Samtidig forklaring: Sådan oprettes en iOS-app med flere tråde

Samtidighed i iOS er et massivt emne. Så i denne artikel vil jeg zoome ind på et underemne vedrørende køer og Grand Central Dispatch (GCD) -rammen.

Især ønsker jeg at undersøge forskellene mellem serielle og samtidige køer samt forskellene mellem synkron og asynkron udførelse.

Hvis du aldrig har brugt GCD før, er denne artikel et godt sted at starte. Hvis du har nogle erfaringer med GCD, men stadig er nysgerrig efter de emner, der er nævnt ovenfor, tror jeg, du stadig vil finde det nyttigt. Og jeg håber, du vil hente en eller to nye ting undervejs.

Jeg oprettede en SwiftUI-ledsager-app for visuelt at demonstrere koncepterne i denne artikel. Appen har også en sjov kort quiz, som jeg opfordrer dig til at prøve før og efter at have læst denne artikel. Download kildekoden her, eller få den offentlige beta her.

Jeg begynder med en introduktion til GCD efterfulgt af en detaljeret forklaring på synkronisering, asynkronisering, seriel og samtidig. Bagefter vil jeg dække nogle faldgruber, når jeg arbejder med samtidighed. Endelig slutter jeg med et resumé og nogle generelle råd.

Introduktion

Lad os starte med en kort introduktion til GCD og sende køer. Du er velkommen til at springe til Sync vs Async- sektionen, hvis du allerede er bekendt med emnet.

Samtidighed og Grand Central forsendelse

Samtidighed giver dig mulighed for at drage fordel af, at din enhed har flere CPU-kerner. For at gøre brug af disse kerner skal du bruge flere tråde. Tråde er dog et lavt niveau værktøj, og det er ekstremt vanskeligt at styre tråde manuelt på en effektiv måde.

Grand Central Dispatch blev oprettet af Apple for over 10 år siden som en abstraktion for at hjælpe udviklere med at skrive multi-threaded kode uden manuelt at oprette og administrere trådene selv.

Med GCD tog Apple en asynkron designtilgangtil problemet. I stedet for at oprette tråde direkte bruger du GCD til at planlægge arbejdsopgaver, og systemet udfører disse opgaver for dig ved at udnytte ressourcerne bedst muligt. GCD håndterer oprettelse af de nødvendige tråde og planlægger dine opgaver på disse tråde og flytter byrden ved trådadministration fra udvikleren til systemet.

En stor fordel ved GCD er, at du ikke behøver at bekymre dig om hardwarressourcer, når du skriver din samtidige kode. GCD administrerer en trådpulje til dig, og den skaleres fra et enkeltkernet Apple Watch helt op til en mange-kerne MacBook Pro.

Afsendelse køer

Dette er de vigtigste byggesten i GCD, som giver dig mulighed for at udføre vilkårlige kodeblokke ved hjælp af et sæt parametre, som du definerer. Opgaverne i ekspeditionskøer startes altid på en først ind, først ud (FIFO) måde. Bemærk, at jeg sagde startede , fordi afslutningstiden for dine opgaver afhænger af flere faktorer og er ikke garanteret at være FIFO (mere om det senere.)

Generelt er der tre slags køer tilgængelige for dig:

  • Hovedforsendelseskøen (seriel, foruddefineret)
  • Globale køer (samtidige, foruddefinerede)
  • Private køer (kan være serielle eller samtidige, du opretter dem)

Hver app kommer med en Main kø, som er en seriel kø, som henretter opgaver på den røde tråd. Denne kø er ansvarlig for at tegne din applikations brugergrænseflade og reagere på brugerinteraktioner (berør, rul, panorering osv.) Hvis du blokerer denne kø for længe, ​​ser din iOS-app ud til at fryse, og din macOS-app viser den berygtede strand kugle / drejehjul.

