fn build_basic_aggregates<G>(
    input: Collection<G, (Row, Row)>,
    aggrs: Vec<(usize, AggregateExpr)>,
) -> Arrangement<G, Row>
where
    G: Scope,
    G::Timestamp: Lattice,
{
    // We are only using this function to render multiple basic aggregates and
    // stitch them together. If that's not true we should complain.
    soft_assert_or_log!(
        aggrs.len() > 1,
        "Unexpectedly computing {} basic aggregations together but we expected to be doing more than one",
        aggrs.len(),
    );
    let mut to_collect = Vec::new();
    for (index, aggr) in aggrs {
        let result = build_basic_aggregate(input.clone(), index, &aggr);
        to_collect.push(result.as_collection(move |key, val| (key.clone(), (index, val.clone()))));
    }
    differential_dataflow::collection::concatenate(&mut input.scope(), to_collect)
        .reduce_abelian::<_, RowSpine<_, _, _, _>>("ReduceFuseBasic", {
            let mut row_packer = Row::default();
            move |_key, input, output| {
                for ((_, row), _) in input.iter() {
                    let datum = row.unpack_first();
                    row_packer.push(datum);
                }
                output.push((row_packer.finish_and_reuse(), 1));
            }
        })
}
/// Build the dataflow to compute and arrange multiple non-accumulable,
/// non-hierarchical aggregations on `input`.
///
/// This function assumes that we are explicitly rendering multiple basic aggregations.
/// For each aggregate, we render a different reduce operator, and then fuse
/// results together into a final arrangement that presents all the results
/// in the order specified by `aggrs`.
Build the dataflow to compute and arrange multiple non-accumulable,
non-hierarchical aggregations on `input`.

This function assumes that we are explicitly rendering multiple basic aggregations.
For each aggregate, we render a different reduce operator, and then fuse
results together into a final arrangement that presents all the results
in the order specified by `aggrs`.
Build the dataflow to compute and arrange multiple non - accumulable non - hierarchical aggregations on ` input ` . This function assumes that we are explicitly rendering multiple basic aggregations . For each aggregate we render a different reduce operator and then fuse results together into a final arrangement that presents all the results in the order specified by ` aggrs ` .
fn build_basic_aggregates<G>(
    input: Collection<G, (Row, Row)>,
    aggrs: Vec<(usize, AggregateExpr)>,
) -> Arrangement<G, Row>
where
    G: Scope,
    G::Timestamp: Lattice,
{
    // We are only using this function to render multiple basic aggregates and
    // stitch them together. If that's not true we should complain.
    soft_assert_or_log!(
        aggrs.len() > 1,
        "Unexpectedly computing {} basic aggregations together but we expected to be doing more than one",
        aggrs.len(),
    );
    let mut to_collect = Vec::new();
    for (index, aggr) in aggrs {
        let result = build_basic_aggregate(input.clone(), index, &aggr);
        to_collect.push(result.as_collection(move |key, val| (key.clone(), (index, val.clone()))));
    }
    differential_dataflow::collection::concatenate(&mut input.scope(), to_collect)
        .reduce_abelian::<_, RowSpine<_, _, _, _>>("ReduceFuseBasic", {
            let mut row_packer = Row::default();
            move |_key, input, output| {
                for ((_, row), _) in input.iter() {
                    let datum = row.unpack_first();
                    row_packer.push(datum);
                }
                output.push((row_packer.finish_and_reuse(), 1));
            }
        })
}
fn build_basic_aggregates < G > ( input : Collection < G , ( Row , Row ) > , aggrs : Vec < ( usize , AggregateExpr ) > , ) -> Arrangement < G , Row > where G : Scope , G :: Timestamp : Lattice , { // We are only using this function to render multiple basic aggregates and // stitch them together. If that's not true we should complain. soft_assert_or_log ! ( aggrs len ( ) 1 " " aggrs len ( ) ) ; let mut to_collect = Vec :: new ( ) ; for ( index , aggr ) in aggrs { let result = build_basic_aggregate ( input . clone ( ) , index , & aggr ) ; to_collect . push ( result . as_collection ( move | key , val | ( key . clone ( ) , ( index , val . clone ( ) ) ) ) ) ; } differential_dataflow :: collection :: concatenate ( & mut input . scope ( ) , to_collect ) . reduce_abelian :: < _ , RowSpine < _ , _ , _ , _ > > ( " " , { let mut row_packer = Row :: default ( ) ; move | _key , input , output | { for ( ( _ , row ) , _ ) in input . iter ( ) { let datum = row . unpack_first ( ) ; row_packer . push ( datum ) ; } output . push ( ( row_packer . finish_and_reuse ( ) , 1 ) ) ; } } ) }
Build the dataflow to compute and arrange multiple non-accumulable,
non-hierarchical aggregations on `input`.
Build the dataflow to compute and arrange multiple non - accumulable non - hierarchical aggregations on ` input ` .
input
Collection<G, (Row, Row)>
aggrs
Vec<(usize, AggregateExpr)>
input
Collection<G, (Row, Row)>
aggrs
Vec<(usize, AggregateExpr)>