differential-dataflow 0.25.0

An incremental data-parallel dataflow platform
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179

//! End-to-end spines benchmark over `key`, `val`, `vec`, and `col` arrangements
//! — an `arrange` + `join` of `String` keys, loaded then queried round by round:
//!
//! * `key` / `val` — the ord-neu `OrdKeySpine` / `OrdValSpine` traces.
//! * `vec` / `col` — the `Vec`- and columnar-trie-backed `Chunk` traces
//!   (`VecChunk` / `ColChunk`), both arranged through the generic `Chunk`
//!   harness via a `ContainerChunker`.
//!
//! Run as `cargo run --release --example spines -- <keys> <size> <mode>`.
//!
//! All four modes feed the same way (each insert allocates a `String`), so the
//! `vec`/`col` comparison is apples-to-apples on chunk layout.

use timely::dataflow::operators::probe::Handle;

use differential_dataflow::input::{Input, InputSession};

use mimalloc::MiMalloc;

#[global_allocator]
static GLOBAL: MiMalloc = MiMalloc;

/// Feeds the dataflow round-by-round. Every mode arranges `String` keys, so they
/// all share this one input harness (each insert allocates a `String`).
struct Workload {
    data_input: InputSession<u64, String, isize>,
    keys_input: InputSession<u64, String, isize>,
}
impl Workload {
    fn insert_data(&mut self, val: usize) { self.data_input.insert(format!("{:?}", val)); }
    fn insert_keys(&mut self, val: usize) { self.keys_input.insert(format!("{:?}", val)); }
    fn advance_to(&mut self, t: u64) {
        self.data_input.advance_to(t); self.data_input.flush();
        self.keys_input.advance_to(t); self.keys_input.flush();
    }
}

fn main() {

    let keys: usize = std::env::args().nth(1).unwrap().parse().unwrap();
    let size: usize = std::env::args().nth(2).unwrap().parse().unwrap();

    let mode: String = std::env::args().nth(3).unwrap();

    println!("Running [{:?}] arrangement", mode);

    let timer1 = ::std::time::Instant::now();
    let timer2 = timer1.clone();

    timely::execute_from_args(std::env::args(), move |worker| {

        let mut probe = Handle::new();
        let mut workload: Workload = worker.dataflow(|scope| {

            use differential_dataflow::operators::arrange::Arrange;

            match mode.as_str() {
                "key" => {
                    use differential_dataflow::trace::implementations::ord_neu::{OrdKeyBatcher, RcOrdKeyBuilder, OrdKeySpine};
                    let (data_input, data) = scope.new_collection::<String, isize>();
                    let (keys_input, keys) = scope.new_collection::<String, isize>();
                    let data = data.arrange::<OrdKeyBatcher<String,_,isize>, RcOrdKeyBuilder<String,_,isize>, OrdKeySpine<String,_,isize>>();
                    let keys = keys.arrange::<OrdKeyBatcher<String,_,isize>, RcOrdKeyBuilder<String,_,isize>, OrdKeySpine<String,_,isize>>();
                    keys.join_core(data, |_k, &(), &()| Option::<()>::None)
                        .probe_with(&mut probe);
                    Workload { data_input, keys_input }
                },
                "val" => {
                    use differential_dataflow::trace::implementations::ord_neu::{OrdValBatcher, RcOrdValBuilder, OrdValSpine};
                    let (data_input, data) = scope.new_collection::<String, isize>();
                    let (keys_input, keys) = scope.new_collection::<String, isize>();
                    let data = data.map(|x| (x, ())).arrange::<OrdValBatcher<String,(),_,isize>, RcOrdValBuilder<String,(),_,isize>, OrdValSpine<String,(),_,isize>>();
                    let keys = keys.map(|x| (x, ())).arrange::<OrdValBatcher<String,(),_,isize>, RcOrdValBuilder<String,(),_,isize>, OrdValSpine<String,(),_,isize>>();
                    keys.join_core(data, |_k, &(), &()| Option::<()>::None)
                        .probe_with(&mut probe);
                    Workload { data_input, keys_input }
                },
                "col" => {
                    // The columnar (`UpdatesTyped` trie) `Chunk` trace, arranged through
                    // the same generic `Chunk` harness as `vec` via a
                    // `ContainerChunker<ColChunk>`.
                    use differential_dataflow::Hashable;
                    use differential_dataflow::columnar::trace::{Batcher, Builder, Spine, ColChunk};
                    use differential_dataflow::trace::implementations::chunker::ContainerChunker;
                    use differential_dataflow::operators::arrange::arrangement::arrange_core;
                    use timely::dataflow::channels::pact::Exchange;

