Hive Data Storage Model Explained: Differences Between Internal and External Tables

Hello, Hive enthusiasts! In this blog post, I will introduce you to Hive Data Storage

Model – one of the most essential concepts in Apache Hive: the Hive Data Storage Model. Understanding how data is stored in Hive is crucial for managing large datasets efficiently and optimizing query performance. Hive provides two primary table types Internal Tables and External Tables each serving different purposes based on data ownership and management. In this post, I will explain what these tables are, how they differ, and when to use each type. By the end, you will have a clear understanding of how Hive manages data storage and how to make the right choice for your use case. Let’s dive in!

Introduction to Hive Data Storage Model: Internal vs. External Tables

Hive provides a powerful data storage model that helps manage large datasets efficiently, primarily through Internal (Managed) Tables and External Tables. Understanding the difference between these table types is crucial for optimizing data organization, storage, and retrieval in a Hadoop ecosystem. Internal tables are fully managed by Hive, meaning Hive controls the storage location and deletes the data when the table is dropped. In contrast, External tables store data externally, allowing users to retain it even if the table is removed. Choosing the right table type depends on factors like data ownership, storage flexibility, and performance optimization. In this article, we will explore the Hive data storage model in detail and compare Internal vs. External Tables to help you make the right choice for your use case.

What is the Hive Data Storage Model? Internal vs. External Tables Explained

Apache Hive provides a structured way to store and manage large datasets in a distributed environment using HDFS (Hadoop Distributed File System). The way Hive stores data significantly impacts data management, access control, and storage efficiency. Hive supports two primary types of tables:

1. Internal (Managed) Tables
2. External Tables

Each table type has distinct characteristics, advantages, and use cases. Let’s dive deep into both table types, their differences, and how to use them effectively.

Internal (Managed) Tables in Hive

An Internal Table, also known as a Managed Table, is a table where Hive takes full ownership of both the table metadata and data storage. This means:

• Hive decides where the data is stored (by default in /user/hive/warehouse/).
• When the table is dropped, the data is also deleted from HDFS.
• Suitable for temporary or intermediate datasets used only within Hive.

Example 1: Creating an Internal Table

CREATE TABLE employees (
    id INT,
    name STRING,
    department STRING,
    salary FLOAT
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ',';

What happens here?

• This creates a table named employees inside the Hive warehouse directory (/user/hive/warehouse/employees/).
• If we load data into this table, it is stored in Hive’s default directory.

Example 2: Loading Data into an Internal Table

LOAD DATA INPATH '/input/employees.csv' INTO TABLE employees;

Key points:

• This moves the file from /input/employees.csv to /user/hive/warehouse/employees/.
• If the table is dropped, the data is deleted permanently.

Example 3: Dropping an Internal Table

DROP TABLE employees;

Outcome: Both the table definition and data are removed from HDFS.

External Tables in Hive

An External Table is different from an Internal Table because it only manages metadata, while the actual data remains in an external location.

Characteristics of External Tables:

• The table schema is stored in Hive, but data is not stored in the Hive warehouse directory.
• Dropping the table does not delete the data, only the table metadata.
• Useful for sharing datasets across multiple applications.
• Best suited for large, persistent datasets that should not be controlled by Hive.

Example 4: Creating an External Table

CREATE EXTERNAL TABLE employees_external (
    id INT,
    name STRING,
    department STRING,
    salary FLOAT
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
LOCATION '/data/employees/';

What happens here?

• The data remains in /data/employees/, even if the table is dropped.
• Hive only manages metadata, not the actual files.

Example 5: Dropping an External Table

DROP TABLE employees_external;

Outcome: Only the table metadata is deleted, but data remains in /data/employees/.

Key Differences Between Internal and External Tables

Feature	Internal Table	External Table
Data Storage	Stored in Hive’s warehouse (/user/hive/warehouse/)	Stored in an external location specified by LOCATION
Data Management	Hive fully manages both schema and data	Hive manages only metadata, not the actual data
Data Deletion	Data is deleted when the table is dropped	Data remains even if the table is dropped
Use Case	Best for temporary and intermediate datasets	Best for permanent and shared datasets
Performance	Optimized for fast Hive queries	Allows external data access across different applications

When to Use Internal vs. External Tables in Hive

Choosing between Internal (Managed) Tables and External Tables in Hive depends on how you plan to manage, store, and access your data. Each table type serves different use cases and has specific advantages. Below, we will discuss when to use each type in detail.

1. When to Use an Internal Table

Internal Tables are best suited when Hive is the primary system managing the data. Here’s when you should use them:

a. You want Hive to fully manage the data (storage, updates, deletion)

• Internal Tables store data inside Hive’s default warehouse directory (/user/hive/warehouse/).
• Hive automatically handles data movement, deletion, and modifications.
• Data is removed when the table is dropped, making it ideal for temporary storage.

Example Use Case:

Imagine you are processing log files daily and need to perform multiple transformations before storing them permanently. You can use an Internal Table to store intermediate results, knowing that once your processing is done, Hive will clean up the data when the table is dropped.

