SwiftUI Real-Time Data Architecture (WebSockets, Streams, Consistency)

Real-time features sound simple:

“Just update the UI when data changes.”

In reality they cause:

• race conditions
• duplicate updates
• UI flicker
• inconsistent state
• impossible-to-debug bugs

This post shows how to design real-time data pipelines in SwiftUI that stay:

• consistent
• predictable
• performant
• testable

This is how chat apps, live dashboards, and collaborative tools actually work.

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🧠 The Core Principle

Real-time data is a stream, not a replacement.

Never overwrite your state blindly.

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🧱 1. Single Source of Truth

final class LiveStore<T>: ObservableObject {
    @Published private(set) var value: T
}

All updates flow through this store.

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🌐 2. Event Stream Layer

protocol EventStream {
    func connect()
    func disconnect()
    var events: AsyncStream<ServerEvent> { get }
}

Your UI never talks to sockets directly.

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🔁 3. Reducer-Based Merging

func reduce(current: State, event: ServerEvent) -> State

Never do:

state = newServerState

Merge instead.

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🧭 4. Conflict Handling

If:

• local edits exist
• server pushes arrive

You must reconcile.

Example:

case .remoteUpdate(let serverValue):
    if state.hasLocalChanges {
        state.queue(serverValue)
    } else {
        state.value = serverValue
    }

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🧬 5. Backpressure Control

Throttle UI updates:

for await event in stream.events.throttle(seconds: 0.2) {
    apply(event)
}

Avoid 60 updates per second.

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🧪 6. Testing Streams

let stream = MockStream(events: [.add, .remove])

Assert:

• final state
• no duplicates
• no race bugs

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⚠️ 7. Avoid These

• overwriting state
• mutating in views
• direct socket calls
• no buffering
• no ordering

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🧠 Mental Model

Socket
 → Event Stream
   → Reducer
     → Store
       → UI

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🚀 Final Thoughts

Real-time apps fail when data is treated as state.

Treat it as a stream of intentions, and your UI stays stable.