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Hey, DZone Community! We have an exciting year of research ahead for our beloved Trend Reports. And once again, we are asking for your insights and expertise (anonymously if you choose) — readers just like you drive the content we cover in our Trend Reports. Check out the details for our research survey below. Comic by Daniel Stori API Management Research APIs already do a great job ensuring secure and seamless connections within systems, sure — but can they do even better? At DZone, we're untangling trends like API-first development and democratization to learn how they empower development teams to implement APIs progressively. Take our short research survey ( ~10 minutes) to contribute to our latest findings. We're exploring key topics, including: Streamlining API integrationAI for APIsAPI security, performance, and observabilityMessaging infrastructure Join the API Management Research Over the coming month, we will compile and analyze data from hundreds of respondents; results and observations will be featured in the "Key Research Findings" of our Trend Reports. Your responses help inform the narrative of our Trend Reports, so we truly cannot do this without you. Stay tuned for each report's launch and see how your insights align with the larger DZone Community. We thank you in advance for your help! —The DZone Content and Community team

Apache Parquet in Data Warehousing Parquet files are becoming the de facto standard for columnar data storage in big data ecosystems. This file format is widely used by both sophisticated in-memory data processing frameworks like Apache Spark and more conventional distributed data processing frameworks like Hadoop due to its high-performance compression and effective data storage and retrieval. Major companies like Netflix, Uber, LinkedIn, and Airbnb rely on Parquet as their data storage file format for large-scale data processing. The Rise of Vulnerabilities Although open-source Java libraries are essential for contemporary software development, they frequently introduce serious security flaws that put systems at risk. The risks are highlighted by recent examples: Deep Java Library (CVE-2025-0851): Attackers can write files outside of designated directories due to a path traversal vulnerability in DJL's archive extraction tools. Versions 0.1.0 through 0.31.0 are affected by this vulnerability, which may result in data corruption or illegal system access. Version 0.31.1 has a patch for it.CVE-2022-42003, Jackson Library: Unsafe serialization/deserialization configurations in the well-known JSON parser cause a high-severity problem (CVSS 7.5) that could result in denial-of-service attacks. These illustrations highlight how crucial it is for open-source libraries to have careful dependency management, frequent updates, and security audits. Companies should enforce stringent validation and use automated vulnerability scanning tools. Parquet-Avro Module of Apache Parquet (CVE-2025-30065): During the life cycle of data load into the Enterprise data lake, we use file format conversions like ORC to Avro, Avro to Parquet, and Parquet to Avro. To achieve this conversion, Spark, in this case, uses underlying jars like parquet-avro. On April 2nd, 2025, one such vulnerability was reported on the parquet-avro module with the highest severity rating (CVSS 10.0, “Critical”). When the parquet-avro module deserializes untrusted schemas embedded in Parquet files, it creates a vulnerability known as the Deserialisation of Untrusted Data (CWE-502). In order to cause arbitrary code execution during file parsing, attackers can insert malicious code into these schemas. Since many systems implicitly trust Parquet files in data workflows, this gets around common security controls. Example of an Attack Scenario Exploiting CVE-2025-30065 in Apache Parquet: Step 1: Crafting the Malicious File The attacker first creates a Parquet file embedded with a corrupted Avro schema. This schema includes malicious payloads that are meant to run when ingested, like binary code or scripts. Targeting systems that depend on external or unreliable data sources, the attacker may pose this file as authentic data in order to evade detection. Step 2: Delivery to the Target System The malicious Parquet file is delivered to the victim's system by the attacker. Phishing emails, compromised third-party data providers, or direct uploads to shared repositories could all be used to accomplish this. Files are frequently processed automatically in settings like cloud data pipelines or big data platforms (like Hadoop and Spark), which makes exploitation more likely. Step 3: Exploitation During File Processing The parquet-avro module tries to parse the schema of the Parquet file when it is processed by the vulnerable system. The embedded exploit payload is executed as a result of the deserialisation process's incorrect handling of untrusted data. This gives the attacker the ability to execute arbitrary commands or scripts on the system through remote code execution (RCE). Step 4: Impact on the Victim System Once control is gained, attackers can: Install malware: Deploy ransomware or crypto miners.Exfiltrate data: Steal sensitive datasets stored in the system.Tamper with data pipelines: Inject false data or disrupt workflows.Cause service disruption: Shut down critical services or corrupt files. Mitigation Steps 1. Dependency Audit Investigate To find out which dependencies in your projects are out of date, use tools such as Maven. For example: XML <dependency> <groupId>org.apache.parquet</groupId> <artifactId>parquet-avro</artifactId> <version>1.15.0</version> <!-- vulnerable version --> </dependency> Mitigate Upgrade immediately — patch to the latest stable release, i.e., Apache Parquet 1.15.1, which resolves the issue. Explicitly mentioning the version in the dependencies forces the project to use the patched jar. XML <dependency> <groupId>org.apache.parquet</groupId> <artifactId>parquet-avro</artifactId> <version>1.15.1</version> <!-- patched --> </dependency> 2. Validate File Sources Investigate Magic number checks: Although this isn't a security measure in and of itself, make sure Parquet files start and end with "PAR1" in order to identify non-Parquet files earlySchema validation: To stop deserialization attacks, reject files with unexpected or distorted schemas. Mitigate Wait until systems have been updated with the patched version of Parquet jars before processing Parquet files from untrusted sources. 3. Audit and Monitor Logs Investigate Anomaly detection: Keep track of all Parquet file ingestions and keep an eye out for odd activity, such as unexpected file sources.Sensitive data scanning: Identify and categorize PII, financial information, or medical records in Parquet files Mitigate To identify unusual activity, enable thorough logging for Parquet file ingestion. 4. Restrict Access and Permissions Investigate Role-based access control (RBAC): Use tools such as AWS IAM or Azure AD to restrict file access while upholding the least privilege principle.Network segmentation: To lessen attack surfaces, separate Parquet processing systems from open networks. Mitigate Implement RBAC, zero-trust principles, and network segmentation. JSON // AWS IAM policy snippet for Parquet file access { "Effect": "Allow", "Action": "s3:GetObject", "Resource": "arn:aws:s3:::data-lake/*", "Condition": { "StringEquals": {"aws:MultiFactorAuthPresent": "true"} } } 5. Secure File Sources Investigate Trusted origins only: Unless it's absolutely required, do not process files from sources that are not trustworthy.Sandbox testing: Examine untrustworthy Parquet files in different environments ahead of ingesting them into production. Mitigate Report and remove impacted Parquet files from processing. Adhere to business policies when implementing vulnerability mitigation techniques. Conclusion Organizations dealing with critical data must prioritize the security of their data pipelines to protect against threats such as tampering, exfiltration, and ransomware. As data pipelines often process sensitive information across distributed systems, they are prime targets for attackers seeking to exploit vulnerabilities, disrupt operations, or steal valuable data. Following industry standards and implementing robust security measures is essential to ensure resilience and trustworthiness.

