Apache Cassandra Overview

What is Apache Cassandra?

• Apache Cassandra is a new social media platform

• Apache Cassandra is a programming language

• Apache Cassandra is a type of bird

• Apache Cassandra is a highly scalable and distributed NoSQL database management system.

What are the key features of Apache Cassandra?

• Decentralized architecture, linear scalability, fault tolerance, tunable consistency, and support for multi-data center replication.

• Centralized architecture, linear scalability, fault tolerance, tunable consistency, and support for multi-data center replication.

• Decentralized structure, non-scalable, fault tolerance, rigid consistency, and support for single-data center replication.

• Centralized architecture, exponential scalability, fault intolerance, fixed consistency, and support for single-data center replication.

Explain the architecture of Apache Cassandra.

• The architecture of Apache Cassandra is centralized and relies on a single master node for data distribution.

• The architecture of Apache Cassandra is distributed, masterless, and decentralized with consistent hashing for data distribution.

• Apache Cassandra architecture uses random hashing for data distribution.

• Apache Cassandra architecture is designed to be single-node and does not support distributed systems.

What are the main components of Apache Cassandra?

• Index, Table, View

• Node, Data Center, Cluster, Commit Log, Memtable, SSTable, and Bloom Filter

• CPU, RAM, Hard Drive

• Primary Key, Foreign Key, Unique Key

What is the role of the commit log in Apache Cassandra?

• The role of the commit log in Apache Cassandra is to ensure durability of data by writing all changes to disk before they are applied to the memtable and SSTables.

• The commit log in Apache Cassandra is used for compressing data

• The commit log in Apache Cassandra is responsible for managing user access control

• The commit log in Apache Cassandra is used for indexing data

Explain the concept of eventual consistency in Apache Cassandra.

• Eventual consistency in Apache Cassandra means that the data consistency is guaranteed within a specific time frame after a write operation.

• Eventual consistency in Apache Cassandra means that the data is always immediately consistent across all nodes in the cluster after a write operation.

• Eventual consistency in Apache Cassandra means that the data consistency is only achieved through manual synchronization of nodes after a write operation.

• Eventual consistency in Apache Cassandra means that the data may not be immediately consistent across all nodes in the cluster after a write operation, but will eventually become consistent as it is propagated to all nodes.

What are the advantages of using Apache Cassandra?

• Difficult replication across multiple data centers

• Limited availability and fault tolerance

• Non-linear scalability and rigid data storage

• The advantages of using Apache Cassandra include high availability, fault tolerance, linear scalability, flexible data storage, and easy replication across multiple data centers.

What are the disadvantages of using Apache Cassandra?

• Complex data modeling, lack of support for complex transactions, and hardware/network configuration considerations.

• Limited scalability

• High maintenance requirements

• Inefficient for small datasets

How does Apache Cassandra handle data replication?

• Apache Cassandra handles data replication by storing all data on a single node

• Apache Cassandra handles data replication by deleting data from the cluster

• Apache Cassandra handles data replication by only replicating data within the same node

• Apache Cassandra handles data replication by replicating data across multiple nodes in a cluster.

What are the use cases for Apache Cassandra?

• Social media platforms

• Online gaming

• E-commerce websites

• Weather forecasting

• Apache Cassandra is used for use cases such as real-time analytics, time series data, messaging platforms, recommendation engines, and more.