Apache Cassandra Interview Questions and Answers

1. What is Apache Cassandra?

   • Answer: Apache Cassandra is a free and open-source, distributed, wide-column store, NoSQL database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure.

2. What are the key features of Cassandra?

   • Answer: Key features include scalability, high availability, fault tolerance, linear scalability, data distribution across multiple nodes, tunable consistency levels, and support for massive datasets.

3. Explain the concept of a "wide-column store".

   • Answer: A wide-column store organizes data into rows and columns, but unlike traditional relational databases, columns within a row can be grouped into column families. This allows for flexible schema and efficient handling of large, sparse datasets.

4. How does Cassandra achieve high availability?

   • Answer: Cassandra achieves high availability through replication. Data is replicated across multiple nodes, so if one node fails, the data is still accessible from other replicas.

5. What is a consistency level in Cassandra?

   • Answer: Consistency level defines how many replicas must acknowledge a write operation before it's considered successful. Options include ONE, TWO, THREE, ALL, QUORUM, LOCAL_QUORUM, EACH_QUORUM etc., offering a trade-off between consistency and availability.

6. Explain the concept of data partitioning in Cassandra.

   • Answer: Data partitioning in Cassandra distributes data across nodes based on a partition key. This ensures data is spread evenly and allows for parallel processing of queries.

7. What is a partition key in Cassandra?

   • Answer: The partition key is the primary key component that determines which node a row of data resides on. Choosing an appropriate partition key is crucial for performance.

8. What is a clustering key in Cassandra?

   • Answer: The clustering key is used to sort rows within a partition. It allows for efficient retrieval of data within a partition.

9. Explain the difference between a read repair and a hinted handoff.

   • Answer: Read repair corrects inconsistencies between replicas by comparing data and updating replicas with the most recent data. Hinted handoff queues write operations when a node is unavailable and delivers them once the node is back online.

10. What are the different data types supported by Cassandra?

    • Answer: Cassandra supports various data types including ASCII, BIGINT, BLOB, BOOLEAN, COUNTER, DECIMAL, DOUBLE, FLOAT, INET, INT, TEXT, TIMESTAMP, UUID, VARCHAR, and more. The choice of data type impacts storage efficiency and query performance.

11. How does Cassandra handle schema changes?

    • Answer: Cassandra uses a flexible schema model. Adding new columns to a table doesn't require downtime or data migration. Existing rows are not affected by adding new columns; new columns will simply have null values initially.

12. What are some common use cases for Cassandra?

    • Answer: Common use cases include time-series data, real-time analytics, logging, fraud detection, internet of things (IoT) data, and handling high-volume write workloads.

13. How does Cassandra handle data compaction?

    • Answer: Cassandra uses a background process to compact data, merging smaller SSTables (Sorted String Tables) into larger ones, improving read performance and reducing storage space.

14. What is the role of the Cassandra commit log?

    • Answer: The commit log is a write-ahead log that ensures data durability. It records all writes before they are written to the memtable and SSTables. This prevents data loss in case of node failure.

15. Explain the concept of tombstone in Cassandra.

    • Answer: A tombstone is a marker indicating that a row or column has been deleted. It does not immediately remove the data but marks it for deletion during compaction.

16. What are some performance tuning techniques for Cassandra?

    • Answer: Performance tuning involves choosing appropriate data types, partition keys, and clustering keys, optimizing read and write queries, adjusting heap size, and managing compaction strategy.

17. How do you monitor a Cassandra cluster?

    • Answer: Cassandra monitoring involves using tools like nodetool (command-line utility), JMX, and monitoring systems like Prometheus, Grafana, or Nagios to track metrics like CPU utilization, memory usage, disk space, and latency.

18. What is the difference between Cassandra and other NoSQL databases like MongoDB or Redis?

    • Answer: Cassandra is a distributed wide-column store optimized for high availability and scalability, while MongoDB is a document database and Redis is an in-memory data structure store. Their strengths lie in different use cases.

19. Explain Cassandra's architecture.

    • Answer: Cassandra uses a decentralized, peer-to-peer architecture. There's no single point of failure; each node is responsible for a portion of the data and communicates with other nodes to maintain data consistency and availability.