Understand the Inheritance in PHP Inheritance gives you the ability to use the code from another class. It helps reduce repetition and also makes your code organized. When you create a parent class with common properties and methods, the child class extends it and gets access to that code. The child can also add its own features or override the parent’s methods. Here is an example: PHP class Gibson { public function run() { echo "Running device"; } } class AirCondition extends Gibson { public function run() { echo "Cooling room"; } } $ac = new AirCondition(); $ac->run(); Output: Plain Text Cooling room The AirCondition class extends Gibson. It overrides the run method with its own version. In the following part, you will learn more about the types of inheritance in PHP. Let's move on. Types of Inheritance in PHP We have three types, which are as follows: Single inheritance: One child class inherits from one parent.Multilevel inheritance: A class inherits from a class that already inherited from another class. Hierarchical inheritance: Multiple child classes inherit from the same parent class. Here is an example: PHP class Device { public function start() { echo "Device started"; } } class Gibson extends Device { public function run() { echo "Running device"; } } class AirCondition extends Gibson { public function cool() { echo "Cooling room"; } } Here we have three classes with inheritance: Device, Gibson, and AirCondition. Device has a method start() that outputs "Device started". Gibson extends Device and adds a run() method, so objects of Gibson can use both start() and run(). AirCondition extends Gibson, inheriting both start() and run(), and adds a cool() method. This creates a class multilevel inheritance, where AirCondition can access all three methods: start(), run(), and cool(). Here is another example of hierarchical inheritance: PHP class Gibson { public function run() { echo "Running device"; } } class AirCondition extends Gibson { public function cool() { echo "Cooling room"; } } class Heater extends Gibson { public function heat() { echo "Warming room"; } } The Gibson class has a run() method. Both AirCondition and Heater extend Gibson inheriting its run() method. AirCondition adds a cool() method, while Heater adds a heat() method. This shows how child classes inherit and extend functionality from a parent class. The Reasons for Using PHP Inheritance So, why should you use inheritance in PHP? Inheritance allows child classes to reuse code from the parent class. That reduces redundancy and makes maintenance easier.It helps you organize code, group similar functionality in a parent class, and extend it in child classes.Child classes can override or extend methods from the parent class, offering customization without changing the base functionality.If a common feature needs an update, you can change it in the parent class, and all child classes will inherit the update automatically. Now, let's understand how to override methods and how classes and objects use them. Override Methods in PHP Override methods can happen when a subclass provides a new implementation for a method already existing in the parent class. The child class method must have the same name and parameters. It also should have the same visibility. Let's take an example: PHP class Device { public function start() { echo "Device started"; } } class AirCondition extends Device { public function start() { echo "AirCondition started"; } } $device = new AirCondition(); $device->start(); Output: Plain Text AirCondition started Use the parent Keyword in PHP The parent keyword allows you to call methods or access properties from the parent class. It is useful when you override a method in a child class but still need to call the original method from the parent class. PHP class Device { public function start() { echo "Device started"; } } class AirCondition extends Device { public function start() { echo "AirCondition started. "; parent::start(); // Call the parent class method } } $device = new AirCondition(); $device->start(); Output: Plain Text AirCondition started. Device started. Constructors are not inherited by default. The child class will inherit the parent's constructor if it does not define its own constructor. However, the child class will override the parent constructor if it defines a constructor. Use the parent::__construct() method to call the parent's constructor from the child class: PHP class Device { public function __construct() { echo "Device constructor called\n"; } } class AirCondition extends Device { public function __construct() { parent::__construct(); // Call parent constructor echo "AirCondition constructor called\n"; } } $device = new AirCondition(); Output: Plain Text Device constructor called AirCondition constructor called Here is how it works: AirCondition overrides the parent constructor, but it still calls Device's constructor using parent::__construct().This makes sure that both constructors are executed when it creates an AirCondition object. Single vs. Multilevel Inheritance in PHP Single inheritance means a class inherits from one parent. Multilevel inheritance means a class inherits from a child class that already inherited from another class. Single Inheritance Example PHP class Country { public function capital() { echo "The capital city of this country."; } } class Japan extends Country { public function language() { echo "The language spoken is Japanese."; } } Japan inherits the capital() method from Country. Multilevel Inheritance Example PHP class Country { public function capital() { echo "The capital city of this country."; } } class Asia extends Country { public function continent() { echo "This country is in Asia."; } } class Japan extends Asia { public function language() { echo "The language spoken is Japanese."; } } The Japan class inherits from Asia, which inherits from Country. So, Japan can access the methods capital(), continent(), and language(). Inheritance vs. Interfaces in PHP The inheritance in PHP lets a class take code from one parent class. The child gets methods and properties from the parent and can change them. PHP allows one parent only. While the interfaces define rules a class must follow. They don’t contain code, just method names. A class must write its own code for each method. PHP allows a class to use many interfaces. Here is an example: PHP interface Speak { public function speak(); } class Japan implements Speak { public function speak() { echo "Japan speaks Japanese."; } } Inheritance shares actual code.The PHP Interfaces force classes to follow a structure. Inheritance and Access Modifiers in PHP The access modifiers in PHP control the visibility of methods and properties. They affect how inheritance works, which restricts or allows access to certain parts of a class. The three main access modifiers are: The "public" keyword determines that methods and properties are accessible from anywhere. It includes child classes.The "protected" keyword shows you that methods and properties are accessible within the class and its subclasses but not from outside.The "private" refers to methods and properties that are only accessible within the class they are defined in, not in subclasses or from outside. Here is an example: PHP class Device { public $name; protected $brand; private $serialNumber; public function __construct($name, $brand, $serialNumber) { $this->name = $name; $this->brand = $brand; $this->serialNumber = $serialNumber; } public function getName() { return $this->name; } protected function getBrand() { return $this->brand; } private function getSerialNumber() { return $this->serialNumber; } } class AirCondition extends Device { public function displayInfo() { //=> public method echo $this->getName(); //=> protected method can be accessed by subclass echo $this->getBrand(); //=> This shows error: private method can't be accessed in subclass // echo $this->getSerialNumber(); } } $ac = new AirCondition("Cooler", "Samsung", "12345"); $ac->displayInfo(); Public properties or methods (like name and getName()) can be accessed anywhere.Protected properties or methods (like brand and getBrand()) are accessible within the class and child classes, but not outside.Private properties or methods (like serialNumber and getSerialNumber()) can only be accessed inside the class where they are defined. Conclusion Inheritance lets you reuse code. You write shared features in a parent class. Then, child classes use them. That doesn't require you to repeat the code. A child class can also add new methods or replace ones from the parent. That gives you control. You saw how single, multilevel, and hierarchical inheritance work. Each type shows how one class can build on another. You also learned how to override methods. If a method exists in both parent and child, PHP runs the child’s version. But you can still call the parent’s version with the parent keyword. Thank you for reading!

