Databricks Data Engineer Professional Question 118
Select 3You are tasked with optimizing a Spark job that processes a large dataset stored in a cloud data lake. During the job, you notice significant query performance issues, including prolonged scan times and excessive shuffle operations. Upon investigation, you observe that the dataset consists of thousands of tiny files and the table is over-partitioned. Which of the following strategies can help mitigate these performance issues?
- A
Use file compaction to combine tiny files into larger files.
- B
Repartition the dataset to reduce the number of partitions.
- C
Use a broadcast join to handle the small data files.
- D
Increase the shuffle partitions to better distribute the workload.
- E
Optimize the table using Delta Lake's OPTIMIZE command with ZORDER BY.
Show answer and explanation
Correct answers: A, B, E
Explanation
Tiny files and over-partitioning can cause significant performance issues in Spark due to increased metadata load, inefficient parallelism, and high I/O overhead. File compaction, reducing the number of partitions, and using Delta Lake's OPTIMIZE command are proven strategies to mitigate these issues. These approaches help streamline data processing and improve query performance by reducing file and partition overhead.
- A. Correct.
Compacting tiny files into larger files helps reduce the overhead of opening and scanning numerous small files, which can improve query performance.
- B. Correct.
Reducing the number of partitions can help avoid over-partitioning issues, which otherwise lead to small partitions and inefficient parallelism during query execution.
- C. Incorrect.
Broadcast joins are used to optimize join operations by broadcasting a small dataset to all executors, but they do not directly address the problem of tiny files or over-partitioning.
- D. Incorrect.
Increasing shuffle partitions may amplify the problem of small partitions and increase shuffle overhead, which is counterproductive in this scenario.
- E. Correct.
Delta Lake's OPTIMIZE command with ZORDER BY can compact files and improve locality for query performance, making it an effective solution for managing tiny files and optimizing table performance.