Når vi udfører en langvarig opgave (netværksopkald, beregningsintensivt arbejde osv.), Undgår vi at fryse brugergrænsefladen ved at udføre dette arbejde i en baggrundskø. Derefter opdaterer vi brugergrænsefladen med resultaterne i hovedkøen:

URLSession.shared.dataTask(with: url) { data, response, error in if let data = data { DispatchQueue.main.async { // UI work self.label.text = String(data: data, encoding: .utf8) } } }

Som en tommelfingerregel skal alt UI-arbejde udføres i hovedkøen. Du kan slå hovedtrådkontrollen til i Xcode for at modtage advarsler, når UI-arbejde udføres på en baggrundstråd.

hovedtrådkontrollen kan findes i skemaeditoren

Ud over hovedkøen kommer hver app med flere foruddefinerede samtidige køer, der har forskellige niveauer af servicekvalitet (en abstrakt forestilling om prioritet i GCD.)

For eksempel er her koden for at indsende arbejde asynkront til brugerens interaktive (højeste prioritet) QoS-kø:

DispatchQueue.global(qos: .userInteractive).async { print("We're on a global concurrent queue!") }

Alternativt kan du kalde den globale prioritetskø ved ikke at angive en QoS som denne:

DispatchQueue.global().async { print("Generic global queue") }

Derudover kan du oprette dine egne private køer ved hjælp af følgende syntaks:

let serial = DispatchQueue(label: "com.besher.serial-queue") serial.async { print("Private serial queue") }

Når du opretter private køer, hjælper det med at bruge en beskrivende etiket (såsom omvendt DNS-notation), da dette vil hjælpe dig under fejlfinding i Xcodes navigator, lldb og Instruments:

Som standard er private køer serielle (jeg forklarer, hvad dette betyder snart, lover!) Hvis du vil oprette en privat samtidig kø, kan du gøre det via parameteren valgfri attributter :

let concurrent = DispatchQueue(label: "com.besher.serial-queue", attributes: .concurrent) concurrent.sync { print("Private concurrent queue") }

Der er også en valgfri QoS-parameter. De private køer, du opretter, lander i sidste ende i en af ​​de globale samtidige køer baseret på deres givne parametre.

Hvad er der i en opgave?

Jeg nævnte at sende opgaver til køer. Opgaver kan henvise til enhver blok kode, du sender til en kø ved hjælp af funktionerne synceller async. De kan indsendes i form af en anonym lukning:

DispatchQueue.global().async { print("Anonymous closure") }

Eller inde i et forsendelsesarbejde, der bliver udført senere:

let item = DispatchWorkItem(qos: .utility) { print("Work item to be executed later") }

Uanset om du sender synkront eller asynkront, og om du vælger en seriel eller samtidig kø, udføres hele koden i en enkelt opgave linje for linje. Samtidighed er kun relevant ved evaluering af flere opgaver.

For eksempel, hvis du har 3 sløjfer inden for samme opgave, udføres disse sløjfer altid i rækkefølge:

DispatchQueue.global().async { for i in 0..<10 { print(i) } for _ in 0..<10 { print("?") } for _ in 0..<10 { print("?") } }

Denne kode udskriver altid ti cifre fra 0 til 9 efterfulgt af ti blå cirkler efterfulgt af ti ødelagte hjerter, uanset hvordan du sender den lukning.

Individuelle opgaver kan også have deres eget QoS-niveau (som standard bruger de deres køs prioritet.) Denne skelnen mellem kø QoS og opgave QoS fører til noget interessant opførsel, som vi vil diskutere i sektionen om prioritetsinversion.

Nu spekulerer du måske på, hvad seriel og samtidig handler om. Du undrer dig måske også over forskellene mellem syncog asyncnår du indsender dine opgaver. Dette bringer os til kernen i denne artikel, så lad os dykke ind!

Synkronisering vs Asynkronisering

Når du sender en opgave til en kø, kan du vælge at gøre det synkront eller asynkront ved hjælp af syncog asyncafsendelsesfunktionerne. Synkronisering og asynkronisering påvirker primært kilden til den indsendte opgave, det vil sige køen, hvorfra den sendes fra .

Når din kode når en syncerklæring, blokerer den den aktuelle kø, indtil opgaven er afsluttet. Når opgaven vender tilbage / er afsluttet, returneres kontrol til den, der ringer op, og koden, der følger syncopgaven, fortsætter.

Tænk på det syncsom synonymt med 'blokering'.

