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📚 pgroll Documentation

Concepts

pgroll introduces a few concepts that are important to understand before using the tool.

Migration workflow

pgroll migrations are applied in two steps, following an expand/contract pattern.

migration flow

During the migration start phase, pgroll will perform only additive changes to the database schema. This includes: creating new tables, adding new columns, and creating new indexes. In the cases where a required change is not backwards compatible, pgroll will take the necessary steps to ensure that the current schema is still valid. For example, if a new column is added to a table with a NOT NULL constraint, pgroll will backfill the new column with a default value.

After a successful migration start, the database will contain two versions of the schema: the old version and the new version. The old version of the schema is still available to client applications. This allows client applications to be updated to use the new version of the schema without any downtime.

Once all client applications have been updated to use the latest version of the schema, the complete phase can be run. During the complete phase pgroll will perform all non-additive changes to the database schema. This includes: dropping tables, dropping columns, and dropping indexes. Effectively breaking the old version of the schema.

Multiple schema versions

pgroll maintains multiple versions of the database schema side-by-side. This is achieved by creating a new Postgres schema for each migration that is applied to the database. The schema will contain views on the underlying tables. These views are used to expose different tables or columns to client applications depending on which version of the schema they are configured to use.

For instance, a rename column migration will create a new schema containing a view on the underlying table with the new column name. This allows for the new version of the schema to become available without breaking existing client applications that are still using the old name. In the migration complete phase, the old schema is dropped and the actual column is renamed (views are updated to point to the new column name automatically).

multiple schema versions

For other more complex changes, like adding a NOT NULL constraint to a column, pgroll will duplicate the affected column and backfill it with the values from the old one. For some time the old & new columns will coexist in the same table. This allows for the new version of the schema to expose the column that fulfils the constraint, while the old version still uses the old column. pgroll will take care of copying the values from the old column to the new one, and vice versa, as needed, both by executing the backfill or installing triggers to keep the columns in sync during updates.

Client applications

In order to work with the multiple versioned schema that pgroll creates, clients need to be configured to work with one of them.

This is done by having client applications configure the search path when they connect to the Postgres database.

For example, this fragment for a Go client application shows how to set the search_path after a connection is established:

db, err := sql.Open("postgres", "postgres://postgres:postgres@localhost:5432/postgres?sslmode=disable")
if err != nil {
    return nil, err
}

searchPath := "public_02_add_assignee_column"
log.Printf("Setting search path to %q", searchPath)
_, err = db.Exec(fmt.Sprintf("SET search_path = %s", pq.QuoteIdentifier(searchPath)))
if err != nil {
    return nil, fmt.Errorf("failed to set search path: %s", err)
}

In practice, the searchPath variable would be provided to the application as an environment variable.

What happens if an application doesn't set the search_path?

If an application doesn't set the search_path for the connection, the search_path defaults to the public schema, meaning that the application will be working with the underlying tables directly rather than accessing them through the versioned views.

Installation

Binaries

Binaries are available for Linux, macOS & Windows on our Releases page.

From source

To install pgroll from source, run the following command:

go install github.com/xataio/pgroll@latest

Note: requires Go 1.21 or later.

From package manager - Homebrew

To install pgroll with homebrew, run the following command:

# macOS or Linux
brew tap xataio/pgroll
brew install pgroll

Supported Postgres versions

pgroll supports Postgres versions >= 14.

⚠️ In Postgres 14, row level security policies on tables are not respected by pgroll's versioned views. This is because pgroll is unable to create the views with the (security_invoker = true) option, as the ability to do so was added in Postgres 15. If you use RLS in Postgres 14 pgroll is likely a poor choice of migration tool. All other pgroll features are fully supported across all supported Postgres versions.

Tutorial

This section will walk you through applying your first migrations using pgroll.

We'll apply two migrations to a fresh database and have a look at what pgroll does under the hood.

Prerequisites:

  • pgroll installed and accessible somewhere on your $PATH
  • A fresh Postgres instance against which to run migrations

A good way to get a throw-away Postgres instance for use in the tutorial is to use Docker. Start a Postgres instance in Docker with:

docker run --rm --name for-pgroll -e POSTGRES_PASSWORD=postgres -p 5432:5432 -d postgres:16

The remainder of the tutorial assumes that you have a local Postgres instance accessible on port 5432.

