Managed vs external table in Spark

What Are Managed Tables and External Tables in Spark?

Managed Tables

• Definition: In a managed table, Spark manages both the metadata and the data itself. When you create a managed table, Spark automatically handles the location where the data is stored.

• Data Management: If you drop a managed table, Spark deletes both the metadata and the actual data from storage.

External Tables

• Definition: In an external table, Spark manages only the metadata, while the data location is controlled and maintained by the user. Spark only reads the data from the specified path.

• Data Management: Dropping an external table removes only the metadata; the actual data remains intact.

Example

-- Managed Table
CREATE TABLE managed_table (
    id INT,
    name STRING
) 
USING PARQUET;

-- Insert data into the managed table
INSERT INTO managed_table VALUES (1, 'Alice'), (2, 'Bob');

-- External Table
CREATE TABLE external_table (
    id INT,
    name STRING
)
USING PARQUET
LOCATION '/path/to/external/data';

-- Insert data into the external table
INSERT INTO external_table VALUES (3, 'Charlie'), (4, 'Daisy');

Layman Explanation Along with Technical One

Layman Explanation

• Managed Table: Think of it like renting a fully-furnished apartment where the landlord takes care of everything, including cleaning up after you move out.
• External Table: It’s like owning your own furniture and moving it into an apartment. If you leave, you take your furniture with you.

Technical Explanation

• A managed table stores its data in the Spark warehouse directory, and Spark controls all operations, including cleanup.
• An external table points to data stored outside the warehouse (e.g., on HDFS, S3, or local storage). Spark tracks only the metadata.

Advantages and Disadvantages of Each

Managed Tables

Advantages:

• Simplicity: Data and metadata management is handled by Spark.

• Integration: Ideal for workflows where Spark manages the entire data lifecycle.

Disadvantages:

• Less Control: Spark deletes the data when the table is dropped.

• Portability: Moving data to other systems is more complicated.

External Tables

Advantages:

• Data Preservation: Dropping the table doesn’t delete the data.

• Flexibility: The same data can be used by multiple systems or applications.

• Location Control: Users can store data in specific storage systems or paths.

Disadvantages:

• Manual Management: Users are responsible for ensuring the integrity and availability of the data.

• Complexity: Requires careful setup of storage paths and permissions.

When to Use and When to Avoid Managed and External Tables

Managed Tables

Use When:

• The data lifecycle is entirely managed by Spark.

• You want simplicity in data and metadata management.

• The data is temporary or not shared across multiple systems.

Avoid When:

• You need control over data location.

• The data is shared between multiple applications or platforms.

• You want to ensure the data persists even if the table is dropped.

External Tables

Use When:

• Data resides in a shared storage system like HDFS, S3, or a cloud data lake.

• Multiple applications or platforms need to access the same data.

• You want control over the storage location and format.

Avoid When:

• You want Spark to handle all data management tasks.

• Data integrity and access policies are complex to maintain manually.

Key Takeaways

1. Lifecycle Management:

• Managed tables: Spark controls the entire lifecycle (metadata and data).

• External tables: Spark manages metadata; the user manages data.

  2. Data Deletion:

• Managed tables: Data is deleted when the table is dropped.

• External tables: Data persists even if the table is dropped.

  3. Flexibility:

• Managed tables: Simplified data management for Spark-only workflows.

• External tables: Enhanced flexibility for shared or multi-application environments.

Examples of Real-World Use Cases

Managed Tables

1. Temporary Analytics Pipelines:

   • An organization needs to run periodic analyses on data and doesn’t need the data after the process is completed.

      CREATE TABLE temp_analysis USING PARQUET AS
      SELECT * FROM transactions WHERE amount > 1000;
     

2. Prototyping:

   • During data pipeline development, managed tables simplify testing.

External Tables

1. Shared Data Lake:
   • Data stored on S3 is shared between Spark, Hive, and other analytics tools. An external table ensures Spark does not delete the data accidentally.

      CREATE TABLE shared_data USING PARQUET LOCATION 's3://my-data-lake/sales/';
     

2. Compliance Requirements:
   • In industries like healthcare or finance, raw data must remain intact even if the metadata is removed.
3. Integration with Other Tools:
   • A BI tool like Tableau uses the same data stored in a cloud storage location.