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In an era where the pace of software development and deployment is accelerating, the significance of having a robust and integrated DevOps environment cannot be overstated. Azure DevOps, Microsoft's suite of cloud-based DevOps services, is designed to support teams in planning work, collaborating on code development, and building and deploying applications with greater efficiency and reduced lead times. The objective of this blog post is twofold: first, to introduce Azure DevOps, shedding light on its components and how they converge to form a powerful DevOps ecosystem, and second, to provide a balanced perspective by delving into the advantages and potential drawbacks of adopting Azure DevOps. Whether you're contemplating the integration of Azure DevOps into your workflow or seeking to optimize your current DevOps practices, this post aims to equip you with a thorough understanding of what Azure DevOps has to offer, helping you make an informed decision tailored to your organization's unique requirements. What Is Azure DevOps? Azure DevOps represents the evolution of Visual Studio Team Services, capturing over 20 years of investment and learning in providing tools to support software development teams. As a cornerstone in the realm of DevOps solutions, Azure DevOps offers a suite of tools catering to the diverse needs of software development teams. Microsoft provides this product in the Cloud with Azure DevOps Services or on-premises with Azure DevOps Server. It offers integrated features accessible through a web browser or IDE client. At its core, Azure DevOps comprises five key components, each designed to address specific aspects of the development process. These components are not only powerful in isolation but also offer enhanced benefits when used together, creating a seamless and integrated experience for users. Azure Boards It offers teams a comprehensive solution for project management, including agile planning, work item tracking, and visualization tools. It enables teams to plan sprints, track work with Kanban boards, and use dashboards to gain insights into their projects. This component fosters enhanced collaboration and transparency, allowing teams to stay aligned on goals and progress. Azure Repos It is a set of version control tools designed to manage code efficiently. It provides Git (distributed version control) or Team Foundation Version Control (centralized version control) for source code management. Developers can collaborate on code, manage branches, and track version history with complete traceability. This component ensures streamlined and accessible code management, allowing teams to focus on building rather than merely managing their codebase. Azure Pipelines Azure Pipelines automates the stages of the application's lifecycle, from continuous integration and continuous delivery to continuous testing, build, and deployment. It supports any language, platform, and cloud, offering a flexible solution for deploying code to multiple targets such as virtual machines, various environments, containers, on-premises, or PaaS services. With Azure Pipelines, teams can ensure that code changes are automatically built, tested, and deployed, facilitating faster and more reliable software releases. Azure Test Plans Azure Test Plans provide a suite of tools for test management, enabling teams to plan and execute manual, exploratory, and automated testing within their CI/CD pipelines. Furthermore, Azure Test Plans ensure end-to-end traceability by linking test cases and suites to user stories, features, or requirements. They facilitate comprehensive reporting and analysis through configurable tracking charts, test-specific widgets, and built-in reports, empowering teams with actionable insights for continuous improvement. Thus providing a framework for rigorous testing to ensure that applications meet the highest standards before release. Azure Artifacts It allows teams to manage and share software packages and dependencies across the development lifecycle, offering a streamlined approach to package management. This feature supports various package formats, including npm, NuGet, Python, Cargo, Maven, and Universal Packages, fostering efficient development processes. This service not only accelerates development cycles but also enhances reliability and reproducibility by providing a reliable source for package distribution and version control, ultimately empowering teams to deliver high-quality software products with confidence. Below is an example of architecture leveraging various Azure DevOps services: Image captured from Microsoft Benefits of Leveraging Azure DevOps Azure DevOps presents a compelling array of benefits that cater to the multifaceted demands of modern software development teams. Its comprehensive suite of tools is designed to streamline and optimize various stages of the development lifecycle, fostering efficiency, collaboration, and quality. Here are some of the key advantages: Seamless Integration One of Azure DevOps' standout features is its ability to seamlessly integrate with a plethora of tools and platforms, whether they are from Microsoft or other vendors. This interoperability is crucial for anyone who uses a diverse set of tools in their development processes. Scalability and Flexibility Azure DevOps is engineered to scale alongside your business. Whether you're working on small projects or large enterprise-level solutions, Azure DevOps can handle the load, providing the same level of performance and reliability. This scalability is a vital attribute for enterprises that foresee growth or experience fluctuating demands. Enhanced Collaboration and Visibility Collaboration is at the heart of Azure DevOps. With features like Azure Boards, teams can have a centralized view of their projects, track progress, and coordinate efforts efficiently. This visibility is essential for aligning cross-functional teams, managing dependencies, and ensuring that everyone is on the same page. Continuous Integration and Deployment (CI/CD) Azure Pipelines provides robust CI/CD capabilities, enabling teams to automate the building, testing, and deployment of their applications. This automation is crucial to accelerate their time-to-market and improve the quality of their software. By automating these processes, teams can detect and address issues early, reduce manual errors, and ensure that the software is always in a deployable state, thereby enhancing operational efficiency and software reliability. Drawbacks of Azure DevOps While Azure DevOps offers a host of benefits, it's essential to acknowledge and understand its potential drawbacks. Like any tool or platform, it may not be the perfect fit for every organization or scenario. Here are some of the disadvantages that one might encounter: Vendor Lock-In By adopting Azure DevOps services for project management, version