En asyncerklæring vil derimod udføre asynkront i forhold til den aktuelle kø og returnerer straks kontrollen tilbage til den, der ringer op, uden at vente på, at indholdet af asynclukningen skal udføres. Der er ingen garanti for, hvornår nøjagtigt koden inde i den asynkroniseringslukning udføres.

Nuværende kø?

Det er muligvis ikke indlysende, hvad kilden eller den aktuelle kø er, fordi den ikke altid er udtrykkeligt defineret i koden.

For example, if you call your sync statement inside viewDidLoad, your current queue will be the Main dispatch queue. If you call the same function inside a URLSession completion handler, your current queue will be a background queue.

Going back to sync vs async, let’s take this example:

DispatchQueue.global().sync { print("Inside") } print("Outside") // Console output: // Inside // Outside

The above code will block the current queue, enter the closure and execute its code on the global queue by printing “Inside”, before proceeding to print “Outside”. This order is guaranteed.

Let’s see what happens if we try async instead:

DispatchQueue.global().async { print("Inside") } print("Outside") // Potential console output (based on QoS): // Outside // Inside

Our code now submits the closure to the global queue, then immediately proceeds to run the next line. It will likely print “Outside” before “Inside”, but this order isn’t guaranteed. It depends on the QoS of the source and destination queues, as well as other factors that the system controls.

Threads are an implementation detail in GCD — we do not have direct control over them and can only deal with them using queue abstractions. Nevertheless, I think it can be useful to ‘peek under the covers’ at thread behaviour to understand some challenges we might encounter with GCD.

For instance, when you submit a task using sync, GCD optimizes performance by executing that task on the current thread (the caller.)

There is one exception however, which is when you submit a sync task to the main queue —  doing so will always run the task on the main thread and not the caller. This behaviour can have some ramifications that we will explore in the priority inversion section.

Which one to use?

When submitting work to a queue, Apple recommends using asynchronous execution over synchronous execution. However, there are situations where sync might be the better choice, such as when dealing with race conditions, or when performing a very small task. I will cover these situations shortly.

One large consequence of performing work asynchronously inside a function is that the function can no longer directly return its values (if they depend on the async work that’s being done). It must instead use a closure/completion handler parameter to deliver the results.

To demonstrate this concept, let’s take a small function that accepts image data, performs some expensive computation to process the image, then returns the result:

func processImage(data: Data) -> UIImage? { guard let image = UIImage(data: data) else { return nil } // calling an expensive function let processedImage = upscaleAndFilter(image: image) return processedImage }

In this example, the function upscaleAndFilter(image:) might take several seconds, so we want to offload it into a separate queue to avoid freezing the UI. Let’s create a dedicated queue for image processing, and then dispatch the expensive function asynchronously:

let imageProcessingQueue = DispatchQueue(label: "com.besher.image-processing") func processImageAsync(data: Data) -> UIImage? { guard let image = UIImage(data: data) else { return nil } imageProcessingQueue.async { let processedImage = upscaleAndFilter(image: image) return processedImage } }

There are two issues with this code. First, the return statement is inside the async closure, so it is no longer returning a value to the processImageAsync(data:) function, and currently serves no purpose.

But the bigger issue is that our processImageAsync(data:) function is no longer returning any value, because the function reaches the end of its body before it enters the async closure.

To fix this error, we will adjust the function so that it no longer directly returns a value. Instead, it will have a new completion handler parameter that we can call once our asynchronous function has completed its work:

let imageProcessingQueue = DispatchQueue(label: "com.besher.image-processing") func processImageAsync(data: Data, completion: @escaping (UIImage?) -> Void) { guard let image = UIImage(data: data) else { completion(nil) return } imageProcessingQueue.async { let processedImage = self.upscaleAndFilter(image: image) completion(processedImage) } }

As evident in this example, the change to make the function asynchronous has propagated to its caller, who now has to pass in a closure and handle the results asynchronously as well. By introducing an asynchronous task, you can potentially end up modifying a chain of several functions.

Concurrency and asynchronous execution add complexity to your project as we just observed. This indirection also makes debugging more difficult. That’s why it really pays off to think about concurrency early in your design — it’s not something you want to tack on at the end of your design cycle.

