Principle:Huggingface Datasets Parquet Import
| Knowledge Sources | |
|---|---|
| Domains | Data_Engineering, NLP |
| Last Updated | 2026-02-14 18:00 GMT |
Overview
Parquet Import is the principle of loading Apache Parquet columnar files into the HuggingFace Dataset format.
Description
Apache Parquet is a columnar storage format optimized for analytical workloads. The Parquet Import principle covers reading one or more Parquet files, mapping their schema to HuggingFace Features, and producing an Arrow-backed Dataset or IterableDataset. Because Parquet is already columnar and self-describing, the import is highly efficient: the schema is read from the file metadata, column pruning can skip irrelevant data, and row-group-level predicate pushdown is possible. The underlying Parquet builder transparently handles these optimizations.
Usage
Use Parquet Import when your data is already stored in Parquet format, which is common for data exported from Spark, BigQuery, or the Hugging Face Hub. Parquet is the recommended format for large datasets because it provides built-in compression, efficient columnar access, and fast import times.
Theoretical Basis
Parquet stores data in a columnar layout organized into row groups, with each column chunk independently compressed and encoded. Importing Parquet into Arrow is nearly zero-copy because both formats share the same columnar memory model. The import process reads Parquet metadata to determine the schema, maps Parquet logical types to Arrow types, and memory-maps the deserialized column chunks into Arrow record batches. This makes Parquet the fastest format to import into the HuggingFace Dataset ecosystem.