dbly 0.3.0

Declare your desired database state in git. dbly makes it real.

dbly deploys your database objects — tables, views, functions, procedures, packages, triggers, grants — from version control to PostgreSQL, SQL Server, Oracle and SQLite. You keep one SQL file per object, like normal source code; dbly works out what changed and brings the target database in sync. Think Terraform for your database: declarative, predictable, repeatable.

Why

• Declarative — your repo is the source of truth. No hand-written migration chains, no version-number collisions on parallel branches.
• Plan before apply — preview exactly what will run, then execute. No surprises.
• Idempotent & safe — only the necessary changes are applied. Additive changes go automatically; destructive ones (dropping columns, etc.) are flagged and never run unless you explicitly allow them.
• Multi-database — one workflow across all four engines.

Install

uv sync                 # PostgreSQL + SQLite work out of the box
uv sync --extra oracle  # add the Oracle driver
uv sync --extra mssql   # add the SQL Server driver
uv sync --extra all     # both

Organize your repo

One file per object; folder names map to database schemas. Use any extension you like (.sql, .tbl, .vw, .prc, …) — dbly reads the SQL to know what each object is.

db/
  sales/
    customer.tbl
    v_open_orders.vw
    grants.sql
  init/                 # optional: privileged greenfield groundwork (CREATE DATABASE/ROLE…)
  migrations/           # optional: ordered, run-once ALTERs for renames / data backfills
hooks/pre/  hooks/post/  # optional: .sql or .py hooks (e.g. ArcGIS/ArcPy steps)
.dbignore               # files in the repo that should not be deployed

Most changes are declarative — edit the object file, dbly figures out the additive diff. For changes the diff can't do safely (renaming a column, moving data), drop an ordered script in migrations/ (0001_…sql); it runs exactly once, is recorded in the ledger, and the table it touches defers to it for that deploy. On a fresh database, migrations are baselined (recorded, not run) since the object files already describe the end state.

Connect

A connection profile (reuses the familiar connection.properties format):

environment=postgres            # postgres | sqlserver | oracle | sqlite
service=db.example.com:5432
username=app
password=${DB_PASSWORD}         # ${ENV} placeholders → keep secrets out of the repo (CI/CD-safe)
database=appdb

Use

# preview the changes between the deployed state and a git ref
dbly plan  --to main --target prod.connection.properties

# apply them
dbly apply --to main --target prod.connection.properties

# export a plain SQL script to run by hand (e.g. through a customer VPN, no dbly needed)
dbly plan  --to main --target prod.connection.properties --sql deploy.sql

# what is currently deployed?
dbly status --target prod.connection.properties

# has the database drifted from the desired state?
dbly check  --target prod.connection.properties

# greenfield only: run privileged groundwork once under a superuser profile
dbly init   --init-target super.connection.properties

Typical workflow: edit your object files → commit → dbly plan to review → dbly apply.

Deploying a subset of features is just choosing the git ref you deploy (a release tag or branch). Destructive steps require --allow-destructive.

Built for trunk-based development

Database teams are usually locked out of trunk-based development: migration scripts collide on parallel branches, and "merge" effectively means "deploy to the customer". dbly is designed to break that deadlock for teams who write their logic in SQL, in the database:

• No migration numbers, no collisions. Two developers edit different objects — git merges them like any other code. Integrate early, every day.
• Merge ≠ deploy. The trunk is your desired state; dbly apply --to <tag> ships the ref you choose, in the maintenance window you choose. Release what you want, when you want.
• One trunk, every customer. dbly reads each database's real state, so customers on different versions are no problem.

Integrate continuously, deploy on your own schedule — trunk-based development, finally practical for state-based database developers.

Status

Early alpha — all four engines are implemented and tested against live databases.

License

MIT