20. What is the role of gossip protocol in Cassandra?

    • Answer: Gossip protocol is a decentralized mechanism used for communication between nodes. It helps nodes discover each other, share information about the cluster state, and maintain consistency across the cluster.

21. How does Cassandra handle failures?

    • Answer: Cassandra handles failures through replication and hinted handoff. If a node fails, data remains accessible from replicas. Hinted handoff queues pending writes and delivers them when the node recovers.

22. What are some common Cassandra troubleshooting steps?

    • Answer: Troubleshooting involves checking logs, analyzing metrics, investigating nodetool status, verifying network connectivity, and reviewing query performance using cqlsh.

23. Explain the concept of anti-compaction in Cassandra.

    • Answer: Anti-compaction is a process that helps reclaim space by removing obsolete data such as tombstones. It's a background process that runs periodically.

24. What is a Cassandra seed node?

    • Answer: Seed nodes are a set of nodes initially configured to bootstrap a Cassandra cluster. They are used by other nodes to join the cluster and discover the rest of the nodes.

25. How do you back up and restore a Cassandra cluster?

    • Answer: Backing up involves using tools like sstableloader to copy the data files. Restoring involves stopping the cluster, copying the backups to the data directory, and restarting the cluster.

26. What are some security considerations for Cassandra?

    • Answer: Security involves configuring authentication, authorization, encryption (both in transit and at rest), using strong passwords, and regularly patching the system.

27. Explain Cassandra's use of Thrift and CQL.

    • Answer: Thrift is an older protocol used to interact with Cassandra. CQL (Cassandra Query Language) is a more modern, SQL-like language that's now the preferred way to interact with Cassandra.

28. What are the different storage engines in Cassandra?

    • Answer: Historically, Cassandra primarily used the SSTable-based storage engine. Newer versions have incorporated enhancements and optimizations to this engine.

29. How does Cassandra handle data locality?

    • Answer: Cassandra uses the partition key to distribute data across nodes. Data for a given partition key is typically located on a specific node or set of replicas, enabling efficient data retrieval.

30. What is the role of the snitch in Cassandra?

    • Answer: The snitch is a plugin that provides information about the location of nodes within a cluster. This information is used for better data placement and routing decisions.

31. Explain the concept of token in Cassandra.

    • Answer: Tokens are mathematical functions applied to partition keys to distribute data across the nodes in a ring-like topology. They determine the ownership of data on each node.

32. What are some best practices for designing Cassandra tables?

    • Answer: Best practices include carefully selecting partition keys to minimize hotspots, using appropriate clustering keys for efficient data retrieval, and choosing appropriate data types to optimize storage and performance.

33. How can you improve the performance of Cassandra queries?

    • Answer: Performance improvements can be achieved by using appropriate indexes, optimizing queries using ALLOW FILTERING judiciously, ensuring efficient data modeling, and carefully selecting consistency levels.

34. Explain the concept of a Cassandra datacenter.

    • Answer: A Cassandra datacenter is a logical grouping of nodes that are physically located in a particular geographic area. This is used for managing data replication across different regions for high availability and disaster recovery.

35. What is the difference between a local quorum and a quorum consistency level?

    • Answer: Quorum requires a majority of replicas across the entire cluster to acknowledge a write, while local quorum requires a majority of replicas within a single datacenter.

36. How can you troubleshoot a slow Cassandra query?

    • Answer: Troubleshooting slow queries involves using tracing, examining query plans, analyzing nodetool stats, and checking for inefficient data modeling or index usage.

37. Explain the concept of read repair in more detail.

    • Answer: Read repair involves comparing data from multiple replicas during a read operation. If inconsistencies are detected, replicas are updated to ensure data consistency across the cluster. This is configurable and can impact performance.

38. What are some common metrics to monitor in a Cassandra cluster?

    • Answer: Key metrics include CPU usage, memory usage, disk space, latency, read/write throughput, garbage collection activity, and number of pending mutations.

