Struct DfRecordBatch
pub struct DfRecordBatch {
schema: Arc<Schema>,
columns: Vec<Arc<dyn Array>>,
row_count: usize,
}
Expand description
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.
Fields§
§schema: Arc<Schema>
§columns: Vec<Arc<dyn Array>>
§row_count: usize
Implementations§
§impl RecordBatch
impl RecordBatch
pub fn try_new(
schema: Arc<Schema>,
columns: Vec<Arc<dyn Array>>,
) -> Result<RecordBatch, ArrowError>
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>
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
pub fn new_empty(schema: Arc<Schema>) -> RecordBatch
Creates a new empty RecordBatch
.
pub fn with_schema(self, schema: Arc<Schema>) -> Result<RecordBatch, ArrowError>
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_ref(&self) -> &Arc<Schema>
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>
pub fn project(&self, indices: &[usize]) -> Result<RecordBatch, ArrowError>
Projects the schema onto the specified columns
pub fn num_columns(&self) -> usize
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
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_by_name(&self, name: &str) -> Option<&Arc<dyn Array>>
pub fn column_by_name(&self, name: &str) -> Option<&Arc<dyn Array>>
Get a reference to a column’s array by name.
pub fn remove_column(&mut self, index: usize) -> Arc<dyn Array>
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
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>
pub fn try_from_iter<I, F>(value: I) -> Result<RecordBatch, ArrowError>
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),
]);
pub fn try_from_iter_with_nullable<I, F>(
value: I,
) -> Result<RecordBatch, ArrowError>
pub fn try_from_iter_with_nullable<I, F>( value: I, ) -> Result<RecordBatch, ArrowError>
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
pub fn get_array_memory_size(&self) -> usize
Returns the total number of bytes of memory occupied physically by this batch.
Trait Implementations§
§impl Clone for RecordBatch
impl Clone for RecordBatch
§fn clone(&self) -> RecordBatch
fn clone(&self) -> RecordBatch
1.0.0 · source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
source
. Read more§impl Debug for RecordBatch
impl Debug for RecordBatch
§impl From<&StructArray> for RecordBatch
impl From<&StructArray> for RecordBatch
§fn from(struct_array: &StructArray) -> RecordBatch
fn from(struct_array: &StructArray) -> RecordBatch
§impl From<StructArray> for RecordBatch
impl From<StructArray> for RecordBatch
§fn from(value: StructArray) -> RecordBatch
fn from(value: StructArray) -> RecordBatch
§impl Index<&str> for RecordBatch
impl Index<&str> for RecordBatch
§impl PartialEq for RecordBatch
impl PartialEq for RecordBatch
§impl RecordOutput for &RecordBatch
impl RecordOutput for &RecordBatch
§fn record_output(self, bm: &BaselineMetrics) -> &RecordBatch
fn record_output(self, bm: &BaselineMetrics) -> &RecordBatch
§impl RecordOutput for RecordBatch
impl RecordOutput for RecordBatch
§fn record_output(self, bm: &BaselineMetrics) -> RecordBatch
fn record_output(self, bm: &BaselineMetrics) -> RecordBatch
impl StructuralPartialEq for RecordBatch
Auto Trait Implementations§
impl Freeze for RecordBatch
impl !RefUnwindSafe for RecordBatch
impl Send for RecordBatch
impl Sync for RecordBatch
impl Unpin for RecordBatch
impl !UnwindSafe for RecordBatch
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