As Java continues to evolve, each new release aims to introduce features that improve the language’s performance, usability, and flexibility. By adopting this release, you can stay ahead of the curve and prepare for when these features become stable in future LTS versions. In this article, I will give you a quick overview of significant changes in Java 23, focusing on the most notable JEPs (JDK Enhancement Proposals). Those interested in previous changes can check out my earlier article on Java 21 features, and you can also read Dariusz Wawer’s older piece with a detailed description of Java 17 features (along with a comparison to Java 8). For code examples, you have to add --enable-preview flag to your compiler args. Primitive Types in Patterns, instanceof, and switch (Preview) Primitive types have always been integral to Java, but handling them in patterns, instanceof, and switch constructs was limited. JEP 455 aims to extend pattern matching and the switch statement to support primitive types. Java static String processInput(Object obj) { return switch (obj) { case Integer i -> "Integer value: %s".formatted(i); case Double d -> "Double value: %s".formatted(d); default -> "Unknown type"; }; } public static void main(String[] args) { System.out.println(processInput(10)); // Integer value: 10 System.out.println(processInput(5.5)); // Double value: 5.5 } This enhancement allows developers to write cleaner and more concise code. Class-File API (Second Preview) Java's class file format is crucial for bytecode manipulation and tool development. JEP 466 introduces the second preview of a new Class-File API, simplifying access to and manipulation of Java class files without requiring developers to rely on third-party libraries like ASM or BCEL. This API will greatly benefit those working on frameworks, compilers, or tools that need to inspect or modify class files. With its straightforward design, it enhances flexibility while keeping developers closer to Java’s native mechanisms. You can find a simple example of interacting with a new API below: Java public static void main(String[] args) throws IOException { ClassFile classFile = ClassFile.of(); ClassModel model=classFile.parse(Paths.get("/home/ExampleClass.class")); System.out.println("Class Name: " + model.thisClass()); // Class Name: 7 java23/ExampleClass model.methods().forEach( method -> System.out.println(" - " + method.methodName())); //- <init> //- sayHello } Stream Gatherers (Second Preview) Another preview feature brings very nice enhancements to the Java Stream API. As JEP473 documentation states, the main goals are to make stream pipelines more flexible and expressive and allow custom intermediate operations to manipulate streams of infinite size. Below are a few examples of built-in gathers operations: Java Stream.of("A", "B", "C", "D", "E") .gather(Gatherers.fold(() -> "", (a, b) -> a + b)) .forEach(System.out::println); //ABCDE Stream.of("A", "B", "C", "D") .gather(Gatherers.windowFixed(2)) .forEach(System.out::println); //[A, B] //[C, D] Of course, there is the possibility of creating your gatherers. To do that, you just have to implement the java.util.stream.Gatherer interface. Scoped Values (Third Preview) JEP 481 introduces scoped values, which are an alternative to thread-local variables. They provide a mechanism for sharing values within a specific scope, making it easier to work with multi-threaded applications. Let’s dive into code example: Java public class Jep481ScopedValues { private static ScopedValue<String> X = ScopedValue.newInstance(); public static void main(String[] args) { foo(); } static void foo() { ScopedValue.runWhere(X, "foo", () -> bar()); } static void bar() { System.out.println("Printing X from bar(): " + X.get()); ScopedValue.runWhere(X, "bar", () -> baz()); System.out.println("Printing X from bar(): " + X.get()); } static void baz() { System.out.println("Printing X from baz(): " + X.get()); } } Output: Printing X from bar(): foo Printing X from baz(): bar Printing X from bar(): foo Flexible Constructor Bodies (Second Preview) JEP 482 revisits constructor flexibility in Java. Traditionally, constructor bodies in Java were limited in how they could be structured and how exceptions could be handled. This JEP introduces more flexibility, allowing developers to write more complex initialization logic within constructors, which enhances control over object creation. Java public class Java481FlexibleConstructors extends BigInteger { Java481FlexibleConstructors(long value) throws Exception { if (value < 0) throw new Exception("Invalid value"); //that wasn’t possible before System.out.println("Initialized with value: " + value); super(String.valueOf(value)); } } Other Notable Features in Java 23 In addition to the JEPs I've already covered, Java 23 introduces several other enhancements worth mentioning: Performance improvements (JEP 474: ZGC Generational Mode by Default): Java 23 optimizes the Z Garbage Collector by enabling its generational mode by default. This feature improves the efficiency of memory management, particularly for applications with long-running processes, by segregating young and old objects in the heap to enhance garbage collection performanceSecurity updates (JEP 471: Deprecate the Memory-Access Methods in sun.misc.Unsafe for Removal): This update deprecates certain memory-access methods in sun.misc.Unsafe that were widely used for direct memory manipulation, enhancing the security of the platform.Library enhancements (JEP 467: Markdown Documentation Comments): JEP 467 introduces support for Markdown in Javadoc comments. This feature allows developers to write better-formatted and more readable documentation using Markdown syntax within their code comments, improving both internal and external documentation practicesModule system improvements (JEP 476: Module Import Declarations): JEP 476 adds the ability to use import module declarations in Java source files. This simplifies module management, especially in multi-module projects, by allowing developers to import entire modules, not just individual classes or packages. Summary Java continues to evolve, and version 23 brings a wealth of improvements that will enhance the developer experience, from better handling of primitive types in pattern matching to more flexible constructors and advanced stream processing. Be sure to explore the changes and start incorporating them into your development workflow – as they will probably soon turn from preview features to core ones.

React is a powerful tool for building user interfaces, thanks to its modular architecture, reusability, and efficient rendering with the virtual DOM. However, working with React presents its own set of challenges. Developers often navigate complexities like state management, performance tuning, and scalability, requiring a blend of technical expertise and thoughtful problem-solving to overcome. In this article, we’ll explore the top challenges that React developers face during app development and offer actionable solutions to overcome them. 1. Understanding React’s Component Lifecycle The Challenge React’s component lifecycle methods, especially in class components, can be confusing for beginners. Developers often struggle to identify the right lifecycle method for specific use cases like data fetching, event handling, or cleanup. How to Overcome It Learn functional components and hooks: With the introduction of hooks like useEffect, functional components now offer a cleaner and more intuitive approach to managing lifecycle behaviors. Focus on understanding how useEffect works for tasks like fetching data or performing cleanup.Use visual tools: Tools like React DevTools help visualize the component hierarchy and understand the rendering process better.Practice small projects: Experiment with small projects to learn lifecycle methods in controlled environments. For instance, build a timer app to understand componentDidMount, componentWillUnmount, and their functional equivalents. 2. Managing State Effectively The Challenge State management becomes increasingly complex as an application grows. Managing state across deeply nested components or synchronizing state between components can lead to spaghetti code and performance bottlenecks. How to Overcome It Choose the right tool: Use React’s built-in useState and useReducer for local component state. For global state management, libraries like Redux, Context API, or Zustand can be helpful.Follow best practices: Keep the state minimal and localized where possible. Avoid storing derived or computed values in the state; calculate them when needed.Learn advanced tools: Libraries like React Query or SWR are excellent for managing server state and caching, reducing the complexity of manually synchronizing data.Break down components: Divide your app into smaller, more manageable components to localize state management and reduce dependencies. 3. Performance Optimization The Challenge Performance issues, such as unnecessary re-renders, slow component loading, or large bundle sizes, are common in React applications. How to Overcome It Use memoization: Use React.memo to prevent unnecessary re-renders of functional components and useMemo or useCallback to cache expensive calculations or function definitions.Code splitting and lazy loading: Implement lazy loading using React.lazy and Suspense to split your code into smaller chunks and load them only when needed.Optimize lists with keys: Use unique and stable keys for lists to help React efficiently update and re-render components.Monitor performance: Use tools like Chrome DevTools, React Profiler, and Lighthouse to analyze and improve your app’s performance. 4. Handling Props and Prop Drilling The Challenge Prop drilling, where data is passed down through multiple layers of components, can make the codebase messy and hard to maintain. How to Overcome It Use Context API: React’s Context API helps eliminate excessive prop drilling by providing a way to pass data through the component tree without manually passing props at every level.Adopt state management libraries: Redux, MobX, or Zustand can centralize your state management, making data flow more predictable and reducing prop drilling.Refactor components: Modularize your components and use composition patterns to reduce the dependency on props. 