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Data Denormalization in Cassandra and NoSQL Databases

NoSQL databases like Cassandra use LSM Tree based indexing which is only for primary index. This allows ultra fast write but reads are slow for complex table joins where needed since the database does not allow for SQL-styled table joins natively.

Though recent trends show table joins are also available for some NoSQL databases, more commonly data is deliberately denormalized to optimize for read performance. This is the opposite approach from traditional relational databases where normalization is preferred. Here's how it works:

Denormalization Concept​

In Cassandra and similar NoSQL databases, data is typically denormalized by:

  • Duplicating data across multiple tables
  • Creating tables specific to query patterns
  • Embedding related data rather than referencing it

Example Scenario​

Let's consider an e-commerce application with customers, orders, and products.

Relational (Normalized) Approach​

In a traditional relational database, you'd have:

customers (customer_id, name, email, address)
orders (order_id, customer_id, order_date, status)
order_items (order_id, product_id, quantity, price)
products (product_id, name, description, base_price)

Cassandra (Denormalized) Approach​

In Cassandra, you'd design tables based on query patterns:

  1. Orders by Customer:
CREATE TABLE orders_by_customer (
  customer_id uuid,
  order_id uuid,
  order_date timestamp,
  status text,
  customer_name text,  // Duplicated from customers
  customer_email text,  // Duplicated from customers
  total_amount decimal,
  PRIMARY KEY (customer_id, order_date, order_id)
) WITH CLUSTERING ORDER BY (order_date DESC);
  1. Order Details:
CREATE TABLE order_details (
  order_id uuid,
  product_id uuid,
  product_name text,  // Duplicated from products
  quantity int,
  price decimal,
  PRIMARY KEY (order_id, product_id)
);
  1. Recent Orders (for a dashboard):
CREATE TABLE recent_orders (
  day_bucket text,
  order_time timestamp,
  order_id uuid,
  customer_name text,  // Duplicated
  status text,
  total_amount decimal,
  PRIMARY KEY (day_bucket, order_time, order_id)
) WITH CLUSTERING ORDER BY (order_time DESC);

Notice how the customer name and email appear in multiple places. Product names are also duplicated. This is intentional - it eliminates the need for joins when retrieving data.

Key Benefits​

  1. Fast Reads: Each query can be satisfied by a single table
  2. Horizontal Scalability: Data can be partitioned easily
  3. Query-Driven Design: Tables are structured for specific access patterns

Trade-offs​

  1. Write Complexity: Updates must be applied to multiple places
  2. Increased Storage: Due to data duplication
  3. Consistency Challenges: Must ensure duplicate data stays synchronized

This approach prioritizes read performance and scalability over storage efficiency, making it ideal for high-throughput systems where reads outnumber writes.

Additional Considerations​

Data Modeling Patterns​

When denormalizing in Cassandra, common patterns include:

  • Materialized Views: Pre-computed query results stored as tables
  • Time Series Data: Using time-based partitioning for efficient data retrieval
  • Bucketing: Grouping related data into partitions to avoid hot spots

Handling Updates​

Since data is duplicated, updating a single piece of information might require updates to multiple tables. Strategies include:

  • Batch Updates: Atomic updates across multiple tables
  • Eventual Consistency: Accepting that data might be temporarily inconsistent
  • Background Processes: Asynchronously updating denormalized data

When to Use​

Denormalization in Cassandra works best for:

  • High-volume read workloads: Systems where read happens overwhelmingly more often that writes
  • Time-series data: Retrieving data based on time is often a performance-killer. So bucketing based on datetime allows effient queries that one single partition of Cassandra can serve
  • Systems that need linear scalability: Denormalized query driven design of database model allows linar time read performance instead of higher order complexities since table joins are avoided altogether
  • Applications where query patterns are well-known in advance: If you know query access pattern it is great to denormalize, but sometimes if the access pattern is not known ahead of time, it might become a premature optimization to denormalize data ahead of time

Real-World Examples​

  • Social Media: User profiles duplicated across posts and comments
  • IoT: Sensor data organized by device and time period
  • E-commerce: Product information duplicated in order history and recommendations