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Top Cassandra Interview Questions and Answers for Big Data professionals

Cassandra is an open-source, distributed NoSQL database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. Cassandra is a key-value store, but it also supports richer data structures like collections and user-defined types.

Cassandra is often used for applications that require high scalability, high availability, and low latency. Some of the common use cases for Cassandra include:

  • Real-time analytics
  • Event streaming
  • IoT
  • Gaming
  • Telecommunications
  • Financial services

Cassandra is a popular choice for these use cases because it is scalable, reliable, and easy to use. It is also open-source, which means that it is free to use and modify.

Here are some of the key features of Cassandra:

  • Distributed architecture: Cassandra is a distributed database, which means that data is spread across multiple nodes. This makes it scalable and fault-tolerant.
  • High availability: Cassandra is designed to be highly available, even in the event of node failures. Data is replicated across multiple nodes, so that if one node fails, the data is still available.
  • Low latency: Cassandra is designed to have low latency, which means that queries can be processed quickly. This makes it ideal for applications that require real-time data access.
  • Strong consistency: Cassandra supports strong consistency, which means that all reads and writes will see the same data. This is important for applications that require accurate data.
  • Flexible schema: Cassandra has a flexible schema, which means that you can change the data model without having to rebuild the database. This makes it easy to adapt Cassandra to changing requirements.

If you are looking for a scalable, reliable, and easy-to-use NoSQL database, Cassandra is a good option to consider.

Here are some of the companies that use Cassandra:

  • Netflix
  • Spotify
  • Instagram
  • Twitter
  • Cisco
  • eBay
  • Airbnb
  • Uber
  • Yahoo
  • Soundcloud

Cassandra is a powerful database that can be used for a variety of applications. If you are looking for a scalable, reliable, and easy-to-use NoSQL database, Cassandra is a good option to consider.

Q1. What is Cassandra?

Ans: Apache Cassandra is a highly scalable, distributed NoSQL database system designed to handle large volumes of data across multiple commodity servers while ensuring high availability and fault tolerance. It falls under the category of wide-column store databases and is known for its ability to handle write-heavy workloads, making it suitable for applications where data needs to be written and updated frequently. Cassandra’s architecture is based on a masterless design with no single point of failure, making it well-suited for scenarios where high availability is crucial.

For example, a social media platform like Instagram uses Cassandra to manage user profiles, posts, and interactions. This allows the platform to handle a massive number of users and their data while ensuring quick response times and reliability.

Q2. How does Cassandra achieve high availability and fault tolerance?
Ans: Cassandra achieves high availability and fault tolerance through its distributed architecture and data replication strategy. Data is partitioned across multiple nodes, and each partition is replicated to multiple nodes. This means that if one node goes down, data can still be accessed from other replicas. The replication factor determines how many replicas of each partition are maintained.

For instance, with a replication factor of 3, there will be three copies of each piece of data spread across the cluster. If one node fails, the data can be retrieved from the remaining replicas, ensuring no data loss and minimal downtime.

Q3. What is eventual consistency in Cassandra?
Ans: Eventual consistency is a concept in distributed databases, including Cassandra, where updates to data will eventually propagate to all replicas, ensuring consistency over time. However, at any given moment, replicas might have slightly different versions of data due to the distributed nature of the system and the possibility of network delays.

Consider an e-commerce application where a product’s price is updated. With eventual consistency, all replicas will eventually reflect the updated price, but there might be a short period during which different replicas might return different prices until the updates are fully propagated.

Q4. How does Cassandra handle reads and writes efficiently?
Ans: Cassandra is designed to handle both reads and writes efficiently through its architecture that separates read and write paths. Writes are optimized by using an append-only write model, where new data is appended to the end of existing data files, reducing the need for frequent disk seeks. This approach improves write throughput significantly.

For reads, Cassandra uses a technique called “Bloom filters” to determine whether a particular piece of data exists in a partition or not. Additionally, Cassandra’s data model allows for fast reads by predefining column families and wide rows, enabling efficient retrieval of related data.

Q5. Explain the architecture of Cassandra.
Ans: Cassandra follows a decentralized, masterless architecture known as the “ring” architecture. It comprises nodes organized in a peer-to-peer manner, where each node communicates with other nodes to share and replicate data. The nodes are divided into one or more datacenters, and each datacenter can span multiple geographical locations.