Synchronous execution, by contrast, does not increase complexity. Rather, it allows you to continue using return statements as you did before. A function containing a sync task will not return until the code inside that task has completed. Therefore it does not require a completion handler.

If you are submitting a tiny task (for example, updating a value), consider doing it synchronously. Not only does that help you keep your code simple, it will also perform better — Async is believed to incur an overhead that outweighs the benefit of doing the work asynchronously for tiny tasks that take under 1ms to complete.

If you are submitting a large task, however, like the image processing we performed above, then consider doing it asynchronously to avoid blocking the caller for too long.

Dispatching on the same queue

While it is safe to dispatch a task asynchronously from a queue into itself (for example, you can use .asyncAfter on the current queue), you can not dispatch a task synchronously from a queue into the same queue. Doing so will result in a deadlock that immediately crashes the app!

This issue can manifest itself when performing a chain of synchronous calls that lead back to the original queue. That is, you sync a task onto another queue, and when the task completes, it syncs the results back into the original queue, leading to a deadlock. Use async to avoid such crashes.

Blocking the main queue

Dispatching tasks synchronously from the main queue will block that queue, thereby freezing the UI, until the task is completed. Thus it’s better to avoid dispatching work synchronously from the main queue unless you’re performing really light work.

Serial vs Concurrent

Serial and concurrent affect the destination —  the queue in which your work has been submitted to run. This is in contrast to sync and async, which affected the source.

A serial queue will not execute its work on more than one thread at a time, regardless of how many tasks you dispatch on that queue. Consequently, the tasks are guaranteed to not only start, but also terminate, in first-in, first-out order.

Moreover, when you block a serial queue (using a sync call, semaphore, or some other tool), all work on that queue will halt until the block is over.

A concurrent queue can spawn multiple threads, and the system decides how many threads are created. Tasks always start in FIFO order, but the queue does not wait for tasks to finish before starting the next task, therefore tasks on concurrent queues can finish in any order.

When you perform a blocking command on a concurrent queue, it will not block the other threads on this queue. Additionally, when a concurrent queue gets blocked, it runs the risk of thread explosion. I will cover this in more detail later on.

The main queue in your app is serial. All the global pre-defined queues are concurrent. Any private dispatch queue you create is serial by default, but can be set to be concurrent using an optional attribute as discussed earlier.

It’s important to note here that the concept of serial vs concurrent is only relevant when discussing a specific queue. All queues are concurrent relative to each other.

That is, if you dispatch work asynchronously from the main queue to a private serial queue, that work will be completed concurrently with respect to the main queue. And if you create two different serial queues, and then perform blocking work on one of them, the other queue is unaffected.

To demonstrate the concurrency of multiple serial queues, let’s take this example:

let serial1 = DispatchQueue(label: "com.besher.serial1") let serial2 = DispatchQueue(label: "com.besher.serial2") serial1.async { for _ in 0..<5 { print("?") } } serial2.async { for _ in 0..<5 { print("?") } }

Both queues here are serial, but the results are jumbled up because they execute concurrently in relation to each other. The fact that they’re each serial (or concurrent) has no effect on this result. Their QoS level determines who will generally finish first (order not guaranteed).

If we want to ensure that the first loop finishes first before starting the second loop, we can submit the first task synchronously from the caller:

let serial1 = DispatchQueue(label: "com.besher.serial1") let serial2 = DispatchQueue(label: "com.besher.serial2") serial1.sync { // <---- we changed this to 'sync' for _ in 0..<5 { print("?") } } // we don't get here until first loop terminates serial2.async { for _ in 0..<5 { print("?") } }

This is not necessarily desirable, because we are now blocking the caller while the first loop is executing.

To avoid blocking the caller, we can submit both tasks asynchronously, but to the same serial queue:

let serial = DispatchQueue(label: "com.besher.serial") serial.async { for _ in 0..<5 { print("?") } } serial.async { for _ in 0..<5 { print("?") } } 

Now our tasks execute concurrently with respect to the caller, while also keeping their order intact.