Initialization

pgroll needs to store its own internal state somewhere in the target Postgres database. Initializing pgroll configures this store and makes pgroll ready for first use:

pgroll init

You should see a success message indicating that pgroll has been configured.

What data does pgroll store?

pgroll stores its data in the pgroll schema. In this schema it creates:

  • A migrations table containing the version history for each schema in the database
  • Functions to capture the current database schema for a given schema name
  • Triggers to capture DDL statements run outside of pgroll migrations

First migration

With pgroll initialized, let's run our first migration. Here is a migration to create a table:

{
  "name": "01_create_users_table",
  "operations": [
    {
      "create_table": {
        "name": "users",
        "columns": [
          {
            "name": "id",
            "type": "serial",
            "pk": true
          },
          {
            "name": "name",
            "type": "varchar(255)",
            "unique": true
          },
          {
            "name": "description",
            "type": "text",
            "nullable": true
          }
        ]
      }
    }
  ]
}

Take this file and save it as sql/01_create_users_table.json.

The migration will create a users table with three columns. It is equivalent to the following SQL DDL statement:

CREATE TABLE users(
  id SERIAL PRIMARY KEY,
  name VARCHAR(255) UNIQUE NOT NULL,
  description TEXT
)

To apply the migration to the database run:

pgroll start sql/01_create_users_table.json --complete
What does the --complete flag do here?

pgroll divides migration application into two steps: start and complete. During the start phase, both old and new versions of the database schema are available to client applications. After the complete phase, only the most recent schema is available.

As this is the first migration there is no old schema to maintain, so the migration can safely be started and completed in one step.

For more details about pgroll's two-step migration process, see the Multiple schema versions section.

Now let's add some users to our new table:

INSERT INTO users (name, description)
 SELECT
   'user_' || suffix,
   CASE
     WHEN random() < 0.5 THEN 'description for user_' || suffix
     ELSE NULL
   END
 FROM generate_series(1, 100000) AS suffix;

Execute this SQL to insert 10^5 users into the users table. Roughly half of the users will have descriptions and the other half will have NULL descriptions.

Second migration

Now that we have our users table, lets make a non backwards-compatible change to the schema and see how pgroll helps us by maintaining the old and new schema versions side by side.

Some of the users in our users table have descriptions and others don't. This is because our initial migration set the description column as nullable: true, allowing some users to have NULL values in the description field.

We'd like to change the users table to disallow NULL values in the description field. We also want a description to be set explicitly for all new users, so we don't want to specify a default value for the column.

There are two things that make this migration difficult:

  • We have existing NULL values in our description column that need to be updated to something non-NULL
  • Existing applications using the table are still running and may be inserting more NULL descriptions

pgroll helps us solve both problems by maintaining old and new versions of the schema side-by-side and copying/rewriting data between them as required.

Here is the pgroll migration that will perform the migration to make the description column NOT NULL:

{
  "name": "02_user_description_set_nullable",
  "operations": [
    {
      "alter_column": {
        "table": "users",
        "column": "description",
        "nullable": false,
        "up": "(SELECT CASE WHEN description IS NULL THEN 'description for ' || name ELSE description END)",
        "down": "description"
      }
    }
  ]
}

Save this migration as sql/02_user_description_set_nullable.json and start the migration:

pgroll start 02_user_description_set_nullable.json

After some progress updates you should see a message saying that the migration has been started successfully.

At this point it's useful to look at the table data and schema to see what pgroll has done. Let's look at the data first:

SELECT * FROM users ORDER BY id LIMIT 10

You should see something like this:

+-----+----------+-------------------------+--------------------------+
| id  | name     | description             | _pgroll_new_description  |
+-----+----------+-------------------------+--------------------------+
| 1   | user_1   | <null>                  | description for user_1   |
| 2   | user_2   | description for user_2  | description for user_2   |
| 3   | user_3   | <null>                  | description for user_3   |
| 4   | user_4   | description for user_4  | description for user_4   |
| 5   | user_5   | <null>                  | description for user_5   |
| 6   | user_6   | description for user_6  | description for user_6   |
| 7   | user_7   | <null>                  | description for user_7   |
| 8   | user_8   | <null>                  | description for user_8   |
| 9   | user_9   | description for user_9  | description for user_9   |
| 10  | user_10  | description for user_10 | description for user_10  |

pgroll has added a _pgroll_new_description field to the table and populated the field for all rows using the up SQL from the 02_user_description_set_nullable.json file:

"up": "(SELECT CASE WHEN description IS NULL THEN 'description for ' || name ELSE description END)",

This has copied over all description values into the _pgroll_new_description field, rewriting any NULL values using the provided SQL.

