pub fn render_reduce(
        &mut self,
        input: CollectionBundle<G, Row, T>,
        key_val_plan: KeyValPlan,
        reduce_plan: ReducePlan,
        permutation: Permutation,
    ) -> CollectionBundle<G, Row, T> {
        let KeyValPlan {
            mut key_plan,
            mut val_plan,
        } = key_val_plan;
        let key_arity = key_plan.projection.len();
        let mut row_packer = Row::default();
        let mut row_mfp = Row::default();
        let mut datums = DatumVec::new();
        let mut row_datums = DatumVec::new();
        let (key_val_input, err_input): (
            timely::dataflow::Stream<_, (Result<(Row, Row), DataflowError>, _, _)>,
            _,
        ) = input.flat_map(None, |permutation| {
            if let Some(permutation) = permutation {
                permutation.permute_safe_mfp_plan(&mut key_plan);
                permutation.permute_safe_mfp_plan(&mut val_plan);
            }
            // Determine the columns we'll need from the row.
            let mut demand = Vec::new();
            demand.extend(key_plan.demand());
            demand.extend(val_plan.demand());
            demand.sort();
            demand.dedup();
            // remap column references to the subset we use.
            let mut demand_map = std::collections::HashMap::new();
            for column in demand.iter() {
                demand_map.insert(*column, demand_map.len());
            }
            let demand_map_len = demand_map.len();
            key_plan.permute(demand_map.clone(), demand_map_len);
            val_plan.permute(demand_map, demand_map_len);
            let skips = dataflow_types::plan::reduce::convert_indexes_to_skips(demand);
            move |row_parts, time, diff| {
                let temp_storage = RowArena::new();

                let mut row_datums = row_datums.borrow_with_many(row_parts);

                let mut row_iter = row_datums.drain(..);
                let mut datums_local = datums.borrow();
                // Unpack only the demanded columns.
                for skip in skips.iter() {
                    datums_local.push((&mut row_iter).nth(*skip).unwrap());
                }

                // Evaluate the key expressions.
                let key =
                    match key_plan.evaluate_into(&mut datums_local, &temp_storage, &mut row_mfp) {
                        Err(e) => {
                            return Some((Err(DataflowError::from(e)), time.clone(), diff.clone()))
                        }
                        Ok(key) => key.expect("Row expected as no predicate was used"),
                    };
                // Evaluate the value expressions.
                // The prior evaluation may have left additional columns we should delete.
                datums_local.truncate(skips.len());
                let val = match val_plan.evaluate_iter(&mut datums_local, &temp_storage) {
                    Err(e) => {
                        return Some((Err(DataflowError::from(e)), time.clone(), diff.clone()))
                    }
                    Ok(val) => val.expect("Row expected as no predicate was used"),
                };
                row_packer.clear();
                row_packer.extend(val);
                let row = row_packer.finish_and_reuse();
                return Some((Ok((key, row)), time.clone(), diff.clone()));
            }
        });

        // Demux out the potential errors from key and value selector evaluation.
        use crate::operator::CollectionExt;
        use differential_dataflow::operators::consolidate::ConsolidateStream;
        let (ok, mut err) = key_val_input
            .as_collection()
            .consolidate_stream()
            .flat_map_fallible("OkErrDemux", Some);

        err = err.concat(&err_input);

