What is Hive

What Exactly is Hive?

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.

Why use Hive?

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.

How does Hive work?

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.

History of 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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Architecture of Hive:

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.

Hive clients:

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.

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.

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Hive modes:

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.

Hive storage and processing:

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.

• The metadata information represented in the form of the tables that have been created in Hive will be stored in the Hive metastore database.
• All the data and the query results that are represented in the tables will be stored in the cluster in HDFS.

Hive features:

Below are the topmost features of Hive that made it popular.

1. Hive is a tool that is designed for managing and querying the data which is the structure format in the form of tables.
2. Hive is very fast and scalable.
3. The processing of the data will be moving into the Hadoop distributed file system whereas the schema will be stored in the database.
4. All the databases and the tables will be created first then followed by raising the data loading which will be represented into the proper tables.
5. Hive is capable of providing its extensive support through the file formats like text file, rC file, sequence file, ORC.
6. Hive is considered as SQL inspired language that reduces the complexity of mapreduce programming. It makes use of the familiar concepts that are usually represented in the relational databases like rows, columns, tables, schema, etc.
7. The primary difference between Hive query language and SQL is that Hive executes the queries on the Hadoop infrastructure rather than using the traditional database.
8. Hive provides support for buckets and partitions for faster and simpler data retrieval.
9. It will also provide extensive support through the custom user defined functions which help in performing the task like data filtering and cleaning. The user defined functions can be defined based on the requirements of the programmers.

How Data Flows in the Hive?

1. The Data analyst is the person who is responsible for the execution of a query within the user interface.
2. It is the responsibility of the driver to interact with the query compiler and also retrieve the plan. It usually includes meta data information and the query execution process that is used for the execution of the query. The driver will also pass the query e to give a random check on the requirements and the syntax.
3. It is a time for the compiler to create the job plan that needs to be executed and also communicate the same with the metastore for performing the retrieval of the metadata request .
4. The metastore is responsible for sending the information back to the compiler.
5. There a compiler will be then reliable on the query execution plan which will be moved to the driver.
6. The driver will be responsible for sending the execution plan to the execution engine.
7. The execution engine is responsible for processing the query which is acting as an interface or a bridge between the Hadoop and hive. This process will be executed in the mapreduce. The execution engine will then send their job to their job tracker, identify the name in the namenode and then assign it to the task tracker that is present in the date node. While all these functionalities are being performed, the execution engine will also execute the data operations that are present within the Meta Store.
8. All the results will be retrieved from the data nodes.
9. Once the results are obtained and sent to the execution engine, the results will be sent back to the driver followed by the front end user interface.

When is the local mode used?

• The local mode is used when hadoop is installed under this pseudo mode by having one data node.
• When the data size is smaller when it is related to a single local machine, then this Mode can be used.
• Whenever the processing is very fast on smaller sets of data that is present in the local machine, then this mode can be used.

When can the map reduce node be used?

• If the data is distributed across different nodes and if Hadoop is maintaining multiple data nodes, then the mapreduce node is used.
• Mapreduce not can be used if there are large volumes of data sets that need to be queried and executed in parallel.
• This mode can be used when there is a processing of large volumes of data sets that require better performance.

Apache Hive vs Apache Hbase

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 vs Hive:

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.

Hive vs. Relational Databases:

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.

Limitations of hive:

Every technology or tool will definitely have certain limitations. Below listed are the limitations of hive.

• Hive is not capable of providing its support for OLTP( Online transaction processing) whereas it supports OLAP (online analytical processing).
• Latency is very high
• It is not capable of providing support for sub queries.
• It is not possible to update or delete the operations in the Hive tables.

conclusion:

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