HBase Model in Hadoop
In this article, we will discuss what is Hbase, different types of data storage approaches, why HBase is preferred as compared to other databases, advantages, and problems in HBase.So, let’s go a little bit deep into this article to understand the concept well.
HBase is a database that is an open-source platform and it is the implementation of storage architecture by Google’s Big Table. The HBase database is column-oriented thus it makes it unique from other databases. One of the unique qualities of Hbase is it doesn’t care about data types because we can store different data types of data for the same column in different rows. It contains different sets of tables that maintain the data in key-value format. Hbase is best suitable for sparse data sets which are very common in the case of big data. It can be used to manage structured and semi-structured data and it has many built-in features such as:
- Garbage collection
There are two types of data storage mediums:
In the Row-Oriented data storage approach, the data is stored and retrieved one row at a time. This could lead to several problems, suppose we want only some part of the data from the row but according to this approach you have to retrieve the complete row even if you don’t need it. Apart from that this approach also serves to get help in the case of the OLTP systems operation and it helps in easy to read and write the records. But it is less efficient in the case when we perform operations on a complete database.
In the column-Oriented data storage approach, the data is stored and retrieved based on the columns. Thus the problem which we were facing in the case of the row-oriented approach has been solved because in the column-oriented approach we can filter out the data which are required to us from the whole set of data with the help of corresponding columns. In the column-oriented approach, the read and write operations are slower than others but it can be efficient while performing operations on the entire database and hence it permits very high compression rates.
Preference Of HBase :
- Hbase can handle large databases very easily as compare to other databases and perform the operations in an efficient manner.
- The data expected in Hbase are highly structured and can easily fit in the well-defined schema.
- It is easy and suitable for low latency operations.
- It provides access to the particular row from thousands of records.
- Data in Hbase can be accessed through shell commands or through client API in java.
- Other databases get extremely slow while large databases but in the case of Hbase it handles the large databases in a finite manner.
Advantages of HBase :
- Hbase provides great functionality for analytics in association with Hadoop MapReduce.
- It is capable of handling a very large amount of databases.
- Hbase provides the facility of sharing the database with other users.
- There are many operations like data reading and processing that will take less amount of time as compared to other traditional databases.
- Whenever there is a failure or load sharing problem arises Hbase can automatically recover from the problem because it is internally distributed.
- In Hbase scalability is supported in both linear and modular forms.
- It provides a large consistency.
Problems in HBase :
- In HBase the memory blocks requirements and hardware Requirement for allocation during an operation is expensive.
- To store the Large file which is in binary format is very difficult in HBase.
- There is no query optimizer present in HBase due to which it is unable to perform SQL functions and it also doesn’t support SQL structures.
- Though there is a lot of functionality provided by Hbase it can’t serve as a complete replacement of the traditional models because some of the traditional features are still not supported by HBase.
- Hbase does not provide any transaction support.
- HBase does not support any default indexing functionality.