Handling Data Migrations

Suppose you manually make changes to your database, and now it's your responsibility to inform other developers to execute them. Additionally, you need to keep track of which changes should be applied to production machines in the next deployment. GoFr supports data migrations for MySQL, Postgres, Redis, ClickHouse & Cassandra which allows altering the state of a database, be it adding a new column to existing table or modifying the data type of existing column or adding constraints to an existing table, setting and removing keys etc.

Usage

Creating Migration Files

It is recommended to maintain a migrations directory in your project root to enhance readability and maintainability.

Migration file names

It is recommended that each migration file should be numbered in the format of YYYYMMDDHHMMSS when the migration was created. This helps prevent numbering conflicts and allows for maintaining the correct sort order by name in different filesystem views.

Create the following file in migrations directory.

Filename : 20240226153000_create_employee_table.go

package migrations

import "gofr.dev/pkg/gofr/migration"


const createTable = `CREATE TABLE IF NOT EXISTS employee
(
    id             int         not null
        primary key,
    name           varchar(50) not null,
    gender         varchar(6)  not null,
    contact_number varchar(10) not null
);`

func createTableEmployee() migration.Migrate {
	return migration.Migrate{
		UP: func(d migration.Datasource) error {
			_, err := d.SQL.Exec(createTable)
			if err != nil {
				return err
			}
			return nil
		},
	}
}

migration.Datasource have the datasources whose migrations are supported i.e., Redis and SQL (MySQL and PostgreSQL). All migrations always run in a transaction.

For MySQL, it is highly recommended to use IF EXISTS and IF NOT EXIST in DDL commands as MySQL implicitly commits these commands.

Create a function which returns all the migrations in a map

Filename : all.go

package migrations

import "gofr.dev/pkg/gofr/migration"

func All() map[int64]migration.Migrate {
	return map[int64]migration.Migrate{
		20240226153000: createTableEmployee(),
	}
}

Migrations run in ascending order of keys in this map.

Initialisation from main.go

package main

import (
	"gofr.dev/examples/using-migrations/migrations"
	"gofr.dev/pkg/gofr"
)

func main() {
	// Create a new application
	a := gofr.New()

	// Add migrations to run
	a.Migrate(migrations.All())

	// Run the application
	a.Run()
}

When we run the app we will see the following logs for migrations which ran successfully.

INFO [16:55:46] Migration 20240226153000 ran successfully

GoFr maintains the records in the database itself which helps in tracking which migrations have already been executed and ensures that only migrations that have never been run are executed.

Migration Records

SQL

Migration records are stored and maintained in gofr_migrations table which has the following schema:

REDIS

Migration records are stored and maintained in a Redis Hash named gofr_migrations where key is the version and value contains other details in JSON format.

Example :

Key : 20240226153000

Value : {"method":"UP","startTime":"2024-02-26T15:03:46.844558+05:30","duration":0}

Where,

Version : Migration version is the number provided in the map, i.e., sequence number.

Start Time : Time when Migration Started in UTC.

Duration : Time taken by Migration since it started in milliseconds.

Method : It contains the method(UP/DOWN) in which migration ran. (For now only method UP is supported)

Migrations in Cassandra

GoFr provides support for migrations in Cassandra but does not guarantee atomicity for individual Data Manipulation Language (DML) commands. To achieve atomicity during migrations, users can leverage batch operations using the NewBatch, BatchQuery, and ExecuteBatch methods. These methods allow multiple queries to be executed as a single atomic operation.

Alternatively, users can construct their batch queries using the BEGIN BATCH and APPLY BATCH statements to ensure that all the commands within the batch are executed successfully or not at all. This is particularly useful for complex migrations involving multiple inserts, updates, or schema changes in a single transaction-like operation.

When using batch operations, consider using a LoggedBatch for atomicity or an UnloggedBatch for improved performance where atomicity isn't required. This approach provides a way to maintain data consistency during complex migrations.

Note: The following example assumes that user has already created the KEYSPACE in cassandra. A KEYSPACE in Cassandra is a container for tables that defines data replication settings across the cluster.

package migrations

import (
    "gofr.dev/pkg/gofr/migration"
)

const (
	createTableCassandra = `CREATE TABLE IF NOT EXISTS employee (
                            id int PRIMARY KEY,
                            name text,
                            gender text,
                            number text
                            );`
	
	addCassandraRecords = `BEGIN BATCH
                           INSERT INTO employee (id, name, gender, number) VALUES (1, 'Alison', 'F', '1234567980');
                           INSERT INTO employee (id, name, gender, number) VALUES (2, 'Alice', 'F', '9876543210');
                           APPLY BATCH;
                           `
	
	employeeDataCassandra = `INSERT INTO employee (id, name, gender, number) VALUES (?, ?, ?, ?);`
)

func createTableEmployeeCassandra() migration.Migrate {
    return migration.Migrate{
        UP: func(d migration.Datasource) error {
            // Execute the create table statement
            if err := d.Cassandra.Exec(createTableCassandra); err != nil {
                return err
            }

            // Batch processes can also be executed in Exec as follows:
			if err := d.Cassandra.Exec(addCassandraRecords); err != nil {
				return err
			}	

            // Create a new batch operation
            batchName := "employeeBatch"
            if err := d.Cassandra.NewBatch(batchName, 0); err != nil { // 0 for LoggedBatch
                return err
            }

            // Add multiple queries to the batch
            if err := d.Cassandra.BatchQuery(batchName, employeeDataCassandra, 1, "Harry", "M", "1234567980"); err != nil {
                return err
            }

            if err := d.Cassandra.BatchQuery(batchName, employeeDataCassandra, 2, "John", "M", "9876543210"); err != nil {
                return err
            }

            // Execute the batch operation
            if err := d.Cassandra.ExecuteBatch(batchName); err != nil {
                return err
            }

            return nil
        },
    }
}

Check out the example to add and run migrations in GoFr: Visit GitHub