Databricks Data Engineer Professional Question 37
Single answerA data engineering team is designing a Delta table to store clickstream data from a high-traffic website. The table will be queried frequently for reports segmented by region and event_type. The dataset is expected to grow rapidly, and the team wants to ensure optimal query performance while minimizing small file issues. Which partitioning strategy should the team use?
- A
Partition by
regionandevent_type - B
Partition by
event_typeandtimestamp - C
Partition by
timestampandregion - D
Do not partition the table
Show answer and explanation
Correct answer: A
Explanation
Partitioning is a critical strategy to optimize query performance in large datasets. The decision on partitioning columns should be guided by the query patterns and the need to prune data effectively. In this scenario, the primary queries are segmented by region and event_type, making them the optimal partitioning columns. Using timestamp as a partition column would lead to excessive granularity, while not partitioning would result in inefficient queries due to the lack of data pruning.
- A. Correct.
Partitioning by
regionandevent_typealigns with the query patterns, ensuring efficient data pruning during queries. This strategy also avoids overly granular partitions that could lead to small file issues. - B. Incorrect.
Including
timestampin the partitioning strategy might create excessive partition granularity, as timestamps are highly unique, leading to small file issues and poorer query performance. - C. Incorrect.
While partitioning by
timestampandregioncould help with certain time-based queries, it doesn't align well with the primary query patterns of segmenting data byregionandevent_type. Additionally, it risks creating too many partitions if thetimestampgranularity is high. - D. Incorrect.
Not partitioning the table would result in poor query performance for the given use case, as the entire dataset would need to be scanned for queries segmented by
regionandevent_type.