Storing Large and Complex Java Objects in Cassandra Using Spring: Best Practices and Design Strategy

When developing modern Spring-based applications, it’s not uncommon to deal with large Java objects composed of multiple fields, nested lists, and a mix of data types like LocalDate, String, and int. Storing such complex structures efficiently in a Cassandra database presents unique challenges—and opportunities. This article will guide you through the best practices for modeling and persisting large nested objects in Cassandra, with a focus on Spring Data Cassandra.

The Challenge: A Large Java Object with Nested Lists

Imagine you’re working with a Java object that looks like this:

• ~50 fields
• Some fields are List<T> of other custom objects
• Those inner objects may contain lists themselves
• Data types include primitives, dates, and large strings

The first question that comes up is:

Should I store this whole object in a single Cassandra table, or break it into multiple tables?

Let’s explore the pros and cons of each approach and walk through an effective strategy.

Option 1: Store the Entire Object in One Table

✅ Pros

• Simpler to implement and query
• Easier to map using Spring Data Cassandra
• Ensures atomic writes and reads (entire object at once)

❌ Cons

• Row size limits: Cassandra isn’t ideal for very large rows
• Performance bottlenecks: Loading large lists can hurt read/write throughput
• Schema inflexibility: Difficult to evolve or change structure later
• Collection limits: Cassandra collections (List, Set, Map) have a size threshold (~64KB per item, up to 2 billion cells per partition, but practically much less)

Option 2: Split into Multiple Tables

This is the preferred approach for complex, nested, or large-scale data structures.

✅ Pros

• Scales better and avoids row-size limits
• Easier to update parts of the object independently
• You can optimize each table’s partitioning and indexing

❌ Cons

• More complex to implement and maintain
• No native joins in Cassandra—requires application-level joins or batch queries
• Must manually ensure consistency between tables

Recommended Strategy: Hybrid Approach with Spring Data Cassandra

Here’s a structured plan:

1. Analyze Access Patterns

• Will the entire object be fetched often?
• Are some lists more frequently accessed or updated?
• Use these insights to split tables along access boundaries.

2. Use UDTs for Small Nested Structures

Cassandra supports User Defined Types (UDTs) which are great for embedding small objects:

@UserDefinedType
public class Address {
    private String street;
    private String city;
}

3. Create Separate Tables for Large Lists

Instead of:

private List<Invoice> invoices;

Use:

@Table
public class Invoice {
    @PrimaryKey
    private UUID parentObjectId;
    private LocalDate date;
    private BigDecimal amount;
}

4. Use UUIDs for Linking Entities

Use UUID as foreign keys or references between your root entity and nested tables. This keeps data well-linked without coupling.

5. Design with Denormalization in Mind

In Cassandra, read efficiency > write normalization. You may duplicate some data to simplify queries.

6. Manage Consistency with Batches or Application Logic

Use logged batches sparingly for related writes, or implement eventual consistency in your app layer.

Example: Breaking Down a Complex Java Object

public class CustomerProfile {
    private UUID id;
    private String name;
    private List<Address> addresses; // UDT
    private List<Order> orders;      // Stored in separate table
}

Tables:

• customer_profile (root table)
• customer_orders_by_customer_id (table for orders)
• order_items_by_order_id (table for items in an order)

Spring Data Cassandra Tips

• Annotate entities with @Table, UDTs with @UserDefinedType
• Use @CassandraType(type = DataType.Name.UDT) to embed UDTs
• Use repositories or custom DAO patterns for nested data retrieval

Conclusion

When storing complex and large objects in Cassandra:

• Avoid monolithic tables that try to do too much
• Split nested lists into separate tables
• Use UDTs for small, reusable objects
• Model your data based on queries, not just structure

By following these best practices, you ensure scalability, maintainability, and performance in your Spring + Cassandra applications.