I am working with the retry operator in RxSwift. According to its documentation, it "resubscribes" to the source observable when it encounters an error.
This is all well and good. However, I'm not exactly sure how to reason about the "source observable" when it contains hot observables, or a mixture of hot/cold observables.
An example I am sure about:
let coldObservable = Observable<Int>.interval(.seconds(1), scheduler: MainScheduler.instance)
let coldObservableRetry = coldObservable.flatMapLatest { num in
if num % 3 == 0 { return .just(num) }
else { return .error() }
}
.retry(2) // retry is inclusive of original attempt
coldObservableRetry.subscribe(onNext: { print ($0)} ) // prints 1, 2, 1, 2, before erroring out.
An example I am not sure about:
let hotObservable = Observable<Int>.interval(.seconds(1), scheduler: MainScheduler.instance).publish().connect()
let hotObservableRetry = hotObservable.flatMapLatest { num in
if num % 3 == 0 { return .just(num) }
else { return .error() }
}
.retry(2)
hotObservableRetry.subscribe(onNext: { print ($0)} ) // What happens here?
Another example I am not sure about:
let coldObservable = Observable<Int>.interval(.seconds(1), scheduler: MainScheduler.instance)
let delayedHotObservable = Observable<Int>.interval(.seconds(1), scheduler: MainScheduler.instance).delay(.milliseconds(100), scheduler: MainScheduler.instance).publish().connect()
let mixtureObservableRetry = Observable.combineLatest(coldObservable, delayedHotObservable).map { $0 + $1 }.flatMapLatest { num in
if num % 3 == 0 { return .just(num) }
else { return .error() }
}
.retry(5)
mixtureObservableRetry.subscribe(onNext: { print ($0)} ) // What happens here? What does it even mean to resubscribe to a combineLatest with a hot and a cold observable?
Yet another example I am not sure about:
let coldObservable = Observable<Int>.interval(.seconds(1), scheduler: MainScheduler.instance)
let hotObservable = Observable<Int>.interval(.seconds(1), scheduler: MainScheduler.instance).publish().connect()
let mixtureObservable2Retry = coldObservable.flatMapLatest { _ in hotObservable }
.flatMapLatest { num in
if num % 3 == 0 { return .just(num) }
else { return .error() }
}
.retry(2)
mixtureObservable2Retry.subscribe(onNext: { print ($0)} ) // What happens here?
Many of your examples don't compile, so what happens is that you get an compile error. :-) But to answer the question asked...
There are some important things to keep in mind.
Each operator subscribes to its source observable(s) and generates a new observable.
When a cold observable is subscribed to, it will emit 0...N next events and then a stop event (which is either completed or error.) It will give each subscription its own set of events.
Hot observables don't start emitting until their connect is called. All the subscriptions will share the same set of events.
Lastly in this case, debug is your friend.
So for your first example you aren't sure about (adjusted so it will compile and run with some debug operators added):
func example() {
let hotObservable = Observable<Int>.interval(.seconds(1), scheduler: MainScheduler.instance)
.debug("before publish")
.publish()
let hotObservableRetry = hotObservable
.debug("after publish")
.map { (num) -> Int in
guard num % 3 != 0 else { throw MyError() }
return num
}
.debug("after map")
.retry(2)
.debug("after retry")
_ = hotObservableRetry.subscribe()
_ = hotObservable.connect()
}
Will produce the below output.
Here are some things to notice about the output that will help in the learning process.
The subscribes happen in reverse order.
Once the first error is emitted, the retry operator re-subscribes to the map operator's observable which resubscribes to the publish's observable. Since the publish's observable is hot, the resubscribe chain stops there. The timer's observable does not get resubscribed.
The above are the key points to understand to answer this question.
after retry -> subscribed
after map -> subscribed
after publish -> subscribed
before publish -> subscribed
before publish -> Event next(0)
after publish -> Event next(0)
after map -> Event error(MyError())
after map -> isDisposed
after publish -> isDisposed
after map -> subscribed
after publish -> subscribed
before publish -> Event next(1)
after publish -> Event next(1)
after map -> Event next(1)
after retry -> Event next(1)
before publish -> Event next(2)
after publish -> Event next(2)
after map -> Event next(2)
after retry -> Event next(2)
before publish -> Event next(3)
after publish -> Event next(3)
after map -> Event error(MyError())
after retry -> Event error(MyError())
after retry -> isDisposed
after map -> isDisposed
after publish -> isDisposed
before publish -> Event next(4)
before publish -> Event next(5)
before publish -> Event next(6)
...
