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A collection of concurrent ring buffers
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README.md
bulk_test.go
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mpmc.go
mpsc.go
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queue.go
ring.go
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util.go

README.md

One Ring to Queue Them All

Well, no, it's not really just one ring, but a collection of concurrent ring buffers for different scenarios, so it's even better! These queues don't use CAS operations to make them suitable for low latency/real-time environments and as a side effect of that, they preserve total order of messages. As a reward for finding flaws/bugs in this, I offer 64bit of random numbers for each.

A couple of things in it were inspired by the very cool LMAX Disruptor, so thanks @mjpt777! It's not anywhere near as intrusive and opinionated as the Disruptor though. It's not a framework and its main goal is to be (very) simple.

The MPMC design is similar to http://www.1024cores.net/home/lock-free-algorithms/queues/bounded-mpmc-queue, but with FAA instad of CAS.

Description

The package contains 4 related but different implementations

  1. SPSC - Single Producer/Single Consumer - For cases when you just need to send messages from one thread/goroutine to another
  2. MPSC - Multi-Producer/Single Consumer - When you need to send messages from many threads/goroutines into a single receiver
  3. SPMC - Single Producer/Multi-Consumer - When you need to distribute messages from a single thread to many others
  4. MPMC - Multi-Producer/Multi-Consumer - Many-to-Many

At the moment, all queues only support sending pointers (of any type). You can send non pointer types, but it will cause heap allocation. But you can not receive anything but pointers, don't even try, it will blow up.

If you build it with -tags debug, then all functions will be instrumented to check types at runtime.

There are 2 tests in the package that intentionally use value types to demonstrate it.

How to use it

Common interface

var queeue = onering.New{Size: N}.QueueType()
queue.Put(*T)
queue.Get(**T)
queue.Consume(fn(onering.Iter, *T))
queue.Close()

Simplest case

   import "github.com/pltr/onering"
   var queue = onering.New{Size: 8192}.MPMC()

   var src = int64(5)
   queue.Put(&src)
   queue.Close()
   var dst *int64
   // .Get expects a pointer to a pointer
   for queue.Get(&dst) {
       if *dst != src {
           panic("i don't know what's going on")
       }
   }

Single consumer batching case

Batching consumption is strongly recommended in all single consumer cases, it's expected to have both higher throughput and lower latency

    import "github.com/pltr/onering"
    var queue = onering.New{Size: 8192}.SPSC()

    var src = int64(5)
    queue.Put(&src)
    queue.Put(6) // WARNING: this will allocate memory on the heap and copy the value into it
    queue.Close()

    queue.Consume(func(it onering.Iter, dst *int64) {
        if *dst != src {
            panic("i don't know what's going on")
        }
        it.Stop()
    })
    // still one element left in the queue
    var dst *int64
    // Get will always expect a pointer to a pointer
    if !queue.Get(&dst) || *dst != 6 {
        panic("uh oh")
    }
    fmt.Println("Yay, batching works")

You can run both examples by go run cmd/examples.go

Warnings

Currently this is highly experimental, so be careful. It also uses some dirty tricks to get around go's typesystem. If you have a type mismatch between your sender and receiver or try to receive something unexpected, it will likely blow up.

Build it with -tags debug to ensure it's not the case.

FAQ

  • Why four different implementations instead of just one (MPMC)? There are optimizations to be made in each case. They can have significant effect on performance.

  • Which one should I use? If you're not sure, MPMC will likely to be the safest choice. However, MPMC queues are almost never a good design choice.

  • I think I found a bug/something doesn't work as expectd Feel free to open an issue

  • How fast is it? I haven't seen any faster, especially when it comes to latency and its distribution (see below)

  • Did someone actually ask those questions above? No

Some benchmarks

Macbook pro 2.9 GHz Intel Core i7 (2017)

GOGC=off go test -bench=. -benchtime=3s -run=none

Rings:

BenchmarkRingSPSC_GetPinned-8      	200000000	        12.5 ns/op
BenchmarkRingSPSC_GetNoPin-8       	200000000	        15.6 ns/op
BenchmarkRingSPSC_Consume-8        	200000000	        12.5 ns/op
BenchmarkRingMPSC_GetPinned-8      	100000000	        29.6 ns/op
BenchmarkRingMPSC_GetNoPin1CPU-8   	100000000	        20.5 ns/op
BenchmarkRingMPSC_Consume-8        	100000000	        29.4 ns/op
BenchmarkRingSPMC_Pinned-8         	100000000	        35.0 ns/op
BenchmarkRingSPMC_NoPin1CPU-8      	100000000	        24.8 ns/op
BenchmarkRingSPMC_Consume-8        	100000000	        32.4 ns/op
BenchmarkRingMPMC/100P100C-8       	100000000	        40.3 ns/op
BenchmarkRingMPMC/4P4C_Pinned-8    	100000000	        42.8 ns/op
BenchmarkRingMPMC/4P4C_1CPU-8      	100000000	        37.0 ns/op

Go channels:

BenchmarkChanMPMC_Pinned4P4C-8     	50000000	        86.6 ns/op
BenchmarkChan/SPSC_Pinned-8        	100000000	        54.8 ns/op
BenchmarkChan/SPSC_1CPU-8          	100000000	        46.3 ns/op
BenchmarkChan/SPMC_Pinned100C-8    	10000000	       388 ns/op
BenchmarkChan/SPMC_1CPU-8          	100000000	        45.6 ns/op
BenchmarkChan/MPSC_Pinned100P-8    	10000000	       401 ns/op
BenchmarkChan/MPSC_1CPU-8          	100000000	        46.1 ns/op

You can generally expect a 2-10x increase in performance, especially if you use a multicore setup. Do note that batching methods in them do not increase latency but, in fact, do the opposite.

Here's some (however flawed - it's hard to measure it precisely, so had to sample) latency distribution (run with -tags histogram):

GOGC=off go test -tags histogram -bench=. -benchtime=3s -run=none

BenchmarkResponseTimesRing-8
[Sample size: 4096 messages] 50: 25ns	75: 25ns	90: 25ns	99: 25ns	99.9: 25ns	99.99: 25ns	99.999: 25ns	99.9999: 25ns
[Sample size: 4096 messages] 50: 13ns	75: 13ns	90: 21ns	99: 31ns	99.9: 31ns	99.99: 31ns	99.999: 31ns	99.9999: 31ns
[Sample size: 4096 messages] 50: 13ns	75: 14ns	90: 14ns	99: 28ns	99.9: 36ns	99.99: 39ns	99.999: 40ns	99.9999: 40ns
[Sample size: 4096 messages] 50: 13ns	75: 14ns	90: 14ns	99: 28ns	99.9: 37ns	99.99: 43ns	99.999: 50ns	99.9999: 55ns

BenchmarkResponseTimesChannel-8
[Sample size: 4096 messages] 50: 86ns	75: 104ns	90: 104ns	99: 104ns	99.9: 104ns	99.99: 104ns	99.999: 104ns	99.9999: 104ns
[Sample size: 4096 messages] 50: 92ns	75: 119ns	90: 144ns	99: 222ns	99.9: 244ns	99.99: 244ns	99.999: 244ns	99.9999: 244ns
[Sample size: 4096 messages] 50: 101ns	75: 130ns	90: 154ns	99: 179ns	99.9: 216ns	99.99: 255ns	99.999: 276ns	99.9999: 276ns

This is WIP, so the API is unstable at the moment - there are no guarantees about anything

Also: https://github.com/kellabyte/go-benchmarks/tree/master/queues Special thanks to @kellabyte and @egonelbre

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