fn build_bucketed<G>(
    input: Collection<G, (Row, Row)>,
    BucketedPlan {
        aggr_funcs,
        skips,
        buckets,
    }: BucketedPlan,
) -> Arrangement<G, Row>
where
    G: Scope,
    G::Timestamp: Lattice,
{
    // Gather the relevant values into a vec of rows ordered by aggregation_index
    let mut packer = Row::default();
    let input = input.map(move |(key, row)| {
        let mut values = Vec::with_capacity(skips.len());
        let mut row_iter = row.iter();
        for skip in skips.iter() {
            packer.push((&mut row_iter).nth(*skip).unwrap());
            values.push(packer.finish_and_reuse());
        }

        (key, values)
    });

    // Repeatedly apply hierarchical reduction with a progressively coarser key.
    let mut stage = input.map(move |(key, values)| ((key, values.hashed()), values));
    for b in buckets.into_iter() {
        stage = build_bucketed_stage(stage, aggr_funcs.clone(), b);
    }

    // Discard the hash from the key and return to the format of the input data.
    let partial = stage.map(|((key, _hash), values)| (key, values));

    // Build a series of stages for the reduction
    // Arrange the final result into (key, Row)
    partial.reduce_abelian::<_, RowSpine<_, _, _, _>>("ReduceMinsMaxes", {
        let mut row_packer = Row::default();
        move |_key, source, target| {
            // Negative counts would be surprising, but until we are 100% certain we wont
            // see them, we should report when we do. We may want to bake even more info
            // in here in the future.
            if source.iter().any(|(_val, cnt)| cnt < &0) {
                // XXX: This reports user data, which we perhaps should not do!
                for (val, cnt) in source.iter() {
                    if cnt < &0 {
                        log::error!("[customer-data] Negative accumulation in ReduceMinsMaxes: {:?} with count {:?}", val, cnt);
                    }
                }
            } else {
                for (aggr_index, func) in aggr_funcs.iter().enumerate() {
                    let iter = source.iter().map(|(values, _cnt)| values[aggr_index].iter().next().unwrap());
                    row_packer.push(func.eval(iter, &RowArena::new()));
                }
                target.push((row_packer.finish_and_reuse(), 1));
            }
        }
    })
}
/// Build the dataflow to compute and arrange multiple hierarchical aggregations
/// on non-monotonic inputs.
///
/// This function renders a single reduction tree that computes aggregations with
/// a priority queue implemented with a series of reduce operators that partition
/// the input into buckets, and compute the aggregation over very small buckets
/// and feed the results up to larger buckets.
///
/// Note that this implementation currently ignores the distinct bit because we
/// currently only perform min / max hierarchically and the reduction tree
/// efficiently suppresses non-distinct updates.
Build the dataflow to compute and arrange multiple hierarchical aggregations
on non-monotonic inputs.

This function renders a single reduction tree that computes aggregations with
a priority queue implemented with a series of reduce operators that partition
the input into buckets, and compute the aggregation over very small buckets
and feed the results up to larger buckets.