In the next example you showed, the combineLatest operator resubscribes to the hot observable, but the connectable observable does not resubscribe to its source.
Using the new Combine framework in iOS 13.
Suppose I have an upstream publisher sending values at a highly irregular rate - sometimes seconds or minutes may go by without any values, and then a stream of values may come through all at once. I'd like to create a custom publisher that subscribes to the upstream values, buffers them and emits them at a regular, known cadence when they come in, but publishes nothing if they've all been exhausted.
For a concrete example:
t = 0 to 5000ms: no upstream values published
t = 5001ms: upstream publishes "a"
t = 5002ms: upstream publishes "b"
t = 5003ms: upstream publishes "c"
t = 5004ms to 10000ms: no upstream values published
t = 10001ms: upstream publishes "d"
My publisher subscribed to the upstream would produce values every 1 second:
t = 0 to 5000ms: no values published
t = 5001ms: publishes "a"
t = 6001ms: publishes "b"
t = 7001ms: publishes "c"
t = 7001ms to 10001ms: no values published
t = 10001ms: publishes "d"
None of the existing publishers or operators in Combine seem to quite do what I want here.
throttle and debounce would simply sample the upstream values at a certain cadence and drop ones that are missing (e.g. would only publish "a" if the cadence was 1000ms)
delay would add the same delay to every value, but not space them out (e.g. if my delay was 1000ms, it would publish "a" at 6001ms, "b" at 6002ms, "c" at 6003ms)
buffer seems promising, but I can't quite figure out how to use it - how to force it to publish a value from the buffer on demand. When I hooked up a sink to buffer it seemed to just instantly publish all the values, not buffering at all.
I thought about using some sort of combining operator like zip or merge or combineLatest and combining it with a Timer publisher, and that's probably the right approach, but I can't figure out exactly how to configure it to give the behavior I want.
Edit
Here's a marble diagram that hopefully illustrates what I'm going for:
Upstream Publisher:
-A-B-C-------------------D-E-F--------|>
My Custom Operator:
-A----B----C-------------D----E----F--|>
Edit 2: Unit Test
Here's a unit test that should pass if modulatedPublisher (my desired buffered publisher) works as desired. It's not perfect, but it stores events (including the time received) as they're received and then compares the time intervals between events, ensuring they are no smaller than the desired interval.
func testCustomPublisher() {
let expectation = XCTestExpectation(description: "async")
var events = [Event]()
let passthroughSubject = PassthroughSubject<Int, Never>()
let cancellable = passthroughSubject
.modulatedPublisher(interval: 1.0)
.sink { value in
events.append(Event(value: value, date: Date()))
print("value received: \(value) at \(self.dateFormatter.string(from:Date()))")
}
// WHEN I send 3 events, wait 6 seconds, and send 3 more events
passthroughSubject.send(1)
passthroughSubject.send(2)
passthroughSubject.send(3)
DispatchQueue.main.asyncAfter(deadline: .now() + .milliseconds(6000)) {
passthroughSubject.send(4)
passthroughSubject.send(5)
passthroughSubject.send(6)
DispatchQueue.main.asyncAfter(deadline: .now() + .milliseconds(4000)) {
// THEN I expect the stored events to be no closer together in time than the interval of 1.0s
for i in 1 ..< events.count {
let interval = events[i].date.timeIntervalSince(events[i-1].date)
print("Interval: \(interval)")
// There's some small error in the interval but it should be about 1 second since I'm using a 1s modulated publisher.
XCTAssertTrue(interval > 0.99)
}
expectation.fulfill()
}
}
wait(for: [expectation], timeout: 15)
}
The closest I've gotten is using zip, like so:
public extension Publisher where Self.Failure == Never {
func modulatedPublisher(interval: TimeInterval) -> AnyPublisher<Output, Never> {
let timerBuffer = Timer
.publish(every: interval, on: .main, in: .common)
.autoconnect()
return timerBuffer
.zip(self, { $1 }) // should emit one input element ($1) every timer tick
.eraseToAnyPublisher()
}
}
This properly attunes the first three events (1, 2, and 3), but not the second three (4, 5, and 6). The output:
value received: 1 at 3:54:07.0007
value received: 2 at 3:54:08.0008
value received: 3 at 3:54:09.0009
value received: 4 at 3:54:12.0012
value received: 5 at 3:54:12.0012
value received: 6 at 3:54:12.0012
I believe this is happening because zip has some internal buffering capacity. The first three upstream events are buffered and emitted on the Timer's cadence, but during the 6 second wait, the Timer's events are buffered - and when the second set ups upstream events are fired, there are already Timer events waiting in the queue, so they're paired up and fired off immediately.
