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Swift Actor 持久化

使用 Swift Actor 实现线程安全的数据持久化——结合内存缓存与文件存储,从设计上消除数据竞争。适用于需要持久化数据并消除数据竞争或线程安全问题的场景。

Swift Actor 持久化 是什么?

本技能提供了一套基于 Swift Actor 构建线程安全数据持久化层的完整模式。它通过 Actor 模型在编译时强制保证序列化访问,从而彻底消除数据竞争,无需手动使用锁或 DispatchQueue。该模式将内存缓存与文件存储相结合,支持快速读取和持久化写入。支持 Swift 5.5+,适用于 iOS/macOS 应用中的本地数据存储、离线优先架构以及任何需要共享可变状态且需避免线程安全问题的场景。通过提供一个泛型 Actor 类 `LocalRepository`(兼容 `Codable & Identifiable` 类型),该技能展示了如何构建一个既保证线程安全又易于集成的持久化层,并能与 `@Observable` ViewModel 结合实现响应式 UI 更新。

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

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文档

Swift Actor 实现线程安全持久化

使用 Swift Actor 构建线程安全数据持久化层的模式。结合内存缓存与文件存储,利用 Actor 模型在编译时消除数据竞争。

使用场景

  • 在 Swift 5.5+ 中构建数据持久化层
  • 需要对共享可变状态进行线程安全访问
  • 希望消除手动同步机制(锁、DispatchQueue)
  • 构建具有本地存储的离线优先应用

核心模式

基于 Actor 的仓库

Actor 模型保证序列化访问——由编译器强制确保无数据竞争。

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 中同步加载(此时 Actor 隔离尚未生效)
        self.cache = Self.loadSynchronously(from: fileURL)
    }

    // MARK: - 公开 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: - 私有方法

    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

由于 Actor 隔离,所有调用会自动变为异步:

let repository = LocalRepository<Question>()

// 读取——基于内存缓存的快速 O(1) 查找
let question = await repository.find(by: "q-001")
let allQuestions = await repository.loadAll()

// 写入——原子地更新缓存并持久化到文件
尝试 await repository.save(newQuestion)
尝试 await repository.delete("q-001")

结合 @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 {
        尝试 await repository.save(question)
        questions = await repository.loadAll()
    }
}

关键设计决策

决策理由
Actor(而非类加锁)编译器强制线程安全,无需手动同步
内存缓存 + 文件持久化快速读取缓存,持久化写入磁盘
同步初始化加载避免异步初始化的复杂性
以 ID 为键的字典通过标识符实现 O(1) 查找
泛型约束 Codable & Identifiable可复用于任何模型类型
原子文件写入 (.atomic)防止崩溃时部分写入

最佳实践

  • 对跨越 Actor 边界的所有数据使用 Sendable 类型
  • 保持 Actor 的公开 API 最小化——仅暴露领域操作,而非持久化细节
  • 使用 .atomic 写入——防止应用在写入过程中崩溃时数据损坏
  • 在 init 中同步加载——异步初始化器增加了复杂性,但对本地文件收益甚微
  • 结合 @Observable ViewModel——实现响应式 UI 更新

应避免的反模式

  • 在新的 Swift 并发代码中使用 DispatchQueue 或 NSLock 代替 Actors
  • 向外部调用者暴露内部缓存字典
  • 未经验证就使文件 URL 可配置
  • 忘记所有 Actor 方法调用都需要 await——调用者必须处理异步上下文
  • 使用 nonisolated 绕过 Actor 隔离(违背了设计初衷)

适用场景

  • iOS/macOS 应用中的本地数据存储(用户数据、设置、缓存内容)
  • 稍后同步到服务器的离线优先架构
  • 应用中多个部分并发访问的任何共享可变状态
  • 用现代 Swift 并发取代传统的基于 DispatchQueue 的线程安全方案

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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