Now let's look at the table schema:

DESCRIBE users

You should see something like this:

+-------------------------+------------------------+-----------------------------------------------------------------+
| Column                  | Type                   | Modifiers                                                       |
+-------------------------+------------------------+-----------------------------------------------------------------+
| id                      | integer                |  not null default nextval('_pgroll_new_users_id_seq'::regclass) |
| name                    | character varying(255) |  not null                                                       |
| description             | text                   |                                                                 |
| _pgroll_new_description | text                   |                                                                 |
+-------------------------+------------------------+-----------------------------------------------------------------+
Indexes:
    "_pgroll_new_users_pkey" PRIMARY KEY, btree (id)
    "_pgroll_new_users_name_key" UNIQUE CONSTRAINT, btree (name)
Check constraints:
    "_pgroll_add_column_check_description" CHECK (_pgroll_new_description IS NOT NULL) NOT VALID
Triggers:
    _pgroll_trigger_users__pgroll_new_description BEFORE INSERT OR UPDATE ON users FOR EACH ROW EXECUTE FUNCTION _pgroll_trigger_users__pgroll_new_description>
    _pgroll_trigger_users_description BEFORE INSERT OR UPDATE ON users FOR EACH ROW EXECUTE FUNCTION _pgroll_trigger_users_description()

The _pgroll_new_description column has a NOT NULL CHECK constraint, but the old description column is still nullable.

We'll talk about what the two triggers on the table do later.

For now, let's look at the schemas in the database:

\dn

You should see something like this:

+-----------------------------------------+-------------------+
| Name                                    | Owner             |
+-----------------------------------------+-------------------+
| pgroll                                  | postgres          |
| public                                  | pg_database_owner |
| public_01_create_users_table            | postgres          |
| public_02_user_description_set_nullable | postgres          |
+-----------------------------------------+-------------------+

We have two schemas: one corresponding to the old schema, public_01_create_users_table, and one for the migration we just started, public_02_user_description_set_nullable. Each schema contains one view on the users table. Let's look at the view in the first schema:

\d+ public_01_create_users_table.users

The output should contain something like this:

 SELECT users.id,
    users.name,
    users.description
   FROM users;

and for the second view:

\d+ public_02_user_description_set_nullable.users

The output should contain something like this:

 SELECT users.id,
    users.name,
    users._pgroll_new_description AS description
   FROM users;

The second view exposes the same three columns as the first, but its description field is mapped to the _pgroll_new_description field in the underlying table.

By choosing to access the users table through either the public_01_create_users_table.users or public_02_user_description_set_nullable.users view, applications have a choice of which version of the schema they want to see; either the old version without the NOT NULL constraint on the description field or the new version with the constraint.

When we looked at the schema of the users table, we saw that pgroll has created two triggers:

_pgroll_trigger_users__pgroll_new_description BEFORE INSERT OR UPDATE ON users FOR EACH ROW EXECUTE FUNCTION _pgroll_trigger_users__pgroll_new_description>
_pgroll_trigger_users_description BEFORE INSERT OR UPDATE ON users FOR EACH ROW EXECUTE FUNCTION _pgroll_trigger_users_description()

These triggers are used by pgroll to ensure that any values written into the old description column are copied over to the _pgroll_new_description column (rewriting values using the up SQL from the migration) and to copy values written to the _pgroll_new_description column back into the old description column (rewriting values using thedown SQL from the migration).

Let's see the first of those triggers in action.

First set the search path for your Postgres session to use the old schema:

SET search_path = 'public_01_create_users_table'

Now insert some data into the users table through the users view:

INSERT INTO users(name, description) VALUES ('Alice', 'this is Alice'), ('Bob', NULL)

This inserts two new users into the users table, one with a description and one without.

Let's check that the data was inserted:

SELECT * FROM users WHERE name = 'Alice' or name = 'Bob'

Running this query should show:

+--------+-------+---------------------+
| id     | name  | description         |
+--------+-------+---------------------+
| 100001 | Alice | this is Alice       |
| 100002 | Bob   | NULL                |
+--------+-------+---------------------+

The trigger should have copied the data that was just written into the old description column (without the NOT NULL constraint) into the _pgroll_new_description column (with the NOT NULL constraint )using the up SQL from the migration.

