swift-composable-architecture-mirror/Sources/ComposableArchitecture/Documentation.docc/Articles/Performance.md

Performance

Learn how to improve the performance of features built in the Composable Architecture.

As your features and application grow you may run into performance problems, such as reducers becoming slow to execute, SwiftUI view bodies executing more often than expected, and more. This article outlines a few common pitfalls when developing features in the library, and how to fix them.

• View stores
• Sharing logic with actions
• CPU-intensive calculations
• High-frequency actions
• Compiler performance

View stores

A common performance pitfall when using the library comes from constructing ViewStores, which is the object that observes changes to your feature's state. When constructed naively, using either view store's initializer ViewStore/init(_:observe:)-3ak1y or the SwiftUI helper WithViewStore, it will observe every change to state in the store:

WithViewStore(self.store, observe: { $0 }) { viewStore in 
  // This is executed for every action sent into the system 
  // that causes self.store.state to change. 
}

Most of the time this observes far too much state. A typical feature in the Composable Architecture holds onto not only the state the view needs to present UI, but also state that the feature only needs internally, as well as state of child features embedded in the feature. Changes to the internal and child state should not cause the view's body to re-compute since that state is not needed in the view.

For example, if the root of our application was a tab view, then we could model that in state as a struct that holds each tab's state as a property:

@Reducer
struct AppFeature {
  struct State {
    var activity: Activity.State
    var search: Search.State
    var profile: Profile.State
  }
  // ...
}

If the view only needs to construct the views for each tab, then no view store is even needed because we can pass scoped stores to each child feature view:

struct AppView: View {
  let store: StoreOf<AppFeature>

  var body: some View {
    // No need to observe state changes because the view does
    // not need access to the state.

    TabView {
      ActivityView(
        store: self.store
          .scope(state: \.activity, action: { .activity($0) })
      )
      SearchView(
        store: self.store
          .scope(state: \.search, action: { .search($0) })
      )
      ProfileView(
        store: self.store
          .scope(state: \.profile, action: { .profile($0) })
      )
    }
  }
}

This means AppView does not actually need to observe any state changes. This view will only be created a single time, whereas if we observed the store then it would re-compute every time a single thing changed in either the activity, search or profile child features.

If sometime in the future we do actually need some state from the store, we can start to observe only the bare essentials of state necessary for the view to do its job. For example, suppose that we need access to the currently selected tab in state:

@Reducer
struct AppFeature {
  enum Tab { case activity, search, profile }
  struct State {
    var activity: Activity.State
    var search: Search.State
    var profile: Profile.State
    var selectedTab: Tab
  }
  // ...
}

Then we can observe this state so that we can construct a binding to selectedTab for the tab view:

struct AppView: View {
  let store: StoreOf<AppFeature>

  var body: some View {
    WithViewStore(self.store, observe: { $0 }) { viewStore in
      TabView(
        selection: viewStore.binding(get: \.selectedTab, send: { .tabSelected($0) })
      ) {
        ActivityView(
          store: self.store.scope(state: \.activity, action: { .activity($0) })
        )
        .tag(AppFeature.Tab.activity)
        SearchView(
          store: self.store.scope(state: \.search, action: { .search($0) })
        )
        .tag(AppFeature.Tab.search)
        ProfileView(
          store: self.store.scope(state: \.profile, action: { .profile($0) })
        )
        .tag(AppFeature.Tab.profile)
      }
    }
  }
}

However, this style of state observation is terribly inefficient since every change to AppFeature.State will cause the view to re-compute even though the only piece of state we actually care about is the selectedTab. The reason we are observing too much state is because we use observe: { $0 } in the construction of the WithViewStore, which means the view store will observe all of state.

To chisel away at the observed state you can provide a closure for that argument that plucks out the state the view needs. In this case the view only needs a single field:

WithViewStore(self.store, observe: \.selectedTab) { viewStore in
  TabView(selection: viewStore.binding(send: { .tabSelected($0) }) {
    // ...
  }
}

In the future, the view may need access to more state. For example, suppose Activity.State holds onto an unreadCount integer to represent how many new activities you have. There's no need to observe all of Activity.State to get access to this one field. You can observe just the one field.

