HBase is a distributed, scalable, big data store that is modeled after Google's BigTable. It uses HDFS for storage and is written in Java. HBase provides a key-value data model and allows for fast lookups by row keys. It does not support SQL queries or transactions. Clients can access HBase data via Java APIs, REST, Thrift or MapReduce. The architecture consists of a master server and multiple region servers that host regions and serve client requests.
Hadoop is an open-source software framework for distributed storage and processing of large datasets using the MapReduce programming model. It includes HDFS for data storage and MapReduce for data processing across clusters of compute nodes. HDFS provides high throughput access to application data and is suitable for applications that have large data sets. It provides reliability through data replication and distributed architecture.
MapReduce is a programming model and framework developed by Google for processing and generating large datasets in a distributed computing environment. It allows parallel processing of large datasets across clusters of computers using a simple programming model. It works by breaking the processing into many small fragments of work that can be executed in parallel by the different machines, and then combining the results at the end.
HBase is a distributed, scalable, big data store that is modeled after Google's BigTable. It uses HDFS for storage and is written in Java. HBase provides a key-value data model and allows for fast lookups by row keys. It does not support SQL queries or transactions. Clients can access HBase data via Java APIs, REST, Thrift or MapReduce. The architecture consists of a master server and multiple region servers that host regions and serve client requests.
Hadoop is an open-source software framework for distributed storage and processing of large datasets using the MapReduce programming model. It includes HDFS for data storage and MapReduce for data processing across clusters of compute nodes. HDFS provides high throughput access to application data and is suitable for applications that have large data sets. It provides reliability through data replication and distributed architecture.
MapReduce is a programming model and framework developed by Google for processing and generating large datasets in a distributed computing environment. It allows parallel processing of large datasets across clusters of computers using a simple programming model. It works by breaking the processing into many small fragments of work that can be executed in parallel by the different machines, and then combining the results at the end.
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系 架统 构
• Client
– 包含访问 HBase 的接口并维护 cache 来加快对 HBase 的访问
• Zookeeper
– 保证任何时候,集群中只有一个 HMaster
– 实时监控 Region Server 的上线和下线信息,并实时通知给
HMaster
– 存储了 -ROOT- 表的位置
• HMaster
– 为 Region server 分配 region ,负责 Region server 的负载均衡
– 发现失效的 Region server 并对其上的 Region 进行迁移
– 管理用户对 table 的增删改查操作
• Region Server
– Region server 维护 Region ,处理对这些 region 的 IO 请求
– Region server 负责切分在运行过程中变得过大的 region
– 包含一个 HLog ,是预写式日志,用于灾难恢复