This is an interesting problem. I played with various combinations of Timer.publish, buffer, zip, and throttle, but I couldn't get any combination to work quite the way you want. So let's write a custom subscriber.
What we'd really like is an API where, when we get an input from upstream, we also get the ability to control when the upstream delivers the next input. Something like this:
extension Publisher {
/// Subscribe to me with a stepping function.
/// - parameter stepper: A function I'll call with each of my inputs, and with my completion.
/// Each time I call this function with an input, I also give it a promise function.
/// I won't deliver the next input until the promise is called with a `.more` argument.
/// - returns: An object you can use to cancel the subscription asynchronously.
func step(with stepper: #escaping (StepEvent<Output, Failure>) -> ()) -> AnyCancellable {
???
}
}
enum StepEvent<Input, Failure: Error> {
/// Handle the Input. Call `StepPromise` when you're ready for the next Input,
/// or to cancel the subscription.
case input(Input, StepPromise)
/// Upstream completed the subscription.
case completion(Subscribers.Completion<Failure>)
}
/// The type of callback given to the stepper function to allow it to continue
/// or cancel the stream.
typealias StepPromise = (StepPromiseRequest) -> ()
enum StepPromiseRequest {
// Pass this to the promise to request the next item from upstream.
case more
// Pass this to the promise to cancel the subscription.
case cancel
}
With this step API, we can write a pace operator that does what you want:
extension Publisher {
func pace<Context: Scheduler, MySubject: Subject>(
_ pace: Context.SchedulerTimeType.Stride, scheduler: Context, subject: MySubject)
-> AnyCancellable
where MySubject.Output == Output, MySubject.Failure == Failure
{
return step {
switch $0 {
case .input(let input, let promise):
// Send the input from upstream now.
subject.send(input)
// Wait for the pace interval to elapse before requesting the
// next input from upstream.
scheduler.schedule(after: scheduler.now.advanced(by: pace)) {
promise(.more)
}
case .completion(let completion):
subject.send(completion: completion)
}
}
}
}
This pace operator takes pace (the required interval between outputs), a scheduler on which to schedule events, and a subject on which to republish the inputs from upstream. It handles each input by sending it through subject, and then using the scheduler to wait for the pace interval before asking for the next input from upstream.
Now we just have to implement the step operator. Combine doesn't give us too much help here. It does have a feature called “backpressure”, which means a publisher cannot send an input downstream until the downstream has asked for it by sending a Subscribers.Demand upstream. Usually you see downstreams send an .unlimited demand upstream, but we're not going to. Instead, we're going to take advantage of backpressure. We won't send any demand upstream until the stepper completes a promise, and then we'll only send a demand of .max(1), so we make the upstream operate in lock-step with the stepper. (We also have to send an initial demand of .max(1) to start the whole process.)
Okay, so need to implement a type that takes a stepper function and conforms to Subscriber. It's a good idea to review the Reactive Streams JVM Specification, because Combine is based on that specification.
What makes the implementation difficult is that several things can call into our subscriber asynchronously:
The upstream can call into the subscriber from any thread (but is required to serialize its calls).
After we've given promise functions to the stepper, the stepper can call those promises on any thread.
We want the subscription to be cancellable, and that cancellation can happen on any thread.
All this asynchronicity means we have to protect our internal state with a lock.
We have to be careful not to call out while holding that lock, to avoid deadlock.
We'll also protect the subscriber from shenanigans involving calling a promise repeatedly, or calling outdated promises, by giving each promise a unique id.
Se here's our basic subscriber definition:
import Combine
import Foundation
public class SteppingSubscriber<Input, Failure: Error> {
public init(stepper: #escaping Stepper) {
l_state = .subscribing(stepper)
}
public typealias Stepper = (Event) -> ()
public enum Event {
case input(Input, Promise)
case completion(Completion)
}
public typealias Promise = (Request) -> ()
public enum Request {
case more
case cancel
}
public typealias Completion = Subscribers.Completion<Failure>
private let lock = NSLock()
// The l_ prefix means it must only be accessed while holding the lock.
private var l_state: State
private var l_nextPromiseId: PromiseId = 1
private typealias PromiseId = Int
private var noPromiseId: PromiseId { 0 }
}
Notice that I moved the auxiliary types from earlier (StepEvent, StepPromise, and StepPromiseRequest) into SteppingSubscriber and shortened their names.
Now let's consider l_state's mysterious type, State. What are all the different states our subscriber could be in?
We could be waiting to receive the Subscription object from upstream.