        // Render the reduce plan
        render_reduce_plan(reduce_plan, ok, err, key_arity, permutation)
    }
/// Renders a `MirRelationExpr::Reduce` using various non-obvious techniques to
/// minimize worst-case incremental update times and memory footprint.
Renders a `MirRelationExpr::Reduce` using various non-obvious techniques to
minimize worst-case incremental update times and memory footprint.
Renders a ` MirRelationExpr :: Reduce ` using various non - obvious techniques to minimize worst - case incremental update times and memory footprint .
pub fn render_reduce(
        &mut self,
        input: CollectionBundle<G, Row, T>,
        key_val_plan: KeyValPlan,
        reduce_plan: ReducePlan,
        permutation: Permutation,
    ) -> CollectionBundle<G, Row, T> {
        let KeyValPlan {
            mut key_plan,
            mut val_plan,
        } = key_val_plan;
        let key_arity = key_plan.projection.len();
        let mut row_packer = Row::default();
        let mut row_mfp = Row::default();
        let mut datums = DatumVec::new();
        let mut row_datums = DatumVec::new();
        let (key_val_input, err_input): (
            timely::dataflow::Stream<_, (Result<(Row, Row), DataflowError>, _, _)>,
            _,
        ) = input.flat_map(None, |permutation| {
            if let Some(permutation) = permutation {
                permutation.permute_safe_mfp_plan(&mut key_plan);
                permutation.permute_safe_mfp_plan(&mut val_plan);
            }
            // Determine the columns we'll need from the row.
            let mut demand = Vec::new();
            demand.extend(key_plan.demand());
            demand.extend(val_plan.demand());
            demand.sort();
            demand.dedup();
            // remap column references to the subset we use.
            let mut demand_map = std::collections::HashMap::new();
            for column in demand.iter() {
                demand_map.insert(*column, demand_map.len());
            }
            let demand_map_len = demand_map.len();
            key_plan.permute(demand_map.clone(), demand_map_len);
            val_plan.permute(demand_map, demand_map_len);
            let skips = dataflow_types::plan::reduce::convert_indexes_to_skips(demand);
            move |row_parts, time, diff| {
                let temp_storage = RowArena::new();
                let mut row_datums = row_datums.borrow_with_many(row_parts);
                let mut row_iter = row_datums.drain(..);
                let mut datums_local = datums.borrow();
                // Unpack only the demanded columns.
                for skip in skips.iter() {
                    datums_local.push((&mut row_iter).nth(*skip).unwrap());
                }
                // Evaluate the key expressions.
                let key =
                    match key_plan.evaluate_into(&mut datums_local, &temp_storage, &mut row_mfp) {
                        Err(e) => {
                            return Some((Err(DataflowError::from(e)), time.clone(), diff.clone()))
                        }
                        Ok(key) => key.expect("Row expected as no predicate was used"),
                    };
                // Evaluate the value expressions.
                // The prior evaluation may have left additional columns we should delete.
                datums_local.truncate(skips.len());
                let val = match val_plan.evaluate_iter(&mut datums_local, &temp_storage) {
                    Err(e) => {
                        return Some((Err(DataflowError::from(e)), time.clone(), diff.clone()))
                    }
                    Ok(val) => val.expect("Row expected as no predicate was used"),
                };
                row_packer.clear();
                row_packer.extend(val);
                let row = row_packer.finish_and_reuse();
                return Some((Ok((key, row)), time.clone(), diff.clone()));
            }
        });
        // Demux out the potential errors from key and value selector evaluation.
        use crate::operator::CollectionExt;
        use differential_dataflow::operators::consolidate::ConsolidateStream;
        let (ok, mut err) = key_val_input
            .as_collection()
            .consolidate_stream()
            .flat_map_fallible("OkErrDemux", Some);
        err = err.concat(&err_input);
        // Render the reduce plan
        render_reduce_plan(reduce_plan, ok, err, key_arity, permutation)
    }
pub fn render_reduce ( & mut self , input : CollectionBundle < G , Row , T > , key_val_plan : KeyValPlan , reduce_plan : ReducePlan , permutation : Permutation , ) -> CollectionBundle < G , Row , T > { let KeyValPlan { mut key_plan , mut val_plan , } = key_val_plan ; let key_arity = key_plan . projection . len ( ) ; let mut row_packer = Row :: default ( ) ; let mut row_mfp = Row :: default ( ) ; let mut datums = DatumVec :: new ( ) ; let mut row_datums = DatumVec :: new ( ) ; let ( key_val_input , err_input ) : ( timely :: dataflow :: Stream < _ , ( Result < ( Row , Row ) , DataflowError > , _ , _ ) > , _ , ) = input . flat_map ( None , | permutation | { if let Some ( permutation ) = permutation { permutation . permute_safe_mfp_plan ( & mut key_plan ) ; permutation . permute_safe_mfp_plan ( & mut val_plan ) ; } // Determine the columns we'll need from the row. let mut demand = Vec :: new ( ) ; demand . extend ( key_plan . demand ( ) ) ; demand . extend ( val_plan . demand ( ) ) ; demand . sort ( ) ; demand . dedup ( ) ; // remap column references to the subset we use. let mut demand_map = std :: collections :: HashMap :: new ( ) ; for column in demand . iter ( ) { demand_map . insert ( * column , demand_map . len ( ) ) ; } let demand_map_len = demand_map . len ( ) ; key_plan . permute ( demand_map . clone ( ) , demand_map_len ) ; val_plan . permute ( demand_map , demand_map_len ) ; let skips = dataflow_types :: plan :: reduce :: convert_indexes_to_skips ( demand ) ; move | row_parts , time , diff | { let temp_storage = RowArena :: new ( ) ; let mut row_datums = row_datums . borrow_with_many ( row_parts ) ; let mut row_iter = row_datums . drain ( .. ) ; let mut datums_local = datums . borrow ( ) ; // Unpack only the demanded columns. for skip in skips . iter ( ) { datums_local . push ( ( & mut row_iter ) . nth ( * skip ) . unwrap ( ) ) ; } // Evaluate the key expressions. let key = match key_plan . evaluate_into ( & mut datums_local , & temp_storage , & mut row_mfp ) { Err ( e ) => { return Some ( ( Err ( DataflowError :: from ( e ) ) , time . clone ( ) , diff . clone ( ) ) ) } Ok ( key ) => key . expect ( " " ) , } ; // Evaluate the value expressions. // The prior evaluation may have left additional columns we should delete. datums_local . truncate ( skips . len ( ) ) ; let val = match val_plan . evaluate_iter ( & mut datums_local , & temp_storage ) { Err ( e ) => { return Some ( ( Err ( DataflowError :: from ( e ) ) , time . clone ( ) , diff . clone ( ) ) ) } Ok ( val ) => val . expect ( " " ) , } ; row_packer . clear ( ) ; row_packer . extend ( val ) ; let row = row_packer . finish_and_reuse ( ) ; return Some ( ( Ok ( ( key , row ) ) , time . clone ( ) , diff . clone ( ) ) ) ; } } ) ; // Demux out the potential errors from key and value selector evaluation. use crate :: operator :: CollectionExt ; use differential_dataflow :: operators :: consolidate :: ConsolidateStream ; let ( ok , mut err ) = key_val_input . as_collection ( ) . consolidate_stream ( ) . flat_map_fallible ( " " , Some ) ; err = err . concat ( & err_input ) ; // Render the reduce plan render_reduce_plan ( reduce_plan , ok , err , key_arity , permutation ) }
Renders a `MirRelationExpr::Reduce` using various non-obvious techniques to
minimize worst-case incremental update times and memory footprint.
Renders a ` MirRelationExpr :: Reduce ` using various non - obvious techniques to minimize worst - case incremental update times and memory footprint .
&mut self
input
CollectionBundle<G, Row, T>
key_val_plan
KeyValPlan
reduce_plan
ReducePlan
permutation
Permutation
&mut self
input
CollectionBundle<G, Row, T>
key_val_plan
KeyValPlan
reduce_plan
ReducePlan
permutation
Permutation