MySQL Cluster vs. MariaDB Galera: Choosing the Right High Availability Solution

• Uses a separate storage engine called NDB Cluster.
• Requires schema changes for your existing tables to work with the distributed nature of NDB.
• Offers high availability (data remains accessible even with node failures) and scalability (add more nodes for increased performance).
• Programming involves working with the NDB API, which is different from the standard MySQL API.

MariaDB Galera:

• Leverages a replication technology called Galera Cluster.
• Works with the standard storage engine (like InnoDB) used by MySQL and MariaDB.
• Offers high availability with multi-master setup (all nodes can receive writes).
• Maintains data consistency through a voting system (reads reflect the latest committed data).
• Programming involves using standard MySQL or MariaDB syntax for interacting with the database.

Here's an analogy:

• MySQL Cluster: Imagine a completely new filing cabinet system with a different way to organize documents.
• MariaDB Galera: Imagine syncing multiple filing cabinets with the same organization system, ensuring everyone has the latest version of each document.

Choosing between them depends on your needs:

• If you need high scalability and don't mind schema changes, MySQL Cluster might be a good fit.
• If you prefer a familiar API, high availability, and multi-master writes, MariaDB Galera is a strong option.

import ndb

# Connect to NDB Cluster using connection string (replace with your details)
connection_string = "host=your_ndb_host,port=your_ndb_port"
client = ndb.Client(connection_string)

# Define your NDB model class (represents a table)
class MyModel(ndb.Model):
  name = ndb.StringProperty()
  value = ndb.IntegerProperty()

# Create an instance of your model
new_model = MyModel(name="Example", value=10)

# Save the model to the cluster
new_model.put()

# Query for models (using NDB API calls)
query = MyModel.query()
results = query.fetch()

# Process the results (access data using model properties)
for result in results:
  print(f"Name: {result.name}, Value: {result.value}")

MariaDB Galera with Standard MySQL API (Python example):

import mysql.connector

# Connect to a node in the MariaDB Galera cluster (replace with your details)
connection = mysql.connector.connect(
  user="your_username",
  password="your_password",
  host="your_galera_host",
  database="your_database"
)

cursor = connection.cursor()

# Standard SQL statements for interacting with the database
cursor.execute("CREATE TABLE IF NOT EXISTS MyTable (id INT PRIMARY KEY, data VARCHAR(255))")
cursor.execute("INSERT INTO MyTable (id, data) VALUES (%s, %s)", (1, "Example data"))
connection.commit()  # Important for writes in Galera

# Standard SQL queries
cursor.execute("SELECT * FROM MyTable")
results = cursor.fetchall()

# Process the results (access data using column indexes)
for result in results:
  print(f"ID: {result[0]}, Data: {result[1]}")

cursor.close()
connection.close()

Key differences:

• MySQL Cluster uses the ndb library and specific API calls for working with NDB models.
• MariaDB Galera uses the standard mysql.connector library and regular SQL statements for interacting with the database.

• Built-in functionality of MySQL and MariaDB.
• One server acts as the master (receives writes), while others are slaves (replicate data asynchronously).
• Offers good read scalability (read from slaves) and some level of fault tolerance (failover to a slave on master failure).
• Limitations: Not truly "high availability" as writes are not available during failover. Data consistency can be an issue with asynchronous replication.

Cloud-based Managed Database Services:

• Services like Amazon Aurora (MySQL compatible), Google Cloud SQL, or Microsoft Azure Database for MySQL offer built-in high availability and scalability features.
• Easy to set up and manage, often with automatic backups and disaster recovery options.
• Can be cost-effective depending on your usage.
• Vendor lock-in and potential limitations on customization compared to self-hosted solutions.

Other Distributed SQL Databases:

• Options like PostgreSQL or CockroachDB offer native distributed architecture without needing clustering solutions.
• Can handle high availability and scalability inherently.
• Might require learning a new database system and potentially adapting your applications.

NoSQL Databases:

• Consider NoSQL databases like Cassandra or MongoDB if your data model is schema-less or requires high write throughput.
• Offer horizontal scaling and fault tolerance.
• Not a direct replacement for relational databases, might require changes in your application logic.

The best alternative depends on your specific needs. Here's a quick guide:

• Need basic high availability and read scalability? Traditional master-slave replication might suffice.
• Looking for ease of use and managed services? Cloud-based options are a good choice.
• Require true multi-master writes and strong consistency? MariaDB Galera or a distributed SQL database could be ideal.
• Focus on high write throughput and schema flexibility? NoSQL databases might be worth considering.