Cassandra is a distributed database management system that is open source, featuring a wide-column store and NoSQL database designed to handle large amounts of data across many commodity servers. Its architecture ensures high availability with no single point of failure, making it ideal for businesses that demand reliability and scalability. Developed by the Apache Software Foundation and written in Java, Cassandra excels in real-time analytics, recommendation engines, and event logging systems.

Its wide-column store format allows for efficient storage and retrieval of large datasets, even when the schema varies between rows. This flexibility makes it a preferred choice for applications where dynamic data structures are crucial.

Basics of Cassandra

1. Data Model

• Keyspace: Acts as a namespace defining data replication on nodes.
• Column Family: Equivalent to tables in relational databases. Each row can have a unique set of columns, offering flexibility in data storage.

2. Cassandra Architecture

• Decentralized: Operates as a peer-to-peer system with no master-slave configuration, ensuring every node is equal.
• Ring Architecture: Nodes are arranged in a ring structure, distributing responsibility for subsets of data evenly. This prevents a single point of failure.
• Data Replication: Cassandra replicates data across nodes to ensure fault tolerance. The replication factor specifies the number of data copies across the cluster.

3. Data Distribution and Partitioning

• Partition Key: Determines how data is distributed across nodes using hashing.
• Clustering Columns: Defines data order within a partition, enabling efficient queries.

4. Cassandra Query Language (CQL)

CQL resembles SQL but is tailored for Cassandra’s NoSQL nature. It supports schema definition, data manipulation, and querying with operations such as creating indexes and managing keyspaces.

When to Use Cassandra

Cassandra is best suited for use cases requiring:

• Large-Scale Data Handling: Distributed across multiple servers.
• High Availability: Ideal for 24/7 applications with no downtime.
• Write-Heavy Applications: Handles high-frequency updates efficiently.
• Distributed Systems: Seamlessly spans multiple data centers.
• Time-Series Data: Stores event logs or sensor data queried over time.

Steps to Set Up Cassandra on Linux

1. Install Cassandra

Verify Java version:

Add the Apache repository:

Install HTTPS transport:

Add Cassandra repository keys:

Update package index and install Cassandra:

Check the Cassandra service status:

2. Connect to Cassandra

Use cqlsh to interact with the Cassandra database:

Integrating Cassandra with Spring Boot

Step 1: Create a New Spring Boot Project

1. Visit Spring Initializr to generate a project.
2. Fill in the details:
   • Project: Maven
   • Language: Java
   • Spring Boot Version: 3.3.5
   • Dependencies: Spring Web, Spring Data for Apache Cassandra
3. Download and extract the project.

**Step 2: Configure **application.properties

Add the following properties to connect your Spring Boot application with Cassandra:

Step 3: Create a Keyspace and Table

Launch cqlsh and create a keyspace:

Create a users table:

Step 4: Define an Entity Class

Step 5: Create a Repository

Step 6: Implement a Controller

Step 7 : Test the REST API with Postman (API testing tool)

Create a User (POST Request)

• Request Type : POST
• URL : http://localhost:8080/users
• Headers : Content-Type : application/json
• Body :

Getting All Users (GET Request)

• Request Type : GET
• URL : http://localhost:8080/users

Update a User (PUT Request)

• Request Type : PUT
• URL : http://localhost:8080/users/550e8400-e29b-41d4-a716-446655440000
• Body :

Delete a User (DELETE Request)

• Request Type : DELETE
• URL : http://localhost:8080/users/550e8400-e29b-41d4-a716-446655440000

Step 8 : Optionally, inspect the table in the terminal using cqlsh

Unveiled Use Cases of Cassandra

• Real-Time Analytics: Processes vast data sets for actionable insights.
• IoT Applications: Manages data from connected devices efficiently.
• E-commerce Platforms: Handles product catalog and user data seamlessly.
• Social Media: Supports high write and read throughput.
• Healthcare: Maintains patient records and processes real-time health data.

Conclusion

Integrating Cassandra with Spring Boot offers a robust solution for applications requiring high scalability, reliability, and performance. By combining Cassandra’s distributed nature with Spring Boot’s simplicity, developers can build applications capable of handling complex data workloads effortlessly.

At Inexture Solutions, we specialize in creating tailored solutions using cutting-edge technologies like Cassandra and Spring Boot. Contact us to elevate your next project with innovative and scalable software solutions.