Databricks Data Engineer Professional Question 80
Select 3You are working on a streaming pipeline in Databricks using Structured Streaming. The pipeline processes real-time sales data and performs a stream-static join with a Delta Lake table containing product details. You notice that the state information for the join is growing significantly over time, impacting performance. Which actions can you take to optimize the state management and performance of the stream-static join?
- A
Use Delta Lake's Z-Ordering on the static table to optimize data locality during the join.
- B
Enable watermarking on the stream to discard old state information.
- C
Repartition the streaming DataFrame to match the partitioning of the static Delta table.
- D
Ensure that the join condition uses indexed columns from the static Delta table.
- E
Reduce the batch interval of the streaming query to process data more frequently.
Show answer and explanation
Correct answers: A, C, D
Explanation
Stream-static joins in Structured Streaming require careful tuning to manage state growth and maintain performance. Actions like optimizing the static Delta table with Z-Ordering, ensuring partition alignment, and leveraging indexed columns in join conditions can significantly reduce state size and improve query efficiency. Watermarking and batch interval adjustments, while useful for other purposes, do not directly address state management in stream-static joins.
- A. Correct.
Z-Ordering optimizes the physical layout of the Delta Lake table, improving data locality and reducing the amount of data scanned during the join, which helps state management.
- B. Incorrect.
Watermarking is primarily used to manage late data and does not directly impact the state growth of stream-static joins.
- C. Correct.
Repartitioning the streaming DataFrame to align with the partitioning of the static Delta table can improve query performance by reducing shuffle and state size during the join.
- D. Correct.
Using indexed columns from the static Delta table in the join condition helps Databricks optimize the join by efficiently accessing relevant rows, reducing the state size.
- E. Incorrect.
Reducing the batch interval affects data processing frequency but does not directly optimize the state size or performance of the stream-static join.