Note that if we make our single queue concurrent via the optional parameter, we go back to the jumbled results, as expected:

let concurrent = DispatchQueue(label: "com.besher.concurrent", attributes: .concurrent) concurrent.async { for _ in 0..<5 { print("?") } } concurrent.async { for _ in 0..<5 { print("?") } }

Sometimes you might confuse synchronous execution with serial execution (at least I did), but they are very different things. For example, try changing the first dispatch on line 3 from our previous example to a sync call:

let concurrent = DispatchQueue(label: "com.besher.concurrent", attributes: .concurrent) concurrent.sync { for _ in 0..<5 { print("?") } } concurrent.async { for _ in 0..<5 { print("?") } }

Suddenly, our results are back in perfect order. But this is a concurrent queue, so how could that happen? Did the sync statement somehow turn it into a serial queue?

The answer is no!

This is a bit sneaky. What happened is that we did not reach the async call until the first task had completed its execution. The queue is still very much concurrent, but inside this zoomed-in section of the code. it appears as if it were serial. This is because we are blocking the caller, and not proceeding to the next task, until the first one is finished.

If another queue somewhere else in your app tried submitting work to this same queue while it was still executing the sync statement, that work will run concurrently with whatever we got running here, because it’s still a concurrent queue.

Which one to use?

Serial queues take advantage of CPU optimizations and caching, and help reduce context switching.

Apple recommends starting with one serial queue per subsystem in your app —  for example one for networking, one for file compression, etc. If the need arises, you can later expand to a hierarchy of queues per subsystem using the setTarget method or the optional target parameter when building queues.

If you run into a performance bottleneck, measure your app’s performance then see if a concurrent queue helps. If you do not see a measurable benefit, it’s better to stick to serial queues.

Pitfalls

Priority Inversion and Quality of Service

Priority inversion is when a high priority task is prevented from running by a lower priority task, effectively inverting their relative priorities.

This situation often occurs when a high QoS queue shares a resources with a low QoS queue, and the low QoS queue gets a lock on that resource.

But I wish to cover a different scenario that is more relevant to our discussion —  it’s when you submit tasks to a low QoS serial queue, then submit a high QoS task to that same queue. This scenario also results in priority inversion, because the high QoS task has to wait on the lower QoS tasks to finish.

GCD resolves priority inversion by temporarily raising the QoS of the queue that contains the low priority tasks that are ‘ahead’ of, or blocking, your high priority task.

It’s kind of like having cars stuck in frontof an ambulance. Suddenly they’re allowed to cross the red light just so that the ambulance can move (in reality the cars move to the side, but imagine a narrow (serial) street or something, you get the point :-P)

To illustrate the inversion problem, let’s start with this code:

 enum Color: String { case blue = "?" case white = "⚪️" } func output(color: Color, times: Int) { for _ in 1...times { print(color.rawValue) } } let starterQueue = DispatchQueue(label: "com.besher.starter", qos: .userInteractive) let utilityQueue = DispatchQueue(label: "com.besher.utility", qos: .utility) let backgroundQueue = DispatchQueue(label: "com.besher.background", qos: .background) let count = 10 starterQueue.async { backgroundQueue.async { output(color: .white, times: count) } backgroundQueue.async { output(color: .white, times: count) } utilityQueue.async { output(color: .blue, times: count) } utilityQueue.async { output(color: .blue, times: count) } // next statement goes here }

We create a starter queue (where we submit the tasks from), as well as two queues with different QoS. Then we dispatch tasks to each of these two queues, each task printing out an equal number of circles of a specific colour (utility queueis blue, background is white.)

Because these tasks are submitted asynchronously, every time you run the app, you’re going to see slightly different results. However, as you would expect, the queue with the lower QoS (background) almost always finishes last. In fact, the last 10–15 circles are usually all white.

But watch what happens when we submit a sync task to the background queue after the last async statement. You don’t even need to print anything inside the sync statement, just adding this line is enough:

// add this after the last async statement, // still inside starterQueue.async backgroundQueue.sync {}

The results in the console have flipped! Now, the higher priority queue (utility) always finishes last, and the last 10–15 circles are blue.

To understand why that happens, we need to revisit the fact that synchronous work is executed on the caller thread (unless you’re submitting to the main queue.)

In our example above, the caller (starterQueue) has the top QoS (userInteractive.) Therefore, that seemingly innocuous sync task is not only blocking the starter queue, but it’s also running on the starter’s high QoS thread. The task therefore runs with high QoS, but there are two other tasks ahead of it on the same background queue that have background QoS. Priority inversion detected!

