# Schema Migrations

Zulip uses the [standard Django system for doing schema
migrations](https://docs.djangoproject.com/en/1.10/topics/migrations/).
There is some example usage in the [new feature
tutorial](new-feature-tutorial.html).

This page documents some important issues related to writing schema
migrations.

* **Large tables**: For large tables like Message and UserMessage, you
  want to take precautions when adding columns to the table,
  performing data backfills, or building indexes. We have a
  `zerver/lib/migrate.py` library to help with adding columns and
  backfilling data. For building indexes on these tables, we should do
  this using SQL with postgres's CONCURRENTLY keyword.

* **Numbering conflicts across branches**: If you've done your schema
  change in a branch, and meanwhile another schema change has taken
  place, Django will now have two migrations with the same number. To
  fix this, you need to renumber your migration(s), fix up
  the "dependencies" entries in your migration(s), and rewrite your
  git history as needed.  There is a tutorial
  [here](migration-renumbering.html) that walks you though that
  process.

* **Atomicity**.  By default, each Django migration is run atomically
  inside a transaction.  This can be problematic if one wants to do
  something in a migration that touches a lot of data and would best
  be done in batches of e.g. 1000 objects (e.g. a `Message` or
  `UserMessage` table change).  There is a new Django feature added in
  [Django 1.10][migrations-non-atomic] that makes it possible to add
  `atomic=False` at the top of a `Migration` class and thus not have
  the entire migration in a transaction.  This should make it possible
  to use the batch update tools in `zerver/lib/migrate.py` (originally
  written to work with South) for doing larger database migrations.

* **Making large migrations work**.  Major migrations should have a
few properties:

  * **Unit tests**.  You'll want to carefully test these, so you might
    as well write some unit tests to verify the migration works
    correctly, rather than doing everything by hand.  This often saves
    a lot of time in re-testing the migration process as we make
    adjustments to the plan.
  * **Run in batches**.  Updating more than 1K-10K rows (depending on
    type) in a single transaction can lock up a database.  It's best
    to do lots of small batches, potentially with a brief sleep in
    between, so that we don't block other operations from finishing.
  * **Rerunnability/idempotency**.  Good migrations are ones where if
    operational concerns (e.g. it taking down the Zulip server for
    users) interfere with it finishing, it's easy to restart the
    migration without doing a bunch of hand investigation.  Ideally,
    the migration can even continue where it left off, without needing
    to redo work.
  * **Multi-step migrations**.  For really big migrations, one wants
  to split the transition into into several commits that are each
  individually correct, and can each be deployed independently:

    1. First, do a migration to add the new column to the Message table
      and start writing to that column (but don't use it for anything)
    2. Second, do a migration to copy values from the old column to
    the new column, to ensure that the two data stores agree.
    3. Third, a commit that stops writing to the old field.
    4. Any cleanup work, e.g. if the old field were a column, we'd do
       a migration to remove it entirely here.

    This multi-step process is how most migrations on large database
    tables are done in large-scale systems, since it ensures that the
    system can continue running happily during the migration.

[migrations-non-atomic]: https://docs.djangoproject.com/en/1.10/howto/writing-migrations/#non-atomic-migrations