The architecture consists of several key components:

  • Node: A single instance of Cassandra running on a machine.
  • Datacenter: A logical grouping of related nodes that share the same network proximity.
  • Cluster: A collection of datacenters forming a single Cassandra deployment.
  • Keyspace: A container for tables, similar to a schema in relational databases.
  • Table: A structured collection of data organized in rows and columns.
  • Column Family: A unit of storage for a set of rows with similar columns.
  • Node Coordinator: The node responsible for handling client requests for a particular piece of data.
  • Gossip Protocol: Nodes communicate with each other to share information about their status and the data they store.

The architecture’s distributed nature ensures high availability, fault tolerance, and scalability.

Q6. What is a CQL in Cassandra?
Ans: CQL (Cassandra Query Language) is a SQL-like language used to interact with the Cassandra database. It allows developers to define and manipulate data using a familiar syntax while taking advantage of Cassandra’s distributed architecture and data model. CQL supports operations like creating keyspaces, tables, inserting and updating data, querying data, and more.

For example, a simple CQL query to create a table for storing user profiles might look like:

CREATE TABLE user_profiles (
first_name TEXT,
last_name TEXT,
email TEXT

Q7. What is a partition key in Cassandra?
Ans: In Cassandra, a partition key is a column or set of columns used to determine the distribution of data across nodes. Data is partitioned into different sets based on the partition key value. Each partition is stored on a different node in the cluster, allowing Cassandra to scale out horizontally.

For instance, in a table that stores customer orders, the partition key might be the customer’s ID. Orders for different customers would be stored in separate partitions based on their IDs, enabling efficient distribution and retrieval of data.

Q8. Explain the concept of compaction in Cassandra.
Ans: Compaction in Cassandra is the process of merging and compacting SSTable (Sorted String Table) files to optimize storage and improve read performance. Over time, as data is updated and new data is inserted, SSTables accumulate. Compaction reduces the number of SSTables by merging them into larger files, eliminating obsolete data and reclaiming space.

Cassandra uses two types of compaction: “Size-Tiered Compaction” and “Leveled Compaction.” Size-Tiered Compaction merges SSTables based on size, while Leveled Compaction organizes SSTables into multiple levels to balance read and write performance.

Q9. How does Cassandra ensure data durability?
Ans: Cassandra ensures data durability through its write path and replication strategy. When data is written, it is first written to a commit log on disk for crash recovery. Then, it’s written to an in-memory data structure called the memtable. Once the memtable reaches a certain size, it’s flushed to an SSTable on disk.

Replication ensures data durability by maintaining multiple copies of data across nodes and datacenters. When a write operation is acknowledged by the required number of replicas (determined by the replication factor), it’s considered durable. In the event of node failures, data can still be retrieved from other replicas.

Q10. Can you explain how data consistency is maintained in Cassandra?
Ans: Cassandra provides tunable consistency levels that allow you to control how data consistency is achieved during read and write operations. Consistency levels range from “ALL” (requiring all replicas to acknowledge) to “ONE” (requiring only one replica to acknowledge).

For example, if a “QUORUM” consistency level is chosen, Cassandra ensures that the majority of replicas (N/2 + 1) agree on the value during a read or write operation. This allows for a balance between data availability and consistency, depending on the desired trade-off.

Q11. What is a secondary index in Cassandra?
Ans: A secondary index in Cassandra allows you to query data based on columns that are not part of the primary key. While primary key columns are used for data distribution, secondary indexes enable efficient queries on non-primary key columns. However, using secondary indexes can lead to performance issues and should be used judiciously.

For instance, in a table storing products, you might create a secondary index on the “category” column to quickly retrieve products belonging to a specific category.

Q12. How does compaction affect read and write performance?
Ans: Compaction in Cassandra impacts both read and write performance. During the compaction process, smaller SSTables are merged into larger ones, reducing the number of files and improving read performance since fewer files need to be searched.

On the other hand, compaction can temporarily affect write performance, as it involves reading and rewriting data. However, well-tuned compaction strategies ensure that write performance remains acceptable, and the benefits of improved read performance outweigh the short-term write performance impact.

Q13. What is the purpose of the Tombstone marker in Cassandra?
Ans: A Tombstone marker in Cassandra is used to mark a deleted record. Instead of immediately deleting data, Cassandra adds a Tombstone marker to indicate that the record has been deleted. This marker is propagated to replicas during compaction, ensuring that the deletion is eventually applied to all replicas.

