SQL Clustering - Vitess

Like rest of us I started my journey as part of different organizations by developing and deploying monolith applications. Typically an application instance hosted somewhere on lcoud and a DB instance will do the job connected to it for small scale organization’s automation solution or business website. If the audience locality is wider then multi regional replicas of DB and application will do the job. In fact even today in most small/medium scaled organizations few multi regional replicas will do the job may be adding a Kafka layer for high throughput or there are managed solutions many cloud providers used to facilitate target audience.

But for medium to large scale organizations they require a solution that comes handy by facilitating underlying complexities on the go. Since the term coined “Data is a new Oil” the importance of databases facilitations of specific use cases require more out of the box DB cluster based solutions. There are many SQL or No-SQL based cluster solutions addressing specific use cases and Vitess is one of the prominent one.

Cluster-based databases are an essential component of modern application architectures, especially for businesses and organizations that require high availability, scalability, and fault tolerance. These databases distribute data across multiple nodes, enabling applications to handle massive traffic while ensuring minimal downtime. Popular options include Vitess, CockroachDB, Amazon Aurora, Google Spanner, and YugabyteDB. Each of these databases is designed with unique features catering to specific use cases. For instance, Google Spanner offers global consistency, while CockroachDB shines with its ease of deployment and multi-region support. Vitess, however, stands out in the crowd as a solution specifically built to scale MySQL databases horizontally, making it a favorite for high-traffic applications.

For most of us who started their career learning databases with MySQL the prominence of Vitess is unique in many ways because it comes with fully loaded features with the best practices evolved over time.

Since ZooKeeper is part of its default features so it takes care of high throughput essentially by choosing a Leader Election, Metadata and Broker Management amd Cluster health monitoring. Extra features it has to offer include Topology service (Tablets, Shards configurations), Service Discovery and Configuration Management.?

Why Vitess is Special

Vitess is a cutting-edge, open-source database clustering system designed to manage extremely large volumes of data and traffic. Originally developed by YouTube, it combines the simplicity of MySQL with the scalability of a distributed system. Some standout features of Vitess include:

• Horizontal Scalability: Vitess enables sharding, which partitions large datasets across multiple servers to scale horizontally.
• High Availability: With built-in failover and replication capabilities, Vitess ensures minimal downtime.
• SQL Query Routing: It abstracts sharding from the application layer by routing queries to the appropriate shard, simplifying application development.
• Compatibility with MySQL: Applications can seamlessly integrate with Vitess without significant changes to the existing MySQL-based codebase.
• Observability and Monitoring: Vitess provides robust tools for monitoring database performance and operations.
• Kubernetes Integration: It is designed to work efficiently in Kubernetes environments, enabling automated scaling and management.
• Built-In VReplication: Vitess’s replication capabilities are versatile, supporting workflows like materialized views and data migration.

These features make Vitess a go-to solution for companies handling massive datasets and high query loads while maintaining MySQL compatibility.

Setting Up Vitess

Vitess comes with comprehensive documentation for its installation, configuration and management but here is a step-by-step guide to setting up Vitess on a target machine, along with the recommended hardware specifications:

Recommended Hardware Specifications:

• CPU: At least 4 cores (8 recommended for production).
• RAM: Minimum 16GB (32GB+ for high traffic environments).
• Disk: SSD storage with at least 500GB (1TB+ recommended for large datasets).
• Operating System: Linux (Ubuntu 20.04 or later).
• Network: High-speed network connection for efficient data replication and cluster communication.

Installation Steps:

·?????? Install Dependencies:

• Update the system:

 sudo apt update && sudo apt upgrade -y        

• Install required packages:

 sudo apt install -y git wget curl build-essential python3 python3-pip        

• Install Go: Download and install Go (required to build Vitess):

 wget https://go.dev/dl/go1.20.5.linux-amd64.tar.gz        

 sudo tar -C /usr/local -xzf go1.20.5.linux-amd64.tar.gz        

 echo "export PATH=$PATH:/usr/local/go/bin" >> ~/.bashrc        

? source ~/.bashrc        

·?????? Clone Vitess Repository:

git clone https://github.com/vitessio/vitess.git        

cd vitess        

• Build Vitess:

make build        

• Configure Vitess:

Set up a topology service (e.g., etcd or ZooKeeper).

Configure MySQL instances as per the Vitess documentation.

Initialize the cluster by defining shards and tablets.

Run Vitess Components:

Start the Vitess components such as vtgate, vttablet, and vtctld using provided scripts or Kubernetes configurations.

• Verify the Setup:

Access the Vitess control interface to ensure all components are running:

Open up in browser or use curl https://<server-ip>:15000        

By following these steps, you’ll have a functional Vitess cluster ready to handle large-scale MySQL workloads efficiently. This setup is ideal for organizations looking to achieve seamless scalability and high availability for their database systems.

What will be a futuristic DB cluster for next generation of applications?

Well there are a lot of basic automation concerns if part of a cluster DB can have far reach even in current and upcoming marketplace like Auto Normalization, Auto shard behavior based on usage (for example Read/Write shards autonomous management), Essential Subquery Management/Creation based on real-time usage and much more.?

Current article is not to build an argument related to Vitess only there could be 1000s of scenarios and use cases as per your goals but just to visualize modern clusters at glance. My personal favorite includes Google BigQuery and Amazon Redshift which are both columnar databases with awesome performance for data warehousing solutions.?

Please comment to add to my knowledge.