5. Debugging React Applications The Challenge Debugging React applications, especially large ones, can be time-consuming. Issues like untracked state changes, unexpected renders, or complex data flows make it harder to pinpoint bugs. How to Overcome It Use React DevTools: This browser extension allows developers to inspect the component tree, view props and state, and track rendering issues.Leverage console logs and breakpoints: Use console.log strategically or set breakpoints in your IDE to step through the code and understand the flow.Write unit tests: Use testing libraries like React Testing Library and Jest to write unit and integration tests, making it easier to catch bugs early.Follow best practices: Always follow a clean code approach and document key sections of your code to make debugging simpler. 6. Integrating Third-Party Libraries The Challenge React’s ecosystem is vast, and integrating third-party libraries often leads to compatibility issues, performance hits, or conflicts. How to Overcome It Research before integrating: Always check the library's documentation, community support, and recent updates. Ensure it’s actively maintained and compatible with your React version.Isolate dependencies: Encapsulate third-party library usage within specific components to reduce the impact on the rest of your codebase.Test integration: Implement thorough testing to ensure the library functions as expected without introducing new issues. 7. SEO Challenges in Single-Page Applications (SPAs) The Challenge React applications often face issues with search engine optimization (SEO) because SPAs dynamically render content on the client side, making it hard for search engines to index the pages effectively. How to Overcome It Server-side rendering (SSR): Use frameworks like Next.js to render pages on the server, ensuring they are SEO-friendly.Static site generation (SSG): For content-heavy applications, consider generating static HTML at build time using tools like Gatsby.Meta tags and dynamic headers: Use libraries like react-helmet to manage meta tags and improve the discoverability of your application. 8. Keeping Up With React Updates The Challenge React is constantly evolving, with new features, hooks, and best practices emerging regularly. Keeping up can be daunting for developers juggling multiple projects. How to Overcome It Follow official channels: Stay updated by following the official React blog, GitHub repository, and documentation.Join the community: Participate in forums, React conferences, and developer communities to learn from others’ experiences.Schedule regular learning: Dedicate time to learning new React features, such as Concurrent Mode or Server Components, and practice implementing them in sample projects. 9. Cross-Browser Compatibility The Challenge Ensuring React applications work seamlessly across all browsers can be challenging due to differences in how browsers interpret JavaScript and CSS. How to Overcome It Test regularly: Use tools like BrowserStack or Sauce Labs to test your app on multiple browsers and devices.Use polyfills: Implement polyfills like Babel for backward compatibility with older browsers.Write cross-browser CSS: Follow modern CSS best practices and avoid browser-specific properties when possible. 10. Scaling React Applications The Challenge As applications grow, maintaining a well-structured codebase becomes harder. Issues like unclear component hierarchies, lack of modularization, and increased technical debt can arise. How to Overcome It Adopt a component-driven approach: Break your application into reusable, well-defined components to promote scalability and maintainability.Enforce coding standards: Use linters like ESLint and formatters like Prettier to ensure consistent code quality.Document the architecture: Maintain clear documentation for your app’s architecture and component hierarchy, which helps onboard new developers and reduces confusion.Refactor regularly: Allocate time to revisit and improve existing code to reduce technical debt. Conclusion React is a powerful tool, but it presents its share of challenges, like any technology. By understanding these challenges and implementing the suggested solutions, React developers can build robust, high-performance applications while maintaining a clean and scalable codebase.

Interrupt testing is all about throwing real-world curveballs at your mobile app. Things like: Incoming callsText messagesNotificationsNetwork changes (Wi-Fi to data, or vice versa)Battery alertsLock screen events Why bother to test it? Because such ordinary tasks can cause your app to crash or freeze especially if it’s not adapted to them. Imagine the interrupt testing as the ultimate user experience stress test. If your app can handle an unexpected phone call and bounce back without missing a single beat, then your app is perfect. Why Interrupt Testing (Really) Matters Look, users have no patience for apps that break when “real life” happens. What if your user is mid-purchase, then a call comes in, and your app loses track of their cart? That’s a revenue killer. Or let’s say a text message arrives, and your app’s audio keeps playing in the background when it should pause. That’s a surefire way to annoy your audience. In short, interrupt testing = fewer headaches, happier users, and better ratings. The Ripple Effect Fewer crashes: Users are less likely to uninstall.Higher retention: People trust apps that just work.More 5-star reviews: Stability is huge in the world of app store ratings. Common Interrupt Scenarios Let's quickly address the numerous scenarios that you must examine: Incoming calls: Mostly on Android, phone calls can push your app into the background or pause it.SMS and push notifications: These can be pop up banners, partial overlays, or they can force user's attention elsewhere.Battery warnings: If your phone has a low battery that turns the device off abruptly, you may even get a system notification function that disables your app. Network switches: Going from Wi-Fi only to 4G or losing signal while some requests are still being processed can be the recipe of crashes. Lock screen events: When the user locks their screen, its background tasks might or might not get killed. App switches: Suppose the user heads to Facebook and then returns to your app. Does your app remember where they left off? Miss handling any one of these and your app’s stability takes a nosedive. The Lifecycle Factor Each smartphone operating system has its approach to managing background and foreground states: Android: When a call comes in, activities enter a phase of onPause, onStop, onDestroy. A single unexpected phone call can cause a quick lineup of those transitions. iOS: Active applications can transit through more than one state, such as active to inactive to background, and they may also be suspended or even terminated if the OS needs resources. During these transitions, the failure to store important data or to manage events can lead to disaster when the user returns. For example, it may be about the loss of a session token, an empty shopping cart, or an incomplete form entry. Moreover, interrupt testing makes you think carefully about the persistence of data. It is not just about finding the bug; rather, it is about building your app architecture in such a way that you are always able to adapt to changes in the lifecycle. Handling Interrupts Without a Scripted Guide Let’s face it. No, there is no single recipe to carry out interrupt testing. It’s more of a mindset. You create your application in such a way that it can respond gracefully to the unexpected, then confirm those assumptions in the real conditions. This might include: Context preservation: To enable users to continue from the point they stopped if their app got terminated in the background, save your session data in local storage or a server. Resilient networking: It is what switches are made from a Wi-Fi network to cellular networks, and the offline queues and reconnection logic are how it is handled. Graceful pauses: Do audio/video, and any other tasks in progress get paused and later can they resume? To cut a long story short, what I mean is you apply the feature of resilience to your tasks from the very beginning. The next stage is to utilize the collected data to verify the outcomes. Busting the “One Device is Enough” Myth A common misconception: “I tested it on my phone, and it worked great.” In reality, real users use various devices and OS versions, each one with its own quirks. Different OS editions: Android 12 may handle background apps very differently from Android 9. Also, iOS 16 could have new policies that iOS 15 did not. Manufacturer changes: There are a few manufacturers who sell custom battery savers or memory manager apps that shut down all background apps (at the slightest sign of inactivity). Diverse hardware: On the other hand, a high-end device might be kinder on memory and a very low-end phone model is likely to crush your app the moment a phone call arrives. If your testing strategy is confined to just one device, it is likely that you are not testing for all groups of users. Simplifying Your QA Process No bullet list here — just key principles: Prepare for chaos: Accept the fact that an interruption may arise any time. Design the application in such a way. Preserve data: Never think that an application can hold on to unsaved data in the memory. The OS may free memory anytime while you’re away.. Stay lean: Do not overcomplicate. A couple of targeted tests can point out the majority of lapses in handling interruptions. When done in the above way, interrupt testing becomes a natural conclusion of your QA philosophy rather than a burden. Wrapping It Up Interrupt testing isn't optional anymore — it’s essential. Apps that survive everyday interruptions earn higher retention, better ratings, and happier users. So, is your app ready for the real world?