Tombstone markers prevent data from reappearing due to inconsistencies that might occur during the replication process. They help maintain data integrity and consistency across the distributed cluster.

Q14. Explain how data modeling in Cassandra differs from traditional relational databases.
Ans: Data modeling in Cassandra is quite different from traditional relational databases. While relational databases use normalized structures to minimize data redundancy, Cassandra promotes denormalization and replicates data across multiple nodes for performance and availability.

In Cassandra, data modeling involves designing tables based on query patterns rather than minimizing redundancy. Wide rows with multiple columns are common, allowing data to be retrieved in a single query without complex joins. The goal is to structure data to minimize the need for joins and maximize query efficiency.

Q15. How does Cassandra handle node failures?
Ans: Cassandra’s architecture is designed to handle node failures gracefully. When a node fails, the data it was responsible for is still accessible from other replicas. The system uses the concept of hinted handoffs to temporarily store data on other nodes until the failed node recovers. Once the node is back online, Cassandra reconciles the data and ensures consistency.

Additionally, Cassandra’s gossip protocol constantly shares information about node status. If a node is detected as down, the system can adjust the replication factor to maintain data availability until the node is restored.

Q16. Can you explain the CAP theorem and how it applies to Cassandra?
Ans: The CAP theorem states that in a distributed system, you can’t simultaneously guarantee all three of the following: Consistency, Availability, and Partition tolerance. Cassandra prioritizes Availability and Partition tolerance over strong Consistency, adhering to the AP side of the CAP theorem.

In situations where network partitions occur, Cassandra allows nodes to continue functioning and serving data. This ensures high availability and partition tolerance, even if it means sacrificing strong consistency temporarily. However, Cassandra does provide tunable consistency levels, allowing developers to choose the level of consistency that suits their application’s requirements.

Q17. What is a Write Ahead Log (WAL) in Cassandra?
Ans: The Write Ahead Log (WAL) in Cassandra is a mechanism used for crash recovery. When data is written to a node, it’s first stored in a commit log (WAL) on disk before being written to memory and flushed to an SSTable. This ensures that data modifications are persisted on disk before they are applied in-memory.

In the event of a crash or failure, the commit log is used to recover data changes that were not yet applied to SSTables, ensuring data durability and consistency.

Q18. How does Cassandra handle data distribution across nodes?
Ans: Cassandra uses a partitioner to determine how data is distributed across nodes. The partitioner converts the partition key value into a token, which is used to determine which node in the cluster will store the data. This ensures an even distribution of data across nodes in the cluster.

For instance, the RandomPartitioner uses a hash function to distribute data randomly, while the Murmur3Partitioner ensures a more even distribution of tokens and data.

Q19. What is a materialized view in Cassandra?
Ans: A materialized view in Cassandra is a precomputed table that allows you to query data in a different way from the base table, without the need for complex joins. Materialized views are created based on the data from the base table, and they provide an efficient way to retrieve data using alternative keys or sorting orders.

For instance, if you have a base table storing user data, you can create a materialized view that organizes the data by user’s age, allowing you to quickly query users within specific age ranges.

Q20. Explain the anti-entropy mechanism in Cassandra.
Ans: The anti-entropy mechanism in Cassandra ensures data consistency between replicas by periodically comparing data and repairing inconsistencies. Cassandra uses a process called “Read Repair” during read operations and “Hinted Handoff” during write operations to detect and correct inconsistencies.

Read Repair involves comparing data from multiple replicas during a read query and returning the most recent version. Hinted Handoff temporarily stores data on other nodes when a node is unavailable, allowing data to be written and then reconciled when the failed node is restored.

Q21. How does compaction strategy selection impact performance?
Ans: Choosing the appropriate compaction strategy in Cassandra is important for maintaining optimal performance. The two main strategies are “Size-Tiered Compaction” and “Leveled Compaction.”

  • Size-Tiered Compaction: Prioritizes merging SSTables of similar sizes. It’s efficient for write-heavy workloads, but can lead to inefficient read patterns and disk space usage as larger SSTables accumulate.

  • Leveled Compaction: Organizes SSTables into levels, reducing read amplification and improving read performance. It’s suitable for read-heavy workloads but can generate more disk I/O.

