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This article introduces the relevant knowledge of "what is the hive component architecture". In the operation of actual cases, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

Hive execution flowchart:

[Pratical Hive.pdf] study notes, each chapter as the main line, supplemented by the official website materials to complete.

Component architecture

Client component

Hive-cli

JDBC/ODBC

Toad or SQuirreL

HCatalog

Metadata management component, the main functions are as follows

Official introduction

Provides a common schema environment for multiple tools

Allows for connectors to tools to read data from and write data to Hive's warehouse

Lets users share data across tools

Creates a relational structure to Hadoop data

Abstracts away the how and where of data storage

Hides schema and storage changes from users

HiveServer2

Interface service component

Execution-Engine

MR

Executive engine component

Tez

Execute engine components, omitting the shuffle process

Tez avoids disk IO by avoiding expensive shuffle and shorts while leveraging more efficient map side joins. Tez also utilizes a costbased optimizer, which helps produce faster execution plans. Combine this with the ORC file format geared

Toward SQL performance and you have a query engine performing up to 100x faster than native MapReduce-

Hive-on-Spark

Storage: Hadoop

Storing http://www.0398hfyy.com based on hdfs file

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