hiveexternal的简单介绍

Hive External Tables: Efficient Data Management and Querying in Hive

Introduction:

Hive External Tables provide a way to efficiently manage and query data stored outside of the Hive warehouse. Whether it is data residing in a different Hadoop cluster or an external data source like Amazon S3, Hive External Tables allow for seamless integration and easy querying using the familiar Hive SQL-like language. In this article, we will explore the benefits of using Hive External Tables and how they can be implemented.

I. Overview of Hive External Tables:

A. What are Hive External Tables?

1. Definition: Hive External Tables are tables in Hive that are not managed by Hive. They are created to access data residing outside of the default Hive warehouse.

2. Location: The data files for external tables can be stored in any Hadoop-supported file system or an external storage system.

B. Benefits of Using Hive External Tables:

1. Data Isolation: By storing data separately from the Hive warehouse, external tables provide data isolation and minimize the interference with other Hive operations.

2. Flexible Data Sources: Hive External Tables allow accessing data from various sources such as HDFS, Amazon S3, Azure Blob Storage, and more. This enables the integration of different data sources into a single Hive environment.

3. Data Sharing: External tables facilitate sharing data with other applications or systems outside of Hive, enabling seamless data integration and collaboration.

4. Cost-Effective Storage: Data stored in external tables can be saved in a more cost-effective storage system, such as Amazon S3, reducing storage costs.

II. Creating and Querying Hive External Tables:

A. Creating External Tables:

1. Syntax: To create an external table, use the CREATE EXTERNAL TABLE command.

2. Specify File Format: Specify the file format of the external data, such as TEXTFILE, PARQUET, ORC, etc.

3. Define Location: Specify the location of the data files for the external table.

4. Define Table Schema: Define the table schema to match the structure of the external data.

B. Querying External Tables:

1. SQL-Like Queries: Similar to regular Hive tables, external tables can be queried using Hive SQL-like language, making it easy to retrieve the data stored outside the Hive warehouse.

2. Performance Considerations: While querying external tables, certain performance considerations should be taken into account, such as data locality and data format optimizations.

3. Metadata Management: Hive External Tables maintain metadata information about the external data, allowing for efficient query planning and execution.

III. Examples of Hive External Tables:

A. External Tables in Amazon S3: Learn how to create and query external tables using data stored in Amazon S3, enabling cost-effective data storage and processing leveraging the power of Hive.

B. External Tables with an On-Premise Hadoop Cluster: Understand how to integrate external data residing in an on-premise Hadoop cluster with Hive, allowing for seamless data integration and queryability.

Conclusion:

Hive External Tables provide a powerful way to manage and query data stored outside of the default Hive warehouse efficiently. By enabling seamless integration with different data sources, easy querying, and cost-effective storage options, Hive External Tables offer flexibility and scalability to Hive users. Understanding their benefits and how to implement them can greatly enhance data management and analysis capabilities in the Hive ecosystem.