                    let (data_input, data) = scope.new_collection::<String, isize>();
                    let (keys_input, keys) = scope.new_collection::<String, isize>();
                    let data = data.map(|x| (x, ()));
                    let keys = keys.map(|x| (x, ()));

                    type Ba = Batcher<String, (), u64, isize>;
                    type Bu = Builder<String, (), u64, isize>;
                    type Sp = Spine<String, (), u64, isize>;
                    type Chu = ContainerChunker<ColChunk<(String, (), u64, isize)>>;
                    let exchange = || Exchange::new(|u: &((String, ()), u64, isize)| (u.0).0.hashed().into());
                    let data = arrange_core::<_, _, Chu, Ba, Bu, Sp>(data.inner, exchange(), "DataArrange");
                    let keys = arrange_core::<_, _, Chu, Ba, Bu, Sp>(keys.inner, exchange(), "KeysArrange");
                    // `ColChunk`'s cursor yields `Val = columnar::Ref<()> = ()`, not `&()`.
                    keys.join_core(data, |_k, _, _| Option::<()>::None)
                        .probe_with(&mut probe);
                    Workload { data_input, keys_input }
                },
                "vec" => {
                    // The `Vec`-backed `Chunk` trace, fed like the row modes (each
                    // insert allocates a `String`) but arranged through the `Chunk`
                    // harness via a `ContainerChunker<VecChunk>`.
                    use differential_dataflow::Hashable;
                    use differential_dataflow::trace::chunk::vec::{ChunkBatcher, ChunkBuilder, ChunkSpine, VecChunk};
                    use differential_dataflow::trace::implementations::chunker::ContainerChunker;
                    use differential_dataflow::operators::arrange::arrangement::arrange_core;
                    use timely::dataflow::channels::pact::Exchange;

                    let (data_input, data) = scope.new_collection::<String, isize>();
                    let (keys_input, keys) = scope.new_collection::<String, isize>();
                    let data = data.map(|x| (x, ()));
                    let keys = keys.map(|x| (x, ()));

                    type Ba = ChunkBatcher<String, (), u64, isize>;
                    type Bu = ChunkBuilder<String, (), u64, isize>;
                    type Sp = ChunkSpine<String, (), u64, isize>;
                    type Chu = ContainerChunker<VecChunk<String, (), u64, isize>>;
                    let exchange = || Exchange::new(|u: &((String, ()), u64, isize)| (u.0).0.hashed().into());
                    let data = arrange_core::<_, _, Chu, Ba, Bu, Sp>(data.inner, exchange(), "DataArrange");
                    let keys = arrange_core::<_, _, Chu, Ba, Bu, Sp>(keys.inner, exchange(), "KeysArrange");
                    keys.join_core(data, |_k, &(), &()| Option::<()>::None)
                        .probe_with(&mut probe);
                    Workload { data_input, keys_input }
                },
                _ => {
                    panic!("unrecognized mode: {:?} (expected `key`, `val`, `vec`, or `col`)", mode);
                }
            }
        });

        // Load up data in batches.
        let mut counter = 0;
        let mut t: u64 = 1;
        while counter < 10 * keys {
            let mut i = worker.index();
            while i < size {
                let val = (counter + i) % keys;
                workload.insert_data(val);
                i += worker.peers();
            }
            counter += size;
            workload.advance_to(t);
            while probe.less_than(&t) {
                worker.step();
            }
            t += 1;
        }
        println!("{:?}\tloading complete", timer1.elapsed());

        let mut queries = 0;

        while queries < 10 * keys {
            let mut i = worker.index();
            while i < size {
                let val = (queries + i) % keys;
                workload.insert_keys(val);
                i += worker.peers();
            }
            queries += size;
            workload.advance_to(t);
            while probe.less_than(&t) {
                worker.step();
            }
            t += 1;
        }

        println!("{:?}\tqueries complete", timer1.elapsed());

    }).unwrap();

    println!("{:?}\tshut down", timer2.elapsed());

}