Datastax Interview Questions and Answers for 7 years experience

1. What is Apache Cassandra and how does it differ from traditional relational databases?

   • Answer: Apache Cassandra is a free and open-source, distributed, wide-column store, NoSQL database management system. Unlike traditional relational databases (like MySQL or PostgreSQL) which use a schema-on-write approach and are optimized for ACID transactions and complex joins, Cassandra is schema-less (schema-on-read) and highly scalable, prioritizing availability and partition tolerance over strong consistency. It excels at handling large volumes of data and high write throughput, making it ideal for applications needing high availability and linear scalability. Key differences include data modeling (tables vs. column families), consistency levels, and query capabilities.

2. Explain the Cassandra architecture. Discuss the roles of nodes, data centers, and racks.

   • Answer: Cassandra is a decentralized, peer-to-peer architecture. Data is distributed across multiple nodes in a cluster. Nodes are organized into data centers, and within data centers, into racks. This hierarchical structure helps in managing data locality and improving performance. Each node has a unique identifier and holds a portion of the overall data. Data is replicated across multiple nodes within and across data centers for high availability and fault tolerance. The system automatically handles node failures and data replication to maintain continuous operation.

3. Describe the concept of consistency levels in Cassandra. What are the trade-offs involved in choosing a consistency level?

   • Answer: Cassandra offers various consistency levels to control the trade-off between consistency and availability. Options range from ONE (read from a single replica), to QUORUM (read from a majority of replicas), to ALL (read from all replicas). Choosing a higher consistency level increases the reliability of data reads but reduces availability in case of node failures. Lower consistency levels prioritize availability but risk reading stale data. The choice depends on the application’s needs; for example, a financial transaction might need ALL consistency, while a social media feed might tolerate a lower level.

4. Explain the concept of data modeling in Cassandra. How do you design a Cassandra table for optimal performance?

   • Answer: Cassandra data modeling focuses on designing tables around expected read and write patterns. The key is to efficiently partition data to minimize read latency. The primary key defines how data is partitioned and clustered. A well-designed primary key uses a partition key to distribute data across nodes and a clustering key to order data within each partition. Careful consideration of data access patterns is crucial; optimizing for frequent read patterns is paramount. Over-partitioning can lead to performance bottlenecks, while poorly chosen keys can cause hotspots.

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