Managed vs External Tables

In an interview, questions about managed vs. external tables in PySpark are likely to focus on concepts, practical applications, and potential scenarios where one is preferable over the other. Here are some areas to prepare for:

1. Definition and Differences

• Question: "What is a managed table, and how does it differ from an external table in PySpark?"

• Expected Answer: Explain that managed tables are fully managed by Spark, where both data and metadata are handled by Spark. External tables, however, only have metadata managed by Spark, with the actual data stored at an external location, specified by the LOCATION clause. Highlight that dropping a managed table removes both metadata and data, while dropping an external table only deletes the metadata.

2. Lifecycle and Data Management

• Question: "What happens to the data when a managed table or an external table is dropped?"

• Expected Answer: When a managed table is dropped, Spark deletes both the table metadata and the actual data files from storage. In contrast, when an external table is dropped, only the metadata is deleted, and the underlying data files remain intact.

3. Use Cases and Scenarios

• Question: "In what scenarios would you use a managed table versus an external table?"

• Expected Answer: Managed tables are ideal when Spark should handle the entire lifecycle of data (e.g., temporary data, testing, or if Spark is the primary tool managing that data). External tables are preferable when the data is shared across multiple applications, needs to be retained after the table is dropped, or is stored in external storage systems (e.g., HDFS, S3).

4. Location and Storage Path

• Question: "Where are managed and external tables stored in PySpark?"

• Expected Answer: Managed tables are stored in the warehouse directory set by spark.sql.warehouse.dir, typically under the default /user/hive/warehouse path. For external tables, the data is stored at the location specified in the LOCATION clause of the CREATE TABLE statement, such as a directory in HDFS or an S3 bucket.

5. Performance and Storage Considerations

• Question: "Are there any performance differences between managed and external tables?"

• Expected Answer: Generally, there are no intrinsic performance differences between managed and external tables, as performance depends more on factors like file format, partitioning, and data locality. However, managed tables might have slight operational advantages within Spark-managed environments, where data location optimizations are possible.

6. Example Creation Commands

• Question: "Can you give examples of how to create a managed table and an external table in PySpark?"

• Expected Answer: For a managed table:

    CREATE TABLE managed_table (id INT, name STRING) USING parquet;
  

  For an external table:

    CREATE TABLE external_table (id INT, name STRING) USING parquet LOCATION '/path/to/external/data';
  

7. Compliance and Data Governance

• Question: "How would you approach data governance requirements for managed and external tables?"

• Expected Answer: Managed tables are useful when data is fully controlled by Spark, as access control can be managed directly. External tables, however, require additional data governance since the data may be accessed by other applications and stored externally, so additional security or compliance measures may be needed, like access control on HDFS or S3.

Tips:

• Emphasize data lifecycle management and how it impacts data retention.

• Be ready to explain specific use cases where one type is more advantageous.

• Mention data-sharing requirements if data is used across multiple applications or platforms.

Being able to articulate the practical aspects and trade-offs of managed vs. external tables will show a solid understanding of PySpark’s data management capabilities.