Key features and concepts of Cassandra
Category : Cassandra
| Sub Category : Cassandra | By Prasad Bonam Last updated: 2023-08-19 18:57:33
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Key features and concepts of Cassandra:
Cassandra is an open-source, distributed NoSQL database management system designed to handle massive amounts of data across multiple commodity servers, while providing high availability, fault tolerance, and scalability. It was originally developed by Facebook and later open-sourced as an Apache project.
Here are some key features and concepts of Cassandra:
Distributed Architecture: Cassandra is designed to distribute data across multiple nodes in a cluster. This architecture allows for high availability and fault tolerance. Each node in the cluster can handle read and write requests independently.
NoSQL: Cassandra is classified as a NoSQL database because it does not rely on a traditional relational data model. Instead, it uses a schema-less approach, where data is organized into columns and rows within column families, similar to a table structure but with more flexibility.
Column-Oriented Storage: Cassandra stores data in a column-oriented format, making it well-suited for handling read-heavy workloads and analytical queries. This storage model allows for efficient retrieval of specific columns while avoiding the need to scan entire rows.
High Availability: Cassandra provides mechanisms for ensuring high availability of data even in the presence of hardware failures. Data is replicated across multiple nodes, and if one node fails, the data can be retrieved from another replica.
Scalability: Cassandra is designed to scale out horizontally by adding more nodes to the cluster. This allows it to handle large amounts of data and high traffic loads while maintaining performance.
CAP Theorem: Cassandra is designed with the principles of the CAP theorem in mind, which states that a distributed system cannot simultaneously guarantee Consistency, Availability, and Partition tolerance. Cassandra prioritizes Availability and Partition tolerance, providing tunable consistency levels.
Eventual Consistency: Cassandra uses an eventual consistency model, meaning that changes made to the system will eventually propagate to all replicas. This approach allows for high availability and low-latency operations but may lead to temporary inconsistencies until data is fully propagated.
Data Replication Strategies: Cassandra supports different data replication strategies, such as SimpleStrategy and NetworkTopologyStrategy, which determine how data is replicated across nodes and data centers in the cluster.
Query Language: Cassandra uses the CQL (Cassandra Query Language) for interacting with the database. CQL is similar to SQL, making it more familiar to developers transitioning from relational databases.
Tunable Data Consistency: Cassandra allows you to tune the level of data consistency for read and write operations. You can choose between strong consistency, eventual consistency, and various levels in between based on your applications requirements.
Secondary Indexes: Cassandra supports secondary indexes, allowing you to query data based on non-primary key columns. However, using secondary indexes should be carefully considered due to potential performance implications.
Use Cases: Cassandra is often used in scenarios requiring high scalability, fault tolerance, and low-latency data access. It is commonly used in applications like real-time analytics, sensor data storage, time-series data, social media platforms, and more.
Overall, Cassandra is a powerful choice for applications that demand high availability, scalability, and fault tolerance while accommodating the challenges of distributed data storage and retrieval.