Databricks Data Engineer Professional Question 45
Select 2You are working on a batch processing pipeline in Databricks to process large volumes of data stored in a Delta Lake table. The processing job is taking longer than expected, and you want to optimize the performance of the pipeline. Which of the following actions will help improve the performance of the batch processing job?
- A
Repartition the data to increase the number of partitions before writing.
- B
Use the OPTIMIZE command on the Delta Lake table to compact small files into larger ones.
- C
Enable Auto Optimize on the Delta Lake table to automatically optimize file sizes during write operations.
- D
Increase the number of executor cores in the cluster configuration.
- E
Use Z-ordering on commonly filtered columns in the Delta Lake table.
Show answer and explanation
Correct answers: B, E
Explanation
To optimize the performance of a batch processing job in Databricks, focusing on the layout and structure of the data is critical. The OPTIMIZE command reduces the number of small files, lowering the metadata and I/O overhead. Additionally, Z-ordering further improves performance by clustering data relevant to common filters, reducing the data read during processing. Together, these techniques significantly enhance performance for batch processing jobs.
- A. Incorrect.
Repartitioning the data to increase partitions can sometimes help with parallelism, but it may also lead to overhead if the partitions are not balanced or the data is overly fragmented. This is not guaranteed to improve batch processing performance in this scenario.
- B. Correct.
The OPTIMIZE command compacts small files into larger ones, reducing the overhead caused by reading multiple small files. This directly improves the performance of batch processing jobs.
- C. Incorrect.
Enabling Auto Optimize helps during write operations but does not retroactively optimize already existing small files. This option alone will not address the current performance issue for batch processing.
- D. Incorrect.
Increasing the number of executor cores can improve cluster performance in some cases, but it does not address the underlying issue of small files or query optimization in this scenario.
- E. Correct.
Z-ordering improves query performance by co-locating related data based on the commonly filtered columns. This reduces the amount of data read during filtering, which optimizes batch processing jobs.