFT OUTER JOIN persons
#   ON animals.ownerId = persons.id


# Execute against any DB-API 2.0 connection
import sqlite3

conn = sqlite3.connect('app.db')
Animal.bind(conn)

# Parameterized execution with model hydration
animals = Animal.query().where('species', '=', 'dog').orderBy('name').run()

# Or take the SQL and parameters and execute them yourself
sql, params = Animal.query().where('species', '=', 'dog').to_sql()
# sql:    "SELECT * FROM animals WHERE species = ?"
# params: ('dog',)

Models define their own schema, so a column change is a migration:

from sustained.migrations import Migrator
from sustained.schema import Integer, String, Text

class User(Model):
    tableName = 'users'
    tableColumns = {
        'id': Integer(primary_key=True, autoincrement=True),
        'email': String(120, unique=True, nullable=False),
    }

migrator = Migrator(conn, [])
migrator.up(models=[User])         # creates the users table

User.tableColumns['bio'] = Text()
migrator.plan([User])              # the migration the next run would generate
migrator.up(models=[User])         # adds only the bio column
migrator.down()                    # rolls it back

From the shell, a config module names the connection, the migrations directory, and the models:

# sustained_config.py
import sqlite3

def get_connection():
    return sqlite3.connect('app.db')

migrations_dir = 'migrations'
models = [User]

$ sustained plan        # pending migrations, validation problems, model drift
$ sustained rehearse    # run it all, forwards and back, then roll back
$ sustained migrate     # apply it for real
$ sustained down        # --steps N (0 or more) or --to ID

See Schema and Migrations for SQL file migrations, repeatables, checksum validation, baseline, and the Athena rules.

Documentation

The documentation includes:

• Getting Started builds a working application in one sitting, against SQLite from the standard library.
• Recipes pairs a common task with the code that does it.
• The guides cover one area each: models, queries, dialects and drivers, filtering, grouping, relations and joins, execution, pooling, and async, and schema and migrations at length.
• The API reference gives every public name its signature, return type, and the conditions that raise.

The support policy lists the supported databases and Python versions and states the deprecation policy. The changelog lists released versions.

Development

To install from source:

git clone https://github.com/wetherc/sustained.git
cd sustained
python3 -m pip install -e .

This project uses pre-commit to format code, lint, type check, and run the test suite before each commit:

pip install pre-commit
pre-commit install