What is HBase?
HBase is one of the open source distributed databases designed to store record-oriented data throughout a scalable machine cluster. Professionals refer HBase as a “sparse, distributed, consistent, persistent, multi-dimensional, sorted map.” Didn’t get it? Let explain a bit –
- Sparse – If a row has null value in a column, it doesn’t take space
- Distributed – rows are spread across several machines
- Consistent – It is strongly consistent
- Persistent – HBase stores data on disk with a log, hence it sticks around
- Sorted – All the rows in HBase are stored in sorted order so that user can seek them faster
- Multi-dimensional – Data stored in HBase is addressable in several dimensions- rows, tables, columns, versions, etc.
Why companies are using NoSQL store even if they have a relational database?
We are not saying that relational database is useless. In fact, relational databases are terrific offering killer features-
- Ability to decompose the physical data storage into different conceptual buckets
- Modify the state of many related values atomically.
But then there is a subset of use cases that include unique requirements for relational data. Less emphasis on relationship webs that need complex transactions for correctness; and more emphasis on large data streams that accrue over time, and require linear access uniqueness.
For those new use cases, HBase has been added to their toolkit.
HBase can leverage the distributed processing paradigm available in HDFS. It can host large tables with billions of rows with millions of columns and run all over a cluster of commodity hardware. HBase is a robust and sturdy database that takes help of MapReduce to combine real-time query capabilities with value store speed and batch processing. In simple words, with HBase, companies can make a query for individual records and obtain aggregate analytic reports.
- Scalability – HBase supports scalability in both modular and linear format
- Sharding – Sharding of tables is supported by HBase. It is also configurable.
- Consistency – HBase also supports consistent read and write operations
- Distributed storage – Distributed storage like HDFS is supported by HBase
- Failover support – HBase supports automatic failover
- API support – Java APIs are supported by HBase
- Backup support – Backup support for Hadoop MapReduce jobs in Hbase tables is available in HBase.
- MapReduce support – MapReduce support is available for parallel processing of bulk data
- Real-time processing – HBase supports block cache and Bloom filters to make real-time processing easy.
HBase is different from a relational database and needs a unique approach to modeling the data. It defines a four-dimensional data model and the below four coordinates explain each cell –
- Row key – Each row contains a unique row key. The row key doesn’t include data type and is treated internally as a byte array.
- Column family – row data is organized within column families; each row has the same set of column families, however across rows, the same column families don’t require the same column qualifiers. HBase stores column families within own data files.
- Column qualifier – Column families explain actual columns known as column qualifiers.
- Version – Each column can have configurable version numbers. You can access the data for certain version of a column qualifier.
Why HBase is the foremost choice of all the NoSQL stores?
Choosing HBase is a key area of investment. There are three factors that influence the decision making –
- HBase is a strongly consistent store – HBase is a CP store and not the AP store. Its consistency is amazing if used for an apt reason.
- It’s a high-quality project – It is well respected in the community. Social networking platform like Facebook built its whole messaging infrastructure on HBase.
Hadoop ecosystem had an operational presence at Salesforce. Experts are applying Hadoop in the product for ages, and they know how it works. HBase uses HDFS for persistence and provides first-class integration with MapReduce.
How HBase works?
HBase scales in a linear way in order to provide a primary key to each table. Each key space is distributed into sequential block allotted to a region. The RegionServers control each region and distribute the load uniformly in a collected environment.
In HBase, you get automating data sharding support, which eliminates the need for manual intervention.
After deployment of HBase, HMaster and Zookeeper server are configured to offer cluster topology data to the HBase clients. Client apps linked to these utilities and acquire the list of RegionServers, key ranges, and regions information. It assists the client to determine the accurate data position and connect to the RegionServer directly. Caching support is provided by RegionServers that help in accessing rows frequently. This enhances the performance.
Major reasons to use HBase are as under-
Even if HBase offers multiple great functionalities, it is still not a ‘Fit for all’ solution. You need to consider following key areas prior using HBase for the application-
- Data volume – The data volume is one of the common things to consider. You should have PETA data bytes that have to be processed in a distributed environment.
- Application type – Hbase is unsuitable for transactional apps, relational analytics, large volume MapReduce jobs, etc. If you have a variable schema with different rows or if you are going for a key dependent access to stored data, you can use HBase.
- Hardware environment – HBase runs on top of HDFS that works efficiently with a massive amount of nodes. If you are using good hardware, HBase can work for you.
- No relational features are needed
- Quick access to data
These are the things making HBase so popular among Hadoop and Big data solutions companies. If you are planning to deploy HBase, do consider the above-discussed scenarios for better and efficient performance.