Hive External Tables

Hive is a powerful data warehousing tool that allows users to query and analyze large datasets stored in various file formats. One important feature of Hive is the ability to create external tables, which are tables that are not managed by Hive itself. In this article, we will explore the concept of Hive external tables and how they can be used.

What are External Tables?

External tables in Hive are tables that are created on top of data files stored outside of the Hive data warehouse. These files can be located in Hadoop Distributed File System (HDFS), local file system, or any other file system accessible by Hive. Unlike managed tables, external tables do not have control over the underlying data files, which means that the data files can be modified or deleted without affecting the table definition.

Creating External Tables

To create an external table in Hive, we need to define the table schema and specify the location of the data files. Here's an example of creating an external table in Hive using the SQL-like HiveQL language:

CREATE EXTERNAL TABLE employees (
    id INT,
    name STRING,
    salary DECIMAL
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
STORED AS TEXTFILE
LOCATION '/user/hive/warehouse/employees';

In the above example, we create an external table named "employees" with three columns: "id", "name", and "salary". The table is stored as a text file and its data files are located at the specified HDFS path.

Querying External Tables

Once the external table is created, we can query it just like any other table in Hive. Here's an example of running a simple query on the "employees" table:

SELECT name, salary
FROM employees
WHERE salary > 50000;

Hive will read the data files associated with the external table and perform the query. The results will be returned in the desired format, such as a table or a file.

Benefits of External Tables

There are several benefits of using external tables in Hive:

  1. Data Independence: External tables allow us to separate the storage of data files from the Hive data warehouse. This means that we can use existing data files or share data with other systems without the need to import or copy the data into Hive.

  2. Flexibility: With external tables, we have the flexibility to choose different file formats, storage locations, and access methods for our data. We can use compressed files, columnar formats, or even remote data sources as external tables.

  3. Performance: By using external tables, we can leverage the data locality feature of Hadoop. This means that the data files are stored near the compute nodes, reducing network overhead and improving query performance.

Conclusion

In this article, we explored the concept of Hive external tables and how they can be used in data warehousing. We learned that external tables provide data independence, flexibility, and improved performance. By using external tables, we can easily integrate existing data files into Hive and leverage the power of Hive for querying and analyzing large datasets.

If you're interested in learning more about Hive external tables, check out the official Hive documentation for detailed information and examples.

Sequence Diagram

sequenceDiagram
    participant User
    participant Hive
    participant HDFS

    User->>Hive: CREATE EXTERNAL TABLE employees
    Hive->>HDFS: Access data files
    Note over Hive: External table is<br/>created with<br/>metadata and<br/>data location
    User->>Hive: SELECT name, salary<br/>FROM employees
    Hive->>HDFS: Fetch data files
    Hive->>User: Return query results

The above sequence diagram illustrates the flow of creating an external table and querying it in Hive. The user interacts with Hive by executing SQL-like statements, which are then processed by Hive. Hive accesses the data files stored in HDFS based on the table definition and returns the query results to the user.

Gantt Chart

gantt
    title Hive External Tables

    section Table Creation
    Define Schema         :a1, 2022-01-01, 1d
    Specify Data Location :a2, after a1, 1d
    Create External Table :a3, after a2, 1d

    section Querying
    Run Query             :b1, after a3, 1d
    Fetch Data            :b2, after b1, 1d
    Return Results        :b3, after b2, 1d

The Gantt chart above visualizes the timeline of creating an external table and querying it in Hive. The table creation process involves defining the schema, specifying the data location, and creating the external table. Once the table is created, the user can run queries, which include fetching the data files and returning the query results.

In conclusion, Hive external tables provide a flexible and efficient way to work with data files stored outside of the Hive data warehouse. With external tables, we can easily integrate existing data files into Hive and leverage the power of Hive for data analysis and querying. By understanding the concept and benefits of external tables, we can make informed decisions when designing and implementing data warehousing solutions using Hive.