A company stores Apache Parquet-formatted files in Amazon S3. The company uses an AWS Glue Data Catalog to store the table metadata and Amazon Athena to query and analyze the data. The tables have a…

Converting Parquet files (a columnar format) to CSV (a row-oriented format) would likely increase, not reduce, query runtime because columnar formats are generally more efficient for analytical queries due to better compression and projection pushdown capabilities.

Converting Parquet files to Apache ORC format, while also a columnar format, would not address the core performance bottleneck caused by a large number of partitions. Any performance difference between Parquet and ORC would be marginal compared to the impact of optimizing partition discovery.

Partition projection in Amazon Athena significantly reduces query runtime for tables with a very large number of partitions by allowing Athena to infer partition locations and values based on a defined pattern, rather than listing them from the AWS Glue Data Catalog. This eliminates the overhead of retrieving extensive metadata, making partition pruning more efficient and speeding up queries on small subsets of data.

Adding more partitions to a table would exacerbate the problem if the bottleneck is partition discovery from the Glue Data Catalog, as it increases the amount of metadata Athena needs to process. While filtering partitions is good practice, simply adding more is counterproductive to reducing query runtime.