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swift-actor-persistence

在 Swift 中利用 Actor 實現執行緒安全的資料持久化——結合記憶體快取與檔案儲存,從設計上消除資料競爭。適用於在 Swift 中進行資料持久化,且需要從根本上解決資料競爭或執行緒安全問題的情境。

swift-actor-persistence 是什麼?

swift-actor-persistence 是一個專門針對 Swift 5.5+ 的技術模式,旨在透過 Actor 模型建立執行緒安全的資料持久化層。它結合了記憶體快取與檔案備份儲存,讓編譯器在編譯時就強制消除資料競爭,無需手動使用鎖或 DispatchQueue。此技術適用於 iOS/macOS 應用中的本地資料儲存(如使用者資料、設定、快取)、離線優先架構以及任何需要多個部分並發存取共用可變狀態的場景。設計上採用泛型 Actor Repository,支援 Codable 與 Identifiable 協定,可重複用於任何模型類型,並提供 O(1) 的記憶體查詢與原子性檔案寫入,確保崩潰時資料不損毀。

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/swift-actor-persistence

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說明文件

使用 Swift Actor 實現執行緒安全的持久化

使用 Swift Actor 建立執行緒安全資料持久化層的模式。結合記憶體快取與檔案備份儲存,利用 Actor 模型在編譯時消除資料競爭。

啟動時機

  • 在 Swift 5.5+ 中建立資料持久化層
  • 需要對共用可變狀態進行執行緒安全存取
  • 希望消除手動同步機制(鎖、DispatchQueues)
  • 建構離線優先的應用程式,具備本地儲存

核心模式

基於 Actor 的儲存庫

Actor 模型保證序列化存取——無資料競爭,由編譯器強制執行。

public actor LocalRepository<T: Codable & Identifiable> where T.ID == String {
    private var cache: [String: T] = [:]
    private let fileURL: URL

    public init(directory: URL = .documentsDirectory, filename: String = "data.json") {
        self.fileURL = directory.appendingPathComponent(filename)
        // 在 init 中同步載入(此時 Actor 隔離尚未生效)
        self.cache = Self.loadSynchronously(from: fileURL)
    }

    // MARK: - 公開 API

    public func save(_ item: T) throws {
        cache[item.id] = item
        try persistToFile()
    }

    public func delete(_ id: String) throws {
        cache[id] = nil
        try persistToFile()
    }

    public func find(by id: String) -> T? {
        cache[id]
    }

    public func loadAll() -> [T] {
        Array(cache.values)
    }

    // MARK: - 私有

    private func persistToFile() throws {
        let data = try JSONEncoder().encode(Array(cache.values))
        try data.write(to: fileURL, options: .atomic)
    }

    private static func loadSynchronously(from url: URL) -> [String: T] {
        guard let data = try? Data(contentsOf: url),
              let items = try? JSONDecoder().decode([T].self, from: data) else {
            return [:]
        }
        return Dictionary(uniqueKeysWithValues: items.map { ($0.id, $0) })
    }
}

使用方式

由於 Actor 隔離,所有呼叫都會自動變成非同步:

let repository = LocalRepository<Question>()

// 讀取——從記憶體快取進行 O(1) 快速查詢
let question = await repository.find(by: "q-001")
let allQuestions = await repository.loadAll()

// 寫入——原子性地更新快取並持久化到檔案
try await repository.save(newQuestion)
try await repository.delete("q-001")

結合 @Observable ViewModel

@Observable
final class QuestionListViewModel {
    private(set) var questions: [Question] = []
    private let repository: LocalRepository<Question>

    init(repository: LocalRepository<Question> = LocalRepository()) {
        self.repository = repository
    }

    func load() async {
        questions = await repository.loadAll()
    }

    func add(_ question: Question) async throws {
        try await repository.save(question)
        questions = await repository.loadAll()
    }
}

關鍵設計決策

決策理由
Actor(而非 class + 鎖)編譯器強制執行執行緒安全,無需手動同步
記憶體快取 + 檔案持久化從快取快速讀取,寫入磁碟持久耐用
同步 init 載入避免非同步初始化的複雜度
以 ID 為鍵的字典根據識別碼進行 O(1) 查詢
泛型於 Codable & Identifiable可重複用於任何模型類型
原子性檔案寫入 (.atomic)防止崩潰時的部分寫入

最佳實踐

  • 使用 Sendable 類型 處理所有跨 Actor 邊界的資料
  • 保持 Actor 的公開 API 最小化——只暴露領域操作,而非持久化細節
  • 使用 .atomic 寫入 防止應用程式在寫入中途崩潰時導致資料損毀
  • 在 init 中同步載入——非同步初始化增加複雜度,但對於本地檔案回報有限
  • 結合 @Observable ViewModel 實現反應式 UI 更新

應避免的反模式

  • 在全新的 Swift 並行程式碼中使用 DispatchQueue 或 NSLock 替代 Actor
  • 將內部快取字典暴露給外部呼叫者
  • 在未經驗證的情況下讓檔案 URL 可配置
  • 忘記所有 Actor 方法呼叫都需 await——呼叫者必須處理非同步上下文
  • 使用 nonisolated 繞過 Actor 隔離(違背其目的)

使用時機

  • iOS/macOS 應用中的本地資料儲存(使用者資料、設定、快取內容)
  • 離線優先架構,稍後再同步到伺服器
  • 任何應用程式中多個部分並發存取的共用可變狀態
  • 用現代 Swift 並行程式取代傳統基於 DispatchQueue 的執行緒安全機制

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

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