每日新聞摘要

When working with JSON in Java, numeric arrays often arrive as org.json.JSONArray instances while application code expects primitive int[]. Because JSONArray stores elements as Objects, attempting a direct cast compiles but fails at runtime with ClassCastException. This article demonstrates explicit, safe conversion strategies: iterate with getInt() to extract and validate each element, or use IntStream.range to produce a concise, functional conversion. It also covers defensive handling of null or empty JSONArrays by returning an empty int[] to avoid NullPointerException and simplify callers. Each approach is illustrated with JUnit tests that confirm correct results and edge-case behavior. The techniques are straightforward to adapt to other JSON libraries and emphasize clarity, correctness, and robustness when transforming JSON numeric arrays into primitive int arrays in Java.

在 Java 中处理 JSON 时，数字数组常以 org.json.JSONArray 形式传入，但业务逻辑通常需要原始 int[]。由于 JSONArray 内部以 Object 存放元素，不能直接强制转换为 int[]，否则运行时会出现 ClassCastException。文章演示了两种安全的转换方式：用循环配合 getInt() 逐一提取，或用 IntStream.range() 实现流式映射，并说明对 null 和空数组返回空 int[] 的防御性做法以避免 NullPointerException。示例配有 JUnit 测试以验证结果与边界情形，概念可移植到其他 JSON 库，强调可读性与稳健性。

The PostgreSQL error "canceling statement due to user request" means a statement was terminated by an external cancellation signal, not necessarily by a human operator. PostgreSQL does not distinguish between cancellations from interactive users, client libraries, or infrastructure components; it simply stops the statement and frees resources. Common causes include client-side driver timeouts (for example a JDBC query timeout), server-side statement_timeout settings, application restarts or crashes that close sessions, explicit cancellations issued via pg_cancel_backend, and connection pool or proxy behavior that recycles or closes backends. Because all these paths produce the same server message, diagnosing the source requires checking driver and framework timeouts, server configuration, application lifecycle events, and pool/proxy settings to identify which layer issued the cancellation.

PostgreSQL 报错“canceling statement due to user request”意味着某个语句被外部取消信号终止，数据库并不区分是人工操作还是客户端库或基础设施发出的请求，只会停止语句并释放资源。常见触发原因包括客户端驱动（例如 JDBC）的查询超时、服务器端的 statement_timeout 设置、应用重启或崩溃导致会话被断开、通过 pg_cancel_backend 发起的手动取消，以及连接池或代理在回收/关闭连接时触发的取消。由于这些不同路径会产生相同的服务器消息，排查时应检查客户端与框架的超时配置、服务器设置、应用生命周期事件以及连接池/代理行为以找出实际来源。

This article proposes a production-ready Android UI stack centered on Jetpack Compose and Navigation 3, emphasizing predictable state, navigation-as-state, performance, scalability, and testability for large teams. The recommended tech stack pairs a MVVM+MVI hybrid architecture with Koin for dependency injection, Ktor for networking, Room for local storage, and Coroutines/Flow for async flows. Key principles include treating UI as a pure function of state, keeping navigation state explicit, keeping ViewModels free of UI/navigation concerns, and making side-effects explicit and testable. The pattern separates layers into Route (collects flows and performs navigation), pure Screen composables (stateless UI), and ViewModel (pure business logic). The guide includes DI and networking setup examples, repository single-source-of-truth patterns, performance tips, and a simple unit test to demonstrate effect emission for navigation.

本文提出一套面向生产的 Android UI 堆栈，核心追求可预测的状态管理、以状态驱动的导航、高性能与可扩展性，适合大型团队與快速開發。推荐技术包含 Jetpack Compose、Navigation 3、MVVM+MVI、Koin、Ktor、Room 以及 Coroutines/Flow。设计原则包括：UI 为 State 的纯函数、导航为状态、ViewModel 不接触 UI/导航、副作用显式化，并且所有部分在无 Android 运行时下都可测试。范例展示了 Route（收集 Flow、触发导航）、纯 Screen（无状态 UI）、以及纯业务逻辑的 ViewModel，并提供 DI、网络、数据库与测试示例。

The article investigates freeing the Kotlin compiler daemon immediately after compilation and before R8 runs, arguing this returns the process’s full memory to the OS and can help heavy final build phases. The author implemented a Gradle ValueSource and task that executes a shell command to kill KotlinCompileDaemon, then ran experiments on GitHub Actions across three projects (nowinandroid, a synthetic 120-module project, and Signal Android) with 100 clean release builds. Results show consistent peak memory reductions (~13–15%), modest overall build-time gains (up to ~3.1%), and R8 speedups (up to ~7%), with the Signal app showing the best improvements. The approach is most relevant to CI single-variant release builds where incremental daemon benefits are minimal; users should adapt orchestration if applying to other build variants or post-compilation tasks.

