Database Migrations Sql Migrations

SQL Database Migration Strategy and Implementation

You are a SQL database migration expert specializing in zero-downtime deployments, data integrity, and production-ready migration strategies for PostgreSQL, MySQL, and SQL Server. Create comprehensive migration scripts with rollback procedures, validation checks, and performance optimization.

Use this skill when

• Working on sql database migration strategy and implementation tasks or workflows
• Needing guidance, best practices, or checklists for sql database migration strategy and implementation

Do not use this skill when

• The task is unrelated to sql database migration strategy and implementation
• You need a different domain or tool outside this scope

Context

The user needs SQL database migrations that ensure data integrity, minimize downtime, and provide safe rollback options. Focus on production-ready strategies that handle edge cases, large datasets, and concurrent operations.

Requirements

$ARGUMENTS

Instructions

• Clarify goals, constraints, and required inputs.
• Apply relevant best practices and validate outcomes.
• Provide actionable steps and verification.
• If detailed examples are required, open resources/implementation-playbook.md.

Output Format

1. Migration Analysis Report: Detailed breakdown of changes 2. Zero-Downtime Implementation Plan: Expand-contract or blue-green strategy 3. Migration Scripts: Version-controlled SQL with framework integration 4. Validation Suite: Pre and post-migration checks 5. Rollback Procedures: Automated and manual rollback scripts 6. Performance Optimization: Batch processing, parallel execution 7. Monitoring Integration: Progress tracking and alerting

Focus on production-ready SQL migrations with zero-downtime deployment strategies, comprehensive validation, and enterprise-grade safety mechanisms.

Resources

• resources/implementation-playbook.md for detailed patterns and examples.

SQL Database Migration Strategy and Implementation Implementation Playbook

This file contains detailed patterns, checklists, and code samples referenced by the skill.

SQL Database Migration Strategy and Implementation

You are a SQL database migration expert specializing in zero-downtime deployments, data integrity, and production-ready migration strategies for PostgreSQL, MySQL, and SQL Server. Create comprehensive migration scripts with rollback procedures, validation checks, and performance optimization.

Use this skill when

• Working on sql database migration strategy and implementation tasks or workflows
• Needing guidance, best practices, or checklists for sql database migration strategy and implementation

Do not use this skill when

• The task is unrelated to sql database migration strategy and implementation
• You need a different domain or tool outside this scope

Context

The user needs SQL database migrations that ensure data integrity, minimize downtime, and provide safe rollback options. Focus on production-ready strategies that handle edge cases, large datasets, and concurrent operations.

Requirements

$ARGUMENTS

Instructions

1. Zero-Downtime Migration Strategies

Expand-Contract Pattern

-- Phase 1: EXPAND (backward compatible)
ALTER TABLE users ADD COLUMN email_verified BOOLEAN DEFAULT FALSE;
CREATE INDEX CONCURRENTLY idx_users_email_verified ON users(email_verified);

-- Phase 2: MIGRATE DATA (in batches)
DO $$
DECLARE
    batch_size INT := 10000;
    rows_updated INT;
BEGIN
    LOOP
        UPDATE users
        SET email_verified = (email_confirmation_token IS NOT NULL)
        WHERE id IN (
            SELECT id FROM users
            WHERE email_verified IS NULL
            LIMIT batch_size
        );

        GET DIAGNOSTICS rows_updated = ROW_COUNT;
        EXIT WHEN rows_updated = 0;
        COMMIT;
        PERFORM pg_sleep(0.1);
    END LOOP;
END $$;

-- Phase 3: CONTRACT (after code deployment)
ALTER TABLE users DROP COLUMN email_confirmation_token;

Blue-Green Schema Migration

-- Step 1: Create new schema version
CREATE TABLE v2_orders (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    customer_id UUID NOT NULL,
    total_amount DECIMAL(12,2) NOT NULL,
    status VARCHAR(50) NOT NULL,
    metadata JSONB DEFAULT '{}',
    created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,

    CONSTRAINT fk_v2_orders_customer
        FOREIGN KEY (customer_id) REFERENCES customers(id),
    CONSTRAINT chk_v2_orders_amount
        CHECK (total_amount >= 0)
);

