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//! Reduce the collection to one occurrence of each distinct element.
//!
//! The `distinct_total` and `distinct_total_u` operators are optimizations of the more general
//! `distinct` and `distinct_u` operators for the case in which time is totally ordered.
use std::default::Default;
use timely::order::TotalOrder;
use timely::dataflow::*;
use timely::dataflow::operators::Unary;
use timely::dataflow::channels::pact::Pipeline;
use timely_sort::Unsigned;
use lattice::Lattice;
use ::{Data, Collection, Diff};
use hashable::{Hashable, UnsignedWrapper};
use collection::AsCollection;
use operators::arrange::{Arrange, Arranged, ArrangeBySelf};
use trace::{BatchReader, Cursor, Trace, TraceReader};
use trace::implementations::ord::OrdKeySpine as DefaultKeyTrace;
/// Extension trait for the `distinct` differential dataflow method.
pub trait DistinctTotal<G: Scope, K: Data, R: Diff> where G::Timestamp: TotalOrder+Lattice+Ord {
/// Reduces the collection to one occurrence of each distinct element.
///
/// # Examples
///
/// ```
/// extern crate timely;
/// extern crate differential_dataflow;
///
/// use differential_dataflow::input::Input;
/// use differential_dataflow::operators::DistinctTotal;
///
/// fn main() {
/// ::timely::example(|scope| {
/// // report the number of occurrences of each key
/// scope.new_collection_from(1 .. 10).1
/// .map(|x| x / 3)
/// .distinct_total();
/// });
/// }
/// ```
fn distinct_total(&self) -> Collection<G, K, isize>;
/// Reduces the collection to one occurrence of each distinct element.
///
/// This method is a specialization for when the key is an unsigned integer fit for distributing
/// the data.
///
/// # Examples
///
/// ```
/// extern crate timely;
/// extern crate differential_dataflow;
///
/// use differential_dataflow::input::Input;
/// use differential_dataflow::operators::DistinctTotal;
///
/// fn main() {
/// ::timely::example(|scope| {
/// // report the number of occurrences of each key
/// scope.new_collection_from(1 .. 10u32).1
/// .map(|x| x / 3)
/// .distinct_total_u();
/// });
/// }
/// ```
fn distinct_total_u(&self) -> Collection<G, K, isize> where K: Unsigned+Copy;
}
impl<G: Scope, K: Data+Default+Hashable, R: Diff> DistinctTotal<G, K, R> for Collection<G, K, R>
where G::Timestamp: TotalOrder+Lattice+Ord {
fn distinct_total(&self) -> Collection<G, K, isize> {
self.arrange_by_self()
.distinct_total_core()
.map(|k| k.item)
}
fn distinct_total_u(&self) -> Collection<G, K, isize> where K: Unsigned+Copy {
self.map(|k| (UnsignedWrapper::from(k), ()))
.arrange(DefaultKeyTrace::new())
.distinct_total_core()
.map(|k| k.item)
}
}
/// Extension trait for the `distinct_total_core` differential dataflow method.
pub trait DistinctTotalCore<G: Scope, K: Data, R: Diff> where G::Timestamp: TotalOrder+Lattice+Ord {
/// Applies `distinct` to arranged data, and returns a collection of output data.
///
/// # Examples
///
/// ```
/// extern crate timely;
/// extern crate differential_dataflow;
///
/// use differential_dataflow::input::Input;
/// use differential_dataflow::operators::arrange::Arrange;
/// use differential_dataflow::operators::distinct::DistinctTotalCore;
/// use differential_dataflow::trace::Trace;
/// use differential_dataflow::trace::implementations::ord::OrdKeySpine;
/// use differential_dataflow::hashable::OrdWrapper;
///
/// fn main() {
/// ::timely::example(|scope| {
///
/// // wrap and order input, then group manually.
/// scope.new_collection_from(1 .. 10u32).1
/// .map(|x| (OrdWrapper { item: x / 3 }, ()))
/// .arrange(OrdKeySpine::new())
/// .distinct_total_core();
/// });
/// }
/// ```
fn distinct_total_core(&self) -> Collection<G, K, isize>;
}
impl<G: Scope, K: Data, R: Diff, T1> DistinctTotalCore<G, K, R> for Arranged<G, K, (), R, T1>
where
G::Timestamp: TotalOrder+Lattice+Ord,
T1: TraceReader<K, (), G::Timestamp, R>+Clone+'static,
T1::Batch: BatchReader<K, (), G::Timestamp, R> {
fn distinct_total_core(&self) -> Collection<G, K, isize> {
let mut trace = self.trace.clone();
self.stream.unary_stream(Pipeline, "DistinctTotal", move |input, output| {
input.for_each(|capability, batches| {
let mut session = output.session(&capability);
for batch in batches.drain(..).map(|x| x.item) {
let (mut batch_cursor, batch_storage) = batch.cursor();
let (mut trace_cursor, trace_storage) = trace.cursor_through(batch.lower()).unwrap();
while batch_cursor.key_valid(&batch_storage) {
let key = batch_cursor.key(&batch_storage);
let mut count = R::zero();
// Compute the multiplicity of this key before the current batch.
trace_cursor.seek_key(&trace_storage, key);
if trace_cursor.key_valid(&trace_storage) && trace_cursor.key(&trace_storage) == key {
trace_cursor.map_times(&trace_storage, |_, diff| count = count + diff);
}
// Take into account the current batch. At each time, check whether the
// "presence" of the key changes. If it was previously present (i.e. had
// nonzero multiplicity) but now is no more, emit -1. Conversely, if it is
// newly present, emit +1. In both remaining cases, the result remains
// unchanged (note that this is better than the naive approach which would
// eliminate the "previous" record and immediately re-add it).
batch_cursor.map_times(&batch_storage, |time, diff| {
let old_distinct = !count.is_zero();
count = count + diff;
let new_distinct = !count.is_zero();
if old_distinct != new_distinct {
session.give((key.clone(), time.clone(), if old_distinct { -1 } else { 1 }));
}
});
batch_cursor.step_key(&batch_storage);
}
// Tidy up the shared input trace.
trace.advance_by(batch.upper());
trace.distinguish_since(batch.upper());
}
});
})
.as_collection()
}
}