Apache Hive vs. Apache HBase: Which is the Query Performance Champion?

In this world of technology, big data is an intriguing thing. When you deal with large sets of data day in and day out, it is difficult to keep track of them. You may wonder where do all these data exactly go? Well, it gets stored in the database system. The database contains tables and modules to store a large amount of data as they keep being processed from time to time. 

Here, Apache Hive and Apache Hbase are two database systems that help in storing a large amount of data. The purpose of these two is similar. But before comparing these two systems, it is important to understand their working and features individually. 

Apache Hive

Apache Hive is precisely a data warehouse system that usually serves the purpose of summarization of data, that can be further analyzed and queries can be made using SQL. 

Hive’s position is on top of an open-source platform called Hadoop, which makes the processing of big data easy. There are a few aspects of it that are functionally important. 

  • Schema can be stored in the database.
  • The data that is processed, can be stored in the Hadoop Distributed File System.
  • It provides Online Analytical Processing.
  • Relatively fast.

Apache Hbase

Apache Hbase or Hadoop is also a storage warehouse for big data. Its main feature is that  Hadoop uses Mapreduce for the processing of big data in a distributed system. 

The main features of Hbase are listed below:

  • Data in Hbase can be replicated while in clusters.
  • The Hbase query language, that is the Java API, is comparatively easy for clients.
  • Can be scaled in a linear way.
  • Read and writes are provided.

The Working of Apache Hive

The dynamics of Apache Hive work sequentially in the following way:

  1. The interface of Hive sends queries to database drivers so it can be executed.
  2. With the help of the compiler, the driver takes in the queries to understand the syntax.
  3. The metadata request is sent to the metastore by the compiler.
  4. The requirements are thoroughly analyzed by the compiler and then it sends it to the driver. 
  5. After a complete analysis, the driver will send the execution plan to the engine for execution.
  6. The metadata operations are executed by the engine. 
  7. The data nodes provide the results.
  8. The results are sent to the Hive by the driver.

Working of Hbase

The Hbase has three important aspects that cover the entire working of the system. Mentioned below are a short description of the components and how they work:

#1. Hmaster

In this section, the data is assigned from one region to another in a cluster, providing which, load can be balanced. 

  • It manages the cluster.
  • The administration is done well for updating or deleting data from a table, or infact a table itself.
  • Hmaster handles DDL operations.
  • Hmaster also manages the changes made in metadata by clients.

#2. Region Server

In this server, the working nodes handle the read, write, delete and update operations. More specifically it has:

  • Block Cache: This performs all the read cache. The data that is used most frequently, gets stored in the read cache. 
  • Memstore-This performs all the write cache. The data that has not been written gets stored in the write cache. 

#3. Zookeeper

The main function of Zookeeper in Hbase is to co-ordinate region servers and to recover any regional server during or after a crash.

  •  Zookeeper also keeps a through track on regional servers and if there is any client that needs access or some sort of communication establishment among other servers then they need to contact Zookeeper first. 
  • Zookeeper keeps information on all the configurations. 
  • In short, it acts as a help desk assistant. 

Module Comparison Between Apache Hive & Hbase

Both Hive and Hbase serve the same purpose, in an organized way. The structure of both the database systems is such that they both help in a way to access the required data in such a way that it ensures the execution of query time is as less as possible.

The partitioning of tables in both the systems is comparatively different but both can calculate the size of clusters in a more precise way. 

Query Performance Comparison Between Apache Hive and Hbase

There are certain differences between the Apache Hive and Hbase in the way they work and we have explained them briefly below.

Hive as an Analyst

The main role of Hive is to deal with analytical queries as densely as possible. Hive uses Hive query language which serves the queries, and to add a note, the structure of the language is more or less similar to SQL language.

It has the ability to reduce the volume size of data that is scanned only by declaring the partitions, which Hive has access to. 

Hive can also transform the queries into something called Apache Tez.

Hbase- a Manager

The role of Hbase is not to perform analytical queries as it is not provided with query languages. Instead, it works like a manager that handles all the data.

It has something called JRuby shell that allows data to be manipulated. It performs CRUD operation which means Create, Read, Update, Delete. 

So, even if it does not perform a thorough analysis query, it can surely handle operations well enough.

More Differences

  • The data model of Apache Hive does not come with an indexing system but the partitioning and the divisions of rows and columns provide a sense of indexing in an organized way. 
  • The data model of Hbase has an indexing system and not only does it have indexing, but it also has multiple layers of indexing available for a better performance. 


To put the differences in a simple way, both the database systems have the same agenda, that is they both are for database management but they both perform different operations and have different mechanisms.

While Apache Hive specializes in performing analysis queries, the Hbase specializes in following the CRUD. Hive ensures that the volume of data is decreased while Hbase ensures that data is ingested. Although they are complementary to each other, they both play an important role in Hadoop Distributed File System. In the essence of technology, they both are used in Hadoop simultaneously and manage data in their own logical way. 

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