The choice depends on the application’s read and write patterns, and it’s crucial to monitor and tune compaction settings for optimal performance.

Q22. How does Cassandra handle data distribution in a multi-datacenter setup?
Ans: In a multi-datacenter setup, Cassandra uses a “Datacenter-Aware” replication strategy to ensure data availability and fault tolerance. Each datacenter contains multiple nodes, and the replication factor is defined per datacenter.

When data is written, it’s replicated to nodes within the local datacenter and, if needed, to remote datacenters based on the replication factor. This ensures that data is available even in the event of datacenter failures.

Q23. What are the factors to consider when choosing a replication factor?
Ans: Choosing the right replication factor in Cassandra depends on several factors:

  • Availability: A higher replication factor improves availability since more replicas can serve data. However, it increases write and storage overhead.

  • Consistency: Higher replication factors increase the number of replicas that must acknowledge a write operation for consistency. This may impact write performance.

  • Network Latency: If datacenters are distributed geographically, consider network latency for inter-datacenter communication when defining replication factors.

  • Data Sensitivity: Critical data might require higher replication factors to ensure availability and durability.

  • Storage Overhead: Replicating data across multiple nodes increases storage usage. Consider your available disk space.

Choosing an appropriate replication factor requires balancing these factors based on your application’s requirements.

Q24. Can you explain the tunable consistency levels in Cassandra?
Ans: Cassandra provides tunable consistency levels to control the level of data consistency during read and write operations. These levels determine how many replicas must acknowledge a read or write before the operation is considered successful.

Common consistency levels include:

  • ALL: Requires all replicas to acknowledge the operation. Ensures strong consistency but can impact availability.

  • QUORUM: Requires a majority of replicas (N/2 + 1) to acknowledge. Balances consistency and availability.

  • ONE: Requires only one replica to acknowledge. Maximizes availability but may sacrifice consistency.

The choice depends on the desired trade-off between consistency and availability, and it can be adjusted on a per-operation basis.

Q25. What is the purpose of the “Nodetool” utility in Cassandra?
Ans: Nodetool is a command-line utility in Cassandra that provides administrators with tools for managing and monitoring Cassandra clusters. It allows you to perform tasks such as viewing cluster information, managing compaction, repairing data, adjusting replication settings, and more.

For example, you can use Nodetool to view the status of nodes in the cluster, check compaction statistics, and trigger repairs to resolve data inconsistencies.

Q26. How does Cassandra handle hotspots and uneven data distribution?
Ans: Cassandra uses the concept of “Virtual Nodes” (vnodes) to mitigate hotspots and uneven data distribution. Each physical node is responsible for multiple vnodes, allowing the cluster to distribute data more evenly.

When vnodes are used, the token range for data distribution is divided into smaller segments, reducing the chance of a single node becoming a hotspot. This approach improves data distribution and ensures that the workload is balanced across nodes.

Q27. What is a batch statement in Cassandra?
Ans: A batch statement in Cassandra allows you to group multiple read or write operations into a single atomic operation. This ensures that either all operations in the batch are executed, or none of them are. Batches can include multiple INSERT, UPDATE, DELETE, and SELECT statements.

Batches are useful for maintaining data consistency and reducing the number of round-trip requests to the database. However, excessive use of batches can lead to performance issues, so they should be used judiciously.

Q28. How does Cassandra handle schema changes?
Ans: Cassandra allows schema changes to be made without taking the entire cluster offline. New columns can be added to existing tables, and tables can be altered to modify column types, add secondary indexes, or change compaction strategies.

Cassandra supports “Online Schema Changes,” where nodes can continue serving data while the schema change is being applied. However, schema changes might require data migration or compaction to ensure consistency across replicas.

Q29. Explain the concept of “tombstone overload” in Cassandra.
Ans: Tombstone overload occurs when a large number of tombstones (markers indicating deleted data) accumulate in a partition. This can impact read performance and compaction efficiency, as compaction must process these tombstones.

Tombstone overload often results from incorrect data modeling, where many updates or deletes are performed on a partition. To mitigate this, it’s important to design your data model and application logic in a way that minimizes the creation of unnecessary tombstones.

Q30. How can you optimize data modeling for time-series data in Cassandra?
Ans: Time-series data can be optimized in Cassandra by using techniques like “time-windowed compaction” and “time-bucketed tables.” Time-windowed compaction ensures that SSTables containing expired data are compacted more frequently, reducing the impact of tombstones.