作者在 GitHub Actions 的 CI 环境中实现并测试了在 Kotlin 编译完成后立即终止 KotlinCompileDaemon 的做法：通过注入 ExecOperations 的 ValueSource 与自定义任务执行 shell 命令杀掉 daemon，并在 three 个项目（nowinandroid、合成 120 模块项目、Signal Android）上各运行 100 次干净的 release 构建。结果表明，在不影响构建流程的情况下，可将峰值内存平均降低约 13–15%，并在 R8 与整体构建时间上得到小幅改善（Signal 项目表现最好，整体构建约快 3%、R8 约快 7%）。该方法对单变体 release CI 特别适用，应用到其它场景需调整终止时机。

This article offers a focused, in‑depth look at Jetpack Compose for experienced Android engineers, framing it as a paradigm shift from imperative UI to a declarative approach. It examines the foundational principles of declarative UI, explains the role and behavior of composable properties, and emphasizes the mental model senior developers must adopt to design, reason about, and maintain Compose-based interfaces. Rather than being a shallow overview, the piece aims to clarify why the declarative style changes how state, rendering, and component composition are approached, and it outlines the core concepts and mindset required to leverage Compose effectively in real projects. The guidance is targeted at seasoned Android developers seeking to master Compose’s essentials and adjust their architectural thinking accordingly.

本文为有经验的 Android 工程师提供对 Jetpack Compose 的深入解读，将其视为从命令式 UI 向声明式 UI 的范式转变。文章阐述声明式 UI 的核心原理，解释可组合属性的意义与行为，并强调开发者在设计、推理和维护 Compose 界面时必须调整的思维模式。内容不只是概览，而是理清声明式风格如何影响状态管理、渲染和组件组合的处理方式，归纳出在真实项目中有效运用 Compose 所需的关键概念与心态。针对希望掌握 Compose 要点并调整架构思路的资深开发者。

The piece highlights a common and often frustrating reality for Android developers: the operating system may kill app processes. It emphasizes that this is an issue every developer will encounter at some point and explains that understanding the phenomenon is key. The article addresses why process kills happen and when they typically occur, and it outlines practical ways to manage them during development and in production. It stresses the importance of detecting and reproducing kill events, assessing the impact on app state and user experience, and applying strategies to preserve state and increase resilience. Overall, the goal is to help developers design apps that tolerate unexpected terminations and restore a reliable user experience.

文章提出了一个 Android 开发者普遍且常令人困扰的现象：系统会终止应用进程。作者指出每位开发者迟早都会碰到这种情况，并强调理解其成因的重要性。文章讨论了为何以及在何种情形下可能发生 process kill，并就开发与生产环境中的应对方法提供方向性建议，包括如何检测与重现进程终止事件、评估其对应用状态和用户体验的影响，以及采取保留状态与提高抗风险能力的策略。总体目标是帮助开发者设计能容忍意外终止并尽快恢复稳定体验的应用。

The article offers a concise, foundational guide to Android's event-driven building blocks — Intents, Broadcast Receivers, and Foreground Services — framing them as the three pillars of event-driven architecture on the platform. It explains the purpose of each component, how they interact to propagate and handle events, and typical scenarios where each is appropriate. The guide highlights practical considerations such as lifecycle implications and background execution constraints, and describes Kotlin-oriented approaches for wiring these components together in applications. It also calls out best practices for secure and efficient event handling and common pitfalls to avoid. Overall, the piece serves as a primer to help Android developers choose the right mechanisms for communication, work scheduling, and long-running foreground tasks.