Let's check. Set the search path to the new version of the schema:

SET search_path = 'public_02_user_description_set_nullable'

and find the users we just inserted:

SELECT * FROM users WHERE name = 'Alice' or name = 'Bob'

The output should look like this:

+--------+-------+---------------------+
| id     | name  | description         |
+--------+-------+---------------------+
| 100001 | Alice | this is Alice       |
| 100002 | Bob   | description for Bob |
+--------+-------+---------------------+

Notice that the trigger installed by pgroll has rewritten the NULL value inserted into the old schema by using the up SQL from the migration definition.

Completing the migration

Once the old version of the database schema is no longer required (perhaps the old applications that depend on the old schema are no longer in production) the current migration can be completed:

pgroll complete

After the migration has completed, the old version of the schema is no longer present in the database:

\dn

shows something like:

+-----------------------------------------+-------------------+
| Name                                    | Owner             |
+-----------------------------------------+-------------------+
| pgroll                                  | postgres          |
| public                                  | pg_database_owner |
| public_02_user_description_set_nullable | postgres          |
+-----------------------------------------+-------------------+

Only the new version schema public_02_user_description_set_nullable remains in the database.

Let's look at the schema of the users table to see what's changed there:

DESCRIBE users

shows something like:

+-------------+------------------------+-----------------------------------------------------------------+----------+--------------+-------------+
| Column      | Type                   | Modifiers                                                       | Storage  | Stats target | Description |
+-------------+------------------------+-----------------------------------------------------------------+----------+--------------+-------------+
| id          | integer                |  not null default nextval('_pgroll_new_users_id_seq'::regclass) | plain    | <null>       | <null>      |
| name        | character varying(255) |  not null                                                       | extended | <null>       | <null>      |
| description | text                   |  not null                                                       | extended | <null>       | <null>      |
+-------------+------------------------+-----------------------------------------------------------------+----------+--------------+-------------+
Indexes:
    "_pgroll_new_users_pkey" PRIMARY KEY, btree (id)
    "_pgroll_new_users_name_key" UNIQUE CONSTRAINT, btree (name)

The extra _pgroll_new_description has been renamed to description and the old description column has been removed. The column is now marked as NOT NULL.

pgroll has allowed us to safely roll out this change to the description column.

Summary

We've seen:

  • how to apply a couple of pgroll migrations to a database.
  • how pgroll separates migrations into start and complete phases.
  • how data is backfilled to meet constraints at the beginning of the start phase.
  • that during the start phase, pgroll uses multiple schema to present different versions of an underlying table to client applications.
  • that data written into the old schema version is copied over into the new schema, and vice-versa.
  • that completing a migration removes the old schema version and cleans up the underlying table, putting it in its final state.

Command line reference

The pgroll CLI offers the following subcommands:

The pgroll CLI has the following top-level flags:

  • --postgres-url: The URL of the postgres instance against which migrations will be run.
  • --schema: The Postgres schema in which migrations will be run (default "public").
  • --pgroll-schema: The Postgres schema in which pgroll will store its internal state (default: "pgroll").
  • --lock-timeout: The Postgres lock_timeout value to use for all pgroll DDL operations, specified in milliseconds (default 500).
  • --role: The Postgres role to use for all pgroll DDL operations (default: "", which doesn't set any role).

Each of these flags can also be set via an environment variable:

  • PGROLL_PG_URL
  • PGROLL_SCHEMA
  • PGROLL_STATE_SCHEMA
  • PGROLL_LOCK_TIMEOUT
  • PGROLL_ROLE

The CLI flag takes precedence if a flag is set via both an environment variable and a CLI flag.

Init

pgroll init initializes pgroll for first use.

$ pgroll init

This will create a new schema in the database called pgroll (or whatever value is specified with the --pgroll-schema switch).

The tables and functions in this schema store pgroll's internal state and are not intended to be modified outside of pgroll CLI.

Start

pgroll start starts a pgroll migration:

$ pgroll start sql/03_add_column.json

This starts the migration defined in the sql/01_create_table.json file.

After starting a migration there will be two schema versions in the database; one for the old schema before the migration and one for the new version with the schema changes.

