A comparable row-oriented representation of a collection of [Array].

[Row]s are normalized for sorting, and can therefore be very efficiently compared, using memcmp under the hood, or used in non-comparison sorts such as radix sort. This makes the row format ideal for implementing efficient multi-column sorting, grouping, aggregation, windowing and more, as described in more detail in this blog post.

For example, given three input [Array], [RowConverter] creates byte sequences that compare the same as when using lexsort.

   ┌─────┐   ┌─────┐   ┌─────┐
   │     │   │     │   │     │
   ├─────┤ ┌ ┼─────┼ ─ ┼─────┼ ┐              ┏━━━━━━━━━━━━━┓
   │     │   │     │   │     │  ─────────────▶┃             ┃
   ├─────┤ └ ┼─────┼ ─ ┼─────┼ ┘              ┗━━━━━━━━━━━━━┛
   │     │   │     │   │     │
   └─────┘   └─────┘   └─────┘
               ...
   ┌─────┐ ┌ ┬─────┬ ─ ┬─────┬ ┐              ┏━━━━━━━━┓
   │     │   │     │   │     │  ─────────────▶┃        ┃
   └─────┘ └ ┴─────┴ ─ ┴─────┴ ┘              ┗━━━━━━━━┛
    UInt64      Utf8     F64

          Input Arrays                          Row Format
    (Columns)

[Rows] must be generated by the same [RowConverter] for the comparison to be meaningful.

Basic Example

# use std::sync::Arc;
# use arrow_row::{RowConverter, SortField};
# use arrow_array::{ArrayRef, Int32Array, StringArray};
# use arrow_array::cast::{AsArray, as_string_array};
# use arrow_array::types::Int32Type;
# use arrow_schema::DataType;

let a1 = Arc::new(Int32Array::from_iter_values([-1, -1, 0, 3, 3])) as ArrayRef;
let a2 = Arc::new(StringArray::from_iter_values(["a", "b", "c", "d", "d"])) as ArrayRef;
let arrays = vec![a1, a2];

// Convert arrays to rows
let converter = RowConverter::new(vec![
    SortField::new(DataType::Int32),
    SortField::new(DataType::Utf8),
]).unwrap();
let rows = converter.convert_columns(&arrays).unwrap();

// Compare rows
for i in 0..4 {
    assert!(rows.row(i) <= rows.row(i + 1));
}
assert_eq!(rows.row(3), rows.row(4));

// Convert rows back to arrays
let converted = converter.convert_rows(&rows).unwrap();
assert_eq!(arrays, converted);

// Compare rows from different arrays
let a1 = Arc::new(Int32Array::from_iter_values([3, 4])) as ArrayRef;
let a2 = Arc::new(StringArray::from_iter_values(["e", "f"])) as ArrayRef;
let arrays = vec![a1, a2];
let rows2 = converter.convert_columns(&arrays).unwrap();

assert!(rows.row(4) < rows2.row(0));
assert!(rows.row(4) < rows2.row(1));

// Convert selection of rows back to arrays
let selection = [rows.row(0), rows2.row(1), rows.row(2), rows2.row(0)];
let converted = converter.convert_rows(selection).unwrap();
let c1 = converted[0].as_primitive::<Int32Type>();
assert_eq!(c1.values(), &[-1, 4, 0, 3]);

let c2 = converted[1].as_string::<i32>();
let c2_values: Vec<_> = c2.iter().flatten().collect();
assert_eq!(&c2_values, &["a", "f", "c", "e"]);

Lexsort

The row format can also be used to implement a fast multi-column / lexicographic sort

# use arrow_row::{RowConverter, SortField};
# use arrow_array::{ArrayRef, UInt32Array};
fn lexsort_to_indices(arrays: &[ArrayRef]) -> UInt32Array {
    let fields = arrays
        .iter()
        .map(|a| SortField::new(a.data_type().clone()))
        .collect();
    let converter = RowConverter::new(fields).unwrap();
    let rows = converter.convert_columns(arrays).unwrap();
    let mut sort: Vec<_> = rows.iter().enumerate().collect();
    sort.sort_unstable_by(|(_, a), (_, b)| a.cmp(b));
    UInt32Array::from_iter_values(sort.iter().map(|(i, _)| *i as u32))
}