Cassandra Deep Dive for System Design Interviews
Cassandra shows up when the system design problem has a very specific shape: massive write volume, huge data size, multi-region availability, and a tolerance for eventual consistency.
It’s the database you reach for when “never go down” matters more than “always perfectly up to date,” and when you’d rather scale horizontally across many nodes than bet everything on one big primary.
Cassandra makes a bold trade-off: it sacrifices query flexibility for write performance and availability. Every write is a sequential append. There is no master node that can fail and take down the cluster. Data automatically replicates across nodes and data centers.
These architectural choices enable performance characteristics that traditional databases cannot match, but they also require a fundamentally different approach to data modeling.
The challenge in interviews is demonstrating that you understand both sides of this trade-off. Proposing Cassandra for the wrong workload, like ad-hoc analytics or systems requiring complex joins, signals inexperience. Proposing it correctly, with well-designed partition keys and query-first modeling, signals depth.
This chapter covers the practical Cassandra knowledge that matters in system design interviews: query-first data modeling, partition key design, consistency tuning, and the anti-patterns.