Note that this implementation currently ignores the distinct bit because we
currently only perform min / max hierarchically and the reduction tree
efficiently suppresses non-distinct updates.
Build the dataflow to compute and arrange multiple hierarchical aggregations on non - monotonic inputs . This function renders a single reduction tree that computes aggregations with a priority queue implemented with a series of reduce operators that partition the input into buckets and compute the aggregation over very small buckets and feed the results up to larger buckets . Note that this implementation currently ignores the distinct bit because we currently only perform min / max hierarchically and the reduction tree efficiently suppresses non - distinct updates .
fn build_bucketed<G>(
    input: Collection<G, (Row, Row)>,
    BucketedPlan {
        aggr_funcs,
        skips,
        buckets,
    }: BucketedPlan,
) -> Arrangement<G, Row>
where
    G: Scope,
    G::Timestamp: Lattice,
{
    // Gather the relevant values into a vec of rows ordered by aggregation_index
    let mut packer = Row::default();
    let input = input.map(move |(key, row)| {
        let mut values = Vec::with_capacity(skips.len());
        let mut row_iter = row.iter();
        for skip in skips.iter() {
            packer.push((&mut row_iter).nth(*skip).unwrap());
            values.push(packer.finish_and_reuse());
        }
        (key, values)
    });
    // Repeatedly apply hierarchical reduction with a progressively coarser key.
    let mut stage = input.map(move |(key, values)| ((key, values.hashed()), values));
    for b in buckets.into_iter() {
        stage = build_bucketed_stage(stage, aggr_funcs.clone(), b);
    }
    // Discard the hash from the key and return to the format of the input data.
    let partial = stage.map(|((key, _hash), values)| (key, values));
    // Build a series of stages for the reduction
    // Arrange the final result into (key, Row)
    partial.reduce_abelian::<_, RowSpine<_, _, _, _>>("ReduceMinsMaxes", {
        let mut row_packer = Row::default();
        move |_key, source, target| {
            // Negative counts would be surprising, but until we are 100% certain we wont
            // see them, we should report when we do. We may want to bake even more info
            // in here in the future.
            if source.iter().any(|(_val, cnt)| cnt < &0) {
                // XXX: This reports user data, which we perhaps should not do!
                for (val, cnt) in source.iter() {
                    if cnt < &0 {
                        log::error!("[customer-data] Negative accumulation in ReduceMinsMaxes: {:?} with count {:?}", val, cnt);
                    }
                }
            } else {
                for (aggr_index, func) in aggr_funcs.iter().enumerate() {
                    let iter = source.iter().map(|(values, _cnt)| values[aggr_index].iter().next().unwrap());
                    row_packer.push(func.eval(iter, &RowArena::new()));
                }
                target.push((row_packer.finish_and_reuse(), 1));
            }
        }
    })
}
fn build_bucketed < G > ( input : Collection < G , ( Row , Row ) > , BucketedPlan { aggr_funcs , skips , buckets , } : BucketedPlan , ) -> Arrangement < G , Row > where G : Scope , G :: Timestamp : Lattice , { // Gather the relevant values into a vec of rows ordered by aggregation_index let mut packer = Row :: default ( ) ; let input = input . map ( move | ( key , row ) | { let mut values = Vec :: with_capacity ( skips . len ( ) ) ; let mut row_iter = row . iter ( ) ; for skip in skips . iter ( ) { packer . push ( ( & mut row_iter ) . nth ( * skip ) . unwrap ( ) ) ; values . push ( packer . finish_and_reuse ( ) ) ; } ( key , values ) } ) ; // Repeatedly apply hierarchical reduction with a progressively coarser key. let mut stage = input . map ( move | ( key , values ) | ( ( key , values . hashed ( ) ) , values ) ) ; for b in buckets . into_iter ( ) { stage = build_bucketed_stage ( stage , aggr_funcs . clone ( ) , b ) ; } // Discard the hash from the key and return to the format of the input data. let partial = stage . map ( | ( ( key , _hash ) , values ) | ( key , values ) ) ; // Build a series of stages for the reduction // Arrange the final result into (key, Row) partial . reduce_abelian :: < _ , RowSpine < _ , _ , _ , _ > > ( " " , { let mut row_packer = Row :: default ( ) ; move | _key , source , target | { // Negative counts would be surprising, but until we are 100% certain we wont // see them, we should report when we do. We may want to bake even more info // in here in the future. if source . iter ( ) . any ( | ( _val , cnt ) | cnt < & 0 ) { // XXX: This reports user data, which we perhaps should not do! for ( val , cnt ) in source . iter ( ) { if cnt < & 0 { log :: error ! ( " " val cnt ) ; } } } else { for ( aggr_index , func ) in aggr_funcs . iter ( ) . enumerate ( ) { let iter = source . iter ( ) . map ( | ( values , _cnt ) | values [ aggr_index ] . iter ( ) . next ( ) . unwrap ( ) ) ; row_packer . push ( func . eval ( iter , & RowArena :: new ( ) ) ) ; } target . push ( ( row_packer . finish_and_reuse ( ) , 1 ) ) ; } } } ) }
Build the dataflow to compute and arrange multiple hierarchical aggregations
on non-monotonic inputs.
Build the dataflow to compute and arrange multiple hierarchical aggregations on non - monotonic inputs .
input
BucketedPlan
input
BucketedPlan