Technically you can do this by mapping your state into a tuple, but because tuples are not Equatable you will need to provide an explicit removeDuplicates argument:

WithViewStore(
  self.store, 
  observe: { (selectedTab: $0.selectedTab, unreadActivityCount: $0.activity.unreadCount) },
  removeDuplicates: ==
) { viewStore in 
  TabView(selection: viewStore.binding(get: \.selectedTab, send: { .tabSelected($0) }) {
    ActivityView(
      store: self.store.scope(state: \.activity, action: { .activity($0) })
    )
    .tag(AppFeature.Tab.activity)
    .badge("\(viewStore.unreadActivityCount)")

    // ...
  }
}

Alternatively, and recommended, you can introduce a lightweight, equatable ViewState struct nested inside your view whose purpose is to transform the Store's full state into the bare essentials of what the view needs:

struct AppView: View {
  let store: StoreOf<AppFeature>
  
  struct ViewState: Equatable {
    let selectedTab: AppFeature.Tab
    let unreadActivityCount: Int
    init(state: AppFeature.State) {
      self.selectedTab = state.selectedTab
      self.unreadActivityCount = state.activity.unreadCount
    }
  }

  var body: some View {
    WithViewStore(self.store, observe: ViewState.init) { viewStore in 
      TabView {
        ActivityView(
          store: self.store
            .scope(state: \.activity, action: { .activity($0) })
        )
        .badge("\(viewStore.unreadActivityCount)")

        // ...
      }
    }
  }
}

This gives you maximum flexibility in the future for adding new fields to ViewState without making your view convoluted.

This technique for reducing view re-computations is most effective towards the root of your app hierarchy and least effective towards the leaf nodes of your app. Root features tend to hold lots of state that its view does not need, such as child features, and leaf features tend to only hold what's necessary. If you are going to employ this technique you will get the most benefit by applying it to views closer to the root. At leaf features and views that need access to most of the state, it is fine to continue using observe: { $0 } to observe all of the state in the store.

Sharing logic with actions

There is a common pattern of using actions to share logic across multiple parts of a reducer. This is an inefficient way to share logic. Sending actions is not as lightweight of an operation as, say, calling a method on a class. Actions travel through multiple layers of an application, and at each layer a reducer can intercept and reinterpret the action.

It is far better to share logic via simple methods on your Reducer conformance. The helper methods can take inout State as an argument if it needs to make mutations, and it can return an Effect<Action>. This allows you to share logic without incurring the cost of sending needless actions.

For example, suppose that there are 3 UI components in your feature such that when any is changed you want to update the corresponding field of state, but then you also want to make some mutations and execute an effect. That common mutation and effect could be put into its own action and then each user action can return an effect that immediately emits that shared action:

@Reducer
struct Feature {
  struct State { /* ... */ }
  enum Action { /* ... */ }

  var body: some Reducer<State, Action> {
    Reduce { state, action in
      switch action {
      case .buttonTapped:
        state.count += 1
        return .send(.sharedComputation)

      case .toggleChanged:
        state.isEnabled.toggle()
        return .send(.sharedComputation)

      case let .textFieldChanged(text):
        state.description = text
        return .send(.sharedComputation)

      case .sharedComputation:
        // Some shared work to compute something.
        return .run { send in
          // A shared effect to compute something
        }
      }
    }
  }
}

This is one way of sharing the logic and effect, but we are now incurring the cost of two actions even though the user performed a single action. That is not going to be as efficient as it would be if only a single action was sent.

Besides just performance concerns, there are two other reasons why you should not follow this pattern. First, this style of sharing logic is not very flexible. Because the shared logic is relegated to a separate action it must always be run after the initial logic. But what if instead you need to run some shared logic before the core logic? This style cannot accommodate that.

Second, this style of sharing logic also muddies tests. When you send a user action you have to further assert on receiving the shared action and assert on how state changed. This bloats tests with unnecessary internal details, and the test no longer reads as a script from top-to-bottom of actions the user is taking in the feature:

let store = TestStore(initialState: Feature.State()) {
  Feature()
}

store.send(.buttonTapped) {
  $0.count = 1
}
store.receive(\.sharedComputation) {
  // Assert on shared logic
}
store.send(.toggleChanged) {
  $0.isEnabled = true
}
store.receive(\.sharedComputation) {
  // Assert on shared logic
}
store.send(.textFieldChanged("Hello") {
  $0.description = "Hello"
}
store.receive(\.sharedComputation) {
  // Assert on shared logic
}

So, we do not recommend sharing logic in a reducer by having dedicated actions for the logic and executing synchronous effects.

Instead, we recommend sharing logic with methods defined in your feature's reducer. The method has full access to all dependencies, it can take an inout State if it needs to make mutations to state, and it can return an Effect<Action> if it needs to execute effects.

The above example can be refactored like so:

@Reducer
struct Feature {
  struct State { /* ... */ }
  enum Action { /* ... */ }

  var body: some Reducer<State, Action> {
    Reduce { state, action in
      switch action {
      case .buttonTapped:
        state.count += 1
        return self.sharedComputation(state: &state)

      case .toggleChanged:
        state.isEnabled.toggle()
        return self.sharedComputation(state: &state)

      case let .textFieldChanged(text):
        state.description = text
        return self.sharedComputation(state: &state)
      }
    }
  }

  func sharedComputation(state: inout State) -> Effect<Action> {
    // Some shared work to compute something.
    return .run { send in
      // A shared effect to compute something
    }
  }
}

This effectively works the same as before, but now when a user action is sent all logic is executed at once without sending an additional action. This also fixes the other problems we mentioned above.

