apache/cassandra
Cassandra
Cassandra is a distributed NoSQL database and wide-column store designed for high availability and linear scalability. It functions as a fault-tolerant distributed system that utilizes an LSM-tree storage engine to optimize write throughput and manage massive datasets.
The system is a CQL-compliant database, using a structured query language to manage and retrieve tabular data stored across multiple nodes. It organizes information into rows and columns based on a flexible schema and primary keys.
The project provides capabilities for horizontal database scaling, distributed data partitioning, and high-volume tabular querying. It also supports data expiration policies and the execution of user-defined functions for data transformations.
Features
- NoSQL Databases - Provides a distributed NoSQL database designed for high availability and linear scalability of structured data.
- Wide-Column Stores - Provides a wide-column store data model with a flexible schema based on rows and primary keys.
- Data Access and Querying - Provides a CQL-compliant interface for querying structured tabular data organized in rows and columns.
- Data Partitioning - Automatically spreads tabular data across multiple machines to ensure linear scalability and avoid single points of failure.
- Data Replication Strategies - Implements quorum-based replication to balance data consistency and fault tolerance across distributed nodes.
- Tabular Row Storage - Organizes information into rows and columns using primary keys and a structured query language.
- Distributed Consensus Stores - Maintains a distributed data store designed to eliminate single points of failure across multiple servers.
- CQL Interfaces - Uses a structured query language (CQL) to manage and retrieve data from distributed tables.
- Data Partitioning - Automatically partitions data across multiple servers to maintain transparency and scalability during cluster changes.
- Fault Tolerant Storage - Replicates data across multiple nodes to ensure the system remains operational during hardware or software failures.
- High-Volume Tabular Querying - Uses a standardized query language to retrieve and manage structured tabular information at scale.
- Horizontal Database Scaling - Enables horizontal database scaling by adding new nodes to the cluster without interrupting active services.
- Log-Structured Merge-Trees - Utilizes a log-structured merge-tree storage engine to optimize write throughput via immutable memory tables.
- Gossip Protocols - Employs a gossip protocol for peer-to-peer propagation of cluster membership and node health state.
- Consistent Hashing - Uses consistent hashing to distribute data across a ring of nodes, enabling linear scalability.
- Distributed Data Management - Manages the storage and retrieval of large datasets across a cluster of servers for high availability.
- Data Snapshotting - Supports creating system snapshots of data files for rapid recovery or rollbacks before upgrades.
- Data Transformation Functions - Supports the execution of user-defined functions for complex data transformations and specialized calculations.
- Sorted String Tables - Stores data in sorted string tables that remain immutable and are periodically merged via compaction.
- Server Configuration - Provides a central configuration file to manage operational parameters and system resource behavior.
- Write-Ahead Logging - Implements a commit log to ensure all mutations are persisted to disk before memory updates for crash recovery.
- Cluster Integration Testing - Implements cluster tests using fault injection and lifecycle management to verify correctness across multiple nodes.
- Time-To-Live Expirations - Supports time-to-live (TTL) durations for records to automate data expiration and removal.
- Database Engines - Partitioned row store for distributed data management.
- Database Management Systems - Highly scalable distributed NoSQL database system.
- Database Systems - Scalable, distributed NoSQL database.
- 数据库与数据处理 - Highly scalable partitioned row store for massive datasets.
- Databases and Storage - Partitioned row store for high-availability data.
- Distributed Databases - Scalable and highly available database for high-performance needs.
- Data Engineering - Scalable distributed NoSQL database for large data.