DZone events bring together industry leaders, innovators, and peers to explore the latest trends, share insights, and tackle industry challenges. From Virtual Roundtables to Fireside Chats, our events cover a wide range of topics, each tailored to provide you, our DZone audience, with practical knowledge, meaningful discussions, and support for your professional growth. DZone Events Happening Soon Below, you’ll find upcoming events that you won't want to miss. Best Practices for Building Secure Data Pipelines with Apache Airflow® Date: April 15, 2025Time: 1:00 PM ET Register for Free! Security is a critical but often overlooked aspect of data pipelines. Effective security controls help teams protect sensitive data, meet compliance requirements with confidence, and ensure smooth, secure operations. Managing credentials, enforcing access controls, and ensuring data integrity across systems can become overwhelming—especially while trying to keep Airflow environments up–to-date and operations running smoothly. Whether you're working to improve access management, protect sensitive data, or build more resilient pipelines, this webinar will provide the knowledge and best practices to enhance security in Apache Airflow. Generative AI: The Democratization of Intelligent Systemsive Date: April 16, 2025Time: 1:00 PM ET Register for Free! Join DZone, alongside industry experts from Cisco and Vertesia, for an exclusive virtual roundtable exploring the latest trends in GenAI. This discussion will dive into key insights from DZone's 2025 Generative AI Trend Report, focusing on advancements in GenAI models and algorithms, their impact on code generation, and the evolving role of AI in software development. We’ll examine AI adoption maturity, intelligent search capabilities, and how organizations can optimize their AI strategies for 2025 and beyond. Measuring CI/CD Transformations with Engineering Intelligence Date: April 23, 2025Time: 1:00 PM ET Register for Free! Ready to Measure the Real Impact of Your CI/CD Pipeline? CI/CD pipelines are essential, but how do you know they’re delivering the results your team needs? Join our upcoming webinar: Measuring CICD Transformations with Engineering Intelligence. We’ll be breaking down key metrics for speed, stability, and efficiency—and showing you how to take raw CI/CD data and turn it into real insights that power better decisions. What's Next? DZone has more in store! Stay tuned for announcements about upcoming Webinars, Virtual Roundtables, Fireside Chats, and other developer-focused events. Whether you’re looking to sharpen your skills, explore new tools, or connect with industry leaders, there’s always something exciting on the horizon. Don’t miss out — save this article and check back often for updates!

Multi-factor authentication (MFA) has become an essential tool for safeguarding sensitive systems. As businesses strive to comply with regulatory requirements, the integration of MFA into workflows is now standard practice. However, automating tests for MFA-enabled systems poses unique challenges for QA teams. In this article, we will focus on a critical topic: what challenges arise when testing workflows with MFA, and what strategies can help overcome them? Key Challenges in MFA Test Automation Dependency on External Devices By design, MFA relies on external devices, such as phones, to receive SMS or apps to generate TOTP codes. This reliance complicates test automation, especially when multiple accounts are involved. Email MFA: QA teams commonly use alias-based email structures (e.g., user+alias@domain.com) to streamline account creation. However, these methods might be limited or disabled in corporate settings, complicating automation efforts.SMS MFA: Each user account typically requires a unique phone number. This leads to logistical issues, such as managing physical SIM cards or sharing test phones, which undermines efficiency and scalability.TOTP MFA: Time-based one-time passwords require secure handling of private keys. Automating tests becomes intricate, as these keys are usually inaccessible after initialization. Limited Automation Feasibility MFA workflows interact with external systems, making them hard to automate and often impractical, particularly for third-party services like email providers (e.g., Outlook). Automating such interactions is resource-intensive and often restricted by service providers that block bot connections. Risky Approach #1: Disabling MFA in Test Environments To save time, some teams disable MFA in testing environments. While expedient, this approach introduces significant risks: Increased security risk: Accounts become less secure in testing environments as MFA becomes optional, and divergent behavior from the production environment undermines test validity.Less representative tests: Tests fail to reflect real-world production conditions, increasing the likelihood of undetected bugs surfacing in production.Human errors: Configuration differences between testing and production environments complicate deployments, sometimes resulting in accidental policy misconfigurations in production.Incomplete tests: Key steps like login processes or transaction validation are skipped, reducing the ability to detect issues in critical functionalities. Risky Approach #2: Intercepting MFA in Testing Environments While better than disabling MFA, this approach still risks configuration divergence between environments and potential errors during deployment. A Costly But Effective Approach: Interfacing With Third-Party Providers Collaborating with service providers offering APIs (for email, SMS, or voice) can streamline MFA test automation. Tools like Cypress or Robot Framework can help interface with these APIs to retrieve MFA codes automatically. However, proactive communication with providers is crucial, as they may restrict automated access to their systems. Strategies for Automating and Testing End-to-End MFA Workflows Align Testing Environments With Production Ensuring parity between testing and production environments is essential for identifying potential issues effectively. Leveraging tools to retrieve MFA codes via email, SMS, or APIs can offer several benefits: Improved UX/UI detection: Reproducing production conditions helps identify anomalies in the user experience or interface.Load management: Testing under production-like constraints uncovers system weaknesses, such as throttling issues or usage limits for MFA services.Third-party service validation: Verifies proper integrations and ensures messages aren't lost. Load testing can also reveal vulnerabilities under heavy usage. Collaborative Manual Testing Solutions For manual testing, collaborative solutions can simplify MFA management within QA teams: Email: Use shared mailboxes with aliases (e.g., testing+xyz@company.io) to centralize code reception. Alternatively, virtual mailbox services offer practical solutions for managing email MFA workflows.SMS: Different solutions private virtual phone numbers, avoiding physical devices.TOTP: Securely share secret keys using password managers like Bitwarden or 1Password. This enables teams to access temporary codes without physical devices while managing key access effectively. Automation Tools for MFA Testing Automating MFA tests requires tools that simplify interactions with authentication mechanisms. Specialized APIs streamline this process, reducing the need for complex manual integrations. Examples include: Email APIs: Online services allow generating temporary email addresses to automate code retrieval via APIs.SMS APIs: Virtual phone number providers simplify automating SMS MFA workflows.TOTP APIs: Providers offer solutions for importing private TOTP keys and exposing OTP codes via APIs. Final Thoughts MFA-enforced flows are among the most important workflows for your users as they safeguard privileged, high-impact actions. These flows are essential to ensuring that users can securely log in and perform sensitive transactions, maintaining both functionality and security. Effectively testing workflows that incorporate multi-factor authentication presents challenges for QA teams, often leading to the disabling of MFA in test environments. However, comprehensive testing is crucial to ensure the reliability and security of production systems. The complexities of automating MFA tests — such as managing external devices and interacting with third-party services — highlight the risks of simplistic approaches, which can compromise system security, test relevancy, and user experience. Utilising specialised tools can bridge the gap between test and production environments. These tools offer private phone numbers, email addresses, and TOTP key injection, enabling secure and efficient testing of MFA flows without compromising security. This ensures that MFA workflows function as intended in a precise and replicable manner and allows you to detect potential issues before reaching the production environment. Investing in comprehensive MFA testing with solutions safeguards system integrity, fosters user trust, simplifies security compliance, and supports seamless operational efficiency.