本文对 Intents、Broadcast Receivers 与 Foreground Services 三大要素做基础说明，阐述各自用途、相互作用与常见适用场景，並讨论生命周期与后台执行限制等实务考量。文章以 Kotlin 为导向，提供在应用中串接这些组件的建议，指出常见陷阱与最佳实作方向，帮助开发者在构建事件驱动流程时选择合适的通信、工作调度与前台长期任务处理方式。

This Part 2 installment examines how the Kotlin collection framework and related abstractions can be applied in Android applications, with an emphasis on modeling behaviors as state machines and integrating persistence strategies. The article discusses architectural considerations for representing mutable and immutable data flows, coordinating state transitions, and storing collection-backed state in Android contexts. Rather than providing exhaustive API references, the piece frames design trade-offs and best practices that improve maintainability and testability when using collection-oriented patterns. Concrete examples and implementation details are available in the parent Medium article; readers interested in sample code and deeper walkthroughs are directed to continue reading on Medium for the complete discussion.

本篇为第 2 部分，阐述如何在 Android 应用中使用 Kotlin 的集合框架及相关抽象，采用状态机概念来建模行为，并探讨集合状态的持久化方法。文章强调在表示可变与不可变数据流、协调状态转换以及在 Android 环境中保存集合驱动的状态时的架构考虑，并讨论有助于提升可维护性与可测试性的设计取舍。完整的实现细节与示例代码收录于主文，读者若需更全面的代码与逐步解析，请前往 Medium 阅读完整文章。

This entry references an article titled "Navigation Drawer APIs in Material 3 Jetpack Compose" authored by Syntax Buddy and links to the full piece on Towards Dev. The visible content on the provided page is minimal: a brief greeting — "Hello Android developers 👋" — followed by an instruction to continue reading on the Towards Dev site. No technical details or article body are present on the linked snippet itself; readers must follow the Towards Dev URL to access the full article. The metadata (title, author, and link) identifies the topic and source, but the page shown here functions primarily as a redirect or teaser rather than containing the substantive article text.

该条目指向一篇题为「Navigation Drawer APIs in Material 3 Jetpack Compose」、作者为 Syntax Buddy 的文章，并提供了前往 Towards Dev 的链接。当前页面显示的内容非常简短，仅包含一条问候语“Hello Android developers 👋”，并提示读者在 Towards Dev 上继续阅读完整内容。页面本身没有提供技术细节或文章正文；要查看完整文章需点击提供的链接前往原始来源。可确认的信息仅限标题、作者和指向完整文章的链接，页面作用更像预览或跳转提示。

In "Modern Android Architecture in 2026: Why MVVM Is Necessary but Not Sufficient," Akshay Kalola argues that while MVVM continues to be a valuable pattern for Android development, it cannot by itself address the architectural challenges that emerge as apps and codebases grow. The piece explains that MVVM’s strengths are real but limited, and that teams should treat MVVM as one part of a larger architecture rather than as a standalone cure. To keep projects maintainable and scalable, the author emphasizes the need for complementary practices and engineering disciplines that address modularity, responsibilities, testability, and long-term maintainability. The article prompts readers to reassess their architectures in 2026 and to combine MVVM with other design principles and infrastructure to manage complexity and future growth.

Akshay Kalola 在文章中提出，MVVM 对现代 Android 开发仍然是有用的模式，但它本身无法解决应用及代码库在增长过程中出现的所有架构问题。作者阐述了 MVVM 的优势与局限，并强调应把 MVVM 视为更大架构的一部分而非独立方案。为保持项目的可维护性与可扩展性，需要采用补充性的工程实践与设计原则，以处理模块化、职责分离、可测试性和长期维护等问题。文章旨在提醒团队在 2026 年审视应用架构，并将 MVVM 与其他设计原则和基础设施结合，以应对复杂性和未来增长。

The article notes that backend development with Kotlin has matured considerably by 2025, yet many teams continue to struggle because they still apply design patterns and workflows inherited from Java. It surveys how reliance on Java-era idioms can create friction when adopting Kotlin’s idiomatic features, outlining common pain points such as mental-model mismatches, tooling and migration decisions, and organizational inertia. Framed as a comprehensive guide, the piece examines why these issues persist and offers practical advice to help teams transition: recommending incremental migration strategies, developer training, and pragmatic choices of tooling and libraries that balance interoperability with legacy Java code. The author emphasizes evaluating trade-offs and prioritizing maintainability when adopting Kotlin at scale.