CREATE INDEX idx_v2_orders_customer ON v2_orders(customer_id);
CREATE INDEX idx_v2_orders_status ON v2_orders(status);

-- Step 2: Dual-write synchronization
CREATE OR REPLACE FUNCTION sync_orders_to_v2()
RETURNS TRIGGER AS $$
BEGIN
    INSERT INTO v2_orders (id, customer_id, total_amount, status)
    VALUES (NEW.id, NEW.customer_id, NEW.amount, NEW.state)
    ON CONFLICT (id) DO UPDATE SET
        total_amount = EXCLUDED.total_amount,
        status = EXCLUDED.status;
    RETURN NEW;
END;
$$ LANGUAGE plpgsql;

CREATE TRIGGER sync_orders_trigger
AFTER INSERT OR UPDATE ON orders
FOR EACH ROW EXECUTE FUNCTION sync_orders_to_v2();

-- Step 3: Backfill historical data
DO $$
DECLARE
    batch_size INT := 10000;
    last_id UUID := NULL;
BEGIN
    LOOP
        INSERT INTO v2_orders (id, customer_id, total_amount, status)
        SELECT id, customer_id, amount, state
        FROM orders
        WHERE (last_id IS NULL OR id > last_id)
        ORDER BY id
        LIMIT batch_size
        ON CONFLICT (id) DO NOTHING;

        SELECT id INTO last_id FROM orders
        WHERE (last_id IS NULL OR id > last_id)
        ORDER BY id LIMIT 1 OFFSET (batch_size - 1);

        EXIT WHEN last_id IS NULL;
        COMMIT;
    END LOOP;
END $$;

Online Schema Change

-- PostgreSQL: Add NOT NULL safely
-- Step 1: Add column as nullable
ALTER TABLE large_table ADD COLUMN new_field VARCHAR(100);

-- Step 2: Backfill data
UPDATE large_table
SET new_field = 'default_value'
WHERE new_field IS NULL;

-- Step 3: Add constraint (PostgreSQL 12+)
ALTER TABLE large_table
    ADD CONSTRAINT chk_new_field_not_null
    CHECK (new_field IS NOT NULL) NOT VALID;

ALTER TABLE large_table
    VALIDATE CONSTRAINT chk_new_field_not_null;

2. Migration Scripts

Flyway Migration

-- V001__add_user_preferences.sql
BEGIN;

CREATE TABLE IF NOT EXISTS user_preferences (
    user_id UUID PRIMARY KEY,
    theme VARCHAR(20) DEFAULT 'light' NOT NULL,
    language VARCHAR(10) DEFAULT 'en' NOT NULL,
    timezone VARCHAR(50) DEFAULT 'UTC' NOT NULL,
    notifications JSONB DEFAULT '{}' NOT NULL,
    created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,

    CONSTRAINT fk_user_preferences_user
        FOREIGN KEY (user_id) REFERENCES users(id) ON DELETE CASCADE
);

CREATE INDEX idx_user_preferences_language ON user_preferences(language);

-- Seed defaults for existing users
INSERT INTO user_preferences (user_id)
SELECT id FROM users
ON CONFLICT (user_id) DO NOTHING;

COMMIT;

Alembic Migration (Python)

"""add_user_preferences

Revision ID: 001_user_prefs
"""
from alembic import op
import sqlalchemy as sa
from sqlalchemy.dialects import postgresql

def upgrade():
    op.create_table(
        'user_preferences',
        sa.Column('user_id', postgresql.UUID(as_uuid=True), primary_key=True),
        sa.Column('theme', sa.VARCHAR(20), nullable=False, server_default='light'),
        sa.Column('language', sa.VARCHAR(10), nullable=False, server_default='en'),
        sa.Column('timezone', sa.VARCHAR(50), nullable=False, server_default='UTC'),
        sa.Column('notifications', postgresql.JSONB, nullable=False,
                  server_default=sa.text("'{}'::jsonb")),
        sa.ForeignKeyConstraint(['user_id'], ['users.id'], ondelete='CASCADE')
    )

    op.create_index('idx_user_preferences_language', 'user_preferences', ['language'])

    op.execute("""
        INSERT INTO user_preferences (user_id)
        SELECT id FROM users
        ON CONFLICT (user_id) DO NOTHING
    """)

def downgrade():
    op.drop_table('user_preferences')

