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README.md

Notes Concurrency in Go

In six chapters building block, patterns, scaling issues and internals are discussed.

1 Intro

It's hard.

Coffman Conditions

If at least one of the conditions is not true, we can prevent deadlocks (but it's hard to reason about code).

Livelocks

Busy, no progress (two or more process attempt preventing a deadlock without coordination). Subset of Starvation.

Starvation

One or more greedy process. Livelock a special case, since no process makes progress. Example: polite worker, keep critical section short.

Is it safe?

Reduce API ambiguity, be explicit or do not expose concurrency at all.

Simplicity in the Face of Complexity

with Go’s concurrency primitives, you can more safely and clearly express your concurrent algorithms.

Understanding Real-World Concurrency Bugs in Go suggested otherwise, no?

2 Modeling

Concurrency != Parallelism

Concurrency is a property of the code; parallelism is a property of the running program.

  • code is not parallel — we hope it will run in parallel

If you wanted to write concurrent code, you would model your program in terms of threads and synchronize the access to the memory between them.

What is CSP?

  • 1978
  • input and output are overlooked properties of (concurrent) programs
  • process calculus

A process requires input to run. Other processed might consume output.

How does it help?

Goroutines may help shift thinking about parallelism to thinking about concurrency.

What would a few questions be, if you would need to implement a web server with threads?

  • language support
  • design, thread boundaries
  • optimal number for a pool

If we step back and think about the natural problem, we could state it as such: individual users are connecting to my endpoint and opening a session. The session should field their request and return a response. In Go, we can almost directly represent the natural state of this problem in code: we would create a goroutine for each incoming connection, field the request there (potentially communicating with other goroutines for data/services), and then return from the goroutine's function. How we naturally think about the problem maps directly to the natural way to code things in Go.

Side effect, separation of concerns.

A more natural mapping to the problem space is an enormous benefit, but it has a few beneficial side effects as well. Go's runtime multiplexes goroutines onto OS threads automatically and manages their scheduling for us. This means that optimizations to the runtime can be made without us having to change how we’ve modeled our prob‐ lem; this is classic separation of concerns.

More composable.

Channels, for instance, are inherently composable with other channels. This makes writing large systems simpler because you can coordinate the input from multiple subsystems by easily composing the output together. You can combine input channels with timeouts, cancellations, or messages to other subsystems. Coordinating mutexes is a much more difficult proposition.

The select statement help composition.

Go Approach

  • support CSP and classic style (locks, pool, ...)

So which should you use?

Use whichever is most expressive and/or most simple.

Thread-safe, transparent to the caller.

type Counter struct {
	mu    sync.Mutex
	value int
}

func (c *Counter) Increment() {
	c.mu.Lock()
	defer c.mu.Unlock()
	c.value++
}

Performance.

This is because channels use memory access synchronization to operate, therefore they can only be slower.

3 Building blocks

Goroutines

  • always at least one present

They're not OS threads, and they're not exactly green threads—threads that are managed by a language's runtime - they're a higher level of abstraction known as coroutines. Coroutines are simply concurrent subroutines (functions, closures, or methods in Go) that are nonpreemptive - that is, they cannot be interrupted.

Goroutines don't define their own suspension or reentry points; Go's runtime observes the runtime behavior of goroutines and automatically suspends them when they block and then resumes them when they become unblocked.

In a way this makes them preemptable, but only at points where the goroutine has become blocked.

Meet the host.

Go's mechanism for hosting goroutines is an implementation of what's called an M:N scheduler.

Map M green threads onto N OS threads. Goroutines are scheduled onto green threads. It's a fork-join model, at any point you can start a goroutine and they may join again later - through some synchronisation (e.g. a WaitGroup).

Pop quiz

  • hello or welcome - which one is it?
var wg sync.WaitGroup
salutation := "hello"
wg.Add(1)
go func() {
	defer wg.Done()
	salutation = "welcome"
}()
wg.Wait()
fmt.Println(salutation)

Next one is a bit trickier.

var wg sync.WaitGroup
for _, salutation := range []string{"hello", "greetings", "good day"} {
    wg.Add(1)
    go func() {
        defer wg.Done()
        fmt.Println(salutation)
    }()
}
wg.Wait()
  • loop might exit, before the goroutines start
  • GC won't pickup salutation because it is still referenced
  • solution: pass value as argument to goroutine

Core idea: Goroutines run in the same address space.

