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HBase Basics



HBase is an open-source, distributed, column-oriented database management system that runs on top of the Hadoop Distributed File System (HDFS). It provides a non-relational database that can handle large amounts of structured and semi-structured data. HBase is designed to handle massive amounts of data with high scalability, fault tolerance, and high availability. In this blog post, we will take a closer look at HBase and its usage example.

Overview of HBase

HBase is based on the Google Bigtable model, which is a sparse, distributed, persistent multidimensional sorted map. HBase is written in Java and is a part of the Apache Hadoop project. It is designed to handle large amounts of data with high scalability, fault tolerance, and high availability. HBase is highly configurable and can be tuned to meet specific needs. It supports ACID transactions, and its data model is similar to that of Google Bigtable, with columns, column families, and rows.

HBase stores data in tables, which are divided into regions, and each region is stored in a region server. HBase uses ZooKeeper to manage coordination between the region servers. The data in HBase can be accessed using the HBase shell or through the HBase API. HBase also provides a RESTful interface that allows applications to access HBase data over HTTP.

Advantages of HBase

  1. HBase can store large datasets.
  2. The database can be shared among users.
  3. HBase is cost-effective for storing data ranging from gigabytes to petabytes.
  4. High availability is ensured through failover and replication.

Disadvantages of HBase

  1. HBase does not support SQL structure.
  2. HBase does not support transactions.
  3. HBase sorts data only on the basis of keys.
  4. Cluster memory issues may occur.

Components of HBase

  1. HMaster – HMaster is the implementation of the Master Server in HBase. It assigns regions to region servers and handles DDL operations (create, delete table). It monitors all Region Server instances present in the cluster. In a distributed environment, Master runs several background threads. HMaster has many features like controlling load balancing, failover, etc.
  2. Region Server – HBase tables are divided horizontally by row key range into regions. Regions are the basic building elements of an HBase cluster that consist of the distribution of tables and are comprised of column families. Region Server runs on an HDFS DataNode present in the Hadoop cluster. Regions of the Region Server are responsible for several things, like handling, managing, executing as well as reads and writes HBase operations on that set of regions. The default size of a region is 256 MB.
  3. Zookeeper – It acts as a coordinator in HBase. It provides services like maintaining configuration information, naming, providing distributed synchronization, server failure notification, etc. Clients communicate with Region Servers via Zookeeper.

Usage Example

Suppose we have a dataset containing information about the population of different countries. We can store this data in HBase in a table called “population.” The table can be divided into regions based on the country code. Each record in the table can contain information about the population of a specific country.

To create a table in HBase, we can use the HBase shell. The following command will create a table called “population” with a column family called “pop_data”:

create 'population', 'pop_data'

We can now add records to the “population” table using the following command:

put 'population', 'us', 'pop_data:2010', '308745538'

This command adds a record to the “population” table with the row key “us,” which represents the United States. The record contains a column called “2010” in the “pop_data” column family, which contains the population of the United States in 2010.

We can retrieve data from the “population” table using the following command:

get 'population', 'us'

This command retrieves the record with the row key “us” from the “population” table. The output will contain all the columns in the “pop_data” column family for the “us” row key.

Features of HBase

Distributed and Scalable: HBase is designed to handle large datasets and can scale out horizontally by adding more nodes to the cluster, making it highly distributed and scalable.

Column-oriented Storage: HBase stores data in a column-oriented manner, which allows for efficient data retrieval and aggregation.

Hadoop Integration: HBase is built on top of Hadoop, which means it can leverage Hadoop’s distributed file system (HDFS) for storage and MapReduce for data processing.

Consistency and Replication: HBase provides strong consistency guarantees for read and write operations, and supports replication of data across multiple nodes for fault tolerance.

Built-in Caching: HBase has a built-in caching mechanism that can improve query performance by caching frequently accessed data in memory.

Compression: HBase supports compression of data, which can reduce storage requirements and improve query performance.

Flexible Schema: HBase supports flexible schemas, which means the schema can be updated on the fly without requiring a database schema migration.

Note – HBase is widely used for online analytical operations, such as real-time data updates in banking applications like ATM machines.


HBase is a powerful database management system that provides high scalability, fault tolerance, and high availability. It is designed to handle large amounts of structured and semi-structured data. HBase is based on the Google Bigtable model and provides a non-relational database that supports ACID transactions. In this blog post, we discussed an example of how to use HBase to store and retrieve data. HBase is a valuable tool for managing large datasets, and its flexibility and configurability make it an excellent choice for many use cases.


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