Struct re_sdk::external::arrow::array::RecordBatch

pub struct RecordBatch {
    schema: Arc<Schema>,
    columns: Vec<Arc<dyn Array>>,
    row_count: usize,
}

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

A RecordBatch is a two-dimensional dataset of a number of contiguous arrays, each the same length. A record batch has a schema which must match its arrays’ datatypes.

Record batches are a convenient unit of work for various serialization and computation functions, possibly incremental.

Use the record_batch! macro to create a RecordBatch from literal slice of values, useful for rapid prototyping and testing.

Example:

use arrow_array::record_batch;
let batch = record_batch!(
    ("a", Int32, [1, 2, 3]),
    ("b", Float64, [Some(4.0), None, Some(5.0)]),
    ("c", Utf8, ["alpha", "beta", "gamma"])
);

Fields

schema: Arc<Schema>

columns: Vec<Arc<dyn Array>>

row_count: usize

Implementations

impl RecordBatch

pub fn try_new( schema: Arc<Schema>, columns: Vec<Arc<dyn Array>>, ) -> Result<RecordBatch, ArrowError>

Creates a RecordBatch from a schema and columns.

Expects the following:

• the vec of columns to not be empty
• the schema and column data types to have equal lengths and match
• each array in columns to have the same length

If the conditions are not met, an error is returned.

Example

let id_array = Int32Array::from(vec![1, 2, 3, 4, 5]);
let schema = Schema::new(vec![
    Field::new("id", DataType::Int32, false)
]);

let batch = RecordBatch::try_new(
    Arc::new(schema),
    vec![Arc::new(id_array)]
).unwrap();

pub fn try_new_with_options( schema: Arc<Schema>, columns: Vec<Arc<dyn Array>>, options: &RecordBatchOptions, ) -> Result<RecordBatch, ArrowError>

Creates a RecordBatch from a schema and columns, with additional options, such as whether to strictly validate field names.

See RecordBatch::try_new for the expected conditions.

pub fn new_empty(schema: Arc<Schema>) -> RecordBatch

Creates a new empty RecordBatch.

pub fn with_schema(self, schema: Arc<Schema>) -> Result<RecordBatch, ArrowError>

Override the schema of this RecordBatch

Returns an error if schema is not a superset of the current schema as determined by Schema::contains

pub fn schema(&self) -> Arc<Schema>

Returns the Schema of the record batch.

pub fn schema_ref(&self) -> &Arc<Schema>

Returns a reference to the Schema of the record batch.

pub fn project(&self, indices: &[usize]) -> Result<RecordBatch, ArrowError>

Projects the schema onto the specified columns

pub fn num_columns(&self) -> usize

Returns the number of columns in the record batch.

Example

let id_array = Int32Array::from(vec![1, 2, 3, 4, 5]);
let schema = Schema::new(vec![
    Field::new("id", DataType::Int32, false)
]);

let batch = RecordBatch::try_new(Arc::new(schema), vec![Arc::new(id_array)]).unwrap();

assert_eq!(batch.num_columns(), 1);

pub fn num_rows(&self) -> usize

Returns the number of rows in each column.

Example

let id_array = Int32Array::from(vec![1, 2, 3, 4, 5]);
let schema = Schema::new(vec![
    Field::new("id", DataType::Int32, false)
]);

let batch = RecordBatch::try_new(Arc::new(schema), vec![Arc::new(id_array)]).unwrap();

assert_eq!(batch.num_rows(), 5);

pub fn column(&self, index: usize) -> &Arc<dyn Array>

Get a reference to a column’s array by index.

Panics

Panics if index is outside of 0..num_columns.

pub fn column_by_name(&self, name: &str) -> Option<&Arc<dyn Array>>

Get a reference to a column’s array by name.

pub fn columns(&self) -> &[Arc<dyn Array>]

Get a reference to all columns in the record batch.

pub fn remove_column(&mut self, index: usize) -> Arc<dyn Array>

Remove column by index and return it.

Return the ArrayRef if the column is removed.

Panics

Panics if `index`` out of bounds.

Example

use std::sync::Arc;
use arrow_array::{BooleanArray, Int32Array, RecordBatch};
use arrow_schema::{DataType, Field, Schema};
let id_array = Int32Array::from(vec![1, 2, 3, 4, 5]);
let bool_array = BooleanArray::from(vec![true, false, false, true, true]);
let schema = Schema::new(vec![
    Field::new("id", DataType::Int32, false),
    Field::new("bool", DataType::Boolean, false),
]);

let mut batch = RecordBatch::try_new(Arc::new(schema), vec![Arc::new(id_array), Arc::new(bool_array)]).unwrap();

let removed_column = batch.remove_column(0);
assert_eq!(removed_column.as_any().downcast_ref::<Int32Array>().unwrap(), &Int32Array::from(vec![1, 2, 3, 4, 5]));
assert_eq!(batch.num_columns(), 1);

pub fn slice(&self, offset: usize, length: usize) -> RecordBatch

Return a new RecordBatch where each column is sliced according to offset and length

Panics

Panics if offset with length is greater than column length.

pub fn try_from_iter<I, F>(value: I) -> Result<RecordBatch, ArrowError>

where I: IntoIterator<Item = (F, Arc<dyn Array>)>, F: AsRef<str>,

Create a RecordBatch from an iterable list of pairs of the form (field_name, array), with the same requirements on fields and arrays as RecordBatch::try_new. This method is often used to create a single RecordBatch from arrays, e.g. for testing.