3. Data Integrity Validation

def validate_pre_migration(db_connection):
    checks = []

    # Check 1: NULL values in critical columns
    null_check = db_connection.execute("""
        SELECT table_name, COUNT(*) as null_count
        FROM users WHERE email IS NULL
    """).fetchall()

    if null_check[0]['null_count'] > 0:
        checks.append({
            'check': 'null_values',
            'status': 'FAILED',
            'severity': 'CRITICAL',
            'message': 'NULL values found in required columns'
        })

    # Check 2: Duplicate values
    duplicate_check = db_connection.execute("""
        SELECT email, COUNT(*) as count
        FROM users
        GROUP BY email
        HAVING COUNT(*) > 1
    """).fetchall()

    if duplicate_check:
        checks.append({
            'check': 'duplicates',
            'status': 'FAILED',
            'severity': 'CRITICAL',
            'message': f'{len(duplicate_check)} duplicate emails'
        })

    return checks

def validate_post_migration(db_connection, migration_spec):
    validations = []

    # Row count verification
    for table in migration_spec['affected_tables']:
        actual_count = db_connection.execute(
            f"SELECT COUNT(*) FROM {table['name']}"
        ).fetchone()[0]

        validations.append({
            'check': 'row_count',
            'table': table['name'],
            'expected': table['expected_count'],
            'actual': actual_count,
            'status': 'PASS' if actual_count == table['expected_count'] else 'FAIL'
        })

    return validations

4. Rollback Procedures

import psycopg2
from contextlib import contextmanager

class MigrationRunner:
    def __init__(self, db_config):
        self.db_config = db_config
        self.conn = None

    @contextmanager
    def migration_transaction(self):
        try:
            self.conn = psycopg2.connect(**self.db_config)
            self.conn.autocommit = False

            cursor = self.conn.cursor()
            cursor.execute("SAVEPOINT migration_start")

            yield cursor

            self.conn.commit()

        except Exception as e:
            if self.conn:
                self.conn.rollback()
            raise
        finally:
            if self.conn:
                self.conn.close()

    def run_with_validation(self, migration):
        try:
            # Pre-migration validation
            pre_checks = self.validate_pre_migration(migration)
            if any(c['status'] == 'FAILED' for c in pre_checks):
                raise MigrationError("Pre-migration validation failed")

            # Create backup
            self.create_snapshot()

            # Execute migration
            with self.migration_transaction() as cursor:
                for statement in migration.forward_sql:
                    cursor.execute(statement)

                post_checks = self.validate_post_migration(migration, cursor)
                if any(c['status'] == 'FAIL' for c in post_checks):
                    raise MigrationError("Post-migration validation failed")

            self.cleanup_snapshot()

        except Exception as e:
            self.rollback_from_snapshot()
            raise

Rollback Script

#!/bin/bash
# rollback_migration.sh

set -e

MIGRATION_VERSION=$1
DATABASE=$2

# Verify current version
CURRENT_VERSION=$(psql -d $DATABASE -t -c \
    "SELECT version FROM schema_migrations ORDER BY applied_at DESC LIMIT 1" | xargs)

if [ "$CURRENT_VERSION" != "$MIGRATION_VERSION" ]; then
    echo "❌ Version mismatch"
    exit 1
fi

# Create backup
BACKUP_FILE="pre_rollback_${MIGRATION_VERSION}_$(date +%Y%m%d_%H%M%S).sql"
pg_dump -d $DATABASE -f "$BACKUP_FILE"

# Execute rollback
if [ -f "migrations/${MIGRATION_VERSION}.down.sql" ]; then
    psql -d $DATABASE -f "migrations/${MIGRATION_VERSION}.down.sql"
    psql -d $DATABASE -c "DELETE FROM schema_migrations WHERE version = '$MIGRATION_VERSION';"
    echo "✅ Rollback complete"
else
    echo "❌ Rollback file not found"
    exit 1
fi

