Path: blob/main/crates/polars-io/src/parquet/read/mod.rs
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//! Functionality for reading Apache Parquet files.1//!2//! # Examples3//!4//! ```5//! use polars_core::prelude::*;6//! use polars_io::prelude::*;7//! use std::fs::File;8//!9//! fn example() -> PolarsResult<DataFrame> {10//! let r = File::open("example.parquet").unwrap();11//! let reader = ParquetReader::new(r);12//! reader.finish()13//! }14//! ```1516#[cfg(feature = "cloud")]17mod async_impl;18mod mmap;19mod options;20mod read_impl;21mod reader;22mod utils;2324const ROW_COUNT_OVERFLOW_ERR: PolarsError = PolarsError::ComputeError(ErrString::new_static(25"\26Parquet file produces more than pow(2, 32) rows; \27consider compiling with polars-bigidx feature (polars-u64-idx package on python), \28or set 'streaming'",29));3031#[cfg(feature = "cloud")]32pub use async_impl::ParquetObjectStore;33pub use options::{ParallelStrategy, ParquetOptions};34use polars_error::{ErrString, PolarsError};35pub use polars_parquet::arrow::read::infer_schema;36pub use polars_parquet::read::FileMetadata;37pub use read_impl::{create_sorting_map, try_set_sorted_flag};38pub use reader::ParquetReader;39pub use utils::materialize_empty_df;4041pub mod _internal {42pub use super::mmap::to_deserializer;43pub use super::read_impl::{PrefilterMaskSetting, calc_prefilter_cost};44pub use super::utils::ensure_matching_dtypes_if_found;45}464748