Readparquet

Readparquet refers to the function or process used to load data stored in the Parquet columnar file format into an in-memory data structure such as a dataframe. Parquet is an open, column-oriented storage format designed for efficient analytics, offering strong compression, encoding, and support for nested data types. readparquet implementations aim to preserve data types and metadata while enabling fast access to the desired columns.

Support for readparquet exists across multiple data processing ecosystems. In Python, libraries such as pandas provide read_parquet, often relying on backends like Apache Arrow (pyarrow) or fastparquet. Apache Arrow also exposes lower-level reading interfaces. In Apache Spark, data can be read from Parquet with spark.read.parquet. In R, the arrow package offers read_parquet to import Parquet files. Across these tools, the function typically accepts a path or directory and can handle datasets composed of multiple Parquet files.

Common options include selecting a subset of columns and, in some implementations, applying filters or predicates. The exact syntax and available options vary by library, and features such as predicate pushdown or complex nested type handling depend on the backend in use.

Performance considerations center on reading only the needed columns (column pruning) and leveraging the Parquet layout to minimize I/O. The file's columnar design allows efficient scans for analytics workloads, while large or deeply nested schemas may influence memory usage and processing time. Compatibility and version support can affect schema inference and fidelity when round-tripping data between systems.