A low-latency thread-safe queue in golang implemented using a lock-free ringbuffer and runtime internals
Based on the LMAX Disruptor Pattern
- Much faster than native channels in both SPSC (single-producer-single-consumer) and MPSC (multi-producer-single-consumer) modes in terms of
time/op - More resource efficient in terms of
memory_allocation/opandnum_allocations/opevident while benchmarking large batch size inputs - Handles the case where NUM_WRITER_GOROUTINES > NUM_CPU_CORES much better than native channels
- Selection from multiple ZenQs just like golang's
select{}ensuring fair selection and no starvation - Closing a ZenQ
Benchmarks to support the above claims here
You need Golang 1.19.x or above
$ go get github.com/alphadose/zenq/v2- Simple Read/Write
package main
import (
"fmt"
"github.com/alphadose/zenq/v2"
)
type payload struct {
alpha int
beta string
}
func main() {
zq := zenq.New[payload](10)
for j := 0; j < 5; j++ {
go func() {
for i := 0; i < 20; i++ {
zq.Write(payload{
alpha: i,
beta: fmt.Sprint(i),
})
}
}()
}
for i := 0; i < 100; i++ {
if data, queueOpen := zq.Read(); queueOpen {
fmt.Printf("%+v\n", data)
}
}
}- Selection from multiple ZenQs just like golang's native
select{}. The selection process is fair i.e no single ZenQ gets starved
package main
import (
"fmt"
"github.com/alphadose/zenq/v2"
)
type custom1 struct {
alpha int
beta string
}
type custom2 struct {
gamma int
}
const size = 100
var (
zq1 = zenq.New[int](size)
zq2 = zenq.New[string](size)
zq3 = zenq.New[custom1](size)
zq4 = zenq.New[*custom2](size)
)
func main() {
go looper(intProducer)
go looper(stringProducer)
go looper(custom1Producer)
go looper(custom2Producer)
for i := 0; i < 40; i++ {
// Selection occurs here
if data := zenq.Select(zq1, zq2, zq3, zq4); data != nil {
switch data.(type) {
case int:
fmt.Printf("Received int %d\n", data)
case string:
fmt.Printf("Received string %s\n", data)
case custom1:
fmt.Printf("Received custom data type number 1 %#v\n", data)
case *custom2:
fmt.Printf("Received pointer %#v\n", data)
}
}
}
}
func intProducer(ctr int) { zq1.Write(ctr) }
func stringProducer(ctr int) { zq2.Write(fmt.Sprint(ctr * 10)) }
func custom1Producer(ctr int) { zq3.Write(custom1{alpha: ctr, beta: fmt.Sprint(ctr)}) }
func custom2Producer(ctr int) { zq4.Write(&custom2{gamma: 1 << ctr}) }
func looper(producer func(ctr int)) {
for i := 0; i < 10; i++ {
producer(i)
}
}Benchmarking code available here
Note that if you run the benchmarks with --race flag then ZenQ will perform slower because the --race flag slows
down the atomic operations in golang. Under normal circumstances, ZenQ will outperform golang native channels.
❯ neofetch
'c. [email protected]
,xNMM. ----------------------
.OMMMMo OS: macOS 12.3 21E230 arm64
OMMM0, Host: MacBookAir10,1
.;loddo:' loolloddol;. Kernel: 21.4.0
cKMMMMMMMMMMNWMMMMMMMMMM0: Uptime: 6 hours, 41 mins
.KMMMMMMMMMMMMMMMMMMMMMMMWd. Packages: 86 (brew)
XMMMMMMMMMMMMMMMMMMMMMMMX. Shell: zsh 5.8
;MMMMMMMMMMMMMMMMMMMMMMMM: Resolution: 1440x900
:MMMMMMMMMMMMMMMMMMMMMMMM: DE: Aqua
.MMMMMMMMMMMMMMMMMMMMMMMMX. WM: Rectangle
kMMMMMMMMMMMMMMMMMMMMMMMMWd. Terminal: iTerm2
.XMMMMMMMMMMMMMMMMMMMMMMMMMMk Terminal Font: FiraCodeNerdFontComplete-Medium 16 (normal)
.XMMMMMMMMMMMMMMMMMMMMMMMMK. CPU: Apple M1
kMMMMMMMMMMMMMMMMMMMMMMd GPU: Apple M1
;KMMMMMMMWXXWMMMMMMMk. Memory: 1370MiB / 8192MiB
.cooc,. .,coo:.