As expected, GCD resolves this inversion by raising the QoS of the entire queue to temporarily match the high QoS task. Consequently, all the tasks on the background queue end up running at user interactive QoS, which is higher than the utility QoS. And that’s why the utility tasks finish last!

Side-note: If you remove the starter queue from that example and submit from the main queue instead, you will get similar results, as the main queue also has user interactive QoS.

To avoid priority inversion in this example, we need to avoid blocking the starter queue with the sync statement. Using async would solve that problem.

Although it’s not always ideal, you can minimize priority inversions by sticking to the default QoS when creating private queues or dispatching to the global concurrent queue.

Thread explosion

When you use a concurrent queue, you run the risk of thread explosion if you’re not careful. This can happen when you try to submit tasks to a concurrent queue that is currently blocked (for example with a semaphore, sync, or some other way.) Your tasks will run, but the system will likely end up spinning up new threads to accommodate these new tasks, and threads aren’t cheap.

This is likely why Apple suggests starting with a serial queue per subsystem in your app, as each serial queue can only use one thread. Remember that serial queues are concurrent in relationto other queues, so you still get a performance benefit when you offload your work to a queue, even if it isn’t concurrent.

Race conditions

Swift Arrays, Dictionaries, Structs, and other value types are not thread-safe by default. For example, when you have multiple threads trying to access and modify the same array, you will start running into trouble.

There are different solutions to the readers-writers problem, such as using locks or semaphores. But the relevant solution I wish to discuss here is the use of an isolation queue.

Let’s say we have an array of integers, and we want to submit asynchronous work that references this array. As long as our work only reads the array and does not modify it, we are safe. But as soon as we try to modify the array in one of our asynchronous tasks, we will introduce instability in our app.

It’s a tricky problem because your app can run 10 times without issues, and then it crashes on the 11th time. One very handy tool for this situation is the Thread Sanitizer in Xcode. Enabling this option will help you identify potential race conditions in your app.

tråd sanitizer kan fås i skema editoren

To demonstrate the problem, let’s take this (admittedly contrived) example:

class ViewController: UIViewController { let concurrent = DispatchQueue(label: "com.besher.concurrent", attributes: .concurrent) var array = [1,2,3,4,5] override func viewDidLoad() { for _ in 0...1 { race() } } func race() { concurrent.async { for i in self.array { // read access print(i) } } concurrent.async { for i in 0..<10 { self.array.append(i) // write access } } } }

One of the async tasks is modifying the array by appending values. If you try running this on your simulator, you might not crash. But run it enough times (or increase the loop frequency on line 7), and you will eventually crash. If you enable the thread sanitizer, you will get a warning every time you run the app.

To deal with this race condition, we are going to add an isolation queue that uses the barrier flag. This flag allows any outstanding tasks on the queue to finish, but blocks any further tasks from executing until the barrier task is completed.

Think of the barrier like a janitor cleaning a public restroom (shared resource.) There are multiple (concurrent) stalls inside the restroom that people can use.

Upon arrival, the janitor places a cleaning sign (barrier) blocking any newcomers from entering until the cleaning is done, but the janitor does not start cleaning until all the people inside have finished their business. Once they all leave, the janitor proceeds to clean the public restroom in isolation.

When finally done, the janitor removes the sign (barrier) so that the people who are queued up outside can finally enter.

Here’s what that looks like in code:

class ViewController: UIViewController { let concurrent = DispatchQueue(label: "com.besher.concurrent", attributes: .concurrent) let isolation = DispatchQueue(label: "com.besher.isolation", attributes: .concurrent) private var _array = [1,2,3,4,5] var threadSafeArray: [Int] { get { return isolation.sync { _array } } set { isolation.async(flags: .barrier) { self._array = newValue } } } override func viewDidLoad() { for _ in 0...15 { race() } } func race() { concurrent.async { for i in self.threadSafeArray { print(i) } } concurrent.async { for i in 0..<10 { self.threadSafeArray.append(i) } } } }

We have added a new isolation queue, and restricted access to the private array using a getter and setter that will place a barrier when modifying the array.

