While big data is capable of processing large volumes of data and delivering meaningful insights for the success of an organization, there is a need for some dedicated tools that help in turning the raw data into actionable content. Hadoop is one of the frameworks which is used to store and process big data information. Hive is one of the tools used in Hadoop. Most organizations have been using Hive in their daily transformations and have gained immense success and profits. In this blog, we will discuss about what is hive, its features, components, history, architecture, modes and the significant differences when compared with the other tools. Let’s get started!
Hive, also known as Apache Hive is one of the data warehouse infrastructure based tools that is used for processing the structured data in Hadoop. It is the software that provides multiple facilitations like writing, reading and managing large volumes of data that is present in the distributed storage using the SQL.
Hadoop helps in performing the operations like data analysis, data summarization and data query. HiveQL is the query language that Hive uses for translating the SQL queries into the mapreduce jobs. Hive is specifically used for the batch jobs.
Apache Hive is used to perform the functionalities like data analysis, querying and summarisation. This tool will also help in improving the productivity of the developers when it comes to the cost that is needed because of the increased latency. Hive is one of the technologies or tools that is standing out when compared to the other SQL systems and implemented In The databases. It also includes other functions which are user-defined, which help in providing the effective ways for solving the issues. Connecting the Hive queries to the Hadoop packages like RHipe, RHive is very easy.
Hive usually refers to a particular system that helps in reporting and analyzing the data. The primary goal is to detect and provide meaningful information along with suggestions and conclusions that help in the betterment of the organization. There are many approaches and aspects that need to be included along with some dynamic techniques while performing the data analysis.
Have also allows its users to access the data at any time, also increases the response time which is nothing but the time that a functional unit takes to react based on a particular input. When compared with the other queries, hive has a faster response time and is also highly flexible.
Hive was usually developed in order to provide the flexibility to the non programmers to get familiar with the SQL programming language and also to make them capable of working with large volumes of data. All this takes place through a SQL like interface called HiveQL. In the traditional Times, the relational database systems are usually designed for processing the small volumes of data to medium data sets but not capable of processing large volumes of data like petabytes of data. Hive makes use of batch processing that will help in processing the volumes of data parallely in a distributed database. Hive is responsible for transforming the Hive SQL queries into mapreduce which will be running on the distributed job scheduling Framework called as yet another resource navigator, often called as YARN. It is responsible for acquiring the data that is stored in the distributed storage like HDFS. Highway responsible for storing the database and the metadata in a meta store which is usually a database or a file back store that helps in easy data discovery and abstraction.
Hive also includes a table and a storage management layer called HCatalog that will help in reading the data from the Hive metastore and also provide the integration between mapreduce, hive and Apache pig. HCatalog is responsible for allowing the mapreduce and pig to make use of the same data structures that are used as hive.
The data infrastructure team in Facebook have developed the hive. The Hive Hadoop cluster is capable of storing more than two PB of RAW information or raw data and also be capable of processing it daily along with the loading of 15 TB of data in Facebook. Soon later, apache has taken or acquired the hive and made certain improvements turning out to be a open source platform. It is used by most of the organization's- big giant platforms like Netflix, FINRA, etc.
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The Hive architecture includes three main components called
a. Hive clients
b. Hive services
c. Hive storage and computing
Below represented is the image of the hive architecture.
Let us discuss more about each of the components in the Hive architecture.
There are different direct drivers that are provided by high for communication purposes that can be implemented within different types of applications. Let us say that it is a thrift based application, then it will provide the thrift client for the communication purposes.
The JDBC drivers are used for Java related applications. All these kinds and drivers will communicate again with the Hive server that is present in the Hive services.
The Hive services are used for establishing the client interactions with the hive. If a particular client would like to perform any query related operation, then it needs to be communicated through the hive services.
In hive, this is a command line that is acting as the height service for the data definition language based operations. There will be communication with the drivers on the Hive server and along with the main driver to the Hive services.
The driver that is present in the highest services is called as a main driver which is used in communicating with all kinds of client based applications.
Hive can be operated in two different modes based on the data node size that is present in the Hadoop. these modes the mapreduce mode and the local mode.
The Hive services include the file system, meta store, job client which will be communicating with the highest storage and will be performing the following set of actions.
Below are the topmost features of Hive that made it popular.
Both Hbase and Hive are two different Hadoop based technologies. The Hive engine which is capable of running the mapreduce jobs whereas Hbase is no SQL key or value based database on Hadoop. Hbase can be used for performing real time querying where hive can be used for performing analytical queries. It is possible to write and read the data from hive to Hbase and back again.
Pig is used for handling the structure and semi structured data while Hive is used for handling only structured data. Pig is used for programming whereas Hive is used for creating the reports. The Pig is used by the programmers or the researchers whereas Hive is used by data analysts.
Relational database system makes use of SQL structured query language whereas Hive uses the Hive query language. Only the normalized data will be stored in the relational database management system whereas the normalized and de-normalised data will be stored in the hive. Relational database system does not provide support for partitioning whereas Hive does.
Every technology or tool will definitely have certain limitations. Below listed are the limitations of hive.
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It is used for providing the users with the facility to read, write and manage the large volumes of data using the structured query language. It is an open source Framework which is built on the top of Apache Hadoop, capable of storing and processing large volumes of data sets.
Explain is a command execution plan of a particular query. Below represented is the syntax:
EXPLAIN [EXTENDED|CBO|AST|DEPENDENCY|AUTHORIZATION|LOCKS|VECTORIZATION|ANALYZE] query
Hive is generally a data warehouse infrastructure software that helps in establishing interaction between the Hadoop distributed file system and the users. Hive provides support for the below set of user interfaces like Hive WebUI, hive HD insight, Hive Command line.