A migration can be started and completed with one command by specifying the --complete flag:

$ pgroll start sql/03_add_column.json --complete

This is equivalent to running pgroll start immediately followed by pgroll complete.

⚠️ Using the --complete flag is appropriate only when there are no applications running against the old database schema. In most cases, the recommended workflow is to run pgroll start, then gracefully shut down old applications before running pgroll complete as a separate step.

Complete

pgroll complete completes a pgroll migration, removing the previous schema and leaving only the latest schema.

$ pgroll complete

This completes the most recently started migration.

Running pgroll complete when there is no migration in progress is a no-op.

Completing a pgroll migration removes the previous schema version from the database, leaving only the latest version of the schema.

⚠️ Before running pgroll complete ensure that all applications that depend on the old version of the database schema are no longer live. Prematurely running pgroll complete can cause downtime of old application instances that depend on the old schema.

Rollback

pgroll rollback rolls back the currently active migration.

$ pgroll rollback

This rolls back the currently active migration (an active migration is one that has been started but not yet completed).

Rolling back a pgroll migration means removing the new schema version. The old schema version was still present throughout the migration period and does not require modification.

Migrations cannot be rolled back once completed. Attempting to roll back a migration that has already been completed is a no-op.

⚠️ Before running pgroll rollback ensure that any new versions of applications that depend on the new database schema are no longer live. Prematurely running pgroll rollback can cause downtime of new application instances that depend on the new schema.

Status

pgroll status shows the current status of pgroll within a given schema:

$ pgroll status

{
  "Schema": "public",
  "Version": "27_drop_unique_constraint",
  "Status": "Complete"
}

The status field can be one of the following values:

  • "No migrations" - no migrations have been applied in this schema yet.
  • "In progress" - a migration has been started, but not yet completed.
  • "Complete" - the most recent migration was completed.

The Version field gives the name of the latest schema version.

If a migration is In progress the schemas for both the latest version indicated by the Version field and the previous version will exist in the database.

If a migration is Complete only the latest version of the schema will exist in the database.

The top-level --schema flag can be used to view the status of pgroll in a different schema:

$ pgroll status --schema schema_a

{
  "Schema": "schema_a",
  "Version": "01_create_tables",
  "Status": "Complete"
}

Operations reference

pgroll migrations are specified as JSON files. All migrations follow the same basic structure:

{
  "name": "0x_migration_name",
  "operations": [...]
}

See the examples directory for examples of each kind of operation.

pgroll supports the following migration operations:

Add column

An add column operation creates a new column on an existing table.

add column operations have this structure:

{
  "add_column": {
    "table": "name of table to which the column should be added",
    "up": "SQL expression",
    "column": {
      "name": "name of column",
      "type": "postgres type",
      "comment": "postgres comment for the column",
      "nullable": true|false,
      "unique": true|false,
      "pk": true|false,
      "default": "default value for the column",
      "check": {
        "name": "name of check constraint",
        "constraint": "constraint expression"
      },
      "references": {
        "name": "name of foreign key constraint",
        "table": "name of referenced table",
        "column": "name of referenced column"
      } 
    }
  }
}

Default values are subject to the usual rules for quoting SQL expressions. In particular, string literals should be surrounded with single quotes.

Example add column migrations:

Alter column

An alter column operation alters the properties of a column. The operation supports several sub-operations, described below.

Rename column

A rename column operation renames a column.

rename column operations have this structure:

{
  "alter_column": {
    "table": "table name",
    "column": "old column name",
    "name": "new column name"
  }
}

Example rename column migrations:

Change type

A change type operation changes the type of a column.

change type operations have this structure:

{
  "alter_column": {
    "table": "table name",
    "column": "column name",
    "type": "new type of column",
    "up": "SQL expression",
    "down": "SQL expression"
  }
}

Example change type migrations:

Add check constraint

An add check constraint operation adds a CHECK constraint to a column.

add check constraint migrations have this structure:

{
  "alter_column": {
    "table": "table name",
    "column": "column name",
    "check": {
      "name": "check constraint name",
      "constraint": "constraint expression"
    },
    "up": "SQL expression",
    "down": "SQL expression"
  }
}

Example add check constraint migrations:

Add foreign key

Add foreign key operations add a foreign key constraint to a column.

add foreign key constraints have this structure:

