DEA-C01 · Question #252
A data engineer notices slow query performance on a highly partitioned table that is in Amazon Athena. The table contains daily data for the previous 5 years, partitioned by date. The data engineer…
The correct answer is B. Use partition projection in Athena. Configure the table properties by using a date range from 5. Partition projection eliminates the need for frequent metadata lookups or explicit partition registration in the AWS Glue Data Catalog. By defining a date range in your table's properties (for example, projection.enabled=true, projection.order_date.type=DATE…
Question
A data engineer notices slow query performance on a highly partitioned table that is in Amazon Athena. The table contains daily data for the previous 5 years, partitioned by date. The data engineer wants to improve query performance and to automate partition management. Which solution will meet these requirements?
Options
- AUse an AWS Lambda function that runs daily. Configure the function to manually create new
- BUse partition projection in Athena. Configure the table properties by using a date range from 5
- CReduce the number of partitions by changing the partitioning schema from daily to monthly
- DIncrease the processing capacity of Athena queries by allocating more compute resources.
How the community answered
(19 responses)- A11% (2)
- B84% (16)
- C5% (1)
Explanation
Partition projection eliminates the need for frequent metadata lookups or explicit partition registration in the AWS Glue Data Catalog. By defining a date range in your table's properties (for example, projection.enabled=true, projection.order_date.type=DATE, projection.order_date.range=2020/01/01,NOW, and the appropriate format), Athena will calculate partitions on-the-fly. This both speeds up queries (avoiding catalog calls for thousands of partitions) and removes the need for any external partition-management jobs.
Topics
Community Discussion
No community discussion yet for this question.