Databricks Data Engineer Professional Question 83
Select 3You are working on a streaming application in Databricks that processes real-time sales data stored in a Kafka stream and performs a join with a static Delta table containing product details. After running the stream for a few hours, you notice that the state size in your structured streaming query is growing rapidly, leading to degraded performance. What steps can you take to optimize the query and manage state size effectively?
- A
Use watermarking on the streaming data to limit how much state is retained.
- B
Enable checkpointing to store state information and reduce memory usage.
- C
Filter unnecessary columns from the static Delta table before performing the join.
- D
Configure the join condition to use equality-based conditions only.
- E
Use Delta Lake's OPTIMIZE command on the static table to improve query performance.
Show answer and explanation
Correct answers: A, C, D
Explanation
Managing state size in stream-static joins is critical for maintaining the performance of structured streaming queries. Using watermarking helps control state retention by limiting how long records are kept in memory. Additionally, filtering out unnecessary columns from the static Delta table reduces memory usage during the join. Ensuring the join condition is equality-based allows Spark to use efficient algorithms for joining, further optimizing performance. While checkpointing and the OPTIMIZE command are useful for other aspects of query performance and reliability, they do not directly address the issue of state size growth in this scenario.
- A. Correct.
Using watermarking on the streaming data allows the system to drop old state information that is no longer needed, helping to limit state size growth.
- B. Incorrect.
Checkpointing is used for storing progress and fault tolerance but does not directly reduce state size in memory.
- C. Correct.
Filtering unnecessary columns from the static Delta table reduces the amount of data loaded into memory during the join, helping to optimize the query.
- D. Correct.
Equality-based join conditions are more efficient and allow Spark to optimize the join operation, which can reduce the state size.
- E. Incorrect.
While the OPTIMIZE command improves query performance by compacting files, it does not directly affect the state size of a stream-static join.