Time-bucketed tables involve creating tables with time-based partition keys, such as year-month-day-hour. This allows data to be partitioned based on time intervals, improving read and write performance for time-series queries.

By considering the query patterns and access patterns for your time-series data, you can design tables and choose compaction strategies that optimize performance.

Q31. What is the purpose of the “sstable2json” utility in Cassandra?
Ans: The “sstable2json” utility in Cassandra is used for debugging and analysis. It converts SSTables to a human-readable JSON format, allowing you to inspect the data contained in an SSTable.

This tool is helpful for understanding the structure and contents of SSTables, diagnosing data-related issues, and performing data recovery or migration tasks.

Q32. Explain the differences between an SSTable and a Memtable in Cassandra.
Ans: In Cassandra, an SSTable (Sorted String Table) is a persistent, immutable on-disk data structure that stores data after it has been flushed from memory. SSTables are optimized for read operations and are periodically compacted to manage disk space.

A Memtable, on the other hand, is an in-memory data structure that temporarily stores data before it’s flushed to an SSTable. Memtables are used for write operations, providing high-speed write performance. Once a Memtable reaches a certain size, it’s flushed to an SSTable.

SSTables ensure durability and persistence, while Memtables provide fast write access.

Q33. What is hinted handoff, and why is it important in Cassandra?
Ans: Hinted handoff is a mechanism in Cassandra that allows a coordinator node to temporarily store write requests for a downed replica node. When a node is unavailable, the coordinator stores hints about the write requests that the downed node would have been responsible for.

Once the node is back online, it retrieves these hints and processes the write requests. This ensures that no data is lost and that the repaired node eventually catches up with the missed updates.

Hinted handoff is important for maintaining data consistency and availability in the face of temporary node failures.

Q34. How can you perform data backup and restore in Cassandra?
Ans: Cassandra provides tools like “nodetool snapshot” for creating backups of data. The “snapshot” command takes a snapshot of the data directory on each node and creates hard links to the existing data files, preserving disk space.

To restore data, you can copy the snapshot files back to the data directory on each node and use “nodetool refresh” to load the data. Alternatively, you can use tools like “sstableloader” to restore data from backups efficiently.

It’s important to regularly back up data to prevent data loss in case of failures.

Q35. How does Cassandra handle security and authentication?
Ans: Cassandra offers several security features to protect data and ensure authorized access:

  • Authentication: Cassandra supports various authentication mechanisms, including password-based authentication, LDAP integration, and role-based access control.

  • Authorization: Cassandra uses role-based access control to define roles and assign permissions to users or groups. Roles can be assigned at various levels, such as keyspace, table, or datacenter.

  • Encryption: Data can be encrypted at various levels, including data in transit and data at rest. SSL/TLS encryption can be used for communication between nodes, and client-to-node encryption can be enforced.

  • Audit Logging: Cassandra can log user activities and system events, helping to monitor and track changes.

These security measures help protect data integrity and restrict unauthorized access.

Q36. Explain how repair operations work in Cassandra.
Ans: Repair operations in Cassandra are used to detect and resolve data inconsistencies across replicas. As data is written to different replicas, network issues or node failures can cause replicas to diverge.

Cassandra offers two types of repair:

  • Incremental Repair: Compares data between replicas and repairs only the differences. It’s more efficient but requires frequent scheduling.

  • Full Repair: Compares all data between replicas and ensures complete consistency. It’s more resource-intensive but guarantees full consistency.

Repair operations involve comparing data between replicas, identifying inconsistencies, and reconciling differences. Regular repairs are important to maintain data consistency across the cluster.

Q37. How does Cassandra handle time synchronization across nodes?
Ans: Cassandra nodes require accurate time synchronization to ensure proper functioning, especially for consistency checks and repair operations. Nodes should be configured to use Network Time Protocol (NTP) or a similar mechanism to synchronize their clocks.

Inconsistent time across nodes can lead to data consistency issues, failed repairs, and other operational problems. Ensuring synchronized time across nodes is crucial for the reliability of the Cassandra cluster.

Q38. Can you explain the principles of distributed data design in Cassandra?
Ans: Distributed data design in Cassandra involves considering how data will be distributed across nodes and ensuring efficient data retrieval. Key principles include:

  • Data Duplication: Data is replicated across multiple nodes to ensure availability and fault tolerance.