- NUM_WRITERS -> The number of goroutines concurrently writing to ZenQ/Channel
- INPUT_SIZE -> The number of input payloads to be passed through ZenQ/Channel from producers to consumer
Computed from benchstat of 30 benchmarks each via go test -benchmem -bench=. benchmarks/simple/*.go
name time/op
_Chan_NumWriters1_InputSize600-8 23.2µs ± 1%
_ZenQ_NumWriters1_InputSize600-8 17.9µs ± 1%
_Chan_NumWriters3_InputSize60000-8 5.27ms ± 3%
_ZenQ_NumWriters3_InputSize60000-8 2.36ms ± 2%
_Chan_NumWriters8_InputSize6000000-8 671ms ± 2%
_ZenQ_NumWriters8_InputSize6000000-8 234ms ± 6%
_Chan_NumWriters100_InputSize6000000-8 1.59s ± 4%
_ZenQ_NumWriters100_InputSize6000000-8 309ms ± 2%
_Chan_NumWriters1000_InputSize7000000-8 1.97s ± 0%
_ZenQ_NumWriters1000_InputSize7000000-8 389ms ± 4%
_Chan_Million_Blocking_Writers-8 10.4s ± 2%
_ZenQ_Million_Blocking_Writers-8 2.32s ±21%
name alloc/op
_Chan_NumWriters1_InputSize600-8 0.00B
_ZenQ_NumWriters1_InputSize600-8 0.00B
_Chan_NumWriters3_InputSize60000-8 109B ±68%
_ZenQ_NumWriters3_InputSize60000-8 24.6B ±107%
_Chan_NumWriters8_InputSize6000000-8 802B ±241%
_ZenQ_NumWriters8_InputSize6000000-8 1.18kB ±100%
_Chan_NumWriters100_InputSize6000000-8 44.2kB ±41%
_ZenQ_NumWriters100_InputSize6000000-8 10.7kB ±38%
_Chan_NumWriters1000_InputSize7000000-8 476kB ± 8%
_ZenQ_NumWriters1000_InputSize7000000-8 90.6kB ±10%
_Chan_Million_Blocking_Writers-8 553MB ± 0%
_ZenQ_Million_Blocking_Writers-8 122MB ± 3%
name allocs/op
_Chan_NumWriters1_InputSize600-8 0.00
_ZenQ_NumWriters1_InputSize600-8 0.00
_Chan_NumWriters3_InputSize60000-8 0.00
_ZenQ_NumWriters3_InputSize60000-8 0.00
_Chan_NumWriters8_InputSize6000000-8 2.76 ±190%
_ZenQ_NumWriters8_InputSize6000000-8 5.47 ±83%
_Chan_NumWriters100_InputSize6000000-8 159 ±26%
_ZenQ_NumWriters100_InputSize6000000-8 25.1 ±39%
_Chan_NumWriters1000_InputSize7000000-8 1.76k ± 6%
_ZenQ_NumWriters1000_InputSize7000000-8 47.3 ±31%
_Chan_Million_Blocking_Writers-8 2.00M ± 0%
_ZenQ_Million_Blocking_Writers-8 1.00M ± 0%The above results show that ZenQ is more efficient than channels in all 3 metrics i.e time/op, mem_alloc/op and num_allocs/op for the following tested cases:-
- SPSC
- MPSC with NUM_WRITER_GOROUTINES < NUM_CPU_CORES
- MPSC with NUM_WRITER_GOROUTINES > NUM_CPU_CORES
In SPSC mode ZenQ is faster than channels by 92 seconds in case of input size of 6 * 108 elements
❯ go run benchmarks/simple/main.go
With Input Batch Size: 60 and Num Concurrent Writers: 1
Native Channel Runner completed transfer in: 26.916µs
ZenQ Runner completed transfer in: 20.292µs
====================================================================
With Input Batch Size: 600 and Num Concurrent Writers: 1
Native Channel Runner completed transfer in: 135.75µs
ZenQ Runner completed transfer in: 105.792µs
====================================================================
With Input Batch Size: 6000 and Num Concurrent Writers: 1
Native Channel Runner completed transfer in: 2.100209ms
ZenQ Runner completed transfer in: 510.792µs
====================================================================
With Input Batch Size: 6000000 and Num Concurrent Writers: 1
Native Channel Runner completed transfer in: 1.241481917s
ZenQ Runner completed transfer in: 226.068209ms
====================================================================
With Input Batch Size: 600000000 and Num Concurrent Writers: 1
Native Channel Runner completed transfer in: 1m55.074638875s
ZenQ Runner completed transfer in: 22.582667917s
====================================================================