Question

Why is it technically superior to use a columnar storage format like Parquet instead of a row-based format like CSV for analytical queries involving only a subset of columns?

Accepted Answer

In a row-based format like CSV, data is stored line by line. To access a single column, the system must read every row in the file and parse the entire content into memory, discarding the data from the columns that are not needed. This results in significant input and output overhead because the system processes unnecessary data. Conversely, a columnar format like Parquet stores data column by column. When an analytical query requests only a specific subset of columns, the storage engine reads only those specific data blocks from the disk. This process is called column projection. Because the data for each column is stored contiguously, the system minimizes disk seek operations and reduces the amount of data transferred from storage to memory. Furthermore, columnar formats allow for effective data compression because values within a single column are typically of the same data type and often share similar patterns, which are easier to compress than the mixed data types found in a single row. By reading fewer bytes and utilizing efficient compression, columnar storage significantly reduces the processing time and memory consumption required for analytical queries.

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Why is it technically superior to use a columnar storage format like Parquet instead of a row-based format like CSV for analytical queries involving only a subset of columns?