1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164

#![cfg_attr(docsrs, feature(doc_auto_cfg))]
#![cfg_attr(feature = "simd", feature(portable_simd))]
#![allow(ambiguous_glob_reexports)]

#[cfg(feature = "avro")]
pub mod avro;
pub mod cloud;
#[cfg(any(feature = "csv", feature = "json"))]
pub mod csv;
#[cfg(feature = "parquet")]
pub mod export;
#[cfg(any(feature = "ipc", feature = "ipc_streaming"))]
pub mod ipc;
#[cfg(feature = "json")]
pub mod json;
#[cfg(feature = "json")]
pub mod ndjson;
#[cfg(feature = "cloud")]
pub use cloud::glob as async_glob;

pub mod mmap;
mod options;
#[cfg(feature = "parquet")]
pub mod parquet;
pub mod predicates;
pub mod prelude;
#[cfg(all(test, feature = "csv"))]
mod tests;
pub mod utils;
use once_cell::sync::Lazy;
use regex::Regex;

#[cfg(feature = "partition")]
pub mod partition;
#[cfg(feature = "async")]
pub mod pl_async;

use std::io::{Read, Write};
use std::path::{Path, PathBuf};

#[allow(unused)] // remove when updating to rust nightly >= 1.61
use arrow::array::new_empty_array;
pub use options::*;
use polars_core::frame::ArrowChunk;
use polars_core::prelude::*;

#[cfg(any(feature = "ipc", feature = "avro", feature = "ipc_streaming",))]
use crate::predicates::PhysicalIoExpr;

pub trait SerReader<R>
where
    R: Read,
{
    /// Create a new instance of the `[SerReader]`
    fn new(reader: R) -> Self;

    /// Make sure that all columns are contiguous in memory by
    /// aggregating the chunks into a single array.
    #[must_use]
    fn set_rechunk(self, _rechunk: bool) -> Self
    where
        Self: Sized,
    {
        self
    }

    /// Take the SerReader and return a parsed DataFrame.
    fn finish(self) -> PolarsResult<DataFrame>;
}

pub trait SerWriter<W>
where
    W: Write,
{
    fn new(writer: W) -> Self
    where
        Self: Sized;
    fn finish(&mut self, df: &mut DataFrame) -> PolarsResult<()>;
}

pub trait WriterFactory {
    fn create_writer<W: Write + 'static>(&self, writer: W) -> Box<dyn SerWriter<W>>;
    fn extension(&self) -> PathBuf;
}

pub trait ArrowReader {
    fn next_record_batch(&mut self) -> PolarsResult<Option<ArrowChunk>>;
}

#[cfg(any(feature = "ipc", feature = "avro", feature = "ipc_streaming",))]
pub(crate) fn finish_reader<R: ArrowReader>(
    mut reader: R,
    rechunk: bool,
    n_rows: Option<usize>,
    predicate: Option<Arc<dyn PhysicalIoExpr>>,
    arrow_schema: &ArrowSchema,
    row_index: Option<RowIndex>,
) -> PolarsResult<DataFrame> {
    use polars_core::utils::accumulate_dataframes_vertical;

    let mut num_rows = 0;
    let mut parsed_dfs = Vec::with_capacity(1024);

    while let Some(batch) = reader.next_record_batch()? {
        let current_num_rows = num_rows as IdxSize;
        num_rows += batch.len();
        let mut df = DataFrame::try_from((batch, arrow_schema.fields.as_slice()))?;

        if let Some(rc) = &row_index {
            df.with_row_index_mut(&rc.name, Some(current_num_rows + rc.offset));
        }

        if let Some(predicate) = &predicate {
            let s = predicate.evaluate_io(&df)?;
            let mask = s.bool().expect("filter predicates was not of type boolean");
            df = df.filter(mask)?;
        }

        if let Some(n) = n_rows {
            if num_rows >= n {
                let len = n - parsed_dfs
                    .iter()
                    .map(|df: &DataFrame| df.height())
                    .sum::<usize>();
                if polars_core::config::verbose() {
                    eprintln!("sliced off {} rows of the 'DataFrame'. These lines were read because they were in a single chunk.", df.height().saturating_sub(n))
                }
                parsed_dfs.push(df.slice(0, len));
                break;
            }
        }
        parsed_dfs.push(df);
    }

    let df = {
        if parsed_dfs.is_empty() {
            // Create an empty dataframe with the correct data types
            let empty_cols = arrow_schema
                .fields
                .iter()
                .map(|fld| {
                    Series::try_from((fld.name.as_str(), new_empty_array(fld.data_type.clone())))
                })
                .collect::<PolarsResult<_>>()?;
            DataFrame::new(empty_cols)?
        } else {
            // If there are any rows, accumulate them into a df
            accumulate_dataframes_vertical(parsed_dfs)?
        }
    };

    Ok(if rechunk { df.agg_chunks() } else { df })
}

static CLOUD_URL: Lazy<Regex> =
    Lazy::new(|| Regex::new(r"^(s3a?|gs|gcs|file|abfss?|azure|az|adl|https?)://").unwrap());

/// Check if the path is a cloud url.
pub fn is_cloud_url<P: AsRef<Path>>(p: P) -> bool {
    match p.as_ref().as_os_str().to_str() {
        Some(s) => CLOUD_URL.is_match(s),
        _ => false,
    }
}