We could have received the Subscription from upstream and be waiting for a signal (an input or completion from upstream, or the completion of a promise from the stepper).
We could be calling out to the stepper, which we want to be careful in case it completes a promise while we're calling out to it.
We could have been cancelled or have received completion from upstream.
So here is our definition of State:
extension SteppingSubscriber {
private enum State {
// Completed or cancelled.
case dead
// Waiting for Subscription from upstream.
case subscribing(Stepper)
// Waiting for a signal from upstream or for the latest promise to be completed.
case subscribed(Subscribed)
// Calling out to the stopper.
case stepping(Stepping)
var subscription: Subscription? {
switch self {
case .dead: return nil
case .subscribing(_): return nil
case .subscribed(let subscribed): return subscribed.subscription
case .stepping(let stepping): return stepping.subscribed.subscription
}
}
struct Subscribed {
var stepper: Stepper
var subscription: Subscription
var validPromiseId: PromiseId
}
struct Stepping {
var subscribed: Subscribed
// If the stepper completes the current promise synchronously with .more,
// I set this to true.
var shouldRequestMore: Bool
}
}
}
Since we're using NSLock (for simplicity), let's define an extension to ensure we always match locking with unlocking:
fileprivate extension NSLock {
#inline(__always)
func sync<Answer>(_ body: () -> Answer) -> Answer {
lock()
defer { unlock() }
return body()
}
}
Now we're ready to handle some events. The easiest event to handle is asynchronous cancellation, which is the Cancellable protocol's only requirement. If we're in any state except .dead, we want to become .dead and, if there's an upstream subscription, cancel it.
extension SteppingSubscriber: Cancellable {
public func cancel() {
let sub: Subscription? = lock.sync {
defer { l_state = .dead }
return l_state.subscription
}
sub?.cancel()
}
}
Notice here that I don't want to call out to the upstream subscription's cancel function while lock is locked, because lock isn't a recursive lock and I don't want to risk deadlock. All use of lock.sync follows the pattern of deferring any call-outs until after the lock is unlocked.
Now let's implement the Subscriber protocol requirements. First, let's handle receiving the Subscription from upstream. The only time this should happen is when we're in the .subscribing state, but .dead is also possible in which case we want to just cancel the upstream subscription.
extension SteppingSubscriber: Subscriber {
public func receive(subscription: Subscription) {
let action: () -> () = lock.sync {
guard case .subscribing(let stepper) = l_state else {
return { subscription.cancel() }
}
l_state = .subscribed(.init(stepper: stepper, subscription: subscription, validPromiseId: noPromiseId))
return { subscription.request(.max(1)) }
}
action()
}
Notice that in this use of lock.sync (and in all later uses), I return an “action” closure so I can perform arbitrary call-outs after the lock has been unlocked.
The next Subscriber protocol requirement we'll tackle is receiving a completion:
public func receive(completion: Subscribers.Completion<Failure>) {
let action: (() -> ())? = lock.sync {
// The only state in which I have to handle this call is .subscribed:
// - If I'm .dead, either upstream already completed (and shouldn't call this again),
// or I've been cancelled.
// - If I'm .subscribing, upstream must send me a Subscription before sending me a completion.
// - If I'm .stepping, upstream is currently signalling me and isn't allowed to signal
// me again concurrently.
guard case .subscribed(let subscribed) = l_state else {
return nil
}
l_state = .dead
return { [stepper = subscribed.stepper] in
stepper(.completion(completion))
}
}
action?()
}
The most complex Subscriber protocol requirement for us is receiving an Input:
We have to create a promise.
We have to pass the promise to the stepper.
The stepper could complete the promise before returning.
After the stepper returns, we have to check whether it completed the promise with .more and, if so, return the appropriate demand upstream.
Since we have to call out to the stepper in the middle of this work, we have some ugly nesting of lock.sync calls.
public func receive(_ input: Input) -> Subscribers.Demand {
let action: (() -> Subscribers.Demand)? = lock.sync {
// The only state in which I have to handle this call is .subscribed:
// - If I'm .dead, either upstream completed and shouldn't call this,
// or I've been cancelled.
// - If I'm .subscribing, upstream must send me a Subscription before sending me Input.