2. Data is temporary and used only within Hive

• If the dataset is needed only within Hive for processing and analysis, an Internal Table is the best choice.
• You don’t need to worry about external applications accessing the data.
• Data is automatically cleaned up, reducing storage costs and clutter in HDFS.

Example Use Case:

A data analyst needs to create a temporary summary table of sales transactions for a weekly report. An Internal Table is perfect because once the report is generated, there is no need to retain the data.

CREATE TABLE temp_sales_summary AS
SELECT region, SUM(revenue) AS total_revenue
FROM sales_data
GROUP BY region;

This table will store data temporarily, and when dropped, Hive will remove both metadata and data.

3. You don’t need to access the data outside of Hive

• Internal Tables store data in Hive’s warehouse, making them unsuitable for sharing with external tools.
• If the data is not required by other applications like Spark, Pig, or custom scripts, an Internal Table is a good option.

Example Use Case:

A data science team is working on predictive analytics using Hive queries. Since their entire workflow is inside Hive, storing their training data as an Internal Table simplifies data management.

2. When to Use an External Table

External Tables are best suited for scenarios where Hive is not the only system accessing or managing the data. Here’s when you should use them:

1. You have pre-existing data in HDFS, S3, or other storage systems

• If your data already exists in HDFS, Amazon S3, Azure Blob Storage, or any external storage system, an External Table lets you use Hive to query it without moving it.
• This avoids data duplication, saving storage and improving efficiency.
• Hive only manages the metadata, while the actual data stays in its original location.

Example Use Case:

You receive daily sensor data logs from IoT devices stored in an S3 bucket. Instead of moving this data to Hive’s warehouse, you create an External Table pointing to the S3 location.

CREATE EXTERNAL TABLE sensor_data (
    device_id STRING,
    temperature FLOAT,
    humidity FLOAT
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
LOCATION 's3://iot-data/sensor-logs/';

Now, Hive can query the data, but the actual files remain in S3, making it accessible for other applications like AWS Athena or Spark.

2. Data is shared between multiple applications (e.g., Spark, Pig, Hadoop).

• If the same dataset needs to be accessed by multiple systems, an External Table allows seamless integration.
• External Tables prevent lock-in, meaning your data can be used by Hive, Spark, Pig, or any Hadoop-based tool without worrying about it being deleted.

Example Use Case:

A large e-commerce company stores transaction data in HDFS. While Hive is used for reporting, Spark is used for machine learning, and Pig for ETL. Creating an External Table ensures all tools can access the same data.

CREATE EXTERNAL TABLE transactions (
    order_id STRING,
    customer_id STRING,
    amount FLOAT
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
LOCATION '/data/ecommerce/transactions/';

Now, Hive, Spark, and Pig can all access the same transaction data without duplicating it.

3. You want to retain data even after dropping the Hive table

• If you drop an External Table, only the metadata is deleted, but the data remains in its original location.
• This is useful for datasets that should not be lost even if the Hive table is accidentally removed.

Example Use Case:

A company maintains a historical archive of customer support chats in HDFS. Even if the Hive table is dropped, the raw data should be preserved.

CREATE EXTERNAL TABLE chat_logs (
    chat_id STRING,
    customer STRING,
    support_agent STRING,
    message STRING,
    timestamp TIMESTAMP
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\t'
LOCATION '/archives/chat_data/';

Even if someone runs:

DROP TABLE chat_logs;

• The data in /archives/chat_data/ remains intact.
• Only Hive’s metadata is deleted.

Understanding when to use Internal vs. External Tables helps you make the right decision based on data lifecycle, storage requirements, and application needs.

• Use Internal Tables when Hive fully controls the data, and you don’t need to share it with other systems.
• Use External Tables when the data is shared across multiple applications, stored outside Hive’s warehouse, or needs to persist even if the table is dropped.

Converting an Internal Table to an External Table

If you need to change an Internal Table to an External Table, follow these steps:

Step 1: Check the Table Type

DESCRIBE FORMATTED employees;

Look for Table Type: MANAGED_TABLE.

Step 2: Convert to External Table

ALTER TABLE employees SET TBLPROPERTIES ('EXTERNAL'='TRUE');

Now, the table is an External Table, and data won’t be deleted if you drop it.

Best Practices for Hive Data Storage:

• For large datasets: Use External Tables to avoid unnecessary data duplication.
• For staging or temporary tables: Use Internal Tables since data management is easier.
• For shared data: Use External Tables to allow multiple users or systems to access the same data.
• For long-term storage: Keep data in Parquet or ORC format for better compression and query performance.

Conclusion:

The Hive Data Storage Model plays a vital role in managing big data efficiently. Internal tables are useful when Hive manages the data entirely, making it easier for quick operations. On the other hand, External tables provide more flexibility for accessing data across different applications and retaining data even after the table is dropped. Understanding these differences allows you to optimize storage, improve performance, and ensure data consistency.