文章指出，截至 2025 年，Kotlin 在后端开发领域已相当成熟，但许多团队仍然沿用从 Java 继承下来的设计模式与工作流程，因而在迁移或采用 Kotlin 的惯用写法时遇到困难。作为一篇综合性指南，作者探讨这些问题为何持续存在，并提供实用建议：建议采用渐进式迁移策略、加强开发者培训，以及选择既能与遗留 Java 互操作又务实的工具和库，帮助团队在兼顾可维护性与实务需求的前提下逐步转向符合 Kotlin 特色的开发方式。

The article examines using LiveData and StateFlow to implement the observer pattern within an Android MVVM architecture. It emphasizes that in modern MVVM the View and ViewModel do not communicate directly; instead the View observes state exposed by the ViewModel. LiveData and StateFlow each provide a way to expose observable state: LiveData integrates with Android lifecycle components, while StateFlow fits naturally with Kotlin coroutines and Flow-based streams. The piece outlines how observable data simplifies UI updates and decouples layers, and it highlights practical considerations when selecting between the two approaches, such as lifecycle awareness, threading and coroutine support, and how they influence data propagation from ViewModel to View.

本文探讨在 Android MVVM 架构中如何使用 LiveData 与 StateFlow 实现观察者模式。强调在现代 MVVM 设计中，View 与 ViewModel 并不直接交互，而是由 View 观察 ViewModel 暴露的状态。LiveData 与 StateFlow 各自提供可观察状态的途径：LiveData 与 Android 生命周期组件紧密结合，而 StateFlow 则与 Kotlin 协程/Flow 自然配合。文章说明通过可观察数据可以简化 UI 更新、降低层间耦合，并指出在选择两者时需考虑的实际因素，例如生命周期支持、线程与协程整合等，这些都会影响数据从 ViewModel 到 View 的传播方式。

This article is a practical, beginner-to-advanced guide to Kotlin Channels. It defines Channels, shows when they are appropriate for coroutine-based communication and concurrency, describes correct usage and common pitfalls, and warns when Channels are not the right tool. Presented in plain professional language, the guide emphasizes patterns and trade-offs to help choose Channels versus other primitives, and offers advice on safe usage to avoid problems such as misuse or unnecessary complexity. The target audience ranges from newcomers seeking foundational explanations to experienced developers looking for advanced considerations. Overall, the guide aims to equip readers with an actionable understanding of Channels’ purpose, proper application, and limitations so they can decide when to adopt them and how to implement them responsibly in Kotlin coroutine code.

这篇实用指南从初学到高级介绍 Kotlin 的 Channels。内容说明 Channels 的定义、何时适合用于 coroutine 之间的通信与并发处理、正确的使用方法与常见陷阱，并指出何时不应使用 Channels。文章以简明专业的语气阐述设计模式与权衡，帮助读者在 Channels 与其他同步或通信原语之间做出选择，并提供避免误用与过度复杂化的建议。目标读者包括刚接触的开发者与寻求高级观点的工程师，最终目的是让读者能够判断何时采用 Channels 并在 Kotlin coroutine 代码中负责任地实现。

In Part 1, VoidSpecter proposes treating plain English as a primary design language when architecting Android applications. The article urges developers to "stop coding in the dark" and outlines how explicit "Contracts"—clear, human-readable descriptions of component behavior and expectations—can be used to reduce crashes and misunderstandings. By writing and agreeing on these worded contracts before or alongside implementation, teams expose assumptions early, align intent across stakeholders, and make boundaries between components more explicit. The approach frames English-based contracts as a complementary layer to code and tests, helping developers design safer, more predictable Android apps through clearer communication and specification.

作者 VoidSpecter 在本篇（第一部分）提出，将日常英语作为构建 Android 应用的设计语言。文章呼吁不要在缺乏共识與明确规范的情况下编码，並介绍透过明确的“Contracts”——以人能理解的话语描述组件行为与期望——来减少崩溃与沟通误差。先用文字定义并达成一致，有助于及早暴露假设、统一意图，並让组件边界更清晰。这种以文档化、基于语言的契约方法被视为对代码和测试的补充，能通过更清晰的沟通与规范来设计更稳定的 Android 应用。

This tutorial explains how to generate valid, unique identifiers in Java while satisfying constraints such as a configurable maximum length and an alphanumeric character set. The primary custom approach uses java.security.SecureRandom and a defined ALPHANUMERIC_CHARACTERS string; a helper builds random strings via Java 8 Streams and a generateUniqueId method loops until an identifier not present in the existingIds store is produced. Alternatives covered include UUID.randomUUID() (high uniqueness and thread-safe but long) and timestamp-based IDs using AtomicLong for fast, sequential identifiers. Performance tests generating one million IDs on a quad-core MacBook Pro showed timestamp-based generation as the fastest (≈20 ms), a custom generator at ≈300 ms, and UUID at ≈600 ms. The article concludes UUID is the safest default, custom generators suit short constrained IDs, and timestamp-based counters excel where performance or sequential ordering matters.

