Suppose my db model contains an object User
:
Base = declarative_base() class User(Base): __tablename__ = 'users' id = Column(String(32), primary_key=True, default=...) name = Column(Unicode(100))
and my database contains a users
table with n rows. At some point I decide to split the name
into firstname
and lastname
, and during alembic upgrade head
I would like my data to be migrated as well.
The auto-generated Alembic migration is as follows:
def upgrade(): op.add_column('users', sa.Column('lastname', sa.Unicode(length=50), nullable=True)) op.add_column('users', sa.Column('firstname', sa.Unicode(length=50), nullable=True)) # Assuming that the two new columns have been committed and exist at # this point, I would like to iterate over all rows of the name column, # split the string, write it into the new firstname and lastname rows, # and once that has completed, continue to delete the name column. op.drop_column('users', 'name') def downgrade(): op.add_column('users', sa.Column('name', sa.Unicode(length=100), nullable=True)) # Do the reverse of the above. op.drop_column('users', 'firstname') op.drop_column('users', 'lastname')
There seem to be multiple and more or less hacky solutions to this problem. This one and this one both propose to use execute()
and bulk_insert()
to execute raw SQL statements during a migration. This (incomplete) solution imports the current db model but that approach is fragile when that model changes.
How do I migrate and modify the existing content of column data during an Alembic migration? What is the recommended way, and where is it documented?
¶ Alembic is a lightweight database migration tool for usage with the SQLAlchemy Database Toolkit for Python.
Alembic provides for the creation, management, and invocation of change management scripts for a relational database, using SQLAlchemy as the underlying engine. This tutorial will provide a full introduction to the theory and usage of this tool.
The proposed solution in norbertpy’s answer sounds good at first, but I think it has one fundamental flaw: it would introduce multiple transactions—in between the steps, the DB would be in a funky, inconsistent state. It also seems odd to me (see my comment) that a tool would migrate a DB’s schema without the DB’s data; the two are too closely tied together to separate them.
After some poking around and several conversations (see code snippets in this Gist) I’ve decided for the following solution:
def upgrade(): # Schema migration: add all the new columns. op.add_column('users', sa.Column('lastname', sa.Unicode(length=50), nullable=True)) op.add_column('users', sa.Column('firstname', sa.Unicode(length=50), nullable=True)) # Data migration: takes a few steps... # Declare ORM table views. Note that the view contains old and new columns! t_users = sa.Table( 'users', sa.MetaData(), sa.Column('id', sa.String(32)), sa.Column('name', sa.Unicode(length=100)), # Old column. sa.Column('lastname', sa.Unicode(length=50)), # Two new columns. sa.Column('firstname', sa.Unicode(length=50)), ) # Use Alchemy's connection and transaction to noodle over the data. connection = op.get_bind() # Select all existing names that need migrating. results = connection.execute(sa.select([ t_users.c.id, t_users.c.name, ])).fetchall() # Iterate over all selected data tuples. for id_, name in results: # Split the existing name into first and last. firstname, lastname = name.rsplit(' ', 1) # Update the new columns. connection.execute(t_users.update().where(t_users.c.id == id_).values( lastname=lastname, firstname=firstname, )) # Schema migration: drop the old column. op.drop_column('users', 'name')
Two comments about this solution:
The downgrade()
function can be implemented similarly.
Addendum. See the Conditional Migration Elements section in the Alembic Cookbook for examples of pairing schema migration with data migration.
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