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Hive Tables: Internal vs External #

Introduction to Hive Tables #

In Apache Hive, tables are used to store structured data in a database-like format. Hive supports two types of tables: Internal (Managed) Tables and External Tables. Understanding the differences between these two types of tables is crucial for data management, storage optimization, and use cases in big data environments.

Hive Internal (Managed) Tables #

An Internal (Managed) Table is a table where Hive manages both the metadata and the data. When you create an internal table, Hive takes full ownership of the table’s data, meaning it controls the location where the data is stored and is responsible for deleting the data when the table is dropped.

Key Characteristics of Internal Tables: #

• Data Storage: Data is stored in a directory within Hive’s default warehouse location, usually in HDFS (Hadoop Distributed File System).
• Data Deletion: When an internal table is dropped, the metadata and the data are both deleted from the warehouse location.
• Use Case: Internal tables are preferred when Hive is responsible for managing the data and its lifecycle.

Example of Creating an Internal Table: #

CREATE TABLE employee (
    emp_id INT,
    name STRING,
    department STRING,
    salary FLOAT
);

• By default, if you do not specify EXTERNAL, Hive creates an internal table.
• The table and its data will be stored in the Hive warehouse directory, typically at /user/hive/warehouse/.

Inserting Data into the Internal Table: #

INSERT INTO TABLE employee VALUES
    (101, 'Alice', 'HR', 70000.0),
    (102, 'Bob', 'Finance', 85000.0),
    (103, 'Charlie', 'Engineering', 95000.0);

Querying Data from the Internal Table: #

SELECT * FROM employee;

Dropping an Internal Table: #

DROP TABLE employee;

• This command will delete both the metadata and the data from the Hive warehouse.

Hive External Tables #

An External Table allows you to create a table over existing data without Hive taking control of the data. With external tables, Hive only manages the metadata, leaving the actual data under your control. Dropping the table does not delete the data, only the metadata.

Key Characteristics of External Tables: #

• Data Storage: Data resides outside the Hive warehouse directory, at a location explicitly specified when creating the table.
• Data Deletion: Dropping an external table only deletes the metadata; the data remains intact in its original location.
• Use Case: External tables are ideal when you want Hive to reference data stored elsewhere (e.g., logs, data lakes) but not manage the data itself.

Example of Creating an External Table: #

CREATE EXTERNAL TABLE employee_external (
    emp_id INT,
    name STRING,
    department STRING,
    salary FLOAT
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
LOCATION '/user/data/employee_data/';

• In this example, the data is stored in /user/data/employee_data/ in HDFS or another storage system, but Hive will only manage the metadata.

Loading Data into the External Table: #

Since external tables reference existing data, there is no need to load data explicitly. You just need to ensure the data is present at the specified location.

Querying Data from the External Table: #

SELECT * FROM employee_external;

Dropping an External Table: #

DROP TABLE employee_external;

• This command will remove only the metadata for the external table, leaving the data intact at /user/data/employee_data/.

Comparison: Internal vs External Tables #

Feature	Internal Table	External Table
Data Storage	Stored in Hive’s warehouse directory	Stored outside Hive’s warehouse, specified by the user
Data Management	Hive manages the data	The user manages the data
Data Deletion	Data is deleted when the table is dropped	Data remains even after the table is dropped
Use Cases	When Hive should control the data lifecycle	When data is shared across multiple systems or applications
Default Table Type	Default if no EXTERNAL keyword is specified	Must be specified using EXTERNAL keyword

Code Walkthrough #

1. Create and Query Internal Table:

-- Create an internal table
CREATE TABLE sales_data (
    order_id INT,
    customer_id INT,
    amount FLOAT
);

-- Insert some sample data
INSERT INTO TABLE sales_data VALUES
    (1, 101, 250.75),
    (2, 102, 345.60),
    (3, 103, 123.45);

-- Query the internal table
SELECT * FROM sales_data;

• The table and data are stored within Hive’s warehouse directory (usually /user/hive/warehouse/sales_data/).

2. Create and Query External Table:

-- Create an external table with data in a specific location
CREATE EXTERNAL TABLE sales_data_external (
    order_id INT,
    customer_id INT,
    amount FLOAT
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
LOCATION '/user/data/sales_data_external/';

-- Query the external table
SELECT * FROM sales_data_external;

• The data is located at /user/data/sales_data_external/, and Hive only manages the metadata.

When to Use Internal vs External Tables #

• Internal Tables: Use when Hive should manage the full lifecycle of the data, especially when the data is only relevant to Hive.
• External Tables: Use when data is shared across multiple systems or needs to persist even if Hive tables are dropped (e.g., log files, HDFS-stored data, etc.).

Practical Considerations #

• Performance: The performance of queries on internal and external tables is largely similar, as long as the data is correctly partitioned and optimized. However, the choice between internal and external is more about control over the data lifecycle rather than performance.
• Backup and Recovery: For internal tables, the data and schema are backed up together since they are both managed by Hive. For external tables, only the metadata is part of Hive’s backup, while the data itself needs to be managed independently.

Best Practices #

1. Use External Tables for Large Datasets: If the data is too large and you don’t want Hive to manage it, external tables provide flexibility.
2. Avoid Unintentional Data Deletion: If you want the data to persist even after the table is dropped, always use external tables.
3. Partitioning for Better Performance: Whether internal or external, use partitioning to improve query performance, especially with large datasets.

Conclusion #

Understanding the differences between internal and external tables is crucial for data management in Hive. Internal tables offer simplicity and control, but external tables provide flexibility when working with external data sources or when multiple applications share the same dataset. The decision between the two should be guided by your specific use case and data management strategy.