// - If I'm .stepping, upstream is currently signalling me and isn't allowed to
// signal me again concurrently.
guard case .subscribed(var subscribed) = l_state else {
return nil
}
let promiseId = l_nextPromiseId
l_nextPromiseId += 1
let promise: Promise = { request in
self.completePromise(id: promiseId, request: request)
}
subscribed.validPromiseId = promiseId
l_state = .stepping(.init(subscribed: subscribed, shouldRequestMore: false))
return { [stepper = subscribed.stepper] in
stepper(.input(input, promise))
let demand: Subscribers.Demand = self.lock.sync {
// The only possible states now are .stepping and .dead.
guard case .stepping(let stepping) = self.l_state else {
return .none
}
self.l_state = .subscribed(stepping.subscribed)
return stepping.shouldRequestMore ? .max(1) : .none
}
return demand
}
}
return action?() ?? .none
}
} // end of extension SteppingSubscriber: Publisher
The last thing our subscriber needs to handle is the completion of a promise. This is complicated for several reasons:
We want to protect against a promise being completed multiple times.
We want to protect against an older promise being completed.
We can be in any state when a promise is completed.
Thus:
extension SteppingSubscriber {
private func completePromise(id: PromiseId, request: Request) {
let action: (() -> ())? = lock.sync {
switch l_state {
case .dead, .subscribing(_): return nil
case .subscribed(var subscribed) where subscribed.validPromiseId == id && request == .more:
subscribed.validPromiseId = noPromiseId
l_state = .subscribed(subscribed)
return { [sub = subscribed.subscription] in
sub.request(.max(1))
}
case .subscribed(let subscribed) where subscribed.validPromiseId == id && request == .cancel:
l_state = .dead
return { [sub = subscribed.subscription] in
sub.cancel()
}
case .subscribed(_):
// Multiple completion or stale promise.
return nil
case .stepping(var stepping) where stepping.subscribed.validPromiseId == id && request == .more:
stepping.subscribed.validPromiseId = noPromiseId
stepping.shouldRequestMore = true
l_state = .stepping(stepping)
return nil
case .stepping(let stepping) where stepping.subscribed.validPromiseId == id && request == .cancel:
l_state = .dead
return { [sub = stepping.subscribed.subscription] in
sub.cancel()
}
case .stepping(_):
// Multiple completion or stale promise.
return nil
}
}
action?()
}
}
Whew!
With all that done, we can write the real step operator:
extension Publisher {
func step(with stepper: #escaping (SteppingSubscriber<Output, Failure>.Event) -> ()) -> AnyCancellable {
let subscriber = SteppingSubscriber<Output, Failure>(stepper: stepper)
self.subscribe(subscriber)
return .init(subscriber)
}
}
And then we can try out that pace operator from above. Since we don't do any buffering in SteppingSubscriber, and the upstream in general isn't buffered, we'll stick a buffer in between the upstream and our pace operator.
var cans: [AnyCancellable] = []
func application(_ application: UIApplication, didFinishLaunchingWithOptions launchOptions: [UIApplication.LaunchOptionsKey: Any]?) -> Bool {
let erratic = Just("A").delay(for: 0.0, tolerance: 0.001, scheduler: DispatchQueue.main).eraseToAnyPublisher()
.merge(with: Just("B").delay(for: 0.3, tolerance: 0.001, scheduler: DispatchQueue.main).eraseToAnyPublisher())
.merge(with: Just("C").delay(for: 0.6, tolerance: 0.001, scheduler: DispatchQueue.main).eraseToAnyPublisher())
.merge(with: Just("D").delay(for: 5.0, tolerance: 0.001, scheduler: DispatchQueue.main).eraseToAnyPublisher())
.merge(with: Just("E").delay(for: 5.3, tolerance: 0.001, scheduler: DispatchQueue.main).eraseToAnyPublisher())
.merge(with: Just("F").delay(for: 5.6, tolerance: 0.001, scheduler: DispatchQueue.main).eraseToAnyPublisher())
.handleEvents(
receiveOutput: { print("erratic: \(Double(DispatchTime.now().rawValue) / 1_000_000_000) \($0)") },
receiveCompletion: { print("erratic: \(Double(DispatchTime.now().rawValue) / 1_000_000_000) \($0)") }
)
.makeConnectable()
let subject = PassthroughSubject<String, Never>()
cans += [erratic
.buffer(size: 1000, prefetch: .byRequest, whenFull: .dropOldest)
.pace(.seconds(1), scheduler: DispatchQueue.main, subject: subject)]
cans += [subject.sink(
receiveCompletion: { print("paced: \(Double(DispatchTime.now().rawValue) / 1_000_000_000) \($0)") },
receiveValue: { print("paced: \(Double(DispatchTime.now().rawValue) / 1_000_000_000) \($0)") }
)]
let c = erratic.connect()
cans += [AnyCancellable { c.cancel() }]
return true
}
And here, at long last, is the output:
erratic: 223394.17115897 A
paced: 223394.171495405 A
erratic: 223394.408086369 B
erratic: 223394.739186984 C
paced: 223395.171615624 B
paced: 223396.27056174 C
erratic: 223399.536717127 D
paced: 223399.536782847 D
erratic: 223399.536834495 E
erratic: 223400.236808469 F
erratic: 223400.236886323 finished
paced: 223400.620542561 E
paced: 223401.703613078 F
paced: 223402.703828512 finished
Timestamps are in units of seconds.