The getter needs to be sync in order to directly return a value. The setter can be async, as we don’t need to block the caller while the write is taking place.

We could have used a serial queue without a barrier to solve the race condition, but then we would lose the advantage of having concurrent read access to the array. Perhaps that makes sense in your case, you get to decide.

Conclusion

Thank you so much for reading this far! I hope you learned something new from this article. I will leave you with a summary and some general advice:

Summary

  • Queues always start their tasks in FIFO order
  • Queues are always concurrent relative to other queues
  • Sync vs Async concerns the source
  • Serial vs Concurrent concerns the destination
  • Sync is synonymous with ‘blocking’
  • Async immediately returns control to caller
  • Serial uses a single thread, and guarantees order of execution
  • Concurrent uses multiple-threads, and risks thread explosion
  • Think about concurrency early in your design cycle
  • Synchronous code is easier to reason about and debug
  • Avoid relying on global concurrent queues if possible
  • Consider starting with a serial queue per subsystem
  • Switch to concurrent queue only if you see a measurable performance benefit

Jeg kan godt lide metaforen fra Swift Concurrency Manifesto om at have en 'ø med serialisering i et hav af samtidighed'. Denne stemning blev også delt i dette tweet af Matt Diephouse:

Hemmeligheden ved at skrive samtidig kode er at gøre det meste serielt. Begræns samtidighed til et lille, ydre lag. (Seriekerne, samtidig skal.)

f.eks. i stedet for at bruge en lås til at administrere 5 egenskaber, skal du oprette en ny type, der omslutter dem og bruge en enkelt egenskab inde i låsen.

- Matt Diephouse (@mdiep) 18. december 2019

Når du anvender samtidighed med denne filosofi i tankerne, tror jeg, det vil hjælpe dig med at opnå samtidig kode, der kan begrundes uden at gå tabt i et rod af tilbagekald.

Hvis du har spørgsmål eller kommentarer, er du velkommen til at kontakte mig på Twitter

Besher Al Maleh

Forsidefoto af Onur K på Unsplash

Download den ledsagende app her:

almaleh / Dispatcher Companion app til min artikel om samtidighed. Bidrag til almaleh / Dispatcher-udvikling ved at oprette en konto på GitHub. almaleh GitHub

Tjek nogle af mine andre artikler:

Fyrværkeri - En visuel partikelditor til Swift Generer Swift-kode i farten til macOS og iOS, mens du designer og gentager dine partikeleffekter Besher Al Maleh Fejlfri iOS Du har ikke (altid) brug for [svagt selv] I denne artikel, vi ' Jeg taler om svagt selv inde i Swift-lukninger for at undgå at bevare cyklusser og udforske tilfælde, hvor det måske eller ikke er nødvendigt at fange sig selv svagt. Besher Al Maleh fejlfri iOS

Yderligere læsning:

Introduktion Forklarer, hvordan man implementerer samtidige kodestier i en applikation. Samtidig programmering: API'er og udfordringer · objc.io objc.io udgiver bøger om avancerede teknikker og fremgangsmåder til iOS og OS X-udvikling Florian Kugler Low-Level Concurrency API'er · objc.io objc.io udgiver bøger om avancerede teknikker og fremgangsmåder til iOS og OS X-udvikling Daniel Eggert

//khanlou.com/2016/04/the-GCD-handbook/

Samtidig vs seriel kø i GCD Jeg kæmper for fuldt ud at forstå de samtidige og serielle køer i GCD. Jeg har nogle problemer og håber, at nogen kan svare mig tydeligt og lige nu. Jeg læser, at der oprettes serielle køer ... Bogdan Alexandru Stack Overflow

WWDC-videoer:

Modernisering af Grand Central Dispatch-brug - WWDC 2017 - Videoer - Apple Developer macOS 10.13 og iOS 11 har genopfundet, hvordan Grand Central Dispatch og Darwin-kernen samarbejder, så dine applikationer kan køre ... Apple Developer Building Responsive og effektive apps med GCD - WWDC 2015 - Videoer - Apple Developer watchOS og iOS Multitasking stiller øgede krav til din applikations effektivitet og lydhørhed. Med ekspertvejledning fra ... Apple-udvikleren