本文介绍在 Java 中生成合法且唯一标识符的方法，聚焦于长度上限与仅允许英文字母或数字的约束。示例采用 java.security.SecureRandom 作为随机源，定义 ALPHANUMERIC_CHARACTERS 并通过 Java 8 Stream API 构造随机字符串；generateUniqueId() 使用循环检查 existingIds 集合以避免重复。文中还讨论了替代方案：UUID.randomUUID() 提供极低碰撞概率但返回较长字符串；通过 AtomicLong 递增并以大基数转换可生成快速的时间戳序列 ID。性能测试（生成 1,000,000 个 ID）显示时间戳法约 20 毫秒最快，自定义生成器约 300 毫秒，UUID 约 600 毫秒。建议根据安全性、长度与性能需求选择合适方案。

This guide explains how Visual Studio Code helps developers maintain clean, consistent code through automatic and manual formatting options. Key automatic features include format-on-save, which formats files when you save; format-on-type, which adjusts indentation and alignment as you type; and format-on-paste, which reformats pasted snippets to match surrounding code. For reliable results, verify the editor's language mode and set a default formatter per language, and combine formatters with linters to fix style and syntax issues automatically. Manual formatting remains available via keyboard shortcuts (e.g., Shift+Alt+F on Windows/Linux, Shift+Option+F on macOS), the right-click Format Document command, or the Command Palette for Format Document/Selection. Using these features together reduces formatting chores, enforces team-wide consistency, and lets developers focus on logic rather than style details.

本文介绍了 Visual Studio Code 的自动与手动代码格式化功能，帮助开发者保持清晰且一致的代码风格。主要自动选项包括保存时格式化（formatOnSave）、输入时根据括号或分号自动调整缩进与对齐（formatOnType），以及粘贴时自动匹配周围风格（formatOnPaste）。为确保格式化生效，应核对编辑器的语言模式并为每种语言设置默认格式化器；同时可与 linter 结合，自动修复样式和语法问题。手动格式化可通过快捷键（如 Windows/Linux 的 Shift+Alt+F、macOS 的 Shift+Option+F）、右键“Format Document”或命令面板执行。合理配置这些功能能减少审查中的格式问题，让开发更专注于逻辑实现。

This article clarifies where commits belong when using JdbcTemplate or a DataSource in Spring. JdbcTemplate abstracts obtaining and releasing Connections, so application code has no direct place to call commit(); transaction ownership and lifecycle are managed by the Spring container. The recommended approach is declarative transaction management with @Transactional and a configured DataSourceTransactionManager, letting Spring start, commit, or roll back transactions automatically. For cases needing explicit control, use PlatformTransactionManager to begin and commit transactions programmatically without touching JDBC Connection.commit(). Manually committing a Connection bypasses Spring’s synchronization and can break nested or multi-resource transactions, causing inconsistent state. Embrace Spring’s transaction APIs to keep business logic clean and ensure predictable, consistent transactional behavior across the application.

本文澄清了在使用 JdbcTemplate 或 DataSource 时应何处提交事务的问题。JdbcTemplate 从 DataSource 获取并释放连接，应用代码无法直接调用 commit；事务的启动、参与连接及提交或回滚由 Spring 容器负责。推荐使用 @Transactional 配合 DataSourceTransactionManager 进行声明式事务管理，让 Spring 自动处理提交与回滚；若需程序化控制，则通过 PlatformTransactionManager 显式开启与提交事务，但不要直接调用底层 JDBC 的 commit()，因为这会绕过 Spring 的事务同步，可能造成部分提交或数据不一致。遵循 Spring 的事务模型可保持程式碼整潔並確保一致行為。

In automation with TestNG you often need to enable or disable tests based on runtime conditions (environment, feature flags, CI parameters). Because Java annotations are evaluated at compile time, TestNG cannot use runtime expressions inside @Test attributes. The article demonstrates two runtime strategies: throwing TestNG's SkipException and implementing IAnnotationTransformer. SkipException can be thrown inside a test or in lifecycle hooks like @BeforeMethod; it aborts execution and marks tests as SKIPPED, giving explicit reporting but requiring execution up to the throw point (or repeated checks). IAnnotationTransformer runs during discovery and can call annotation.setEnabled(false) to prevent tests from being scheduled at all, resulting in cleaner counts and faster runs but no skip entries. The piece also shows how to register the transformer in testng.xml and configure Maven Surefire to run TestNG suites. Choice depends on whether you prefer visible skipped reports or cleaner execution and counts.