The erratic publisher's timings are, indeed, erratic and sometimes close in time.
The paced timings are always at least one second apart even when the erratic events occur less than one second apart.
When an erratic event occurs more than one second after the prior event, the paced event is sent immediately following the erratic event without further delay.
The paced completion occurs one second after the last paced event, even though the erratic completion occurs immediately after the last erratic event. The buffer doesn't send the completion until it receives another demand after it sends the last event, and that demand is delayed by the pacing timer.
I've put the the entire implementation of the step operator in this gist for easy copy/paste.
EDIT
There's an even simpler approach to the original one outlined below, which doesn't require a pacer, but instead uses back-pressure created by flatMap(maxPublishers: .max(1)).
flatMap sends a demand of 1, until its returned publisher, which we could delay, completes. We'd need a Buffer publisher upstream to buffer the values.
// for demo purposes, this subject sends a Date:
let subject = PassthroughSubject<Date, Never>()
let interval = 1.0
let pub = subject
.buffer(size: .max, prefetch: .byRequest, whenFull: .dropNewest)
.flatMap(maxPublishers: .max(1)) {
Just($0)
.delay(for: .seconds(interval), scheduler: DispatchQueue.main)
}
ORIGINAL
I know this is an old question, but I think there's a much simpler way to implement this, so I thought I'd share.
The idea is similar to a .zip with a Timer, except instead of a Timer, you would .zip with a time-delayed "tick" from a previously sent value, which can be achieved with a CurrentValueSubject. CurrentValueSubject is needed instead of a PassthroughSubject in order to seed the first ever "tick".
// for demo purposes, this subject sends a Date:
let subject = PassthroughSubject<Date, Never>()
let pacer = CurrentValueSubject<Void, Never>(())
let interval = 1.0
let pub = subject.zip(pacer)
.flatMap { v in
Just(v.0) // extract the original value
.delay(for: .seconds(interval), scheduler: DispatchQueue.main)
.handleEvents(receiveOutput: { _ in
pacer.send() // send the pacer "tick" after the interval
})
}
What happens is that the .zip gates on the pacer, which only arrives after a delay from a previously sent value.
If the next value comes earlier than the allowed interval, it waits for the pacer.
If, however, the next value comes later, then the pacer already has a new value to provide instantly, so there would be no delay.
If you used it like in your test case:
let c = pub.sink { print("\($0): \(Date())") }
subject.send(Date())
subject.send(Date())
subject.send(Date())
DispatchQueue.main.asyncAfter(deadline: .now() + 1.0) {
subject.send(Date())
subject.send(Date())
}
DispatchQueue.main.asyncAfter(deadline: .now() + 10.0) {
subject.send(Date())
subject.send(Date())
}
the result would be something like this:
2020-06-23 19:15:21 +0000: 2020-06-23 19:15:21 +0000
2020-06-23 19:15:21 +0000: 2020-06-23 19:15:22 +0000
2020-06-23 19:15:21 +0000: 2020-06-23 19:15:23 +0000
2020-06-23 19:15:22 +0000: 2020-06-23 19:15:24 +0000
2020-06-23 19:15:22 +0000: 2020-06-23 19:15:25 +0000
2020-06-23 19:15:32 +0000: 2020-06-23 19:15:32 +0000
2020-06-23 19:15:32 +0000: 2020-06-23 19:15:33 +0000
Could Publishers.CollectByTime be useful here somewhere?
Publishers.CollectByTime(upstream: upstreamPublisher.share(), strategy: Publishers.TimeGroupingStrategy.byTime(RunLoop.main, .seconds(1)), options: nil)
Just wanted to mention that I adapted Rob's answer from earlier and converted it to a custom Publisher, in order to allow for a single unbroken pipeline (see comments below his solution). My adaptation is below, but all the credit still goes to him. It also still makes use of Rob's step operator and SteppingSubscriber, as this custom Publisher uses those internally.
