Databricks Data Engineer Professional Question 117
Select 3A data engineering team is processing daily log files in a Databricks environment using Spark. The logs are generated as thousands of small files, each containing a few kilobytes of data. The team notices significant performance degradation during the execution of their Spark jobs. Which of the following are potential reasons for the performance issues caused by the small files?
- A
Increased job scheduling overhead as Spark needs to manage a large number of tasks for small file processing.
- B
Reduced parallelism due to Spark reading small files sequentially by default.
- C
Higher I/O overhead as Spark has to open and close many small files during execution.
- D
Skewed data distribution across partitions, leading to some partitions being much larger than others.
- E
Increased memory usage on Spark executors due to caching of metadata for a large number of small files.
Show answer and explanation
Correct answers: A, C, E
Explanation
Small files introduce performance issues in Spark jobs due to increased job scheduling overhead, higher I/O overhead, and increased memory usage on executors to manage metadata. These inefficiencies can significantly slow down query execution and resource utilization in a distributed environment.
- A. Correct.
Correct: Managing a large number of small files increases job scheduling overhead as Spark generates a task per file. This can create inefficiencies in task scheduling and resource management.
- B. Incorrect.
Incorrect: Spark does not read small files sequentially by default. Instead, it processes files in parallel based on the available resources, though small files can still create inefficiencies.
- C. Correct.
Correct: Small files result in higher I/O overhead because Spark has to open and close many files, which adds latency during job execution.
- D. Incorrect.
Incorrect: Small files themselves do not inherently cause skewed data distribution. Skew typically arises from uneven key distribution or partitioning logic.
- E. Correct.
Correct: Managing a large number of small files increases the metadata footprint, leading to higher memory usage on Spark executors.