In a columnar database, if a row has 40 columns but a query only needs 4 columns, the database only accesses those 4 columns. It doesn't scan the entire row. This is efficient for extraction, especially in Athena. Can you explain how columnar storage improves query performance?
💡 Model Answer
Columnar databases store data by columns rather than rows. When a query requests only a subset of columns, the engine reads just the relevant column files, skipping the rest. This reduces I/O, improves cache locality, and allows the database to apply compression more effectively because values in a column are often similar.
Athena, built on Presto, reads data from S3 in Parquet or ORC formats, which are columnar. By scanning only the needed columns, Athena can return results faster and at lower cost. Additionally, columnar formats support predicate pushdown, where filters are applied before data is read, further reducing the amount of data processed.
The combination of selective column reads, efficient compression, and vectorized execution leads to significant performance gains for analytical workloads, especially when dealing with large datasets where reading entire rows would be prohibitively expensive.
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