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.
mysql mariadb