For example, if you need to execute the shared logic before the core logic, you can do so easily:

case .buttonTapped:
  let sharedEffect = self.sharedComputation(state: &state)
  state.count += 1
  return sharedEffect

You have complete flexibility to decide how, when and where you want to execute the shared logic.

Further, tests become more streamlined since you do not have to assert on internal details of shared actions being sent around. The test reads like a user script of what the user is doing in the feature:

let store = TestStore(initialState: Feature.State()) {
  Feature()
}

store.send(.buttonTapped) {
  $0.count = 1
  // Assert on shared logic
}
store.send(.toggleChanged) {
  $0.isEnabled = true
  // Assert on shared logic
}
store.send(.textFieldChanged("Hello") {
  $0.description = "Hello"
  // Assert on shared logic
}

CPU intensive calculations

Reducers are run on the main thread and so they are not appropriate for performing intense CPU work. If you need to perform lots of CPU-bound work, then it is more appropriate to use an Effect, which will operate in the cooperative thread pool, and then send actions back into the system. You should also make sure to perform your CPU intensive work in a cooperative manner by periodically suspending with Task.yield() so that you do not block a thread in the cooperative pool for too long.

So, instead of performing intense work like this in your reducer:

case .buttonTapped:
  var result = // ...
  for value in someLargeCollection {
    // Some intense computation with value
  }
  state.result = result

...you should return an effect to perform that work, sprinkling in some yields every once in awhile, and then delivering the result in an action:

case .buttonTapped:
  return .run { send in
    var result = // ...
    for (index, value) in someLargeCollection.enumerated() {
      // Some intense computation with value

      // Yield every once in awhile to cooperate in the thread pool.
      if index.isMultiple(of: 1_000) {
        await Task.yield()
      }
    }
    await send(.computationResponse(result))
  }

case let .computationResponse(result):
  state.result = result

This will keep CPU intense work from being performed in the reducer, and hence not on the main thread.

High-frequency actions

Sending actions in a Composable Architecture application should not be thought as simple method calls that one does with classes, such as ObservableObject conformances. When an action is sent into the system there are multiple layers of features that can intercept and interpret it, and the resulting state changes can reverberate throughout the entire application.

Because of this, sending actions does come with a cost. You should aim to only send "significant" actions into the system, that is, actions that cause the execution of important logic and effects for your application. High-frequency actions, such as sending dozens of actions per second, should be avoided unless your application truly needs that volume of actions in order to implement its logic.

However, there are often times that actions are sent at a high frequency but the reducer doesn't actually need that volume of information. For example, say you were constructing an effect that wanted to report its progress back to the system for each step of its work. You could choose to send the progress for literally every step:

case .startButtonTapped:
  return .run { send in
    var count = 0
    let max = await self.eventsClient.count()

    for await event in self.eventsClient.events() {
      defer { count += 1 }
      send(.progress(Double(count) / Double(max)))
    }
  }
}

However, what if the effect required 10,000 steps to finish? Or 100,000? Or more? It would be immensely wasteful to send 100,000 actions into the system to report a progress value that is only going to vary from 0.0 to 1.0.

Instead, you can choose to report the progress every once in awhile. You can even do the math to make it so that you report the progress at most 100 times:

case .startButtonTapped:
  return .run { send in
    var count = 0
    let max = await self.eventsClient.count()
    let interval = max / 100

    for await event in self.eventsClient.events() {
      defer { count += 1 }
      if count.isMultiple(of: interval) {
        send(.progress(Double(count) / Double(max)))
      }
    }
  }
}

This greatly reduces the bandwidth of actions being sent into the system so that you are not incurring unnecessary costs for sending actions.

Compiler performance

In very large SwiftUI applications you may experience degraded compiler performance causing long compile times, and possibly even compiler failures due to "complex expressions." The WithViewStore helpers that come with the library can exacerbate that problem for very complex views. If you are running into issues using WithViewStore, there are two options for fixing the problem.

For example, if your view looks like this:

struct FeatureView: View {
  let store: StoreOf<Feature>

  var body: some View {
    WithViewStore(self.store, observe: { $0 }) { viewStore in
      // A large, complex view inside here...
    }
  }
}

…and you start running into compiler troubles, then you can explicitly specify the type of the view store in the closure:

WithViewStore(self.store, observe: { $0 }) { (viewStore: ViewStoreOf<Feature>) in
  // A large, complex view inside here...
}

Or you can refactor the view to use an @ObservedObject:

struct FeatureView: View {
  let store: StoreOf<Feature>
  @ObservedObject var viewStore: ViewStoreOf<Feature>

  init(store: StoreOf<Feature>) {
    self.store = store
    self.viewStore = ViewStore(self.store, observe: { $0 })
  }

  var body: some View {
    // A large, complex view inside here...
  }
}

Both of these options should greatly improve the compiler's ability to type-check your view.