What is Hive?

Apache Hive is a Data warehouse system which is built to work on Hadoop. It is used to querying and managing large datasets residing in distributed storage. Before becoming a open source project of Apache Hadoop, Hive was originated in Facebook. It provides a mechanism to project structure onto the data in Hadoop and to query that data using a SQL-like language called HiveQL (HQL).

Hive is used because the tables in Hive are similar to tables in a relational database. If you are familiar with SQL, it’s a cakewalk. Many users can simultaneously query the data using Hive-QL.

What is HQL?

Hive defines a simple SQL-like query language to querying and managing large datasets called Hive-QL ( HQL ). It’s easy to use if you’re familiar with SQL Language. Hive allows programmers who are familiar with the language to write the custom MapReduce framework to perform more sophisticated analysis.

Uses of Hive:

1. The Apache Hive distributed storage.

2. Hive provides tools to enable easy data extract/transform/load (ETL)

3. It provides the structure on a variety of data formats.

4. By using Hive, we can access files stored in Hadoop Distributed File System (HDFS is used to querying and managing large datasets residing in) or in other data storage systems such as Apache HBase.

Limitations of Hive:

• Hive is not designed for Online transaction processing (OLTP ), it is only used for the Online Analytical Processing.

• Hive supports overwriting or apprehending data, but not updates and deletes.

• In Hive, sub queries are not supported.

Why Hive is used inspite of Pig?

The following are the reasons why Hive is used in spite of Pig’s availability:

  • Hive-QL is a declarative language line SQL, PigLatin is a data flow language.
  • Pig: a data-flow language and environment for exploring very large datasets.
  • Hive: a distributed data warehouse.

Components of Hive:

  1. Hive Metastore: The Hive Metastore is a critical component that stores metadata information about Hive tables, databases, columns, data types, and more. It acts as a catalog for Hive, allowing users to define and manage schema information. The metadata stored in the Metastore enables Hive to perform optimizations and query planning efficiently.
  2. Hive Query Language (HiveQL): Hive Query Language, often abbreviated as HiveQL, is a SQL-like language specifically designed for querying and manipulating data stored in the Hadoop Distributed File System (HDFS). HiveQL allows users to express complex data transformations and analysis tasks using familiar SQL syntax. Under the hood, Hive translates HiveQL queries into MapReduce or other execution engines for processing.
  3. Hive Execution Engine: Hive supports multiple execution engines that process HiveQL queries and perform data operations. The primary execution engines include:
    • MapReduce: The classic Hadoop batch processing framework.
    • Tez: A more optimized data processing framework that can provide better performance for certain types of queries.
    • Spark: An in-memory data processing framework that can significantly speed up data processing tasks.
  4. Hive Driver: The Hive Driver is responsible for executing queries in Hive. It receives queries from users or applications, compiles them, and submits them to the appropriate execution engine for processing. The driver also manages query execution and handles communication between Hive and the execution engine.
  5. Hive CLI (Command Line Interface): Hive CLI is a command-line tool that provides an interactive interface for users to interact with the Hive environment. Users can submit HiveQL queries, manage databases, tables, and perform various administrative tasks through the CLI.
  6. Hive UDFs (User-Defined Functions): Hive UDFs allow users to extend Hive's functionality by creating custom functions that can be used in HiveQL queries. These functions can perform specialized computations, data transformations, or other operations not provided by default Hive functions.
  7. Hive SerDe (Serializer/Deserializer): Hive SerDe is a framework that enables Hive to read and write data in various formats, including structured and semi-structured data formats such as JSON, XML, Avro, and more. SerDe provides the ability to serialize data for storage and deserialize it for processing.
  8. Hive Web Interface: The Hive Web Interface provides a graphical user interface (GUI) for interacting with Hive. It offers a user-friendly way to submit queries, manage databases and tables, and monitor query execution.
  9. Hive Server: The Hive Server is responsible for serving client requests, including query execution, to Hive. It provides a remote interface for external applications to interact with Hive using various protocols like JDBC and ODBC.
  10. Hive Warehouse Directory: The Hive Warehouse Directory is the default location where Hive stores its data within HDFS. It contains the data files associated with Hive tables and databases.

These components collectively form the Hive ecosystem, enabling users to leverage the power of Hadoop for data storage, management, and analytics through a SQL-like interface. Each component plays a vital role in ensuring the smooth execution of Hive tasks and making big data analytics more accessible to a wider range of users.

 

 

 

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