{
  "alter_column": {
    "table": "table name",
    "column": "column name",
    "references": {
      "name": "name of foreign key reference",
      "table": "name of referenced table",
      "column": "name of referenced column"
    },
    "up": "SQL expression",
    "down": "SQL expression"
  }
}

Example add foreign key migrations:

Add not null constraint

Add not null operations add a NOT NULL constraint to a column.

add not null operations have this structure:

{
  "alter_column": {
    "table": "table name",
    "column": "column name",
    "nullable": false,
    "up": "SQL expression",
    "down": "SQL expression"
  }
}

Example add not null migrations:

Drop not null constraint

Drop not null operations drop a NOT NULL constraint from a column.

drop not null operations have this structure:

{
  "alter_column": {
    "table": "table name",
    "column": "column name",
    "nullable": true,
    "up": "SQL expression",
    "down": "SQL expression"
  }
}

Example drop not null migrations:

Add unique constraint

Add unique operations add a UNIQUE constraint to a column.

add unique operations have this structure:

{
  "alter_column": {
    "table": "table name",
    "column": "column name",
    "unique": {
      "name": "name of unique constraint"
    },
    "up": "SQL expression",
    "down": "SQL expression"
  }
}

Example add unique migrations:

Create index

A create index operation creates a new btree index on a set of columns.

create index operations have this structure:

{
  "create_index": {
    "table": "name of table on which to define the index",
    "name": "index name",
    "columns": [ "names of columns on which to define the index" ]
  }
}

Example create index migrations:

Create table

A create table operation creates a new table in the database.

create table operations have this structure:

{
  "create_table": {
    "name": "name of new table",
    "columns": [...]
    ]
  }
}

where each column is defined as:

{
  "name": "column name",
  "type": "postgres type",
  "comment": "postgres comment for the column",
  "nullable": true|false,
  "unique": true|false,
  "pk": true|false,
  "default": "default value"
  "check": {
    "name": "name of check constraint"
    "constraint": "constraint expression"
  }
  "references": {
    "name": "name of foreign key constraint"
    "table": "name of referenced table"
    "column": "name of referenced column"
  }
},

Default values are subject to the usual rules for quoting SQL expressions. In particular, string literals should be surrounded with single quotes.

Example create table migrations:

Drop column

A drop column operation drops a column from an existing table.

drop column operations have this structure:

{
  "drop_column": {
    "table": "name of table",
    "column": "name of column to drop",
    "down": "SQL expression"
  }
}

Example drop column migrations:

Drop constraint

A drop constraint operation drops a constraint from an existing table.

Only CHECK, FOREIGN KEY, and UNIQUE constraints can be dropped.

drop constraint operations have this structure:

{
  "drop_constraint": {
    "table": "name of table",
    "column": "name of column on which constraint is defined",
    "name": "name of constraint to drop",
    "up": "SQL expression",
    "down": "SQL expression"
  }
}

Example drop constraint migrations:

Drop index

A drop index operation drops an index from a table.

drop index operations have this structure:

{
  "drop_index": {
    "name": "name of index to drop"
  }
}

Example drop index migrations:

Drop table

A drop table operation drops a table.

drop table operations have this structure:

{
  "drop_table": {
    "name": "name of table to drop"
  }
}

Example drop table migrations:

Raw SQL

A raw SQL operation runs arbitrary SQL against the database. This is intended as an 'escape hatch' to allow a migration to perform operations that are otherwise not supported by pgroll.

⚠️ pgroll is unable to guarantee that raw SQL migrations are safe and will not result in application downtime. ⚠️

sql operations have this structure:

{
  "sql": {
    "up": "SQL expression",
    "down": "SQL expression"
  }
}

Example raw SQL migrations:

Rename table

A rename table operation renames a table.

rename table operations have this structure:

{
  "rename_table": {
    "from": "old column name",
    "to": "new column name"
  }
}

Example rename table migrations:

Set replica identity

A set replica identity operation sets the replica identity for a table.

set replica identity operations have this structure:

{
  "set_replica_identity": {
    "table": "name of the table",
    "identity": {
      "type": "full | default | nothing | index"
      "index": "name of the index, if type is 'index'"
    }
  }
}

⚠️ A set replica identity operation is applied directly to the underlying table on migration start. This means that both versions of the table exposed in the old and new version schemas will have the new replica identity set. ⚠️

Example set replica identity migrations: