apache/hbase
Hbase
HBase 是一个分布式、宽列 NoSQL 存储和大数据存储引擎，专为稀疏数据集设计。它作为一个可扩展的列式数据库，构建在 Hadoop 分布式文件系统之上，提供对海量结构化和非结构化数据的实时读写访问。
该系统充当跨语言数据库网关，通过原生远程过程调用、REST 和 Thrift 接口提供连接。它通过主从协调模型脱颖而出，该模型实现了跨集群的水平扩展和容错能力。
该项目涵盖了广泛的功能，包括通过单元级可见性标签实现的细粒度访问控制、可插拔数据压缩和服务器端数据聚合。它还通过 MapReduce 集成支持大数据分析工作流，并允许执行自定义的服务器端逻辑。
操作监控通过系统指标跟踪和基于插件的指标导出提供。
Features
- Columnar Databases - Implements a distributed NoSQL wide-column store built on top of the Hadoop ecosystem for sparse datasets.
- Big Data Storage - Functions as a distributed engine for storing and querying massive volumes of structured and unstructured data.
- Column Family Management - Organizes sparse data into grouped column families for efficient distributed storage and retrieval.
- Hadoop - Integrates with the Hadoop Distributed File System to provide a columnar store for large-scale data analysis.
- Distributed File System Backends - Relies on the Hadoop Distributed File System for durable, replicated persistent storage of data files.
- Sparse Dataset Management - Provides scalable storage and versioning for massive, sparse, column-oriented datasets across a cluster.
- LSM-Tree Storage Engines - Utilizes an LSM-tree storage engine to provide high write throughput via in-memory buffering and sorted flushes.
- Region-Based Partitioning - Implements region-based partitioning by splitting the sorted keyspace into contiguous ranges for horizontal scaling.
- Wide-Column Stores - Organizes data into column families to provide real-time read and write access to high-scale datasets.
- Column-Oriented Disk Storage - Organizes sparse datasets into column-oriented disk storage for scalable, versioned data management.
- Distributed Data Stores - Provides a cluster-based storage system with horizontal scaling and fault tolerance for scalable data retrieval.
- Cell-Level Controls - Enforces fine-grained access control using visibility labels at the individual data cell level.
- Master-Worker Coordination - Employs a master-worker coordination model to manage cluster metadata and region assignments.
- Distributed Storage Clusters - Implements a scalable architecture that aggregates multiple nodes into a unified storage system for massive datasets.
- Distributed File Systems - Relies on a distributed file system like HDFS for durable and replicated storage of underlying data files.
- Big Data Processing - Supports big data processing workflows using map-reduce patterns for large-scale data transformation.
- Cross-Language Data Interfaces - Provides consistent data interaction interfaces via native RPC, REST, and Thrift APIs for clients in multiple programming languages.
- MapReduce Processing Engines - Integrates with MapReduce processing engines to transform and migrate large volumes of data between tables.
- Server-Side Aggregations - Calculates summaries and statistics directly on the server to minimize data transfer to the client.
- Application REST API Gateways - Exposes database operations and cluster status through a standardized REST API gateway.
- Thrift RPC Servers - Ships a dedicated Thrift server to enable cross-language connectivity for database operations.
- Cross-Language Service Gateways - Acts as an entry point that translates REST, Thrift, and RPC requests into internal database protocols.
- Storage Block Compression - Applies pluggable block compression to reduce the physical storage footprint of datasets on disk.
- Multi-Protocol Communication Bridges - Provides a multi-protocol gateway allowing clients to connect via RPC, HTTP, and Thrift.
- Remote Procedure Calls - Uses remote procedure calls for low-latency communication between clients, master nodes, and region servers.
- Remote Procedure Call Protocols - Implements structured messaging protocols for standardized communication between cluster nodes and clients.
- Database Systems - Distributed big data store modeled after Bigtable.
