Cassandra Data Modeling in Azure Cosmos DB

This document provides guidance on how to model your data for Azure Cosmos DB's Cassandra API. Understanding data modeling principles is crucial for achieving optimal performance and scalability.

Key Concepts in Cassandra Modeling

Cassandra's data modeling approach is fundamentally different from relational databases. It's designed around the queries you intend to run, rather than normalization. Here are some core concepts:

• Query-Driven Modeling: Design your tables based on the exact queries you need to execute. Each query should ideally map to a single table.
• Denormalization: Data is often duplicated across tables to facilitate faster read operations. This is a deliberate trade-off for performance.
• Partition Keys: Crucial for distributing data across nodes and determining read performance. A good partition key distributes data evenly and is used in the WHERE clause of your queries.
• Clustering Keys: Define the order of data within a partition. They enable efficient sorting and filtering within a partition.

Designing Your Tables

When creating tables for Azure Cosmos DB's Cassandra API, consider the following best practices:

1. Understand Your Read Patterns

Before creating any tables, thoroughly analyze the application's read requirements. Identify the most frequent and critical queries.

2. Choose Appropriate Partition Keys

The partition key dictates how data is distributed.

• Cardinality: A partition key with high cardinality (many unique values) is generally good for distribution.
• Selectivity: Ensure your queries frequently filter by the partition key to avoid full partition scans, which can be expensive.
• Hot Partitions: Avoid partition keys that lead to a few "hot" partitions with disproportionately large amounts of data or heavy traffic.

For example, if you frequently query user data by `userId`, then `userId` would be a good candidate for a partition key.

CREATE TABLE users (
    userId UUID PRIMARY KEY,
    username text,
    email text
);

3. Leverage Clustering Keys for Sorting

Clustering keys determine the order of rows within a partition. They are essential for efficient range queries and sorting.

Consider a scenario where you need to retrieve recent orders for a user. You can use `orderTimestamp` as a clustering key.

CREATE TABLE user_orders (
    userId UUID,
    orderTimestamp timestamp,
    orderId UUID,
    amount decimal,
    PRIMARY KEY (userId, orderTimestamp)
) WITH CLUSTERING ORDER BY (orderTimestamp DESC);

This table allows efficient retrieval of the latest orders for a specific `userId` by querying `WHERE userId = ? ORDER BY orderTimestamp DESC LIMIT 10;`.

4. Handle Multiple Query Patterns

Since each table is optimized for specific queries, you may need multiple tables that contain denormalized data to support different query patterns.

-- Table for querying by userId
CREATE TABLE user_orders_by_user (
    userId UUID,
    orderTimestamp timestamp,
    orderId UUID,
    amount decimal,
    PRIMARY KEY (userId, orderTimestamp)
) WITH CLUSTERING ORDER BY (orderTimestamp DESC);

-- Table for querying by orderId
CREATE TABLE orders_by_id (
    orderId UUID PRIMARY KEY,
    userId UUID,
    orderTimestamp timestamp,
    amount decimal
);

Performance Considerations

• Tombstones: Avoid frequent deletes or Time-To-Live (TTL) configurations that can lead to a large number of tombstones, impacting read performance.
• Schema Design: Keep your table schemas as lean as possible. Only include columns that are necessary for your queries.
• Batching: Use batch statements cautiously. Large batches can cause performance issues. Consider asynchronous operations instead.