Migrations in Practice



Schema migration - Modifies the database schema, say by adding a new column or changing properties of an existing column. Usually pretty fast but can get complicated if not backwards-compatible.

Data migration - Modifies data already in the database. This has the potential to be quite slow

Django migration - A migration (either kind) that is run automatically on deploy by ./manage.py migrate. These are in files like corehq/apps/<app>/migrations/0001_my_migration.py

Management command - Some data migrations are written as management commands. These are then run either manually via a commcare-cloud command or automatically from inside a django migration using call_command. These are in files like corehq/apps/<app>/management/commands/my_command.py

Private release - If you need to run code from a branch that’s not currently deployed, use a private release.

General Principles

Don’t block deploys - If you write a migration that will take more than 15 minutes to run on any server (typically production will have the most data and therefore be the slowest), take care to run it outside of a deploy, otherwise that deploy will hang. How do you know if your migration will take too long? Run it on staging and compare the amount of data on staging to the amount of data on prod to estimate. If in any kind of doubt, err on the side of caution. In practice, any migration that touches common models - users, apps, domains - will need to be run outside of a deploy, while migrations to tiny tables (thousands of rows) or flagged features with few users may be able to run in a deploy.

Deploys are not instantaneous - Deploys will push new code, run the migrations in that new code, and then switch the servers to the new code. The site will be active this whole time. Users or tasks can add or modify data after the start of the migration and before the code switch and you need to account for that.

All migration states should be valid - Similar to the above, you must consider the states before, during, and after the migration. Will the active code handle all three states correctly?

Master should always be deployable - If you have a PR with a migration that requires manual handling, don’t merge it until you are prepared to handle it.

Remember third parties - We’ll often manage migrations manually for at least prod and india, but third parties run environments that we can’t manage directly. Be sure that whatever changes are necessary will be applied automatically on these environments, though it will likely require running data migrations during deploy. If the change may be disruptive or requires manual handling, we’ll need to communicate it out in advance.

Practical Considerations

Handling new data - There’s likely a code change that writes to the database in the new way going forward. It cannot be deployed until any requisite schema changes have been implemented.

Migrating old data - This will be handled via a django migration, a management command, or both. Typically, small/simple migrations are handled by a django migration and large/complex ones use a django migration that runs a management command. It cannot run until any schema changes are deployed.

Dealing with the gap - We generally can’t pause the servers, put everything to rights, then restart. Rather, we must ensure that we’re saving new data properly before migrating old data, or otherwise ensure that all data from before, during, and after the migration is handled correctly.

Backwards-incompatible changes - These are best avoided. A common workaround is to treat it as two migrations - one to store the data (in duplicate, with any necessary syncing code) the new way, then a later migration to remove the old way from the schema. With couchdb, this is a little easier, since you don’t need to remove the old schema once all the data is migrated.

Idempotence and resumability - If at all possible, you should design your management command to be run multiple times without changing the result, breaking, or redoing work. This means it should expect that some data in the db might already be migrated, and only operate on unmigrated data. This should happen performantly. This will help with some of the below migration strategies and make dealing with unexpected failures much smoother.

Shim code for the transition - Another useful pattern in some circumstances is to write short-lived code that can deal with both versions of the data in the db. This can make the transition much easier. One example of this is overriding the wrap method on a couch Document. Be sure to make a clean-up PR that drops support for the old version to be merged later once it’s no longer needed.

Migration-dependent code - This is the reason the migration is being performed. You need to make sure all data is migrated before code depending on it is released.

Testing - Complex migrations can justify unit tests. These tests are often short-lived, but they can protect against highly disruptive and lengthy data cleanup caused by a bug. With migrations, plan for the worst. Example tests for a management command.

Staging - Prod data is usually much more complex than what you have locally or what you might write for test cases. Before running your migration on prod, run it on staging. However, this can cause issues if you need to change your migration later, or if another migration in the same app conflicts with yours. Be sure to leave staging in a good state.

