Databricks Data Engineer Professional Question 116
Select 3A data engineering team is running a Spark job on Databricks to process a large dataset stored in a distributed file system. However, the job is experiencing significant performance issues. Upon investigation, it is discovered that the dataset consists of thousands of small files partitioned by a high-cardinality column. Which of the following factors are most likely contributing to the performance degradation?
- A
Increased I/O overhead due to a large number of small files
- B
Efficient parallelism due to small files being distributed across multiple nodes
- C
High task scheduling overhead caused by excessive partitioning
- D
Reduced scan efficiency due to reading many small files instead of fewer large files
- E
Improved query optimization because of smaller data partitions
Show answer and explanation
Correct answers: A, C, D
Explanation
The presence of small files and excessive partitioning in a dataset can severely impact Spark query performance. Spark jobs incur additional I/O overhead and task scheduling latency when handling many small files, as well as reduced scan efficiency due to the need to process individual files. Addressing these issues often involves techniques like file compaction and partition optimization to improve query execution speed and resource utilization.
- A. Correct.
Increased I/O overhead occurs because each small file requires individual metadata operations. This adds latency to the Spark job, making it less efficient.
- B. Incorrect.
This is incorrect because small files do not inherently lead to efficient parallelism. In fact, they can cause underutilization of resources as tasks may be too small to leverage available compute power.
- C. Correct.
Excessive partitioning, especially with high-cardinality columns, leads to a significant increase in task scheduling overhead. Each partition corresponds to a task, and managing thousands of tasks introduces latency.
- D. Correct.
Reading many small files reduces scan efficiency because each file introduces additional read operations and metadata lookups. Consolidating files into fewer, larger files improves scan performance.
- E. Incorrect.
This is incorrect because smaller partitions do not necessarily improve query optimization. Over-partitioning can lead to inefficiencies instead of benefits.