39. How does Cassandra handle data compression?

    • Answer: Cassandra supports data compression at the SSTable level. This reduces storage space and improves read performance by reducing the amount of data that needs to be read from disk.

40. What is the role of the memtable in Cassandra?

    • Answer: The memtable is an in-memory data structure that stores newly written data before it's flushed to disk as SSTables. This improves write performance.

41. What are some considerations for choosing a partition key strategy?

    • Answer: Considerations include data distribution, query patterns, and expected write load. The goal is to evenly distribute data across nodes to avoid hotspots.

42. How does Cassandra handle schema updates in a production environment?

    • Answer: Schema updates in production are generally straightforward because of Cassandra's flexible schema. Adding columns is non-disruptive. Altering existing columns might require careful planning and potentially a phased rollout.

43. What are some tools for managing and administering Cassandra?

    • Answer: Tools include nodetool, cqlsh, the Cassandra management web interface, and various third-party monitoring and management tools.

44. Explain the concept of a Cassandra repair.

    • Answer: A repair process actively compares and resolves data inconsistencies across replicas. It's a proactive measure to ensure data consistency across the cluster. It can be resource-intensive and should be scheduled carefully.

45. How do you handle data modeling in Cassandra?

    • Answer: Data modeling involves designing tables with appropriate partition keys and clustering keys to optimize query patterns. It's crucial to anticipate common queries and structure data to efficiently answer them.

46. What are some common performance bottlenecks in Cassandra?

    • Answer: Common bottlenecks include poorly chosen partition keys leading to hotspots, inefficient queries, insufficient resources (CPU, memory, disk I/O), and improperly configured compaction strategies.

47. How does Cassandra handle concurrent writes?

    • Answer: Cassandra handles concurrent writes efficiently using a combination of techniques including Paxos or a similar consensus algorithm to maintain data consistency across replicas. It is designed for high write throughput.

48. Explain the concept of lightweight transactions in Cassandra.

    • Answer: Lightweight transactions (LWTs) are used for conditional updates. They allow an operation to succeed only if a certain condition is met, providing a form of concurrency control.

49. What are some advanced features of Cassandra?

    • Answer: Advanced features include materialized views, user-defined functions (UDFs), and support for various data types and custom serializers.

50. How does Cassandra handle schema migration?

    • Answer: Schema migrations are generally handled by using `ALTER TABLE` statements to add or modify columns. Removing columns can be more complex and might require data migration.

51. What are some best practices for monitoring Cassandra in a production environment?

    • Answer: Best practices involve setting up automated alerts for critical metrics, using comprehensive monitoring tools, and proactively analyzing historical data to identify trends and potential issues.

52. Explain the concept of a Cassandra snapshot.

    • Answer: A snapshot is a point-in-time copy of the data. It is useful for backup and restore operations and for debugging purposes.

53. How does Cassandra handle garbage collection?

    • Answer: Cassandra uses garbage collection to reclaim memory used by objects that are no longer needed. Monitoring garbage collection is important for overall system performance.

54. What are some strategies for handling large datasets in Cassandra?

    • Answer: Strategies include proper data modeling, partitioning to distribute data evenly, and utilizing compression to reduce storage space.

55. How can you optimize Cassandra for specific workloads?

    • Answer: Optimization involves analyzing query patterns, adjusting consistency levels, tuning resource allocation (CPU, memory), and optimizing data modeling for the specific application requirements.

56. Explain the importance of choosing the right consistency level in Cassandra.

    • Answer: Consistency level selection balances data consistency and availability. Choosing a level that is too strong can negatively impact performance while choosing a level that is too weak can compromise data accuracy.

57. What are some common issues encountered when scaling Cassandra?

    • Answer: Issues can include hotspotting due to poor partition key design, network bottlenecks, and resource limitations on individual nodes.

58. How does Cassandra handle upgrades?

    • Answer: Cassandra upgrades typically involve a rolling upgrade process, where nodes are upgraded one at a time to minimize downtime.

59. What are some tools for performance testing Cassandra?

    • Answer: Tools like ycsb (Yahoo! Cloud Serving Benchmark) and other custom performance testing tools can be used to simulate workloads and assess performance.

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