HBase is a column-oriented non-relational database management system that runs on top of Hadoop Distributed File System (HDFS). HBase provides a fault-tolerant way of storing sparse data sets, which are common in many big data use cases. It is well suited for real-time data processing or random read/write access to large volumes of data.
Unlike relational database systems, HBase does not support a structured query language like SQL; in fact, HBase isn’t a relational data store at all. HBase applications are written in Java™ much like a typical Apache MapReduce application. HBase does support writing applications in Apache Avro, REST and Thrift.
An HBase system is designed to scale linearly. It comprises a set of standard tables with rows and columns, much like a traditional database. Each table must have an element defined as a primary key, and all access attempts to HBase tables must use this primary key.
Avro, as a component, supports a rich set of primitive data types including: numeric, binary data and strings; and a number of complex types including arrays, maps, enumerations and records. A sort order can also be defined for the data.
HBase relies on ZooKeeper for high-performance coordination. ZooKeeper is built into HBase, but if you’re running a production cluster, it’s suggested that you have a dedicated ZooKeeper cluster that’s integrated with your HBase cluster.
HBase works well with Hive, a query engine for batch processing of big data, to enable fault-tolerant big data applications.
An HBase column represents an attribute of an object; if the table is storing diagnostic logs from servers in your environment, each row might be a log record, and a typical column could be the timestamp of when the log record was written, or the server name where the record originated.
HBase allows for many attributes to be grouped together into column families, such that the elements of a column family are all stored together. This is different from a row-oriented relational database, where all the columns of a given row are stored together. With HBase you must predefine the table schema and specify the column families. However, new columns can be added to families at any time, making the schema flexible and able to adapt to changing application requirements.
Just as HDFS has a NameNode and slave nodes, and MapReduce has JobTracker and TaskTracker slaves, HBase is built on similar concepts. In HBase a master node manages the cluster and region servers store portions of the tables and perform the work on the data. In the same way HDFS has some enterprise concerns due to the availability of the NameNode HBase is also sensitive to the loss of its master node.
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