As applications scale and demand real-time performance, traditional relational databases may struggle with write throughput and horizontal scaling. Apache Cassandra is a highly scalable, fault-tolerant NoSQL database designed for high availability and big data use cases.

In this post, you’ll learn how to integrate Spring Boot with Apache Cassandra to build cloud-native applications with scalable and resilient data storage. We’ll cover configuration, schema modeling, CRUD operations, performance tips, and real-world production considerations.

Why Cassandra for Scalable Storage?

Apache Cassandra is ideal for:

  • Write-heavy workloads
  • Distributed systems with high availability
  • Real-time analytics
  • Systems needing multi-region replication

Key features:

  • Linear horizontal scalability
  • Masterless architecture
  • Fault tolerance with replication
  • Tunable consistency levels

Add Maven Dependencies

Use Spring Data Cassandra to simplify integration:

<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-data-cassandra</artifactId>
</dependency>

Also ensure your Cassandra instance is running locally or remotely.

Configure Cassandra Connection in application.yml

spring:
data:
cassandra:
keyspace-name: my_keyspace
contact-points: localhost
port: 9042
schema-action: create-if-not-exists
local-datacenter: datacenter1

Set schema-action to none, create, or create-if-not-exists depending on your use case.

Define Cassandra Entity

Use @Table, @PrimaryKey, and @Column annotations to define a table.

@Table("users")
public class User {

    @PrimaryKey
    private UUID id;

    @Column("name")
    private String name;

    @Column("email")
    private String email;

    @Column("created_at")
    private Instant createdAt;

    // Getters and setters
}

Create a Repository Interface

Spring Data Cassandra automatically provides CRUD operations:

public interface UserRepository extends CassandraRepository<User, UUID> {
List<User> findByName(String name);
}

Supports query derivation, pagination, and custom queries using CQL.

Service Layer Example

Encapsulate logic in a service class:

@Service
public class UserService {

    @Autowired
    private UserRepository repository;

    public User createUser(String name, String email) {
        User user = new User();
        user.setId(UUID.randomUUID());
        user.setName(name);
        user.setEmail(email);
        user.setCreatedAt(Instant.now());
        return repository.save(user);
    }

    public List<User> getUsersByName(String name) {
        return repository.findByName(name);
    }
}

Handling Time-Series Data

Cassandra excels at time-series workloads. Use compound primary keys for partitioning:

@PrimaryKeyClass
public class EventKey {
@PrimaryKeyColumn(name = "user_id", ordinal = 0, type = PARTITIONED)
@PrimaryKeyColumn(name = "timestamp", ordinal = 1, type = CLUSTERED)
}

This helps in designing efficient, read-optimized queries with controlled partitions.

Using CQL for Custom Queries

Use @Query for custom CQL queries:

@Query("SELECT * FROM users WHERE email = ?0 ALLOW FILTERING")
List<User> findByEmail(String email);

Note: ALLOW FILTERING should be avoided in production unless necessary. Always design tables for your queries.

Performance Considerations

  • Use prepared statements for better performance
  • Choose the correct partition key to avoid hotspots
  • Set consistency levels based on read/write guarantees
  • Tune the replication factor for fault tolerance
spring.data.cassandra.consistency-level: LOCAL_QUORUM

For heavy production loads, monitor using DataStax OpsCenter or Prometheus exporters.

Best Practices

  • Prefer denormalization over joins (Cassandra is not relational)
  • Design tables around query patterns
  • Use UUIDs or time-based keys for scaling writes
  • Regularly run nodetool cleanup and repair
  • Enable paging for large result sets

Conclusion

Integrating Spring Boot with Cassandra unlocks scalable, fault-tolerant data storage for modern applications. By combining Spring’s developer productivity with Cassandra’s distributed capabilities, you can build systems that handle high traffic, large volumes of data, and mission-critical workloads.

Whether you’re building a microservice, IoT platform, or real-time dashboard, Cassandra and Spring Boot offer a powerful, production-ready stack.