Example Migration: User Logging

Let’s speak about this a bit more concretely with a specific example migration in mind. This is based on a real example, but I’ve idealized it somewhat here for illustration. Here’s a brief description of a migration which will be referred back to throughout this document.

We log all changes to users, keeping track of who changed what. We currently store the ID of the affected user, but now we want to store the username too. This means we’ll need to make the following four changes:

  1. Schema migration: Add a new user_repr field to the log model to hold the username

  2. Data migration: Populate that field for all existing log rows

    1. If this will be run multiple times (more on that below), it should be idempotent and resumable.

      1. Resumability: Rather than update all user changelogs, it should filter out those that already have user_repr set, so subsequent runs can be much much faster.

      2. Idempotence: Running the command multiple times should behave the same as if it were run only once. For example, the command shouldn’t error if it encounters an already migrated log. It also shouldn’t apply a modification where unnecessary, like if the migration appended the user_repr to a string, then running it twice might result in something like "user@example.comuser@example.com"

  3. Handle new data: Modify the logging code to populate the user_repr field going forward.

  4. Migration-dependent code: Update the UI to display the user_repr and make it filterable. We can’t turn this on until all existing logs have user_repr set, or at least they’ll need to anticipate that some rows will be missing that field.

Because this example exclusively adds new data, there’s no cleanup step. Some migrations will need to remove an “old” way of doing things, which is frequently done in an additional PR. For low-risk, simple, single-PR migrations, cleanup might be included in the single PR.

Common types of migrations


If you’re adding a new model or field in postgres that doesn’t need to be back-populated, you can just put the schema migration in the same PR as the associated code changes, and the deploy will apply that migration before the new code goes live. In couch, this type of change doesn’t require a migration at all.

User Logging Example

A “simple” migration would not be suitable for the example user logging migration described above. If you tried to make all those changes in a single PR and let it get deployed as-is, you risk missing data. During deploy, the data migration will be run before the code handling new data properly goes live. Any users modified in this period would not have the user_repr populated. Additionally, the migration might take quite a while to run, which would block the deploy.

Multiple deploys

This is the most robust approach, and is advocated for in the couch-to-sql pattern. You make two PRs:

  • PR 1: Schema migration; handle new data correctly; data migration management command

  • PR 2: Django migration calling the management command; actual code relying on the migration

After the first PR is deployed, you can run the migration in a management command on whatever schedule is appropriate. The Django migration in the second PR calls the command again so we can be sure it’s been run at least once on every environment. On production, where the command has been run manually already, this second run should see that there are no remaining unmigrated rows/documents in the db and be nearly a noop.

Although using two deploys eliminates the risk of an indeterminate state on environments that you control, this risk is still present for third party environments. If the third party doesn’t deploy often and ends up deploying the two PRs together, there’s still a risk of changes happening in the gap between the migration and the new code going live. The magnitude of this risk depends on the functionality being migrated - how much data it touches and how frequently it is used. If necessary, you can mitigate this risk by spacing the deploys so that third parties are likely to deploy them separately. See guidelines for third parties running CommCare.

User Logging Example

Splitting the example user logging migration across two deploys would be a good way to ensure everything is handled correctly. You’d split the changes into two PRs as described above and deploy them separately. The steps would be:

  1. First PR deployed: Now we have the schema change live, and all new changes to users have the user_repr field populated. Additionally, the management command is available for use.

  2. Run the management command: This can be done in a private release any time before the second deploy. This should almost certainly be done on prod. Whether or not it needs to be done on the other Dimagi-managed environments (india, swiss) depends on how much data those environments have.

  3. Second PR deployed: This will run the management command again, but since all logs have already been migrated, it won’t actually make any changes and should run fast - see the migrations best practices section below. This will also make sure third party environments have the change applied. This second PR also finally contains user-facing references to the user_repr field, since by the time the code switch happens, everything will have been migrated.

Single Deploy

While this single-deploy option is tempting compared to waiting weeks to get out a multi-deploy migration, it’s really only suitable for specific situations like custom work and unreleased features, where we can be confident the drawbacks are insignificant.