  • Data Partitioning: Data is partitioned based on a partition key, distributing it across nodes.

  • Data Denormalization: Design tables based on query patterns, allowing data to be retrieved in a single query without complex joins.

  • Data Modeling for Queries: Structure tables based on how data will be queried, optimizing data retrieval.

  • Choosing Partition Key: Choose a partition key that evenly distributes data and avoids hotspots.

  • Avoiding Large Partitions: Limit the size of partitions to ensure efficient query performance.

By following these principles, you can design data models that make the most of Cassandra’s distributed architecture.

Q39. How does compaction impact disk I/O in Cassandra?
Ans: Compaction in Cassandra can impact disk I/O in several ways:

  • Read Amplification: During compaction, data is read from multiple SSTables and written to a new SSTable. This read-and-write process can increase disk I/O.

  • Write Amplification: Compaction generates new SSTables, and during the process, duplicate data is eliminated. This reduces the overall data size but increases write operations, impacting disk write I/O.

  • Background I/O: Compaction runs in the background, consuming disk I/O resources alongside regular read and write operations.

To optimize disk I/O, it’s important to choose compaction strategies that balance read and write amplification and to monitor and tune compaction activities as needed.

Q40. How does Cassandra handle data distribution in a single-datacenter setup?
Ans: In a single-datacenter setup, Cassandra uses a replication strategy to determine how data is distributed across nodes within the datacenter. Each node contains data for a portion of the keyspace.

When data is written, it’s replicated to the required number of nodes within the same datacenter, determined by the replication factor. This ensures data availability and high throughput for read and write operations.

Q41. What is the purpose of “nodetool repair” in Cassandra?
Ans: The “nodetool repair” command in Cassandra is used to perform a full repair operation, ensuring data consistency across replicas. It compares data between nodes and reconciles any differences, bringing replicas back into sync.

Repair operations are important for maintaining data integrity and preventing inconsistencies that can arise due to network issues or node failures.

Q42. How can you optimize performance for read-heavy workloads in Cassandra?
Ans: To optimize performance for read-heavy workloads in Cassandra:

  • Data Denormalization: Design tables based on query patterns to reduce the need for complex joins and ensure efficient data retrieval.

  • Materialized Views: Create materialized views that organize data for specific queries, reducing the need for multiple queries or complex filtering.

  • Leveled Compaction: Use leveled compaction to reduce read amplification and improve read performance.

  • Read Repair: Enable read repair to maintain data consistency and ensure that the latest version of data is returned.

  • Caching: Use Cassandra’s caching mechanisms, such as row caching and key caching, to speed up read operations.

By designing your data model and using appropriate strategies, you can optimize performance for read-heavy workloads.

Q43. Explain how compaction and tombstones are related in Cassandra. Ans: Compaction and tombstones are related in the sense that tombstones are generated when data is deleted, and they are processed during compaction to clean up obsolete data.

When data is deleted in Cassandra, a tombstone marker is created to indicate the deletion. These tombstones are propagated to replicas to ensure eventual consistency. During compaction, the obsolete data marked by tombstones is identified and removed, reclaiming disk space and ensuring that deleted data doesn’t linger indefinitely.

Efficient management of tombstones is crucial to prevent tombstone overload and maintain read and compaction performance.

Q44. What is the purpose of the “nodetool cleanup” command in Cassandra?
Ans: The “nodetool cleanup” command in Cassandra is used to remove obsolete data and tombstones from a node’s data files. It’s typically used after a node has been added to the cluster or after a node has been decommissioned.

When a new node is added or an existing node is decommissioned, it may inherit data from other nodes. The “cleanup” operation ensures that the node’s data files only contain the data it’s responsible for, removing any unnecessary data.

Q45. Can you explain the use of “nodetool drain” in Cassandra?
Ans: The “nodetool drain” command in Cassandra is used to gracefully stop a Cassandra node by flushing all in-memory data to disk and ensuring that all pending data is written to commit logs.

It’s commonly used when performing maintenance tasks, such as restarting a node or upgrading Cassandra. Running “nodetool drain” before stopping a node helps ensure data durability and consistency during the shutdown process.

Remember, these questions and answers serve as a starting point for your preparation and understanding of Cassandra. It’s essential to dive deeper into each topic and gain hands-on experience to become proficient in using Cassandra effectively.

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