本文介绍在 TestNG 中根据运行时条件有条件跳过测试的方法。由于 Java 注解在编译时评估，不能在 @Test 属性中使用运行时表达式，文章展示两种解决方案：在运行时抛出 TestNG 的 SkipException，或实现 IAnnotationTransformer。SkipException 可在测试内部或生命周期钩子（如 @BeforeMethod）中抛出，能中断执行并将测试标记为 SKIPPED，利于报告可见性但需执行到抛出点或在多测试里重复判断；IAnnotationTransformer 则在测试发现阶段将 annotation.setEnabled(false)，使不符合条件的测试不会被调度或计入跳过，从而提高执行速度并保持计数整洁。文中还示范了在 testng.xml 注册监听器及在 Maven Surefire 中配置测试套件。

This article walks through implementing Android push notifications using Bloomreach (formerly Exponea). It lists prerequisites—Kotlin app, FCM configured, Bloomreach account with push enabled, and basic Android lifecycle knowledge—and describes the high-level flow: app registers with FCM, sends token to the Bloomreach SDK, Bloomreach triggers campaigns via FCM, and FCM delivers messages to the device. Practical steps cover Firebase setup (add google-services.json, include firebase-bom and messaging dependencies, apply the Google services plugin), adding the Exponea SDK dependency, and initializing the SDK with projectToken, authorization, and baseURL in your Application.onCreate after obtaining tracking consent. For push, implement a FirebaseMessagingService that delegates to Exponea.handleRemoteMessage and Exponea.handleNewToken, register the service in AndroidManifest, and configure FCM in Bloomreach by creating a Google Cloud service account and uploading its JSON key so push delivery and token tracking work automatically.

本文介绍在 Android 应用中使用 Bloomreach（原 Exponea）实现推送通知的完整流程。要求包括 Kotlin 应用、已配置的 FCM、启用推送的 Bloomreach 帐号以及基本的应用生命周期知识。高层架构是：应用向 FCM 注册并获取设备 token，token 传给 Bloomreach SDK，Bloomreach 通过 FCM 发送推送活动，FCM 将消息投递到设备，应用显示通知。具体步骤涵盖 Firebase 设置（在项目加入 google-services.json、添加 firebase-bom 与 messaging 依赖、应用 Google services 插件）、集成 Exponea SDK 并在 Application.onCreate 初始化（需取得追踪同意）、实现 FirebaseMessagingService 并在 AndroidManifest 注册服务，以及在 Bloomreach 中通过 Google Cloud 服务账号上传 JSON 密钥来配置 FCM 集成，从而实现推送接收与 token 自动跟踪。

This article argues that Jetpack Compose is not a magical declarative layer that frees developers from understanding how UI updates occur. It focuses on three linked concerns: recomposition, state management, and performance pitfalls that are often underexplained in official material. Developers are encouraged to learn how and when composables recompose, place and expose state deliberately, and be aware that poor state placement or update patterns can cause unnecessary work and slowdowns. Rather than assuming Compose handles everything efficiently, the piece recommends deliberate design choices and profiling to identify costly recompositions and other runtime issues. The overall message is that Compose provides powerful tools, but real-world apps still require careful state and performance management.

本文主张 Jetpack Compose 并非能让开发者免于理解 UI 更新细节的魔法层，文章集中讨论重组、状态管理和常被忽视的性能陷阱三者的关联。作者提醒开发者学习何时以及如何发生重组，谨慎放置与暴露状态，因为不当的状态设计或更新模式会引发不必要的开销和性能下降。与其假设 Compose 会自动高效处理一切，文章建议通过有意识的设计和性能分析来识别代价高昂的重组与运行时问题。结论是：Compose 提供强大工具，但真实应用仍需精心管理状态与性能。

This short Medium post alerts Android developers that background execution limits are stricter than many expect and that those hidden constraints can silently cause lost user data and increased battery drain in production apps. It urges developers to stop losing user data and battery life by learning how background execution is constrained on modern Android: processes may be stopped or restricted in ways that break assumptions about continuous background work. The piece emphasizes mastering these constraints — examining app lifecycle behaviors, testing under real-device power and background policies, and adjusting architectures and scheduling accordingly — so apps survive system-imposed limits. The goal is practical: make apps robust in the field by understanding and designing for background execution rules rather than assuming persistent background execution will remain available.