The main drawbacks are:

  • This method requires manually running the Django migrations which are normally only run during deploy. Running migrations manually on a production environment is generally a bad idea.

  • It is possible that there will be a gap in data between the final run of the data migration command and the new going live (due to the sequence of events during a deploy).

If you decide to go down this route you should split your changes into two PRs:

  • PR 1: Schema migration; data migration management command

  • PR 2: Handle new data correctly; Django migration calling the management command; actual code relying on the migration

Once the PRs have both been approved, merge PR 1, then set up a private release containing that change. Merging the PR first will prevent migration conflicts with anyone else working in the area, and it’s a good idea that anything run on a production environment is on the master branch.

Run your schema migration and management command directly:

cchq <ENV> django-manage --release=<NAME> migrate <APP_NAME> cchq <ENV> django-manage --release=<NAME> my_data_migration_command

Then merge PR 2. The subsequent deploy will run your management command again, though it should be very quick this time around, since nearly all data has been migrated, and finally the code changes will go live.

The big limitation here is that there’s a gap between the final run of the management command and go-live (especially with the variation). Any changes in the interim won’t be accounted for. This is sometimes acceptable if you’re confident no such changes will have happened (eg, the migration pertains only to a custom feature, and we know that project won’t have relevant activity during that period).

User Logging Example

Consider attempting to apply our example user logging migration with a single deploy. Make two PRs as described, so they can be merged independently. Then while coordinating with the team, merge the first PR, deploy a private release, and run the schema migration, then the management command.

The second PR can be merged and go live with the next deploy. This django migration will re-run the management command, picking up any new changes since it was previously run. In our case, this should be a small enough data set that it won’t hinder the deploy. However, any changes in the window between that run and go-live will not be migrated. To pick up those changes, you can run the management command a third time after the deploy, which will ensure all user logs have been migrated.

This is still not ideal, since for the period between go-live and this third run, there will be missing data in the DB and that data will be in-use in the UI. Remember also that third party environments will have the management command run only once, on the second deploy (unless we announce this as a required maintenance operation), which would mean their data would have a gap in it.

Best practices for data migrations in Python

Consider codifying boundaries for your migration - This is especially useful for large migrations that might require manual intervention or special handling on third party environments. See detailed instructions in the Auto-Managed Migration Pattern doc.

Don’t fetch all data at once - Instead, use an iterator that streams data in chunks (note that django queryset’s .iter() method does not do this). Some models have their own performant getters, for others, consider queryset_to_iterator for SQL models, iter_update or IterDB for couch models. The chunked function is also helpful for this.

Don’t write all data at once - Instead, write data in chunks (ideally) or individually (if needed, or if performance isn’t a concern). For couch, use IterDB, iter_update, or db.bulk_save. For SQL, use django_bulk_update. Remember though that these bulk options won’t call the save() method of your model, so be sure to check for any relevant side effects or signals that happen there and either trigger them manually or use individual saves in this instance.

Don’t hold all data in memory - Since you’re submitting in chunks anyways, consider writing your changes in chunks as you iterate through them, rather than saving them all up and submitting at the end.

Don’t write from elasticsearch - It’s sometimes necessary to use ES to find the data that needs to be modified, but you should only return the ids of the objects you need, then pull the full objects from their primary database before modifying and writing.

Check your assumptions - Consider what could go wrong and encode your assumptions about the state of the world. Eg: if you expect a field to be blank, check that it is before overwriting. Consider what would happen if your migration were killed in the middle - would that leave data in a bad state? Would the migration need to redo work when run again? Couch data in particular, since it’s less structured than SQL, can contain surprising data, especially in old documents.

Only run migrations when needed - All historical migrations are run whenever a new environment is set up. This means your migrations will be run in every future test run and in every future new production or development environment. If your migration is only relevant to environments that already have data in the old format, decorate it with @skip_on_fresh_install so that it is a noop for new environments.