Cassandra Interview Questions and Answers for 10 years experience

1. What is Cassandra, and what are its core features?

   • Answer: Cassandra is a wide-column store NoSQL database management system designed to handle massive amounts of data across many commodity servers. Its core features include high availability, scalability, fault tolerance, linear scalability, and flexible schema. It's particularly well-suited for applications requiring high write throughput and low latency.

2. Explain the concept of Consistency Levels in Cassandra.

   • Answer: Consistency levels in Cassandra define the degree of data consistency that a read or write operation guarantees. Options range from ONE (read from at least one replica) to ALL (read from all replicas), with various levels in between (QUORUM, LOCAL_QUORUM, EACH_QUORUM). Choosing the appropriate consistency level involves balancing consistency needs with performance requirements. Higher consistency levels offer stronger guarantees but can impact performance.

3. Describe the Cassandra architecture.

   • Answer: Cassandra's architecture is decentralized and peer-to-peer. It consists of nodes, each holding a portion of the data. Data is replicated across multiple nodes to ensure high availability and fault tolerance. Nodes communicate with each other to maintain data consistency and handle requests. Key components include the gossip protocol for communication, a consistent hashing algorithm for data distribution, and a commit log for durability.

4. Explain the concept of data partitioning and replication in Cassandra.

   • Answer: Data partitioning in Cassandra divides data across multiple nodes based on a partition key. This allows for horizontal scalability. Replication creates multiple copies of the data on different nodes to ensure high availability and fault tolerance. The replication factor determines how many copies of each partition exist. The combination of partitioning and replication enables Cassandra to handle massive datasets and withstand node failures.

5. What is a partition key, and why is it crucial in Cassandra?

   • Answer: The partition key is the primary key component that determines how data is distributed across nodes. It's crucial because it dictates data locality and influences read and write performance. Choosing an appropriate partition key is essential for optimal performance. Poorly chosen partition keys can lead to hotspots (nodes handling a disproportionate amount of traffic).

6. How does Cassandra handle data consistency and availability?

   • Answer: Cassandra balances consistency and availability through its use of tunable consistency levels, replication, and the gossip protocol. The gossip protocol ensures that nodes remain aware of each other's status and data location. Replication provides redundancy, and adjustable consistency levels allow developers to trade off consistency for availability based on application requirements. This approach enables Cassandra to achieve high availability even in the face of node failures.

7. Explain the role of the commit log in Cassandra.

   • Answer: The commit log is a write-ahead log that ensures data durability. Before writing data to the data files (SSTables), Cassandra first writes it to the commit log. This ensures that even if the system crashes before data is written to disk, it can be recovered from the commit log after restart. It's a key component of Cassandra's durability guarantee.

8. What are SSTables, and how are they used in Cassandra?

   • Answer: SSTables (Sorted Strings Tables) are immutable files that store Cassandra's data on disk. They are sorted by row key and optimized for read performance. New data is written to memtables, which are then flushed to SSTables periodically. Compaction processes merge and sort SSTables to reduce file size and improve query performance. SSTables are fundamental to Cassandra's efficient data management and retrieval.

9. Explain the concept of compaction in Cassandra.

   • Answer: Compaction is a process in Cassandra that merges and sorts multiple SSTables into fewer, larger SSTables. This improves read performance by reducing the number of files that need to be read for a given query and also reclaims disk space occupied by deleted data. Cassandra offers different compaction strategies (Size-Tiered, Leveled, DateTiered) to optimize for different workloads.

10. Describe different types of Cassandra data models.

    • Answer: Cassandra primarily uses a wide-column store model but can be adapted to model various data structures. Common data models include:
      • Wide rows: Storing multiple columns within a single row, suitable for storing large amounts of related data.
      • Sparse data: Efficiently managing data where many column values might be null.
      • Counter tables: Incrementing or decrementing numerical values efficiently.
      The choice depends on the specific application and data characteristics.

11. What are the different types of Cassandra nodes?

    • Answer: In a basic Cassandra setup, all nodes are essentially the same. However, more complex deployments can involve specialized roles, though not directly part of the core architecture. These roles are often handled through administrative configurations and not inherent node types. The main distinction is between nodes actively participating in data storage and replication, and those that might be down or in maintenance.

12. How does Cassandra handle schema changes?

    • Answer: Cassandra's schema is flexible. Adding new columns doesn't require downtime. Existing queries will simply not see the new column until they're updated, making it quite schema-agnostic. However, removing a column is not straightforward and may require a schema upgrade process. The overall approach to schema changes is less disruptive than many relational databases.

13. Explain the importance of the gossip protocol in Cassandra.

    • Answer: The gossip protocol is a crucial mechanism for maintaining cluster-wide awareness. Nodes constantly exchange information about themselves, their data, and the health of other nodes. This enables Cassandra to dynamically handle node failures, manage data distribution, and ensure consistency across the cluster without relying on a central point of failure.

14. How does Cassandra handle node failures?

    • Answer: Due to its decentralized and replicated architecture, Cassandra is highly resilient to node failures. When a node fails, the gossip protocol alerts other nodes, which continue to serve data from the replicated copies. Data is automatically recovered when the failed node comes back online or is replaced. The replication factor determines the redundancy and availability during failures.

15. What are some common Cassandra performance tuning techniques?

    • Answer: Tuning Cassandra for optimal performance involves various strategies: selecting appropriate consistency levels, optimizing partition key design, configuring appropriate compaction strategies, adjusting heap size, optimizing network configuration, using appropriate read/write consistency levels, ensuring sufficient disk I/O, and monitoring cluster health using tools like nodetool.

16. How do you monitor the health of a Cassandra cluster?

    • Answer: Monitoring a Cassandra cluster is crucial. Tools like `nodetool` provide commands to check node status, token distribution, and other metrics. Monitoring tools like Grafana, Prometheus, and others can be integrated for comprehensive dashboards visualizing key performance indicators (KPIs) like CPU usage, memory consumption, disk I/O, and network latency. Observing these metrics helps identify potential problems and bottlenecks.

17. What are some common Cassandra anti-patterns to avoid?

    • Answer: Common Cassandra anti-patterns include: poorly designed partition keys leading to hotspots, excessive replication factors negatively impacting performance, neglecting compaction strategies, inadequate monitoring, and underestimating the importance of data modeling.

18. Explain Cassandra's use of consistent hashing.

    • Answer: Consistent hashing is used to distribute data across nodes in a way that minimizes data movement when nodes are added or removed. It ensures that only a small portion of data needs to be relocated, enhancing scalability and minimizing disruptions.

19. What are some common challenges faced when working with Cassandra?

    • Answer: Challenges include data modeling complexities, optimizing partition keys, managing schema changes effectively, troubleshooting performance issues, and understanding the tradeoffs between consistency and availability. Proper planning and understanding are crucial.

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