Apache Airflow Alembic provider containing Operators & Hooks.
Project description
Alembic Airflow Provider
An Airflow Provider to use Alembic to manage database migrations Read more here
Setup
Locally
Install the Alembic CLI with pip install alembic
In Airflow
Add airflow-provider-alembic to your requirements.txt or equivalent
Usage
- Create the required files for Alembic in either your
dagsfolder or theincludefolder
mkdir dags/migrations
cd dags/migrations
alembic init .
- Create a revision
alembic revision -m "My Database Revision"
- Edit the revision - adding, modifying, or removing objects as needed
...
def upgrade():
# Use ORM to create objects
op.create_table(
'foo',
sa.Column('id', sa.Integer, primary_key=True),
sa.Column('name', sa.String(50), nullable=False),
sa.Column('description', sa.Unicode(200)),
)
# Or run raw SQL
op.execute("SELECT 1;")
def downgrade():
# Specify the opposite of your upgrade, to rollback
op.drop_table('account')
- Add a Connection to Airflow
For demo purposes, we will add an in-memory SQLite3 Connection named
sqlitevia our.envfile:
AIRFLOW_CONN_SQLITE="sqlite:///:memory:"
- Restart (or start) your project with
astro dev restart - Add a DAG, to run your revision. Because this has
@once, it will run as soon as the DAG is turned on. Future runs for future revisions will need to be triggered.
import os
from datetime import datetime
from airflow.models import DAG
from airflow.models.param import Param
from airflow_provider_alembic.operators.alembic import AlembicOperator
with DAG(
"example_alembic",
schedule="@once", # also consider "None"
start_date=datetime(1970, 1, 1),
params={
"command": Param("upgrade"),
"revision": Param("head")
}
) as dag:
AlembicOperator(
task_id="alembic_op",
conn_id="sqlite",
command="{{ params.command }}",
revision="{{ params.revision }}",
script_location="/usr/local/airflow/dags/migrations",
)
Extra Capabilities
- You can utilize any of the Alembic commands in the
AlembicOperator- such asdowngrade - The
AlembicHookhas methods to run any alembic commands
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