In Apache Spark, both Managed and External tables are used to store the data. However, there are significant differences in how Spark manages the data for these tables:
Managed Tables
- Ownership and Management:
- Spark manages both the metadata and the data for managed tables.
- When you create a managed table, Spark takes care of storing the table data in a default location, typically within a predefined warehouse directory.
- Data Location:
- By default, data for managed tables is stored in the warehouse directory specified by the
spark.sql.warehouse.dirconfiguration.
- By default, data for managed tables is stored in the warehouse directory specified by the
- Lifecycle:
- When a managed table is dropped, both the metadata and the data are deleted by Spark.
CREATE TABLE managed_table (
id INT,
name STRING
)
USING PARQUET;External Tables
- Ownership and Management:
- Spark only manages the metadata for external tables. The data is managed externally, i.e., it resides outside the default Spark warehouse directory.
- Data Location:
- You specify the location of the data when creating an external table. This data can be in HDFS, S3, or any other storage system supported by Spark.
- Lifecycle:
- When an external table is dropped, Spark only deletes the metadata; the actual data remains intact.
CREATE TABLE external_table (
id INT,
name STRING
)
USING PARQUET
LOCATION '/path/to/external/data';| Aspect | Managed Table | External Table |
|---|---|---|
| Data Storage | Data is stored in the Spark warehouse directory. | Data is stored at a user-specified location. |
| Data Management | Spark manages both data and metadata. | Spark manages only the metadata; the user is responsible for managing the data. |
| Data Deletion | Dropping the table deletes both data and metadata. | Dropping the table deletes only the metadata; the data remains. |
| When to Use | Use managed tables when you want Spark to handle the complete lifecycle of the table and data. Suitable for temporary data or data that is fully controlled by Spark. | Use external tables when you have existing data stored in a specific location that you want to query using Spark. Ideal for integrating Spark with external data sources without moving the data. |
Metadata Summary
Metadata is data that describes other data. In databases and data management, metadata includes information about:
- Schema Information:
- Describes tables, columns, data types, constraints, and default values.
- Includes information about views and virtual tables.
- Data Relationships:
- Details how tables are related (e.g., one-to-many, many-to-many).
- Information about indexes used for query performance.
- Permissions and Security:
- Specifies user roles and access permissions.
- Describes security policies, such as encryption and data masking.
- Data Origin and Lineage:
- Provides information about data provenance and transformations.
- Tracks data lineage for auditing and compliance.
- Storage Information:
- For external tables, includes the file locations and storage formats.
- Details about file formats like CSV, Parquet, ORC.
- Statistics and Performance Metrics:
- Includes statistical information about the data, such as row counts and value distributions.
- Provides performance metrics for query optimization.
Importance of Metadata
- Data Management: Organizes and retrieves data efficiently.
- Query Optimization: Helps in creating efficient query execution plans.
- Data Governance: Ensures compliance with regulations and governance policies.
- Data Integration: Facilitates integration across different data sources.