HBase achieves distributed storage of data through a combination of techniques including data partitioning, replication, and distributed consensus protocols.
Firstly, HBase partitions data across multiple nodes in a cluster using a technique called region splitting. Each region is a contiguous range of rows in a table and is stored on a single node. As data is added or deleted, regions can grow or shrink, and HBase will automatically rebalance the regions across the cluster to ensure even distribution of data.
Secondly, HBase replicates data across multiple nodes to ensure fault tolerance and high availability. Each region is replicated to multiple nodes, typically three or more, so that if one node fails, the data can still be accessed from another node.
Finally, HBase uses a distributed consensus protocol called ZooKeeper to coordinate the activities of the nodes in the cluster. ZooKeeper maintains a hierarchical namespace of nodes and provides services such as leader election, configuration management, and distributed locking to ensure that the cluster operates correctly even in the presence of node failures.
For example, in a HBase cluster with three nodes, a table might be partitioned into three regions, with each region stored on a different node. Each region would be replicated to the other two nodes, so that there are three copies of each region in the cluster. If one node fails, HBase can still access the data from the other two nodes.
In the context of cloud computing, Tencent Cloud offers a managed HBase service called TencentDB for HBase, which simplifies the deployment, operation, and maintenance of HBase clusters. TencentDB for HBase leverages Tencent Cloud's high-performance infrastructure and provides automatic scaling, backup and recovery, and security features to ensure reliable and efficient distributed storage of data.