The resulting schema is marked as nullable for each column if the array for that column is has any nulls. To explicitly specify nullibility, use RecordBatch::try_from_iter_with_nullable

Example:

let a: ArrayRef = Arc::new(Int32Array::from(vec![1, 2]));
let b: ArrayRef = Arc::new(StringArray::from(vec!["a", "b"]));

let record_batch = RecordBatch::try_from_iter(vec![
  ("a", a),
  ("b", b),
]);

Another way to quickly create a RecordBatch is to use the record_batch! macro, which is particularly helpful for rapid prototyping and testing.

Example:

use arrow_array::record_batch;
let batch = record_batch!(
    ("a", Int32, [1, 2, 3]),
    ("b", Float64, [Some(4.0), None, Some(5.0)]),
    ("c", Utf8, ["alpha", "beta", "gamma"])
);

pub fn try_from_iter_with_nullable<I, F>( value: I, ) -> Result<RecordBatch, ArrowError>

where I: IntoIterator<Item = (F, Arc<dyn Array>, bool)>, F: AsRef<str>,

Create a RecordBatch from an iterable list of tuples of the form (field_name, array, nullable), with the same requirements on fields and arrays as RecordBatch::try_new. This method is often used to create a single RecordBatch from arrays, e.g. for testing.

Example:

let a: ArrayRef = Arc::new(Int32Array::from(vec![1, 2]));
let b: ArrayRef = Arc::new(StringArray::from(vec![Some("a"), Some("b")]));

// Note neither `a` nor `b` has any actual nulls, but we mark
// b an nullable
let record_batch = RecordBatch::try_from_iter_with_nullable(vec![
  ("a", a, false),
  ("b", b, true),
]);

pub fn get_array_memory_size(&self) -> usize

Returns the total number of bytes of memory occupied physically by this batch.

Note that this does not always correspond to the exact memory usage of a RecordBatch (might overestimate), since multiple columns can share the same buffers or slices thereof, the memory used by the shared buffers might be counted multiple times.

Trait Implementations

impl AsRef<RecordBatch> for TransportChunk

fn as_ref(&self) -> &RecordBatch

Converts this type into a shared reference of the (usually inferred) input type.

impl Clone for RecordBatch

fn clone(&self) -> RecordBatch

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for RecordBatch

fn fmt(&self, f: &mut Formatter<'_>) -> Result<(), Error>

Formats the value using the given formatter.

impl Encode<DataframePart> for RecordBatch

fn encode(&self) -> Result<DataframePart, CodecError>

impl Encode<RerunChunk> for RecordBatch

fn encode(&self) -> Result<RerunChunk, CodecError>

impl From<&StructArray> for RecordBatch

fn from(struct_array: &StructArray) -> RecordBatch

Converts to this type from the input type.

impl From<RecordBatch> for StructArray

fn from(value: RecordBatch) -> StructArray

Converts to this type from the input type.

impl From<RecordBatch> for TransportChunk

fn from(batch: RecordBatch) -> TransportChunk

Converts to this type from the input type.

impl From<StructArray> for RecordBatch

fn from(value: StructArray) -> RecordBatch

Converts to this type from the input type.

impl From<TransportChunk> for RecordBatch

fn from(chunk: TransportChunk) -> RecordBatch

Converts to this type from the input type.

impl FromPyArrow for RecordBatch

fn from_pyarrow_bound(value: &Bound<'_, PyAny>) -> Result<RecordBatch, PyErr>

Convert a Python object to an arrow-rs type.

impl Index<&str> for RecordBatch

fn index(&self, name: &str) -> &<RecordBatch as Index<&str>>::Output

Get a reference to a column’s array by name.

Panics

Panics if the name is not in the schema.

type Output = Arc<dyn Array>

The returned type after indexing.

impl PartialEq for RecordBatch

fn eq(&self, other: &RecordBatch) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl SizeBytes for RecordBatch

fn heap_size_bytes(&self) -> u64

Returns how many bytes self uses on the heap.

fn total_size_bytes(&self) -> u64

Returns the total size of self in bytes, accounting for both stack and heap space.

fn stack_size_bytes(&self) -> u64

Returns the total size of self on the stack, in bytes.

fn is_pod() -> bool

Is Self just plain old data?

impl ToPyArrow for RecordBatch

fn to_pyarrow(&self, py: Python<'_>) -> Result<Py<PyAny>, PyErr>

Convert the implemented type into a Python object without consuming it.

impl StructuralPartialEq for RecordBatch