Databricks Data Engineer Professional Question 115
Select 3You are working on a Spark job that processes large amounts of data stored in a distributed file system. Upon running the job, you notice significant performance degradation. After investigating, you find that the data is stored in hundreds of tiny files, and the dataset is also over-partitioned. Which of the following statements explain why these issues are causing performance problems in Spark?
- A
Each tiny file generates a separate task in Spark, leading to excessive task scheduling overhead.
- B
Over-partitioning results in insufficient data per partition, causing underutilization of cluster resources.
- C
Tiny files and over-partitioning increase the memory usage of executors, which can lead to frequent garbage collection.
- D
Tiny files reduce the number of available shuffle partitions, lowering parallelism and slowing down execution.
- E
Reading tiny files causes high I/O overhead, as Spark needs to open and read metadata for each file.
Show answer and explanation
Correct answers: A, B, E
Explanation
Tiny files and over-partitioning are common issues that can severely impact Spark's performance. Tiny files lead to excessive task scheduling overhead and high I/O overhead due to frequent file system operations. Over-partitioning results in inefficient resource utilization, as the partitions may not contain enough data to fully utilize the cluster's processing power. By reducing the number of tiny files (e.g., through file compaction) and optimizing partitioning, you can significantly improve query performance.
- A. Correct.
Correct: Each tiny file generates a separate task in Spark, and the overhead of scheduling a large number of tasks can significantly degrade performance.
- B. Correct.
Correct: Over-partitioning results in partitions with very small amounts of data, which leads to inefficient utilization of resources, such as CPU and memory.
- C. Incorrect.
Incorrect: Tiny files and over-partitioning do not directly result in increased memory usage or frequent garbage collection. These issues are more related to executor memory management.
- D. Incorrect.
Incorrect: Tiny files do not reduce the number of shuffle partitions. Parallelism is determined by the number of partitions, not by the file size itself.
- E. Correct.
Correct: Reading tiny files causes Spark to perform many file system operations, such as opening, closing, and reading metadata, which leads to high I/O overhead and slower query execution.