Databricks Data Engineer Professional Question 120
Select 3A data engineering team is running a Spark job to process a large dataset stored in a distributed file system. The dataset consists of thousands of small files, each less than 10 KB in size. They observe that the job's performance is significantly slower than expected. Which of the following reasons could explain the performance degradation caused by 'small files'?
- A
Small files increase the number of tasks in a Spark job, leading to higher scheduling overhead.
- B
Each small file introduces a new partition, which can result in skewed data distribution across partitions.
- C
Small files reduce the memory utilization of each Spark executor, causing memory underutilization.
- D
The high number of small files increases I/O overhead for the file system and Spark's job setup time.
- E
Small files increase the compression ratio of the data, reducing storage efficiency.
Show answer and explanation
Correct answers: A, B, D
Explanation
Small files negatively impact Spark performance due to increased scheduling overhead, skewed partitioning, and higher I/O costs. Spark is optimized for processing larger files with fewer partitions, which helps reduce task overhead and improves parallelism. Addressing small file issues by compacting them into larger files can significantly improve query performance.
- A. Correct.
Small files increase the number of tasks because Spark creates one task per partition. If there are many small files, the number of tasks grows, leading to increased scheduling overhead and slower execution.
- B. Correct.
Each small file typically maps to a separate partition. This can result in significant partition skew if some partitions have very little data, causing inefficiencies in processing.
- C. Incorrect.
Small files are not directly related to executor memory utilization. Instead, memory utilization issues are more dependent on data shuffling, caching, or computation logic.
- D. Correct.
The high number of small files increases the time required for Spark to read the data and the overall I/O overhead, as Spark has to open and process each file individually, introducing significant delays.
- E. Incorrect.
Small files do not impact the compression ratio of the data. Compression efficiency is generally unrelated to the size of individual files but rather the compression algorithm and the data structure.