//! Differential dataflow is a high-throughput, low-latency data-parallel programming framework.
//!
//! Differential dataflow programs are written in a collection-oriented style, where you transform
//! collections of records using traditional operations like `map`, `filter`, `join`, and `reduce`.
//! Differential dataflow also includes the less traditional operation `iterate`, which allows you
//! to repeatedly apply differential dataflow transformations to collections.
//!
//! Once you have defined a differential dataflow computation, you may then add records to or remove
//! records from its inputs; the system will automatically update the computation's outputs with the
//! appropriate corresponding additions and removals, and report these changes to you.
//!
//! Differential dataflow is built on the [timely dataflow](https://github.com/frankmcsherry/timely-dataflow)
//! framework for data-parallel programming which automatically parallelizes across multiple threads,
//! processes, and computers. Furthermore, because it uses timely dataflow's primitives, it seamlessly
//! inter-operates with other timely dataflow computations.
//!
//! Differential dataflow is still very much a work in progress, with features and ergonomics still
//! wildly in development. It is generally improving, though.
//!
//! # Examples
//!
//! This fragment creates a collection of pairs of integers, imagined as graph edges, and then counts
//! first the number of times the source coordinate occurs, and then the number of times each count
//! occurs, giving us a sense for the distribution of degrees in the graph.
//!
//! ```ignore
//! // create a degree counting differential dataflow
//! let (mut input, probe) = worker.dataflow(|scope| {
//!
//!     // create edge input, count a few ways.
//!     let (input, edges) = scope.new_collection();
//!
//!     // extract the source field, and then count.
//!     let degrs = edges.map(|(src, _dst)| src)
//!                      .count();
//!
//!     // extract the count field, and then count them.
//!     let distr = degrs.map(|(_src, cnt)| cnt)
//!                      .count();
//!
//!     // report the changes to the count collection, notice when done.
//!     let probe = distr.inspect(|x| println!("observed: {:?}", x))
//!                      .probe();
//!
//!     (input, probe)
//! });
//! ```
//!
//! Now assembled, we can drive the computation like a timely dataflow computation, by pushing update
//! records (triples of data, time, and change in count) at the `input` stream handle. The `probe` is
//! how timely dataflow tells us that we have seen all corresponding output updates (in case there are
//! none).
//!
//! ```ignore
//! loop {
//!     let time = input.epoch();
//!     for round in time .. time + 100 {
//!         input.advance_to(round);
//!         input.insert((round % 13, round % 7));
//!     }
//!
//!     input.flush();
//!     while probe.less_than(input.time()) {
//!        worker.step();
//!     }
//! }
//! ```
//!
//! This example should print out the 100 changes in the output, in this case each reflecting the increase
//! of some node degree by one (typically four output changes, corresponding to the addition and deletion
//! of the new and old counts of the old and new degrees of the affected node).

#![forbid(missing_docs)]
#![allow(array_into_iter)]


use std::fmt::Debug;

pub use collection::{Collection, AsCollection};
pub use hashable::Hashable;
pub use difference::Abelian as Diff;

/// Data type usable in differential dataflow.
///
/// Most differential dataflow operators require the ability to cancel corresponding updates, and the
/// way that they do this is by putting the data in a canonical form. The `Ord` trait allows us to sort
/// the data, at which point we can consolidate updates for equivalent records.
pub trait Data : timely::Data + Ord + Debug { }
impl<T: timely::Data + Ord + Debug> Data for T { }

/// Data types exchangeable in differential dataflow.
pub trait ExchangeData : timely::ExchangeData + Ord + Debug { }
impl<T: timely::ExchangeData + Ord + Debug> ExchangeData for T { }

pub mod hashable;
pub mod operators;
pub mod algorithms;
pub mod lattice;
pub mod trace;
pub mod input;
pub mod difference;
pub mod dynamic;
pub mod collection;
pub mod logging;
pub mod consolidation;
pub mod capture;

/// Configuration options for differential dataflow.
#[derive(Default)]
pub struct Config {
    /// An amount of arrangement effort to spend each scheduling quantum.
    ///
    /// The default value of `None` will not schedule operators that maintain arrangements
    /// other than when computation is required. Setting the value to `Some(effort)` will
    /// cause these operators to reschedule themselves as long as their arrangemnt has not
    /// reached a compact representation, and each scheduling quantum they will perform
    /// compaction work as if `effort` records had been added to the arrangement.
    pub idle_merge_effort: Option<isize>
}

impl Config {
    /// Assign an amount of effort to apply to idle arrangement operators.
    pub fn idle_merge_effort(mut self, effort: Option<isize>) -> Self {
        self.idle_merge_effort = effort;
        self
    }
}

/// Introduces differential options to a timely configuration.
pub fn configure(config: &mut timely::WorkerConfig, options: &Config) {
    if let Some(effort) = options.idle_merge_effort {
        config.set("differential/idle_merge_effort".to_string(), effort);
        config.set::<trace::ExertionLogic>(
            "differential/default_exert_logic".to_string(),
            std::sync::Arc::new(move |batches| {
                let mut non_empty = 0;
                for (_index, count, length) in batches {
                    if *count > 1 { return Some(effort as usize); }
                    if *length > 0 { non_empty += 1; }
                    if non_empty > 1 { return Some(effort as usize); }
                }
                None
            }),
        );
    }
}