differential_dataflow/operators/iterate.rs
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//! Iterative application of a differential dataflow fragment.
//!
//! The `iterate` operator takes as an argument a closure from a differential dataflow collection
//! to a collection of the same type. The output collection is the result of applying this closure
//! an unbounded number of times.
//!
//! The implementation of `iterate` does not directly apply the closure, but rather establishes an
//! iterative timely dataflow subcomputation, in which differences circulate until they dissipate
//! (indicating that the computation has reached fixed point), or until some number of iterations
//! have passed.
//!
//! **Note**: The dataflow assembled by `iterate` does not automatically insert `consolidate` for
//! you. This means that either (i) you should insert one yourself, (ii) you should be certain that
//! all paths from the input to the output of the loop involve consolidation, or (iii) you should
//! be worried that logically cancelable differences may circulate indefinitely.
//!
//! # Details
//!
//! The `iterate` method is written using a `Variable`, which lets you define your own iterative
//! computations when `iterate` itself is not sufficient. This can happen when you have two
//! collections that should evolve simultaneously, or when you would like to rotate your loop and
//! return an intermediate result.
//!
//! Using `Variable` requires more explicit arrangement of your computation, but isn't much more
//! complicated. You must define a new variable from an existing stream (its initial value), and
//! then set it to be a function of this variable (and perhaps other collections and variables).
//!
//! A `Variable` dereferences to a `Collection`, the one corresponding to its value in each iteration,
//! and it can be used in most situations where a collection can be used. The act of setting a
//! `Variable` consumes it and returns the corresponding `Collection`, preventing you from setting
//! it multiple times.
use std::fmt::Debug;
use std::ops::Deref;
use timely::progress::{Timestamp, PathSummary};
use timely::order::Product;
use timely::dataflow::*;
use timely::dataflow::scopes::child::Iterative;
use timely::dataflow::operators::{Feedback, ConnectLoop, Map};
use timely::dataflow::operators::feedback::Handle;
use crate::{Data, Collection};
use crate::difference::{Semigroup, Abelian};
use crate::lattice::Lattice;
/// An extension trait for the `iterate` method.
pub trait Iterate<G: Scope, D: Data, R: Semigroup> {
/// Iteratively apply `logic` to the source collection until convergence.
///
/// Importantly, this method does not automatically consolidate results.
/// It may be important to conclude with `consolidate()` to ensure that
/// logically empty collections that contain cancelling records do not
/// result in non-termination. Operators like `reduce`, `distinct`, and
/// `count` also perform consolidation, and are safe to conclude with.
///
/// # Examples
///
/// ```
/// use differential_dataflow::input::Input;
/// use differential_dataflow::operators::Iterate;
///
/// ::timely::example(|scope| {
///
/// scope.new_collection_from(1 .. 10u32).1
/// .iterate(|values| {
/// values.map(|x| if x % 2 == 0 { x/2 } else { x })
/// .consolidate()
/// });
/// });
/// ```
fn iterate<F>(&self, logic: F) -> Collection<G, D, R>
where
G::Timestamp: Lattice,
for<'a> F: FnOnce(&Collection<Iterative<'a, G, u64>, D, R>)->Collection<Iterative<'a, G, u64>, D, R>;
}
impl<G: Scope, D: Ord+Data+Debug, R: Abelian+'static> Iterate<G, D, R> for Collection<G, D, R> {
fn iterate<F>(&self, logic: F) -> Collection<G, D, R>
where G::Timestamp: Lattice,
for<'a> F: FnOnce(&Collection<Iterative<'a, G, u64>, D, R>)->Collection<Iterative<'a, G, u64>, D, R> {
self.inner.scope().scoped("Iterate", |subgraph| {
// create a new variable, apply logic, bind variable, return.
//
// this could be much more succinct if we returned the collection
// wrapped by `variable`, but it also results in substantially more
// diffs produced; `result` is post-consolidation, and means fewer
// records are yielded out of the loop.
let variable = Variable::new_from(self.enter(subgraph), Product::new(Default::default(), 1));
let result = logic(&variable);
variable.set(&result);
result.leave()
})
}
}
impl<G: Scope, D: Ord+Data+Debug, R: Semigroup+'static> Iterate<G, D, R> for G {
fn iterate<F>(&self, logic: F) -> Collection<G, D, R>
where G::Timestamp: Lattice,
for<'a> F: FnOnce(&Collection<Iterative<'a, G, u64>, D, R>)->Collection<Iterative<'a, G, u64>, D, R> {
// TODO: This makes me think we have the wrong ownership pattern here.
let mut clone = self.clone();
clone
.scoped("Iterate", |subgraph| {
// create a new variable, apply logic, bind variable, return.
//
// this could be much more succinct if we returned the collection
// wrapped by `variable`, but it also results in substantially more
// diffs produced; `result` is post-consolidation, and means fewer
// records are yielded out of the loop.
let variable = SemigroupVariable::new(subgraph, Product::new(Default::default(), 1));
let result = logic(&variable);
variable.set(&result);
result.leave()
}
)
}
}
/// A recursively defined collection.