Size of a Goroutine

In the book: 2.817kb, on my machine 0.007kb.

Also: context switch overhead, but much better in software than on OS level.

$ sudo apt install linux-tools-generic linux-tools-5.0.0-29-generic \
    linux-cloud-tools-5.0.0-29-generic linux-cloud-tools-generic \
    perf-tools-unstable

Result from a perf test:

$ taskset -c 0 perf bench sched pipe -T
# Running 'sched/pipe' benchmark:
# Executed 1000000 pipe operations between two threads

     Total time: 6.158 [sec]

       6.158190 usecs/op
         162385 ops/sec

This benchmark actually measures the time it takes to send and receive a message on a thread, so we'll take the result and divide it by two.

So: 3us per CSW or 300k CSW/s at most. As I am writing this, ~ 2000 CSW/s.

$ make
go test -bench=. -cpu=1
goos: linux
goarch: amd64
pkg: github.com/miku/cignotes/x/csw
BenchmarkContextSwitch   6272942               191 ns/op
PASS
ok      github.com/miku/cignotes/x/csw  2.332s
  • 191ns per CSW, much better.

Package sync

The WaitGroup

WaitGroup is a great way to wait for a set of concurrent operations to > complete when you either don't care about the result of the concurrent operation, or you have other means of collecting their results.

Mutex, RWMutex

A Mutex provides a concurrent-safe way to express exclusive access to these shared resources.

  • put Unlock in defer
  • minimize critical sections

The RWMutex is more fine grained.

This means that an arbitrary number of readers can hold a reader lock so long as nothing else is holding a writer lock.

Cond

A rendezvous point for goroutines waiting for or announcing the occurrence of an event.

Inefficient checks for a condition:

for conditionTrue() == false {
}

Better:

for conditionTrue() == false {
    time.Sleep(1*time.Millisecond)
}

Better:

c := sync.NewCond(&sync.Mutex{})
c.L.Lock()
for conditionTrue() == false {
	c.Wait() // blocked - and goroutine suspended
}
c.L.Unlock()
  • NewCond takes a sync.Locker (interface)
  • Wait, Broadcast and Signal

Internally, the runtime maintains a FIFO list of goroutines waiting to be > signaled; Signal finds the goroutine that’s been waiting the longest and notifies that, whereas Broadcast sends a signal to all goroutines that are waiting. Broadcast is arguably the more interesting of the two methods as it provides a way to communicate with multiple goroutines at once.

A GUI example (but which GUI framework).

To get a feel for what it's like to use Broadcast, let's imagine we're creating a GUI application with a button on it. We want to register an arbitrary number of functions that will run when that button is clicked. A Cond is perfect for this because we can use its Broadcast method to notify all registered handlers.

Once

Odd function, often used.

$ grep -ir sync.Once $(go env GOROOT)/src | wc -l
  • book: 70, me (Go 1.13): 112

Pool

Pool is a concurrent-safe implementation of the object pool pattern.

At a high level, a the pool pattern is a way to create and make available a fixed number, or pool, of things for use. It's commonly used to constrain the creation of things that are expensive (e.g., database connections) so that only a fixed number of them are ever created, but an indeterminate number of operations can still request access to these things.

myPool := &sync.Pool{
	New: func() interface{} {
		fmt.Println("Creating new instance.")
		return struct{}{}
	},
}
myPool.Get()
instance := myPool.Get()
myPool.Put(instance)
myPool.Get()

Channels

A stream.

Like a river, a channel serves as a conduit for a stream of information; values may be passed along the channel, and then read out downstream. For this reason I usually end my chan variable names with the word "Stream."

I used done, queue or the C suffix, like outC or workC or the like.

  • typed
  • unidirectional, bidirectional
invalid operation: <-writeStream
    (receive from send-only type chan<- interface {})

invalid operation: readStream <- struct {} literal
    (send to receive-only type <-chan interface {})

The channel receive can return two values.

stringStream := make(chan string)
go func() {
	stringStream <- "Hello channels!"
}()
salutation, ok := <-stringStream
fmt.Printf("(%v): %v", ok, salutation)

The second return value is a way for a read operation to indicate whether the read off the channel was a value generated by a write elsewhere in the process, or a default value generated from a closed channel.

Channels have states: active or closed.

  • close makes for a universal sentinel value

It is ok to read from a closed channel (but writing panics).

This is to allow support for multiple downstream reads from a single upstream writer on the channel.

