In order to help you to understand better their features, benefits, and drawbacks as well as the practical uses of these databases, we will compare HBase vs Hive in-depth in this blog post.
The following subjects are listed below, we’ll go into detail on each of them:
Hive vs HBase: Overview
Two distinct Hadoop-based technologies are Hive and HBase. HBase is a Hadoop-based NoSQL key/value database, and Hive is a MapReduce engine with SQL-like capabilities.
But just as Facebook can be used for social networking and Google can be used for search, so too can Hive be used for analytical queries and HBase for real-time querying.
To gain a thorough understanding of the above-mentioned subject, let’s examine each of the two technologies separately:
What is Hive?
Hive is a framework made for data warehousing that runs on top of Hadoop. Users are able to perform searches on enormous amounts of data with this type of framework. SQL queries are transformed into MapReduce jobs as their fundamental function.
Hive was developed out of the necessity for Facebook to handle and learn from the massive amounts of data that its expanding social network was producing every day.
After experimenting with a few other systems, the team decided to use Hadoop for processing and storage because it was affordable and scalable.
Hive was developed to enable analysts with proficient SQL abilities (but less knowledge of Java programming) to do queries on the massive amounts of data that Facebook kept in HDFS.
This is the way how Hive was derived with the help of Facebook.
Hive is a well-known Apache project that many businesses use as a foundation for all-purpose, scalable data processing today.
Your SQL query is converted into a set of tasks that can be carried out on a Hadoop cluster by Hive, a program that typically runs on your computer.
Data is arranged into tables by Hive, which enables HDFS data to be given structure. A database called the Metastore is used to store metadata, such as table schemas.
It supports interactive SQL-like query language and data modeling. A MapReduce join across Hive tables is achievable using the Hive query language.
It supports aggregation functions like SUM, COUNT, MAX, and CONCAT as well as simple SQL-like functions like SUBSTR, ROUND, etc.
Additionally, it supports the grouping and sorting of clauses. Additionally, the Hive query language supports the creation of user-defined functions.
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What is HBase?
On top of the HDFS, the distributed column-oriented database HBase was created. HBase is a massively scalable open-source project which is a data model that is comparable to Google’s big table and created to offer speedy random access to enormous quantities of structured data.
Data in the Hadoop File System can be read and written in real-time thanks to HBase, a component of the Hadoop ecosystem. HDFS is used by HBase to store its data.
Using SQL, the Apache Hive data warehouse software makes it easier to read, write, and manage massive datasets that are stored in distributed storage. Onto stored data, the structure can be projected. Users can connect to Hive using a command-line tool and a JDBC driver.
This technology is quickly gaining popularity as a database option for applications that require quick random access to big amounts of data. It is strongly connected with Apache Hadoop and developed on top of it.
Although it hasn’t been a straight replacement for conventional databases, its performance has been purposefully enhanced lately, and it has been essential to many data-driven websites and web applications like Facebook Messenger and others. This is the reason why HBase is in high demand.
Difference between Hive and HBase
It’s time to understand the difference between HBase vs Hive so that you can compare the two databases with ease and have a firm grasp of everything:
| Hive | HBase |
| A Hadoop-based infrastructure for data warehouses. | A distributed, versioned, column-oriented open-source store called HBase is based on Google’s Bigtable. |
| Hive is used to analyze and run ad-hoc queries on big data volumes without the need to learn MapReduce. | HBase is used to store data that is a source or sink for analytical tasks (usually MapReduce). |
| Enables the storing of both structured and unstructured data. Has built-in support for common SQL data types, including INT, FLOAT, and VARCHAR, among others. | Supports only unstructured data types. The user defines the mappings between the names of the data fields and the supported data types through Java. |
| Hive processes petabytes of Hadoop data using SQL queries. It also offers HQL, a query language for Hadoop node data that is similar to SQL. | HBase produces a Hadoop-based GIS that is affordable, versatile, and simple to maintain (HBGIS). |
| Latency is minimal, but there’s a chance it might be inconsistent. | Latency depends on how responsive the machine is, from medium to high. |
Advantages and Disadvantages of Hive
We would like to discuss the benefits and drawbacks of Apache Hive so that you may keep these things in mind as you work on this database.
Advantages
Let’s examine the many benefits of Hive that encourage users to utilize this specific database.
- Hive follows ACID transaction processing.
- It allows sharing of Hive Metastore.
- Provide low-latency analytical processing.
- Better security improvements.
- Transparent in every way to the underlying Map Reduce.
- Flexibility to load data from localFS/HDFS into Hive Tables.
Disadvantages
The following are some of Apache Hive’s drawbacks are given below:
- Hive cant be used when we have unstructured data, whether fully or partially.
- Processing online transactions are not supported in Hive.
- Only batch operations involving huge data sets should use for Hive.
- Even for extremely tiny data sets, hive queries typically have high latency in minutes.
- It cannot be compared to platforms like Oracle, where analyses are performed on a much lower volume of data.
Advantages and Disadvantages of HBase
To help you while you work on this database, we’ll go over the benefits and drawbacks of using HBase.
Advantages
The following are some benefits of using HBase:
- It provides modular and linear scaling.
- Strictly reliable reading and writing.
- Automatic and customizable table sharding.
- Support for automatic failover between RegionServers.
- Convenient foundation classes for using Apache HBase tables to support Hadoop MapReduce jobs.
Disadvantages
HBase has a few drawbacks and issues that limit its usability. Therefore, we shall learn about the drawbacks of HBase in this blog which are listed below:
- Neither relational analytics nor conventional transactional applications can be used with it.
- When performing a huge batch MapReduce, HBase is also not a full replacement for HDFS.
- It does not handle cross-record transactions or joins, speak SQL, or have an optimizer.
- Using it with complex access patterns is not possible in HBase(such as joins).
Check out this YouTube video on Hive Tutorial for Beginners
Conclusion
To give you a thorough understanding of these subjects for your future reference, we have attempted to compare the two key terminologies, HBase vs Hive, in this blog post.
HBase and Hive are the two most often used programming paradigms. Hive, however, takes less time than HBase, according to practical testing.
Additionally, each strategy has advantages and disadvantages, so we must select one based on our requirements and available information.