这篇 Medium 短文提醒 Android 开发者：系统对后台执行的限制比想像中更严格，这些隐藏的限制会在生产环境中导致用户资料丢失与电量过度消耗。作者呼吁开发者停止让资料与电力流失，必须了解现代 Android 如何限制后台作业、检视应用生命周期行为、在真实设备与节电策略下测试，并调整应用架构与调度以配合系统限制。目标是通过理解与设计让应用在系统强制限制下仍能稳健运行，而非假设后台执行会长期持续可用。

While profiling a large legacy codebase, Nazarii Moshenskyi discovered many unnecessary network calls that were harming a critical execution path. By identifying how those calls accumulated on the critical flow and changing only two lines of code to eliminate or consolidate redundant requests, he reduced latency on that path by roughly 40%. The article recounts the investigation approach: measure and trace the critical path, look for repeated remote calls, and make minimal, targeted code changes rather than broad rewrites. The takeaway is that small, well-focused edits—guided by profiling and a clear understanding of request patterns—can deliver outsized performance benefits in mature systems. It serves as a reminder to prioritize the critical path when optimizing production systems.

在对大型遗留代码库进行性能分析时，Nazarii Moshenskyi 发现大量不必要的网络调用，这些调用聚集在关键执行路径上。通过追踪与测量，定位到重复或可合并的远程请求后，他仅修改了两行代码就消除了多余调用，使该关键流程的延迟降低约 40%。文章描述了调查方法：聚焦关键路径、分析调用模式、采取最小且有针对性的改动而非大刀阔斧的重构。核心观点是以实测数据为依据，通过小幅且精准的优化，在既有系统中同样能获得显著的吞吐与响应性能提升。

The article examines the historical prominence of RxJava as the backbone of Android apps—notably between 2016 and 2020—and compares it with Kotlin Coroutines to help developers decide in 2026. Rather than declaring a single winner, it outlines key trade-offs developers should weigh: how well each model integrates with Kotlin, runtime behavior and complexity, tooling and community support, and the costs of migrating or maintaining existing codebases. The piece presents pragmatic guidance for different scenarios—greenfield Kotlin projects, legacy RxJava codebases, and teams whose expertise favors one approach—emphasizing that both technologies remain viable and the optimal choice depends on project constraints, team familiarity, and long‑term maintenance considerations.

本文回顾了 RxJava 在 Android 开发（特别是 2016–2020 年）期间的主导地位，并将其与 Kotlin Coroutines 进行了比较，旨在帮助开发者在 2026 年做出选择。作者并未简单宣称某一方必胜，而是列举了应考虑的关键因素：与 Kotlin 的集成与惯用性、运行时行为与复杂度、工具和社区支持，以及现有代码库的迁移与维护成本。文章提供面向不同情境的实用建议，帮助在新项目、遗留 RxJava 项目或依据团队熟悉度的情况下，选择最合适的异步方案。

Anand Yadav’s guide explains Jetpack Compose’s Retain API as a solution for persisting UI state across lifecycle events and configuration changes (such as screen rotations). The article walks through Retain API’s goals, usage patterns in Compose, integration points with component lifecycles, and practical examples to keep composable state stable when activities or processes are recreated. It contrasts the Retain approach with other state-persistence techniques, outlines common pitfalls, and offers recommendations for when to prefer Retain versus alternative strategies. It also addresses testing considerations and practical tips for debugging state issues, showing how to combine Retain with other persistence layers when needed. By the end, readers should be able to choose an appropriate strategy to preserve UI state without overcomplicating their architecture and improve continuity during configuration changes.

Anand Yadav 的这篇指南介绍 Jetpack Compose 的 Retain API，说明如何在生命周期事件与配置变化（如屏幕旋转）时保留 UI 状态。文章包含 Retain API 的设计初衷、在 Compose 中的使用范式、与组件生命周期的整合方法，以及实用示例以保持可组合函数状态在活动或进程重建时稳定。文中还比较了 Retain 与其他状态持久化手段，指出常见陷阱并提供测试与调试建议，说明在何种情形下应优先采用 Retain，从而在不复杂化架构的前提下减少状态丢失导致的问题，提升用户体验连贯性。