Edit: updated with buffer as part of the modulated operator, otherwise that would be required to be attached to buffer the upstream events.
public extension Publisher {
func modulated<Context: Scheduler>(_ pace: Context.SchedulerTimeType.Stride, scheduler: Context) -> AnyPublisher<Output, Failure> {
let upstream = buffer(size: 1000, prefetch: .byRequest, whenFull: .dropNewest).eraseToAnyPublisher()
return PacePublisher<Context, AnyPublisher>(pace: pace, scheduler: scheduler, source: upstream).eraseToAnyPublisher()
}
}
final class PacePublisher<Context: Scheduler, Source: Publisher>: Publisher {
typealias Output = Source.Output
typealias Failure = Source.Failure
let subject: PassthroughSubject<Output, Failure>
let scheduler: Context
let pace: Context.SchedulerTimeType.Stride
lazy var internalSubscriber: SteppingSubscriber<Output, Failure> = SteppingSubscriber<Output, Failure>(stepper: stepper)
lazy var stepper: ((SteppingSubscriber<Output, Failure>.Event) -> ()) = {
switch $0 {
case .input(let input, let promise):
// Send the input from upstream now.
self.subject.send(input)
// Wait for the pace interval to elapse before requesting the
// next input from upstream.
self.scheduler.schedule(after: self.scheduler.now.advanced(by: self.pace)) {
promise(.more)
}
case .completion(let completion):
self.subject.send(completion: completion)
}
}
init(pace: Context.SchedulerTimeType.Stride, scheduler: Context, source: Source) {
self.scheduler = scheduler
self.pace = pace
self.subject = PassthroughSubject<Source.Output, Source.Failure>()
source.subscribe(internalSubscriber)
}
public func receive<S>(subscriber: S) where S : Subscriber, Failure == S.Failure, Output == S.Input {
subject.subscribe(subscriber)
subject.send(subscription: PaceSubscription(subscriber: subscriber))
}
}
public class PaceSubscription<S: Subscriber>: Subscription {
private var subscriber: S?
init(subscriber: S) {
self.subscriber = subscriber
}
public func request(_ demand: Subscribers.Demand) {
}
public func cancel() {
subscriber = nil
}
}
I am initiating an operation via a REST API in two steps:
Start operation and return a task id
Poll task with the given id and complete the sequence when the operation returns complete.
The polling the task id will return a 202 which indicates that the operation is still in progress and a 200 when it completes. Any other code is an error.
I need to communicate to the subscribers the response of each step.
Previously, I would have the do operator push the response in between steps to a ReplaySubject.
startReboot()
.do(onNext: { response in
operationStatus.next(response)
)
.flatMap({ response in
// If we could not get the task ID from the response we error
guard let taskID = getTaskIDFromJSON(response) else { return Observable.error(API.serverError) }
return Observable.just(taskID)
})
.flatMap({ taskID in
return pollTask(withID: taskID) // internally, it uses retryWhen to check the api again with a five second delay
})
.do(onNext: { response in
operationStatus.next(response)
})
.subscribe(onError: { _ in
showOperationFailedIcon()
}, onCompleted: {
showOperationCompleteIcon()
})
And somewhere else, a subscriber to the subject would do the following:
operationStatus.subscribe(onNext: { response
showResponse(response)
})
So essentially I am showing the progress of the operation and the response we get from each step at the same time.
At the time I was not familiar with Rx to come up with a cleaner solution. But now that I have familiarized my self with it, it seems to me that there should be a solution where we don't use side effects and contain this to a single final observable. Still, I cannot find a way to do it.
I was thinking about something like this:
let opObs = startReboot()
let pollingObs = pollTask(/* where does the id come from? */)
Observable.concat(opObs, pollingObs)
.subscribe(onNext : { response in
showResponse(response)
}, onError: { _ in
showOperationFailedIcon()
}, onCompleted: {
showOperationCompleteIcon()
})
But that would imply that once opObs is done I would need to save the task id in variable outside of the monad and wrap pollingObs to fetch it when it starts - once again introducing side effects.
Is there an operator or combination of operators that I can use to emit the response of each step to a subscriber and also pass it to another observable?
Something like this should work. Note the use of share() to avoid triggering twice the startReboot sequence.
let opObs = startReboot().share()
let pollingObs = opObs.flatMap {
guard let taskID = getTaskIDFromJSON(response) else { return Observable.error(API.serverError)
return pollTask(withID: taskID)
}
Observable.concat(opObs, pollingObs)
.subscribe(onNext : { response in
showResponse(response)
}, onError: { _ in
showOperationFailedIcon()
}, onCompleted: {
showOperationCompleteIcon()
})
I'm rather new at swift and have been doing some research on how to answer this question myself since I want to learn, but I am completely stumped.