///
/// The `Variable` struct allows differential dataflow programs requiring more sophisticated
/// iterative patterns than singly recursive iteration. For example: in mutual recursion two
/// collections evolve simultaneously.
///
/// # Examples
///
/// The following example is equivalent to the example for the `Iterate` trait.
///
/// ```
/// use timely::order::Product;
/// use timely::dataflow::Scope;
///
/// use differential_dataflow::input::Input;
/// use differential_dataflow::operators::iterate::Variable;
///
/// ::timely::example(|scope| {
///
/// let numbers = scope.new_collection_from(1 .. 10u32).1;
///
/// scope.iterative::<u64,_,_>(|nested| {
/// let summary = Product::new(Default::default(), 1);
/// let variable = Variable::new_from(numbers.enter(nested), summary);
/// let result = variable.map(|x| if x % 2 == 0 { x/2 } else { x })
/// .consolidate();
/// variable.set(&result)
/// .leave()
/// });
/// })
/// ```
pub struct Variable<G: Scope, D: Data, R: Abelian+'static>
where G::Timestamp: Lattice {
collection: Collection<G, D, R>,
feedback: Handle<G, Vec<(D, G::Timestamp, R)>>,
source: Option<Collection<G, D, R>>,
step: <G::Timestamp as Timestamp>::Summary,
}
impl<G: Scope, D: Data, R: Abelian> Variable<G, D, R> where G::Timestamp: Lattice {
/// Creates a new initially empty `Variable`.
///
/// This method produces a simpler dataflow graph than `new_from`, and should
/// be used whenever the variable has an empty input.
pub fn new(scope: &mut G, step: <G::Timestamp as Timestamp>::Summary) -> Self {
let (feedback, updates) = scope.feedback(step.clone());
let collection = Collection::<G,D,R>::new(updates);
Variable { collection, feedback, source: None, step }
}
/// Creates a new `Variable` from a supplied `source` stream.
pub fn new_from(source: Collection<G, D, R>, step: <G::Timestamp as Timestamp>::Summary) -> Self {
let (feedback, updates) = source.inner.scope().feedback(step.clone());
let collection = Collection::<G,D,R>::new(updates).concat(&source);
Variable { collection, feedback, source: Some(source), step }
}
/// Set the definition of the `Variable` to a collection.
///
/// This method binds the `Variable` to be equal to the supplied collection,
/// which may be recursively defined in terms of the variable itself.
pub fn set(self, result: &Collection<G, D, R>) -> Collection<G, D, R> {
let mut in_result = result.clone();
if let Some(source) = &self.source {
in_result = in_result.concat(&source.negate());
}
self.set_concat(&in_result)
}
/// Set the definition of the `Variable` to a collection concatenated to `self`.
///
/// This method is a specialization of `set` which has the effect of concatenating
/// `result` and `self` before calling `set`. This method avoids some dataflow
/// complexity related to retracting the initial input, and will do less work in
/// that case.
///
/// This behavior can also be achieved by using `new` to create an empty initial
/// collection, and then using `self.set(self.concat(result))`.
pub fn set_concat(self, result: &Collection<G, D, R>) -> Collection<G, D, R> {
let step = self.step;
result
.inner
.flat_map(move |(x,t,d)| step.results_in(&t).map(|t| (x,t,d)))
.connect_loop(self.feedback);
self.collection
}
}
impl<G: Scope, D: Data, R: Abelian> Deref for Variable<G, D, R> where G::Timestamp: Lattice {
type Target = Collection<G, D, R>;
fn deref(&self) -> &Self::Target {
&self.collection
}
}
/// A recursively defined collection that only "grows".
///
/// `SemigroupVariable` is a weakening of `Variable` to allow difference types
/// that do not implement `Abelian` and only implement `Semigroup`. This means
/// that it can be used in settings where the difference type does not support
/// negation.
pub struct SemigroupVariable<G: Scope, D: Data, R: Semigroup+'static>
where G::Timestamp: Lattice {
collection: Collection<G, D, R>,
feedback: Handle<G, Vec<(D, G::Timestamp, R)>>,
step: <G::Timestamp as Timestamp>::Summary,
}
impl<G: Scope, D: Data, R: Semigroup> SemigroupVariable<G, D, R> where G::Timestamp: Lattice {
/// Creates a new initially empty `SemigroupVariable`.
pub fn new(scope: &mut G, step: <G::Timestamp as Timestamp>::Summary) -> Self {
let (feedback, updates) = scope.feedback(step.clone());
let collection = Collection::<G,D,R>::new(updates);
SemigroupVariable { collection, feedback, step }
}
/// Adds a new source of data to `self`.
pub fn set(self, result: &Collection<G, D, R>) -> Collection<G, D, R> {
let step = self.step;
result
.inner
.flat_map(move |(x,t,d)| step.results_in(&t).map(|t| (x,t,d)))
.connect_loop(self.feedback);
self.collection
}
}
impl<G: Scope, D: Data, R: Semigroup> Deref for SemigroupVariable<G, D, R> where G::Timestamp: Lattice {
type Target = Collection<G, D, R>;
fn deref(&self) -> &Self::Target {
&self.collection
}
}