Crate arrow

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

Please see the arrow crates.io page for feature flags and tips to improve performance.

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 different concrete types. For example, since the following function is generic over all primitively typed arrays, when invoked the Rust compiler will generate specialized implementations with optimized code for each concrete type.

fn sum<T: ArrowPrimitiveType>(array: &PrimitiveArray<T>) -> T::Native
where
    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);

And the following uses ArrayAccessor to implement a generic function over all arrays with comparable values.

fn min<T: ArrayAccessor>(array: T) -> Option<T::Item>
where
    T::Item: Ord
{
    ArrayIter::new(array).filter_map(|v| v).min()
}

assert_eq!(min(&Int32Array::from(vec![4, 2, 1, 6])), Some(1));
assert_eq!(min(&StringArray::from(vec!["b", "a", "c"])), Some("a"));

For more examples, and details consult the arrow_array docs.

Type Erasure / Trait Objects

It is common to write code that handles any type of array, without necessarily knowing its concrete type. This is done using the Array trait and using DataType to determine the appropriate downcast_ref.

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

fn impl_dyn(array: &dyn Array) {
    match array.data_type() {
        // downcast `dyn Array` to concrete `StringArray`
        DataType::Utf8 => impl_string(array.as_any().downcast_ref().unwrap()),
        // downcast `dyn Array` to concrete `Float32Array`
        DataType::Float32 => impl_f32(array.as_any().downcast_ref().unwrap()),
        _ => unimplemented!()
    }
}

You can use the AsArray extension trait to facilitate downcasting:

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_string()),
        DataType::Float32 => impl_f32(array.as_primitive()),
        _ => 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>
where
    T: ArrowPrimitiveType,
    T::Native: FromStr,
    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
where
    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>
where
    I: IntoIterator<Item=&'a str>,
{
    let array = StringArray::from_iter(iter.into_iter().map(Some));
    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:

• All mathematical binary operators, such as sub
• All boolean binary operators such as equality
• cast
• filter
• take
• sort
• some string operators such as substring and length

let array = Int32Array::from_iter(0..100);
// Create a 32-bit integer scalar (single) value:
let scalar = Int32Array::new_scalar(60);
// find all rows in the array that are greater than 60
let predicate = gt(&array, &scalar).unwrap();
// copy all matching rows into a new array
let filtered = filter(&array, &predicate).unwrap();

let expected = Int32Array::from_iter(61..100);
assert_eq!(&expected, filtered.as_primitive::<Int32Type>());

As well as some horizontal operations, such as:

• min and max
• sum

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();

IO

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

• JSON: Reader and Writer
• CSV: Reader and Writer
• IPC: Reader and Writer

Parquet is published as a separate crate

Serde Compatibility

arrow_json::reader::Decoder provides a mechanism to convert arbitrary, serde-compatible structures into RecordBatch.

Whilst likely less performant than implementing a custom builder, as described in arrow_array::builder, this provides a simple mechanism to get up and running quickly

#[derive(Serialize)]
struct MyStruct {
    int32: i32,
    string: String,
}

let schema = Schema::new(vec![
    Field::new("int32", DataType::Int32, false),
    Field::new("string", DataType::Utf8, false),
]);

let rows = vec![
    MyStruct{ int32: 5, string: "bar".to_string() },
    MyStruct{ int32: 8, string: "foo".to_string() },
];

let mut decoder = ReaderBuilder::new(Arc::new(schema)).build_decoder().unwrap();
decoder.serialize(&rows).unwrap();

let batch = decoder.flush().unwrap().unwrap();

// Expect batch containing two columns
let int32 = batch.column(0).as_primitive::<Int32Type>();
assert_eq!(int32.values(), &[5, 8]);

let string = batch.column(1).as_string::<i32>();
assert_eq!(string.value(0), "bar");
assert_eq!(string.value(1), "foo");

Crate Topology

The arrow project is implemented as multiple sub-crates, which are then re-exported by this top-level crate.

Crate authors can choose to depend on this top-level crate, or just the sub-crates they need.

The current list of sub-crates is:

• arrow-arith - arithmetic kernels
• arrow-array - type-safe arrow array abstractions
• arrow-buffer - buffer abstractions for arrow arrays
• arrow-cast - cast kernels for arrow arrays
• arrow-csv - read/write CSV to arrow format
• arrow-data - the underlying data of arrow arrays
• arrow-ipc - read/write IPC to arrow format
• arrow-json - read/write JSON to arrow format
• arrow-ord - ordering kernels for arrow arrays
• arrow-row - comparable row format
• arrow-schema - the logical types for arrow arrays
• arrow-select - selection kernels for arrow arrays
• arrow-string - string kernels for arrow arrays

Some functionality is also distributed independently of this crate:

• arrow-flight - support for Arrow Flight RPC
• arrow-integration-test - support for Arrow JSON Test Format
• parquet - support for Apache Parquet

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.

Re-exports

• pub use arrow_csv as csv;
• pub use arrow_ipc as ipc;
• pub use arrow_json as json;
• pub use arrow_row as row;

Modules

• alloc
  Defines the low-level Allocation API for shared memory regions
• array
  Statically typed implementations of Arrow Arrays
• buffer
  Types of shared memory region
• compute
  Computation kernels on Arrow Arrays
• datatypes
  Defines the logical data types of Arrow arrays.
• error
  Defines ArrowError for representing failures in various Arrow operations.
• ffi
  Contains declarations to bind to the C Data Interface.
• ffi_stream
  Contains declarations to bind to the C Stream Interface.
• pyarrow
  Pass Arrow objects from and to PyArrow, using Arrow’s C Data Interface and pyo3.
• record_batch
  Contains the RecordBatch type and associated traits
• temporal_conversions
  Conversion methods for dates and times.
• tensor
  Arrow Tensor Type, defined in format/Tensor.fbs.
• util
  Utility functions for working with Arrow data

Macros

• downcast_dictionary_array
  Downcast an Array to a DictionaryArray based on its DataType, accepts a number of subsequent patterns to match the data type
• downcast_primitive_array
  Downcast an Array to a PrimitiveArray based on its DataType accepts a number of subsequent patterns to match the data type

Constants

• ARROW_VERSION
  Arrow crate version