One of the challenges of data model derived metadata is keeping it up-to-date when schema changes occur. Conveniently, there is library called South for Django which provides an API for creating schema and data migration scripts. If you aren't using this for your models, you should be. In fact, as of version 2.0.7, Avocado requires it as a hard dependency.

Migration Command

New in Avocado 2.0.15

The migration command acts as an all-or-nothing migration step for your project's metadata. Metadata in Avocado is both descriptive and functional. Therefore it is crucial to ensure metadata is properly up-to-date during a deployment to production (or any remote environment).

When the migration command is run, it performs these steps:

  • Creates a fixture of DataField, DataConcept, DataCategory, and DataConceptField objects
  • Creates a South data migration script that will load the fixture

The data migration script utilizes an internal function avocado.core.backup.safe_load which will:

  • Create a backup fixture of the metadata from the target database
  • Attempt to load the new fixture
  • If the load fails, the backup will be re-loaded back into the database

For extra relief, this is all performed in a transaction (assuming the database supports transactions).

The migration command requires the METADATA_MIGRATION_APP setting be defined in the AVOCADO dict:

    'METADATA_MIGRATION_APP': 'someapp',

This is required so South knows where to manage the migrations as well as where the metadata fixtures should go. All fixtures created in this way will be versioned (like South migrations) with the number prefix, e.g. 0001_.

A word of caution: this command creates a fixture with all metadata in the current database. This means if metadata has been created for models that are not yet in deployed, they will be problematic. A safe guard against this is to ensure the DataFields and DataConcepts are not marked as published.

A common workflow to prevent this from happening is doing this as the last step prior to a deployment. In future revisions of this command, additional checks may be put in place to validate all metadata prior to generating the fixture.

To run the command simply do:

$ python avocado migration

This will print out the details of what it's doing.

Incremental/Fine-grain Control

Using South's datamigration command, keeping the Avocado metadata up-to-date is quite simple. After every schema migration is created, immediately create a data migration to update the metadata corresponding to the schema changes.

Here is an example. You add your first model to the library app:

from django.db import models

class Book(models.Model):
    title = models.CharField(max_length=50)
    author = models.CharField(max_length=50)
    pub_date = models.DateField()

You run the initial schema migration and sync your metadata:

python schemamigration library --initial
python migrate library
python avocado sync library

As a contrived example, you choose to rename pub_date to published because it looks nicer. Once you change the model, you create a new schema migration:

python schemamigration library --empty

The forwards method of the migration class will look something like this:

def forwards(self, orm):
    db.rename_column('library_book', 'pub_date', 'published')

Create a data migration to update the corresponding DataField:

python datamigration library metadata_migration_for_0002

The forwards method of the migration class will look something like this:

def forwards(self, orm):
        f = DataField.objects.get_by_natural_key('')
    except DataField.DoesNotExist:
    f.field_name = 'published'

Now the migrations can be applied which will perform the schema migration and the data migration for the Avocado metadata:

python migrate library

NOTE: Although the above example keeps the schema migration separate from the data migration for metadata, they can technically occur in the same script. This would involve adding the metadata-related migration logic in the schema migration script. The downside to this (and why it is not used in the example) is the lack of separate of concerns between DDL and DML statements. Also, from a coding standpoint, it is more clear and maintainable to keep the scripts separate. The only minor downside to the above approach is that the two migrations must occur in succession to prevent metadata inconsistencies.