Struct datafusion::execution::dataframe_impl::DataFrameImpl

pub struct DataFrameImpl { /* fields omitted */ }

Implementation of DataFrame API

Implementations

impl DataFrameImpl

pub fn new(
    ctx_state: Arc<Mutex<ExecutionContextState>>,
    plan: &LogicalPlan
) -> Self

Create a new Table based on an existing logical plan

Trait Implementations

impl DataFrame for DataFrameImpl

fn select_columns(&self, columns: &[&str]) -> Result<Arc<dyn DataFrame>>

Apply a projection based on a list of column names

fn select(&self, expr_list: Vec<Expr>) -> Result<Arc<dyn DataFrame>>

Create a projection based on arbitrary expressions

fn filter(&self, predicate: Expr) -> Result<Arc<dyn DataFrame>>

Create a filter based on a predicate expression

fn aggregate(
    &self,
    group_expr: Vec<Expr>,
    aggr_expr: Vec<Expr>
) -> Result<Arc<dyn DataFrame>>

Perform an aggregate query

fn limit(&self, n: usize) -> Result<Arc<dyn DataFrame>>

Limit the number of rows

fn sort(&self, expr: Vec<Expr>) -> Result<Arc<dyn DataFrame>>

Sort by specified sorting expressions

fn join(
    &self,
    right: Arc<dyn DataFrame>,
    join_type: JoinType,
    left_cols: &[&str],
    right_cols: &[&str]
) -> Result<Arc<dyn DataFrame>>

Join with another DataFrame

fn repartition(
    &self,
    partitioning_scheme: Partitioning
) -> Result<Arc<dyn DataFrame>>

Repartition a DataFrame based on a logical partitioning scheme.

fn to_logical_plan(&self) -> LogicalPlan

Convert to logical plan

fn collect<'life0, 'async_trait>(
    &'life0 self
) -> Pin<Box<dyn Future<Output = Result<Vec<RecordBatch>>> + Send + 'async_trait>> where
    'life0: 'async_trait,
    Self: 'async_trait, 

Executes this DataFrame and collects all results into a vector of RecordBatch.

fn collect_partitioned<'life0, 'async_trait>(
    &'life0 self
) -> Pin<Box<dyn Future<Output = Result<Vec<Vec<RecordBatch>>>> + Send + 'async_trait>> where
    'life0: 'async_trait,
    Self: 'async_trait, 

Executes this DataFrame and collects all results into a vector of vector of RecordBatch maintaining the input partitioning.

fn schema(&self) -> &DFSchema

Returns the schema from the logical plan

fn explain(&self, verbose: bool) -> Result<Arc<dyn DataFrame>>

Return a DataFrame with the explanation of its plan so far.

fn registry(&self) -> Arc<dyn FunctionRegistry>

Return a FunctionRegistry used to plan udf’s calls

fn union(&self, dataframe: Arc<dyn DataFrame>) -> Result<Arc<dyn DataFrame>>

Calculate the union two DataFrames. The two DataFrames must have exactly the same schema