TL; DR: Optimus Alpha Creates Useful Retrospective Format In this experiment, OpenAI’s new stealthy LLM Optimus Alpha demonstrated exceptional performance in team data analysis, quickly identifying key patterns in complex agile metrics and synthesizing insights about technical debt, value creation, and team dynamics. The model provided a tailored retrospective format based on real team data. Its ability to analyze performance metrics and translate them into solid, actionable retrospective designs represents a significant advancement for agile practitioners. The Rationale Behind Analyzing Team Data In this conversation, I explored Optimus Alpha’s capabilities in analyzing team performance data and designing evidence-based retrospectives. The exchange began with a familiar scenario: a Scrum team stuck in a “feature factory” mindset with stakeholders not embracing agile practices. While Optimus Alpha initially provided a solid retrospective format based on this limited information, the real test came when I introduced comprehensive team performance metrics from my AI for Agile Bootcamp cohort class. The synthesized data of MegaBrain.io’s fictitious MB1 team includes metrics on technical debt, value creation, stakeholder satisfaction, and team autonomy across multiple sprints. This simulated dataset represented the complex reality many Scrum teams face — balancing predictability with technical health, stakeholder demands with value creation, and feature delivery with sustainability. What follows is a remarkable demonstration of how Optimus Alpha absorbed this complex information, identified patterns that would typically require an experienced agile practitioner, and then transformed those insights into a tailored retrospective design with supporting materials. The conversation showcases analytical power and the ability to convert data insights into practical tools for team improvement. So, let’s get into the conversation with Optimus Alpha. Analyzing the Team Data With the Help of Optimus Alpha Round 1 I ran this chat on OpenRouter.ai on April 12, 2025. “SW” refers to myself, “OP” to Optimus Alpha. The text is unedited to preserve its authenticity: SW: My Scrum team has had a hard time; we are stuck in feature factory. Our Product Owner is little more than a Jira monkey, and no one is listening to me, the Scrum Master. Can you suggest a retrospective format my team can use to invite our stakeholders and help them understand that we could be more effective if we would only embrace agile practices? OP: Absolutely, and thank you for sharing your challenge so openly — feature factory symptoms and disengaged roles are all too common. It’s great that you’re ready to create space for everyone, including stakeholders, to reflect and learn together. Here’s a retrospective format designed to include stakeholders and shift the conversation toward agile value, not just output: Optimus Alpha — Retrospective Format #1: “From Feature Factory to Value Factory” Aim: Help the whole group — team and stakeholders — reflect on how you work, see the costs of “just pushing features,” and imagine better ways of working together. 1. Setting the Stage (10 min) Welcome everyone, explain the purpose: “We want to reflect together on how we’re working — not just what we’re building — and explore ways to better create value for our customers and business.”Establish Working Agreements (respect, honesty, curiosity).Remind folks: retros are blameless. 2. The Metaphor: “Factory Floor” Visual (5 min) Draw a simple assembly line on a virtual board or whiteboard.On one side, put “Inputs” (ideas, requests); in the middle, “Our Work,” and outputting “Features.”Ask: “Where does customer value show up here?” (Get a few responses from both teams and stakeholders.) 3. Group Activity: “What’s Working, What’s Not?” (15 min) Split everyone into breakout groups (mixed team and stakeholders).Each group answers: What do we do well right now?Where do we see pain, rework, or missed opportunities for value?Use sticky notes (digital or real), and post answers on the board under “Working” and “Not Working.” 4. Share and Cluster Key Themes (10 min) In the main group, everyone shares stickies.Scrum Master (you!) clusters themes — often you’ll see “no time to validate with users,” “little feedback,” “unclear priorities,” etc. 5. Reflection — Are We Doing Agile or Just Doing Stories? (5 min) Present the Agile Manifesto values or Scrum pillars briefly.Invite all: “Where are we supporting these values today? Where are we struggling?”Give a couple of minutes for silent reflection, then discuss. 6. Co-create Experiments (10 min) In pairs (ideally a stakeholder and a team), brainstorm one small experiment for the next sprint that could address a pain point (e.g., getting quick user feedback, clarifying the value of one backlog item, joint story mapping session, etc.).Each pair shares their idea briefly. 7. Closing and Commitment (5 min) Summarize: “If we want to escape feature factory mode, we all have a role.”Ask each participant: “What will I do differently next sprint?”Agree on 1–2 experiments to run, and how you’ll follow up. Tips and Rationale Stakeholders see they’re part of the system — their requests, engagement, and feedback matter.Makes visible the costs of just shipping features (technical debt, missed value, burnout, lack of learning).Creates joint ownership for change, rather than feeling like the Scrum team is just “whining.” Resources You Might Share Feature Factory SignsAgile ManifestoAtlassian’s Retrospective Techniques Let me know if you want some invitation language for your stakeholders or facilitation tips for handling tricky discussions! Note SW: All links are correct topic-wise and technically, although I had to add the title tags manually. Also, this is not just a good retrospective — it’s a transformational opportunity if followed through. It reframes roles, builds bridges, and ignites reflection on what really matters. For a Feature Factory, it’s a worthwhile approach: small risk, huge potential upside. Round 2 SW: Do you need more information from me to improve your retrospective suggestion? OP: That’s a great offer — thank you! Here are some clarifying questions that could help me refine my suggestion, making it more targeted and actionable for your specific context: 1. Stakeholder Context Who are the stakeholders you want to include? (e.g., internal business partners, external clients, managers, VPs…)What do you think “they” understand about Agile or Scrum now?How engaged or disengaged are they in your team’s work or results? 2. Team Dynamics How large is your team? (Size can affect activity facilitation)How long has your team been working in Scrum?How are your team members currently feeling about your process? Are they frustrated, disengaged, or burned out? 3. Pain Points and Symptoms Could you specify a couple of concrete “feature factory” symptoms you’re seeing? (e.g., relentless backlog churning, little feedback from users, no time for reflection/learning, missing sprint goals, etc.)Are there past attempts at improvement or retros that didn’t land well? 4. Goals for the Retrospective What’s your top wish for the outcome? (e.g., awareness/education, spark for change, specific agreement on next steps)Are you hoping to target a specific agile practice or value (focus, transparency, collaboration, delivering value, etc.)? 5. Risk Appetite Would your group be open to a bit of “creative tension,” or do you need to be more diplomatic and gentle in surfacing issues? The more context you can share, the more tailored (and likely effective) I can make the retrospective format, fitting your team’s culture, goals, and challenges. If you’d like, answer whichever of these jumps out, or just share anything else you think is relevant! SW: Before I answer your questions, please analyze the following team performance information and reconsider your questions. (Note: Here, I copied the synthesized team data from the Excel file into the context window.) OP: Thank you for sharing this comprehensive team performance data. It gives a richer, more nuanced picture than any qualitative conversation alone. Before updating questions or tailoring my retrospective advice, here’s a quick analysis of key patterns: Key Patterns and Insights 1. Feature Factory and Agile Health Experiments per sprint: Variable, sometimes 0, often 2–7 (average ~3–4), so the team is experimenting, but not always consistently.Technical debt %: Frequently high (14–25%), showing this is a persistent drain, likely slowing new value delivery.Education time: Fluctuates widely (as low as 1–2 hours, up to 11+), suggesting learning is sometimes deprioritized.Customer interactions: Moderate, but not always present every sprint (often 3–10). Not bad, but more could drive value.Stakeholder satisfaction: Mostly good, though dips into the 4–6 range sometimes with spikes to 9–10 — indicative of variability. 