  • You can range over channels.
  • You can create buffered channels

An unbuffered channel has a capacity of zero and so it’s already full before any writes. A buffered channel with no receivers and a capacity of four would be full after four writes, and block on the fifth write since it has nowhere else to place the fifth element. Like unbuffered channels, buffered channels are still blocking; the preconditions that the channel be empty or full are just different. In this way, buffered channels are an in-memory FIFO queue for concurrent processes to communicate over.

  • default value for channel: nil

Table of channel states and results.

Encapsulation and ownership.

Example:

chanOwner := func() <-chan int {
	resultStream := make(chan int, 5)
	go func() {
		defer close(resultStream)
		for i := 0; i <= 5; i++ {
			resultStream <- i
		}
	}()
	return resultStream
}
resultStream := chanOwner()
for result := range resultStream {
	fmt.Printf("Received: %d\n", result)
}
fmt.Println("Done receiving!")

Notice how the lifecycle of the resultStream channel is encapsulated within the chan Owner function. It's very clear that the writes will not happen on a nil or closed channel, and that the close will always happen once.

The select statement

If channels are the glue that binds goroutines together, what does that say about the select statement? It is not an overstatement to say that select statements are one of the most crucial things in a Go program with concurrency.

Multiple cases may be available.

c1 := make(chan interface{})
close(c1)
c2 := make(chan interface{})
close(c2)
var c1Count, c2Count int
for i := 1000; i >= 0; i-- {
	select {
	case <-c1:
		c1Count++
	case <-c2:
		c2Count++
	}
}
fmt.Printf("c1Count: %d\nc2Count: %d\n", c1Count, c2Count)
// c1Count: 505
// c2Count: 496

Why random?

A good way to do that is to introduce a random variable into your equa‐ tion—in this case, which channel to select from. By weighting the chance of each channel being utilized equally, all Go programs that utilize the select statement will perform well in the average case.

What is no channel is ready? You can use a timeout channel.

func main() {
    var c <-chan int
    select {
    case <-c:
    case <-time.After(1 * time.Second):
        fmt.Println("Timed out.")
    }
}

Finally, there is a default clause, entered when no other case can be entered. Also in conjuction with for to allow for control.

package main

import (
        "fmt"
        "time"
)

func main() {
        done := make(chan interface{})
        go func() {
                time.Sleep(5 * time.Second)
                close(done)
        }()
        workCounter := 0
loop:
        for {
                select {
                case <-done:
                        break loop
                default:
                }
                // Simulate work
                workCounter++
                time.Sleep(1 * time.Second)
        }
        fmt.Printf("Achieved %v cycles of work before signalled to stop.\n", workCounter)
}

GOMAXPROCS

  • number of OS threads used

Have you used this?

runtime.GOMAXPROCS(runtime.NumCPU())

Debug story:

By increasing GOMAXPROCS beyond the number of logical CPUs we had, we were able to trigger the race conditions much more often, and thus get them corrected faster.

4 Patterns

About twelve patterns:

  • confinement
  • for-select
  • preventing leaks
  • or-channel
  • error handling
  • pipelines
  • fan-in, fan-out
  • or-done-channel
  • tee-channel
  • bridge-channel
  • queueing
  • context package

Preventing Goroutine Leaks

Possible terminations:

  • work done
  • unrecoverable error
  • told to stop work

Example:

Error handling

Example, parallel crawler. Key: Couple result and error - in a struct. Main goroutine can decide. Separate error handling from the producer goroutine.

Fan-in, fan-out

Prime finder.

The tee-channel

Like the tee command, duplicate values - e.g. towards processing and logging.

Context package

Helps to prevent leaks, by providing ways to cancel complete trees of processing threads.

  • WithCancel (when cancelFunc is called)
  • WithDeadline (time.Time)
  • WithTimeout (time.Duration)

On top of the call-graph, use Background (empty) or TODO (also empty, just a marker).

Context can carry some request scoped data - WithValue (maybe use a custom key type for your app).

5 Scale

Problem of error propagation.

Cancellation and Timeouts

  • system saturation, stale data, attempt to prevent deadlocks
  • timeouts, user intervention, parent cancellation, replicated requests

Heartbeats

Replicated Requests

  • request hedging, run multiple request with the same target, use the fastest response

Scholarly metadata fortune cookie.

$ metha-fortune

Rate Limiting

  • token bucket algorithm
  • simple

6 Internals

Work stealing scheduler.

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