I have a function which requests data from a server, and after the data is received, a completion handler is executed which parses the data. Within the previously mentioned completion handler, another function is called which is passed a completion handler itself.
For some reason, the function call within the function is being being skipped, and being finished after the first completion handler is fully executed. This might make more sense with the code below:
func loadSites(forceDownload: Bool){
self.inspectionSites = MyData.getLocallyStoredInspectionSites()
if self.inspectionSites.count < 1 || forceDownload {
self.http.requestSites({(sitesAcquired, jsonObject) -> Void in
guard sitesAcquired else{
SwiftOverlays.removeAllBlockingOverlays()
MyAlertController.alert("Unable to acquire sites from server or locally")
return
}
let result = jsonObject
for (_,subJson):(String, JSON) in result!.dictionaryValue {
let site = InspectionSite()
site.name = subJson[self.currentIndex]["name"].string!
site.city = subJson[self.currentIndex]["city"].string!
site.address = subJson[self.currentIndex]["address"].string!
site.state = subJson[self.currentIndex]["state"].string!
site.zip = subJson[self.currentIndex]["zip"].stringValue
site.siteId = subJson[self.currentIndex]["id"].string!
objc_sync_enter(self) //SAW A STACKOVERFLOW POST WITH THIS, THOUGHT IT MIGHT HELP
MyLocation.geoCodeSite(site, callback:{(coordinates) -> Void in
print("YO!!!! GEOCODING SITE!")
self.localLat = coordinates["lat"]!
self.localLon = coordinates["lon"]!
})
objc_sync_exit(self)
for type in subJson[self.currentIndex]["inspection_types"]{
let newType = InspectionType()
newType.name = type.1["name"].string!
newType.id = type.1["id"].string!
site.inspectionTypes.append(newType)
}
site.lat = self.localLat
print("HEYY!!!! ASSIGNING COORDS")
site.lon = self.localLon
let address = "\(site.address), \(site.city), \(site.state) \(site.zip)"
site.title = site.name
site.subtitle = address
MyData.persistInspectionSite(site)
self.currentIndex++
}
self.inspectionSites = MyData.getLocallyStoredInspectionSites()
SwiftOverlays.removeAllBlockingOverlays()
self.showSitesOnMap(self.proteanMap)
})
}else{
SwiftOverlays.removeAllBlockingOverlays()
self.showSitesOnMap(self.proteanMap)
}
}
I added those print statements which print "YOOO" and "HEYYY" just so I could see what was being executed first, and "HEYY" is always first. I just need to make sure that the geocoding always happens before the object is persisted. I saw a stackoverflow post which mentioned objc_sync_enter(self) for synchronous operation, but im not even sure if it's what I need.
This is the function which geocodes the site (incase it helps):
class func geoCodeSite(site: InspectionSite, callback: ((coordinates: Dictionary<String, String>)->Void)?) {
let geocoder = CLGeocoder()
let address: String = "\(site.address), \(site.city), \(site.state) \(site.zip)"
print(address)
geocoder.geocodeAddressString(address, completionHandler: {(placemarks, error) -> Void in
if((error) != nil){
print("Error", error)
}
if let placemark = placemarks?.first {
MyLocation.mLat = String(stringInterpolationSegment:placemark.location!.coordinate.latitude)
MyLocation.mLon = String(stringInterpolationSegment:placemark.location!.coordinate.longitude)
MyLocation.coordinates = ["lat":mLat, "lon":mLon]
print(MyLocation.coordinates)
callback?(coordinates: MyLocation.coordinates)
}
})
}
I think the behaviour your seeing is expected. You have two levels of asynchronous methods:
requestSites
geoCodeSite
Since the geoCodeSite method is also asynchronous, its callback is executed well after the line:
MyData.persistInspectionSite(site)
So your problem is how to wait till all InspectionSites have geocoded before persisting the site, right?
Dispatch groups can be used to detect when multiple asynchronous events have finished, see my answer here.
How to Implement Dispatch Groups
dispatch_groups are used to fire a callback when multiple async callbacks have finished. In your case, you need to wait for all geoCodeSite async callbacks to complete before persisting your site.
So, create a dispatch group, firing off your geoCodeSite calls, and implement the dispatch callback inside of which you can persist your geocoded sites.
var myGroup = dispatch_group_create()
dispatch_group_enter(myGroup)
...
fire off your geoCodeSite async callbacks
...
dispatch_group_notify(myGroup, dispatch_get_main_queue(), {
// all sites are now geocoded, we can now persist site
})
Don't forget to add
dispatch_group_leave(myGroup)
inside the closure of geoCodeSite! Otherwise dispatch_group will never know when your async call finish.