2. Agile Values and Team Dynamics Scores on autonomy, value creation, NPS, professional satisfaction, psychological safety, courage, respect, openness, and focus mostly trend between 5 and 9, but all dip into the 3–5 range at times, signaling periods of stress (see sprints 5–6, 15, etc.).Diary entries frequently mention: Management interferenceTechnical debt slowing progressPositive retros/workshops that seem to spike morale/value scoresStakeholder tensions, executive pressure, and production outages 3. Other Patterns Predictability is high (60–90%), but that doesn’t always equate to satisfaction or value.Escaped defects, incidents, and new/lost team members are low: this is a stable team, but with recurring process pain. What This Means for Your Retrospective The Team is NOT “Just a Feature Factory” There is some experimentation, customer contact, and educational investment.However, periods of “feature factory stress” (management interference, tech debt response mode, executive pressure) very clearly drive low scores in autonomy, satisfaction, and value creation. When the Team Works Well High autonomy and psychological safety, productive retros, stakeholder workshops, customer feedback => spike in both business and team satisfaction. Biggest Opportunities Consistent focus on value over pure output — variability is still high.Creating a more stable environment for learning, retrospection, and customer feedback, not just monthly or in “good times.”Better defense against management micromanagement and shifting priorities.Bridging perception with stakeholders — communicating value, not just throughput. Revised Follow-Up Questions With this data, fewer clarifying questions are needed, but I’ll tune them: What are your goals for the next retrospective, given these patterns? Awareness/education for stakeholders?Specific improvements (tech debt, stakeholder engagement, value focus)?Who do you want stakeholders to hear from? The team as a whole?Specific customer voices or data?Are there “untouchable” topics, or are you willing to openly discuss management interference and tech debt as a group?What timeframe do stakeholders care about most (week-to-week, quarterly outcomes, etc.)? How much appetite do they have for participating?In your view, which ONE success from the data is worth celebrating with stakeholders, and which ONE pain would you like to see acknowledged or solved? If you answer even a couple of these, I can create a high-impact, data-driven retrospective format (perhaps even using this data as a visual centerpiece to spark joint learning and action among stakeholders and the team). Would you like a sample format that incorporates this data as a mirror for your group? Or do you want to further discuss the questions above? SW: Regarding your follow-up questions: Help the stakeholders understand that the team could create more value for customers and the organization if we leave the feature factory and embrace agile product development. It is about education, opportunities, trust building, overcoming technical debt.The team as a whole.No.At the moment, they are focused on the team meeting its commitments as defined in their year-long product roadmap, a problematic approach in a complex environment.Celebrate: High predictability despite the circumstances. Pain point: technical debt accumulation. Please revise your suggested retrospective format. OP: Thank you for the clear answers! Here’s an improved retrospective format tailored to your needs, data, and objective to educate and inspire stakeholders, shifting them from feature factory thinking to agile, value-driven product development. Retrospective Format #2: “Beyond Commitments: Building Lasting Value” Audience: Scrum team and stakeholdersDuration: 60–75 minutesGoal: To build trust and educate stakeholders on how greater agility, autonomy, and focus on technical health can boost business value — beyond simply hitting features on a roadmap. 1. Welcome and Framing (5 min) “We’ve been very predictable, but our team sees even more potential for value — if we work together to modernize how we deliver.”Ground rules: curiosity, no blame, focus on opportunities.State purpose: “Let’s look at the real story our data tells, and find ways to unlock even more value for our customers and business.” 2. Celebrating Predictability (5 min) Show a simple chart of your team’s high predictability.Acknowledge: “We’re proud to honor commitments, despite challenges. That’s a real team achievement worth celebrating!” 3. Looking Deeper: The Value vs. Output Exercise (10 min) Show two simple graphs: a) Predictability (high, steady)b) Technical debt/Value creation/Stakeholder satisfaction (variable/fluctuating)Prompt: “What do you notice? What stories hide behind the numbers?”Invite both team and stakeholder reactions. 4. Short Team Stories: What’s the Cost of Feature Factory Living? (10 min) Team members each briefly share: A time when technical debt or backlog pressure forced a tradeoff.A time when autonomy/conversation with customers led to real value or insight.Ask: “What might have been possible, if we’d invested differently?” 5. Joint Brainstorm: Obstacles to Value (15 min) Use two columns (“What helps us create value?” / “What blocks it?”) The team and stakeholders add stickies or notes.Common “blockers”: technical debt, rigid roadmaps, lack of room to learn or adapt, excessive management intervention.Common “helpers”: customer feedback, refactoring, team autonomy, experiments, stakeholder trust.Cluster and discuss. Highlight technical debt as the recurring pain point. 6. Opportunities for Change: Data-Driven Reflection (15 min) Show a visual of technical debt trend and its correlation with value creation or satisfaction (even rough/qualitative is fine: “Our biggest dips in value/satisfaction align with tech debt crunch periods…”)Prompt stakeholders: “If we could reduce tech debt and invest in technical health, what new doors might open?”“What would make you more confident to allow the team to explore, experiment, or adjust course?”“How might we move from only delivery, to creating value that lasts?” 7. Pick 1–2 Team + Stakeholder Experiments (10 min) Examples (modify as needed): Block 10% of time for tech debt/health work every sprint; review outcomes with stakeholders.Monthly customer feedback or “value alignment” review — invite a stakeholder to join.Jointly define what “real value” means for a sprint or feature.Hold quarterly review: “Is our roadmap still the highest-leverage way to deliver value?”Agree to try one or two for the next sprint/month. 8. Commitment Rounds and Closing (5 min) Each person (including stakeholders): “One thing I commit to do, say, or advocate for in service of value creation, not just feature delivery.”Thank everyone for honesty, curiosity, and partnership. Facilitation Pointers Use rich data visuals — a couple easy graphs (predictability, tech debt, value creation, satisfaction) spark honest discussion.Encourage the team to speak frankly about technical debt consequences.Invite stakeholders to reflect on what *they* would gain if the team could work with more agility and autonomy (“would we get to market faster, respond to changes better, attract and retain top talent?”).Model mutual respect: “Everyone here wants to deliver business value — but the path isn’t always just through features.” Optional Pre-Work Consider sending the predictability and tech debt charts (and maybe a value creation graph) to stakeholders ahead of the session, with a note: “We’re incredibly proud of our team’s reliability, yet we see a chance to unlock even more business value by working smarter. Looking forward to exploring this with you in our next session!” Let me know if you want: Example data slide templatesStickies/board templates for Miro/Mural/JamboardMore story prompts for your team This format will help you shift the conversation from mere throughput (“predictable factory”) to real, meaningful, sustainable value — for both customers and the business. Note SW: The revised retrospective format is an interesting outcome: Adding real team data helped shift our retrospective from general discussion to focused conversations about what really affects results. Instead of just talking about feelings or processes, we looked at facts — like predictability, tech debt, and team satisfaction. This shift would make it easier for everyone, including stakeholders, to see where we are stuck and what is possible. It turned vague concerns, addressed by the first format, into clear opportunities for smarter decisions and better long-term outcomes. This development perfectly reflects one of the outstanding capabilities of large language models: analyzing large quantities of data, identifying patterns within them, and suggesting next steps. And Optimus Alpha proves to be really good at this. Of course, I asked to deliver all of the before-mentioned artifacts only to be disappointed; Optimus Alpha could not deliver yet. Conclusion: Optimus Alpha’s Retrospective Formats Optimus Alpha’s ability to analyze team data and design targeted retrospectives demonstrates the transformative potential of AI for agile practitioners. Tools like Optimus Alpha can help teams escape the feature factory trap and build more sustainable, value-focused ways of working by quickly identifying patterns in complex metrics and translating them into actionable formats. The model’s insight into the relationship between technical debt and value creation, team autonomy, and stakeholder satisfaction provides evidence-based foundations for retrospectives that might otherwise rely solely on subjective experiences. This approach represents a new frontier for data-informed facilitation — where LLMs serve as information processors and insightful partners in team improvement, easing communication with more data-oriented stakeholders. As agile practices continue to evolve, the opportunity to augment your retrospectives with AI-assisted data analysis could be game-changing: no more “gut-feeling-driven” change requests! Have you experimented with analyzing team data using AI tools? How might an LLM like Optimus Alpha help you identify patterns in your team’s metrics that could inform more impactful retrospectives? Please share your experiences and thoughts on how this approach might enhance your team’s journey toward greater business agility and sustainable value delivery.

