Crate arrow

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A complete, safe, native Rust implementation of Apache Arrow, a cross-language development platform for in-memory data.

Columnar Format

The array module provides statically typed implementations of all the array types as defined by the Arrow Columnar Format.

For example, an Int32Array represents a nullable array of i32

let array = Int32Array::from(vec![Some(1), None, Some(3)]);
assert_eq!(array.len(), 3);
assert_eq!(array.value(0), 1);
assert_eq!(array.is_null(1), true);

let collected: Vec<_> = array.iter().collect();
assert_eq!(collected, vec![Some(1), None, Some(3)]);
assert_eq!(array.values(), [1, 0, 3])

It is also possible to write generic code. For example, the following is generic over all primitively typed arrays:

fn sum<T: ArrowPrimitiveType>(array: &PrimitiveArray<T>) -> T::Native
    T: ArrowPrimitiveType,
    T::Native: Sum
    array.iter().map(|v| v.unwrap_or_default()).sum()

assert_eq!(sum(&Float32Array::from(vec![1.1, 2.9, 3.])), 7.);
assert_eq!(sum(&TimestampNanosecondArray::from(vec![1, 2, 3])), 6);

For more examples, consult the array docs.

Type Erasure / Trait Objects

It is often the case that code wishes to handle any type of array, without necessarily knowing its concrete type. This use-case is catered for by a combination of Array and DataType, with the former providing a type-erased container for the array, and the latter identifying the concrete type of array.

fn impl_string(array: &StringArray) {}
fn impl_f32(array: &Float32Array) {}

fn impl_dyn(array: &dyn Array) {
    match array.data_type() {
        DataType::Utf8 => impl_string(array.as_any().downcast_ref().unwrap()),
        DataType::Float32 => impl_f32(array.as_any().downcast_ref().unwrap()),
        _ => unimplemented!()

It is also common to want to write a function that returns one of a number of possible array implementations. ArrayRef is a type-alias for Arc<dyn Array> which is frequently used for this purpose

fn parse_to_primitive<'a, T, I>(iter: I) -> PrimitiveArray<T>
    T: ArrowPrimitiveType,
    I: IntoIterator<Item=&'a str>,
    PrimitiveArray::from_iter(iter.into_iter().map(|val| T::Native::from_str(val).ok()))

fn parse_strings<'a, I>(iter: I, to_data_type: DataType) -> ArrayRef
    I: IntoIterator<Item=&'a str>,
   match to_data_type {
       DataType::Int32 => Arc::new(parse_to_primitive::<Int32Type, _>(iter)) as _,
       DataType::UInt32 => Arc::new(parse_to_primitive::<UInt32Type, _>(iter)) as _,
       _ => unimplemented!()

let array = parse_strings(["1", "2", "3"], DataType::Int32);
let integers = array.as_any().downcast_ref::<Int32Array>().unwrap();
assert_eq!(integers.values(), [1, 2, 3])

Compute Kernels

The compute module provides optimised implementations of many common operations, for example the parse_strings operation above could also be implemented as follows:

fn parse_strings<'a, I>(iter: I, to_data_type: &DataType) -> Result<ArrayRef>
    I: IntoIterator<Item=&'a str>,
    let array = Arc::new(StringArray::from_iter(iter.into_iter().map(Some))) as _;
    arrow::compute::cast(&array, to_data_type)

let array = parse_strings(["1", "2", "3"], &DataType::UInt32).unwrap();
let integers = array.as_any().downcast_ref::<UInt32Array>().unwrap();
assert_eq!(integers.values(), [1, 2, 3])

This module also implements many common vertical operations:

As well as some horizontal operations, such as:

Tabular Representation

It is common to want to group one or more columns together into a tabular representation. This is provided by RecordBatch which combines a Schema and a corresponding list of ArrayRef.

let col_1 = Arc::new(Int32Array::from_iter([1, 2, 3])) as _;
let col_2 = Arc::new(Float32Array::from_iter([1., 6.3, 4.])) as _;

let batch = RecordBatch::try_from_iter([("col1", col_1), ("col_2", col_2)]).unwrap();


This crate provides readers and writers for various formats to/from RecordBatch

Parquet is published as a separate crate

Memory and Buffers

Advanced users may wish to interact with the underlying buffers of an Array, for example, for FFI or high-performance conversion from other formats. This interface is provided by ArrayData which stores the Buffer comprising an Array, and can be accessed with Array::data

The APIs for constructing ArrayData come in safe, and unsafe variants, with the former performing extensive, but potentially expensive validation to ensure the buffers are well-formed.

An ArrayRef can be cheaply created from an ArrayData using make_array, or by using the appropriate From conversion on the concrete Array implementation.

Safety and Security

Like many crates, this crate makes use of unsafe where prudent. However, it endeavours to be sound. Specifically, it should not be possible to trigger undefined behaviour using safe APIs.

If you think you have found an instance where this is possible, please file a ticket in our issue tracker and it will be triaged and fixed. For more information on arrow’s use of unsafe, see here.

Higher-level Processing

This crate aims to provide reusable, low-level primitives for operating on columnar data. For more sophisticated query processing workloads, consider checking out DataFusion. This orchestrates the primitives exported by this crate into an embeddable query engine, with SQL and DataFrame frontends, and heavily influences this crate’s roadmap.


Defines memory-related functions, such as allocate/deallocate/reallocate memory regions, cache and allocation alignments.

The central type in Apache Arrow are arrays, which are a known-length sequence of values all having the same type. This module provides concrete implementations of each type, as well as an Array trait that can be used for type-erasure.

Defines Bitmap for tracking validity bitmaps

This module contains two main structs: Buffer and MutableBuffer. A buffer represents a contiguous memory region that can be shared via offsets.

Computation kernels on Arrow Arrays

Transfer data between the Arrow memory format and CSV (comma-separated values).

Defines the logical data types of Arrow arrays.

Defines ArrowError for representing failures in various Arrow operations.

Contains declarations to bind to the C Data Interface.

Contains declarations to bind to the C Stream Interface.

Transfer data between the Arrow memory format and JSON line-delimited records. See the module level documentation for the reader and writer for usage examples.

A two-dimensional batch of column-oriented data with a defined schema.

Conversion methods for dates and times.

Arrow Tensor Type, defined in format/Tensor.fbs.