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Hive Architecture

Hive Architecture with tutorial, introduction, environment setup, first app hello world, state, props, flexbox, height and width, listview, scrollview, images, buttons, router, etc.

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Hive Architecture

The following architecture explains the flow of submission of query into Hive.

Hive Architecture

Hive Client

Hive allows writing applications in various languages, including Java, Python, and C++. It supports different types of clients such as:-

  • Thrift Server - It is a cross-language service provider platform that serves the request from all those programming languages that supports Thrift.
  • JDBC Driver - It is used to establish a connection between hive and Java applications. The JDBC Driver is present in the class org.apache.hadoop.hive.jdbc.HiveDriver.
  • ODBC Driver - It allows the applications that support the ODBC protocol to connect to Hive.

Hive Services

The following are the services provided by Hive:-

  • Hive CLI - The Hive CLI (Command Line Interface) is a shell where we can execute Hive queries and commands.
  • Hive Web User Interface - The Hive Web UI is just an alternative of Hive CLI. It provides a web-based GUI for executing Hive queries and commands.
  • Hive MetaStore - It is a central repository that stores all the structure information of various tables and partitions in the warehouse. It also includes metadata of column and its type information, the serializers and deserializers which is used to read and write data and the corresponding HDFS files where the data is stored.
  • Hive Server - It is referred to as Apache Thrift Server. It accepts the request from different clients and provides it to Hive Driver.
  • Hive Driver - It receives queries from different sources like web UI, CLI, Thrift, and JDBC/ODBC driver. It transfers the queries to the compiler.
  • Hive Compiler - The purpose of the compiler is to parse the query and perform semantic analysis on the different query blocks and expressions. It converts HiveQL statements into MapReduce jobs.
  • Hive Execution Engine - Optimizer generates the logical plan in the form of DAG of map-reduce tasks and HDFS tasks. In the end, the execution engine executes the incoming tasks in the order of their dependencies.
Next TopicHive Installation




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