Struct datafusion::physical_plan::repartition::RepartitionExec
source · pub struct RepartitionExec { /* private fields */ }
Expand description
The repartition operator maps N input partitions to M output partitions based on a partitioning scheme. No guarantees are made about the order of the resulting partitions.
Implementations§
source§impl RepartitionExec
impl RepartitionExec
sourcepub fn input(&self) -> &Arc<dyn ExecutionPlan>
pub fn input(&self) -> &Arc<dyn ExecutionPlan>
Input execution plan
sourcepub fn partitioning(&self) -> &Partitioning
pub fn partitioning(&self) -> &Partitioning
Partitioning scheme to use
source§impl RepartitionExec
impl RepartitionExec
sourcepub fn try_new(
input: Arc<dyn ExecutionPlan>,
partitioning: Partitioning
) -> Result<Self>
pub fn try_new(
input: Arc<dyn ExecutionPlan>,
partitioning: Partitioning
) -> Result<Self>
Create a new RepartitionExec
Trait Implementations§
source§impl Debug for RepartitionExec
impl Debug for RepartitionExec
source§impl ExecutionPlan for RepartitionExec
impl ExecutionPlan for RepartitionExec
source§fn children(&self) -> Vec<Arc<dyn ExecutionPlan>> ⓘ
fn children(&self) -> Vec<Arc<dyn ExecutionPlan>> ⓘ
Get a list of child execution plans that provide the input for this plan. The returned list
will be empty for leaf nodes, will contain a single value for unary nodes, or two
values for binary nodes (such as joins). Read more
source§fn with_new_children(
self: Arc<Self>,
children: Vec<Arc<dyn ExecutionPlan>>
) -> Result<Arc<dyn ExecutionPlan>>
fn with_new_children(
self: Arc<Self>,
children: Vec<Arc<dyn ExecutionPlan>>
) -> Result<Arc<dyn ExecutionPlan>>
Returns a new plan where all children were replaced by new plans.
source§fn output_partitioning(&self) -> Partitioning
fn output_partitioning(&self) -> Partitioning
Specifies the output partitioning scheme of this plan
source§fn output_ordering(&self) -> Option<&[PhysicalSortExpr]>
fn output_ordering(&self) -> Option<&[PhysicalSortExpr]>
If the output of this operator is sorted, returns
Some(keys)
with the description of how it was sorted. Read moresource§fn equivalence_properties(&self) -> EquivalenceProperties
fn equivalence_properties(&self) -> EquivalenceProperties
Get the EquivalenceProperties within the plan
source§fn execute(
&self,
partition: usize,
context: Arc<TaskContext>
) -> Result<SendableRecordBatchStream>
fn execute(
&self,
partition: usize,
context: Arc<TaskContext>
) -> Result<SendableRecordBatchStream>
creates an iterator
source§fn metrics(&self) -> Option<MetricsSet>
fn metrics(&self) -> Option<MetricsSet>
source§fn statistics(&self) -> Statistics
fn statistics(&self) -> Statistics
Returns the global output statistics for this
ExecutionPlan
node.source§fn required_input_distribution(&self) -> Vec<Distribution> ⓘ
fn required_input_distribution(&self) -> Vec<Distribution> ⓘ
Specifies the data distribution requirements for all the
children for this operator, By default it’s [Distribution::UnspecifiedDistribution] for each child, Read more
source§fn required_input_ordering(&self) -> Vec<Option<&[PhysicalSortExpr]>> ⓘ
fn required_input_ordering(&self) -> Vec<Option<&[PhysicalSortExpr]>> ⓘ
Specifies the ordering requirements for all the
children for this operator. Read more
source§fn relies_on_input_order(&self) -> bool
fn relies_on_input_order(&self) -> bool
Returns
true
if this operator relies on its inputs being
produced in a certain order (for example that they are sorted
a particular way) for correctness. Read moresource§fn maintains_input_order(&self) -> bool
fn maintains_input_order(&self) -> bool
Returns
false
if this operator’s implementation may reorder
rows within or between partitions. Read moresource§fn benefits_from_input_partitioning(&self) -> bool
fn benefits_from_input_partitioning(&self) -> bool
Returns
true
if this operator would benefit from
partitioning its input (and thus from more parallelism). For
operators that do very little work the overhead of extra
parallelism may outweigh any benefits Read more