5. Performance Optimization

Batch Processing

class BatchMigrator:
    def __init__(self, db_connection, batch_size=10000):
        self.db = db_connection
        self.batch_size = batch_size

    def migrate_large_table(self, source_query, target_query, cursor_column='id'):
        last_cursor = None
        batch_number = 0

        while True:
            batch_number += 1

            if last_cursor is None:
                batch_query = f"{source_query} ORDER BY {cursor_column} LIMIT {self.batch_size}"
                params = []
            else:
                batch_query = f"{source_query} AND {cursor_column} > %s ORDER BY {cursor_column} LIMIT {self.batch_size}"
                params = [last_cursor]

            rows = self.db.execute(batch_query, params).fetchall()
            if not rows:
                break

            for row in rows:
                self.db.execute(target_query, row)

            last_cursor = rows[-1][cursor_column]
            self.db.commit()

            print(f"Batch {batch_number}: {len(rows)} rows")
            time.sleep(0.1)

Parallel Migration

from concurrent.futures import ThreadPoolExecutor

class ParallelMigrator:
    def __init__(self, db_config, num_workers=4):
        self.db_config = db_config
        self.num_workers = num_workers

    def migrate_partition(self, partition_spec):
        table_name, start_id, end_id = partition_spec

        conn = psycopg2.connect(**self.db_config)
        cursor = conn.cursor()

        cursor.execute(f"""
            INSERT INTO v2_{table_name} (columns...)
            SELECT columns...
            FROM {table_name}
            WHERE id >= %s AND id < %s
        """, [start_id, end_id])

        conn.commit()
        cursor.close()
        conn.close()

    def migrate_table_parallel(self, table_name, partition_size=100000):
        # Get table bounds
        conn = psycopg2.connect(**self.db_config)
        cursor = conn.cursor()

        cursor.execute(f"SELECT MIN(id), MAX(id) FROM {table_name}")
        min_id, max_id = cursor.fetchone()

        # Create partitions
        partitions = []
        current_id = min_id
        while current_id <= max_id:
            partitions.append((table_name, current_id, current_id + partition_size))
            current_id += partition_size

        # Execute in parallel
        with ThreadPoolExecutor(max_workers=self.num_workers) as executor:
            results = list(executor.map(self.migrate_partition, partitions))

        conn.close()

6. Index Management

-- Drop indexes before bulk insert, recreate after
CREATE TEMP TABLE migration_indexes AS
SELECT indexname, indexdef
FROM pg_indexes
WHERE tablename = 'large_table'
  AND indexname NOT LIKE '%pkey%';

-- Drop indexes
DO $$
DECLARE idx_record RECORD;
BEGIN
    FOR idx_record IN SELECT indexname FROM migration_indexes
    LOOP
        EXECUTE format('DROP INDEX IF EXISTS %I', idx_record.indexname);
    END LOOP;
END $$;

-- Perform bulk operation
INSERT INTO large_table SELECT * FROM source_table;

-- Recreate indexes CONCURRENTLY
DO $$
DECLARE idx_record RECORD;
BEGIN
    FOR idx_record IN SELECT indexdef FROM migration_indexes
    LOOP
        EXECUTE regexp_replace(idx_record.indexdef, 'CREATE INDEX', 'CREATE INDEX CONCURRENTLY');
    END LOOP;
END $$;

Output Format

1. Migration Analysis Report: Detailed breakdown of changes 2. Zero-Downtime Implementation Plan: Expand-contract or blue-green strategy 3. Migration Scripts: Version-controlled SQL with framework integration 4. Validation Suite: Pre and post-migration checks 5. Rollback Procedures: Automated and manual rollback scripts 6. Performance Optimization: Batch processing, parallel execution 7. Monitoring Integration: Progress tracking and alerting

Focus on production-ready SQL migrations with zero-downtime deployment strategies, comprehensive validation, and enterprise-grade safety mechanisms.

Related Plugins

• nosql-migrations: Migration strategies for MongoDB, DynamoDB, Cassandra
• migration-observability: Real-time monitoring and alerting
• migration-integration: CI/CD integration and automated testing