By Stefan Wolpers CORE

Apache Avro is a widely used data format that keeps things compact and efficient while making it easy to evolve schemas over time. By default, it comes with basic data types like int, long, string, and bytes. But what if you need to store something more specific, like a date or a decimal number? That’s where logical types come in. Logical types let you add semantic meaning to your data. They ensure that values like timestamps or IP addresses are interpreted correctly while still benefiting from Avro’s optimized encoding. We’ll also take a deep dive into a specific use case and how logical types can enhance data security by enforcing structured storage and interpretation of sensitive information. Example Use Case Let’s consider a customer record that contains Personally Identifiable Information (PII), such as an email address and a customer account number. Since this data is sensitive, our approach is to mask the username portion of the email address before storing it and encode the customer account number for added security. When retrieving the data, the system will decode the account number to restore its original value. There are various reasons for encoding customer account numbers. Here are a few of those: Most of the time, IDs are generated using an auto-increment key, so it is easy to know the growth of the system.Also, knowing the IDs of two users makes it easy to determine which user was created in the system first.Simply, when data is persisted in a third-party system, it makes sense to hide the actual internal IDs. The Algorithm for Obfuscating Account Id To obfuscate or lightly encode an account number (or any numeric field), we’ll use a neat little trick called the modular multiplicative inverse. The Idea We want to take a number, transform it in a reversible way, and get it back later — kind of like scrambling and unscrambling. Let’s say the field we want to encode can have values from 0 to 255. Step-by-Step Pick a modulus M, which should be one more than the maximum value the field can hold. In this case, since the max is 255, we choose M = 256.Pick a number P that’s coprime with M (i.e., they share no common factors except 1). Let’s go with P = 9.Now, find the modular inverse of P - a number Q such that P × Q ≡ 1 (mod M). In simpler terms, we want some number that, when multiplied by 9 and taken modulo 256, gives 1. Using an online calculator or doing a bit of math, we find Q = 57. The modulo operation guarantees that the output will always stay within the range 0 to M - 1, ensuring it never goes out of bounds. Now, we’re ready to encode and decode. To encode: Python encoded_value = (original_value * P) % M The encoded value for the original value 195 would be 219. To decode: Python original_value = (encoded_value * Q) % M The original value can be restored now using the above logic. This method is simple, fast, and easily reversible — perfect for lightweight obfuscation where you don’t need full-blown encryption. Actual Implementation Using Avro Logical Types Step 1 Define the Avro Record for UserProfile and annotate the record with annotation @logicalType. Scheme @namespace("com.example.avro.customer") protocol Customer { record UserProfile { long id; @logicalType("accountId") long accountId; @logicalType("email") string userEmail; } } Step 2 Define the logical type for the account ID. Java package org.example.customtypes; import org.apache.avro.LogicalType; import org.apache.avro.LogicalTypes; import org.apache.avro.Schema; public class AccountIdLogicalType extends LogicalType { public static final String ACCOUNT_ID_LOGICAL_TYPE_NAME = "accountId"; public static class TypeFactory implements LogicalTypes.LogicalTypeFactory { private final LogicalType accountIdLogicalType = new AccountIdLogicalType(); @Override public LogicalType fromSchema(Schema schema) { return accountIdLogicalType; } @Override public String getTypeName() { return accountIdLogicalType.getName(); } } public AccountIdLogicalType() { super(ACCOUNT_ID_LOGICAL_TYPE_NAME); } public void validate(Schema schema) { super.validate(schema); if (schema.getType() != Schema.Type.LONG) { throw new IllegalArgumentException("Logical type 'accountId' must be long"); } } } Step 3 Write the custom conversion logic for encoding/decoding the account ID. Here, M is 9223372036854775808, which is Long.MAX_VALUE + 1, and the chosen value of P is 64185959, and Q is computed using an online calculator, which comes to be 1703179806106473815. Java package org.example.customtypes; import org.apache.avro.Conversion; import org.apache.avro.LogicalType; import org.apache.avro.Schema; public class AccountIdConversion extends Conversion<Long> { private static final long PRIME_NUMBER = 64185959L; private static final long PRIME_NUMBER_INVERSE = 1703179806106473815L; private static final AccountIdConversion INSTANCE = new AccountIdConversion(); private static AccountIdConversion get() { return INSTANCE; } public AccountIdConversion() { super(); } @Override public Class<Long> getConvertedType() { return Long.class; } @Override public String getLogicalTypeName() { return AccountIdLogicalType.ACCOUNT_ID_LOGICAL_TYPE_NAME; } @Override public Long fromLong(Long value, Schema schema, LogicalType type) { return (value * PRIME_NUMBER_INVERSE) & Long.MAX_VALUE; } @Override public Long toLong(Long value, Schema schema, LogicalType type) { return (value * PRIME_NUMBER) & Long.MAX_VALUE; } } Note: In this implementation, we’re using bitwise arithmetic to compute the modulus. When M is a power of 2, the operation x % 2^n can be efficiently calculated as x & (2^n - 1). In our case, M = Long.MAX_VALUE + 1, which equals 2^63, so this optimization applies. Step 4 This is the final code for testing. It simply creates an Avro record for UserProfile, writes it to a local file, reads the file again, and prints the record. Java package org.example; import com.example.avro.customer.UserProfile; import org.apache.avro.LogicalTypes; import org.apache.avro.file.DataFileReader; import org.apache.avro.file.DataFileWriter; import org.apache.avro.io.DatumReader; import org.apache.avro.io.DatumWriter; import org.apache.avro.specific.SpecificDatumReader; import org.apache.avro.specific.SpecificDatumWriter; import org.example.customtypes.AccountIdLogicalType; import org.example.customtypes.EmailLogicalType; import java.io.File; import java.io.IOException; public class LogicalTypeExample { public static void main(String[] args) { LogicalTypes.register(EmailLogicalType.EMAIL_LOGICAL_TYPE_NAME, new EmailLogicalType.TypeFactory()); LogicalTypes.register(AccountIdLogicalType.ACCOUNT_ID_LOGICAL_TYPE_NAME, new AccountIdLogicalType.TypeFactory()); UserProfile testUserProfile = UserProfile.newBuilder() .setId(100) .setUserEmail("hello@gmail.com") .setAccountId(23L) .build(); final DatumWriter<UserProfile> userProfileDatumWriter = new SpecificDatumWriter<>(UserProfile.class); File f = new File("query.avro"); try (DataFileWriter<UserProfile> dataFileWriter = new DataFileWriter<>(userProfileDatumWriter)) { dataFileWriter.create(testUserProfile.getSchema(), f); dataFileWriter.append(testUserProfile); } catch (IOException e) { throw new RuntimeException(e); } System.out.println("Written to " + f.getAbsolutePath()); final DatumReader<UserProfile> userProfileDatumReader = new SpecificDatumReader<>(UserProfile.class); try (DataFileReader<UserProfile> userProfileDataFileReader = new DataFileReader<>(f, userProfileDatumReader)) { while (userProfileDataFileReader.hasNext()) { UserProfile record = userProfileDataFileReader.next(); System.out.println("Id : " + record.getId()); System.out.println("Email : " + record.getUserEmail()); System.out.println("AccountId : " + record.getAccountId()); } } catch (IOException e) { throw new RuntimeException(e); } } } System Output Please note that the output is similar to the original Avro Record. The email is masked, and the AccountId is the same as the original. PowerShell Written to /Users/gurmeetsaran/Documents/repo/avro_types/query.avro Id : 100 Email : ******@gmail.com AccountId : 23 Process finished with exit code 0 Step 5 Inspect the data using avro-tools. Here, the persisted data stores encoded value of the accountId and userEmail is also masked. Shell $ avro-tools tojson query.avro 25/04/07 13:26:27 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable {"id":100,"accountId":1476277057,"userEmail":"******@gmail.com"} To check the complete implementation, please check my Github page. Final Thoughts Avro logical types are a great way to add semantic meaning to your data, making it easier to interpret and work with across different parts of your system. Instead of just dealing with raw primitives like strings or longs, logical types let you define what the data actually represents, whether it's a timestamp, a decimal, a UUID, or something custom. One of the biggest advantages is consistency. By using logical types, you can ensure that fields of the same kind — like email addresses or account IDs — are treated the same way throughout your system. This means less boilerplate code, fewer one-off transformations, and a more maintainable schema. In our example, we can standardize how we handle email fields and obfuscated account IDs simply by tagging them with the right logical type annotations. Once that’s in place, all the serialization, deserialization, and even validation logic can follow a common, reusable pattern - which is especially helpful when working at scale or across teams.