orx-concurrent-bag

An efficient and convenient thread-safe grow-only collection, ideal for collecting results concurrently.

convenient: the bag can be shared among threads simply as a shared reference, not even requiring Arc,
efficient: for collecting results concurrently:
- rayon is significantly faster than ConcurrentBag when the elements are small and there is an extreme load (no work at all among push calls),
- ConcurrentBag is significantly faster than rayon when elements are large or there there is some computation happening to evaluate the elements before the push calls,
- you may see the details of the benchmarks at benches/grow.rs.

The bag preserves the order of elements with respect to the order the push method is called.

Examples

Safety guarantees to push to the bag with an immutable reference makes it easy to share the bag among threads.

Using `std::sync::Arc`

Following the common approach of using an Arc, we can share our bag among threads and collect results concurrently.

use orx_concurrent_bag::prelude::*;
use std::{sync::Arc, thread};

let (num_threads, num_items_per_thread) = (4, 8);

let bag = Arc::new(ConcurrentBag::new());
let mut thread_vec: Vec<thread::JoinHandle<()>> = Vec::new();

for i in 0..num_threads {
    let bag = bag.clone();
    thread_vec.push(thread::spawn(move || {
        for j in 0..num_items_per_thread {
            bag.push(i * 1000 + j); // concurrently collect results simply by calling `push`
        }
    }));
}

for handle in thread_vec {
    handle.join().unwrap();
}

let mut vec_from_bag: Vec<_> = unsafe { bag.iter() }.copied().collect();
vec_from_bag.sort();
let mut expected: Vec<_> = (0..num_threads).flat_map(|i| (0..num_items_per_thread).map(move |j| i * 1000 + j)).collect();
expected.sort();
assert_eq!(vec_from_bag, expected);

Using `std::thread::scope`

An even more convenient approach would be to use thread scopes. This allows to use shared reference of the bag across threads, instead of Arc.

use orx_concurrent_bag::prelude::*;
use std::thread;

let (num_threads, num_items_per_thread) = (4, 8);

let bag = ConcurrentBag::new();
let bag_ref = &bag; // just take a reference
std::thread::scope(|s| {
    for i in 0..num_threads {
        s.spawn(move || {
            for j in 0..num_items_per_thread {
                bag_ref.push(i * 1000 + j); // concurrently collect results simply by calling `push`
            }
        });
    }
});

let mut vec_from_bag: Vec<_> = bag.into_inner().iter().copied().collect();
vec_from_bag.sort();
let mut expected: Vec<_> = (0..num_threads).flat_map(|i| (0..num_items_per_thread).map(move |j| i * 1000 + j)).collect();
expected.sort();
assert_eq!(vec_from_bag, expected);

Safety

ConcurrentBag uses a SplitVec as the underlying storage. SplitVec implements PinnedVec which guarantees that elements which are already pushed to the vector stay pinned to their memory locations. This feature makes it safe to grow with a shared reference on a single thread, as implemented by ImpVec.

In order to achieve this feature in a concurrent program, ConcurrentBag pairs the SplitVec with an AtomicUsize.

AtomicUsize fixes the target memory location of each element to be pushed at the time the push method is called. Regardless of whether or not writing to memory completes before another element is pushed, every pushed element receives a unique position reserved for it.
SplitVec guarantees that already pushed elements are not moved around in memory and new elements are written to the reserved position.

The approach guarantees that

only one thread can write to the memory location of an element being pushed to the bag,
at any point in time, only one thread is responsible for the allocation of memory if the bag requires new memory,
no thread reads any of the written elements (reading happens after converting the bag into_inner),
hence, there exists no race condition.

This pair allows a lightweight and convenient concurrent bag which is ideal for collecting results concurrently.

Write-Only vs Read-Write

The concurrent bag is write-only & grow-only bag which is convenient and efficient for collecting elements.

See ConcurrentVec for a read-and-write variant which

guarantees that reading and writing never happen concurrently, and hence,
allows safe iteration or access to already written elements of the concurrent vector,
with a minor additional cost of values being wrapped by an Option.

orx-concurrent-bag 1.1.0