Anil Inamdar, VP & global head of data solutions at Instaclustr by NetApp, navigates through the distinct advantages and potential hurdles of Cassandra and PostgreSQL to empower the database decision-making process like never before.
Cassandra’s Advantages and Ideal Use Cases
Cassandra’s architecture is designed for scale. It can manage millions of concurrent users and operations per second while storing vast amounts of data, and it can increase capacity with no downtime simply by adding nodes to a cluster. Cassandra also preserves continuous availability and uptime—with no single point of failure—and the option to straddle multiple data centers easily.
While this is a powerful database for general usage, Cassandra is especially suited to supporting applications that utilize far more writes than reads, applications that allow for an even spread of data partitions across nodes, and applications that don’t require joins, data aggregates, or frequent data updates. Cassandra shines when tasked with delivering low-latency experiences to global users by replicating data across data centers, handling large write volumes, and storing and retrieving data in real-time across multiple devices. Some ideal use cases for Cassandra include:
- Media streaming
- Online gaming
- Real-time messaging
- Social media data input and analysis
- IoT vehicle-based telematics
- Order tracking
- Transaction logging
- Time-series data storage
- Healthcare data storage and retrieval
It’s also worth noting that Cassandra 5.0 is now in beta, with GA expected soon. Performance improvements and better functionality for AI/ML projects are among the highlights.
Cassandra Challenges
Developers trained in relational database models face an all-too-common challenge when they shift to Cassandra. Making the most of what the NoSQL database offers often means unlearning SQL-based experience and modeling data around queries instead of relations or objects.
Cassandra also requires expertise to operate optimally: developers with relational database experience often start changing Cassandra’s default settings before they understand the impact of those actions, slowing cluster performance. While Cassandra is outstandingly resilient from an availability and reliability perspective, it’s by no means a set-and-forget solution. Developers who neglect to actively monitor, operate, and scale Cassandra to adjust for events (such as surges in write operations) will also suffer performance declines.
Finally, developers often fail to understand how affordable writing is with Cassandra. They produce data models that minimize writes with expenses in mind, e.g., focusing on reducing storage costs, which can lead to inefficient use of Cassandra’s capabilities.