control, continuous integration, and deployment, organizations may find themselves tightly integrated into the Microsoft ecosystem. This dependency could limit flexibility and increase reliance on Microsoft's tools and services, making it challenging to transition to alternative platforms or technologies in the future. Integration Challenges Although Azure DevOps boasts impressive integration capabilities, there can be challenges when interfacing with certain non-Microsoft or legacy systems. Some integrations may require additional customization or the use of third-party tools, potentially leading to increased complexity and maintenance overhead. For organizations heavily reliant on non-Microsoft products, this could pose integration and workflow continuity challenges. Cost Considerations Azure DevOps operates on a subscription-based pricing model, which, while flexible, can become significant at scale, especially for larger teams or enterprises with extensive requirements. The cost can escalate based on the number of users, the level of access needed, and the use of additional features and services. For smaller teams or startups, the pricing may be a considerable factor when deciding whether Azure DevOps is the right solution for their needs. Potential for Over-Complexity With its myriad of features and tools, there's a risk of over-complicating workflows and processes within Azure DevOps. Teams may find themselves navigating through a plethora of options and configurations, which, if not properly managed, can lead to inefficiency rather than improved productivity. Organizations must strike a balance between leveraging Azure DevOps' capabilities and maintaining simplicity and clarity in their processes. While these disadvantages are noteworthy, they do not necessarily diminish the overall value that Azure DevOps can provide to an organization. It's crucial for enterprises and organizations to carefully assess their specific needs, resources, and constraints when considering Azure DevOps as their solution. By acknowledging these potential drawbacks, organizations can plan effectively, ensuring that their adoption of Azure DevOps is strategic, well-informed, and aligned with their operational goals and challenges. Conclusion In the landscape of modern software development, Azure DevOps stands out as a robust and comprehensive platform, offering a suite of tools designed to enhance and streamline the DevOps process. Its integration capabilities, scalability, and extensive features make it an attractive choice for any organization or enterprise. However, like any sophisticated platform, Azure DevOps comes with its own set of challenges and considerations. The vendor lock-in, integration complexities, cost factors, and potential for over-complexity are aspects that organizations need to weigh carefully. It's crucial for enterprises to undertake a thorough analysis of their specific needs, resources, and constraints when evaluating Azure DevOps as a solution. The decision to adopt Azure DevOps should be guided by a strategic assessment of how well its advantages align with the organization's goals and how its disadvantages might impact operations. For many enterprises, the benefits of streamlined workflows, enhanced collaboration, and improved efficiency will outweigh the drawbacks, particularly when the adoption is well-planned and aligned with the organization's objectives.
The ExecutorService in Java provides a flexible and efficient framework for asynchronous task execution. It abstracts away the complexities of managing threads manually and allows developers to focus on the logic of their tasks. Overview The ExecutorService interface is part of the java.util.concurrent package and represents an asynchronous task execution service. It extends the Executor interface, which defines a single method execute(Runnable command) for executing tasks. Executors Executors is a utility class in Java that provides factory methods for creating and managing different types of ExecutorService instances. It simplifies the process of instantiating thread pools and allows developers to easily create and manage executor instances with various configurations. The Executors class provides several static factory methods for creating different types of executor services: FixedThreadPool: Creates an ExecutorService with a fixed number of threads. Tasks submitted to this executor are executed concurrently by the specified number of threads. If a thread is idle and no tasks are available, it remains alive but dormant until needed. Java ExecutorService executor = Executors.newFixedThreadPool(5); CachedThreadPool: Creates an ExecutorService with an unbounded thread pool that automatically adjusts its size based on the workload. Threads are created as needed and reused for subsequent tasks. If a thread remains idle for a certain period, it may be terminated to reduce resource consumption. In a cached thread pool, submitted tasks are not queued but immediately handed off to a thread for execution. If no threads are available, a new one is created. If a server is so heavily loaded that all of its CPUs are fully utilized, and more tasks arrive, more threads will be created, which will only make matters worse. Idle time of threads is default to 60s, after which if they don't have any task thread will be terminated. Therefore, in a heavily loaded production server, you are much better off using Executors.newFixedThreadPool, which gives you a pool with a fixed number of threads, or using the ThreadPoolExecutor class directly, for maximum control. Java ExecutorService executor = Executors.newCachedThreadPool(); SingleThreadExecutor: Creates an ExecutorService with a single worker thread. Tasks are executed sequentially by this thread in the order they are submitted. This executor is useful for tasks that require serialization or have dependencies on each other. Java ExecutorService executor = Executors.newSingleThreadExecutor(); ScheduledThreadPool: Creates an ExecutorService that can schedule tasks to run after a specified delay or at regular intervals. It provides methods for scheduling tasks with fixed delay or fixed rate, allowing for periodic execution of tasks. newWorkStealingPool: Creates a work-stealing thread pool with the target parallelism level. This executor is based on the ForkJoinPool and is capable of dynamically adjusting its thread pool size to utilize all available processor cores efficiently. Overall, the Executors class simplifies the creation and management of executor instances. ExecutorService Tasks can be submitted to an ExecutorService for execution. These tasks are typically instances of Runnable or Callable, representing units of work that need to be executed asynchronously. Below are the methods in ExecutorService. 1. execute(Runnable command): Executes the given task asynchronously. Java ExecutorService executor = Executors.newFixedThreadPool(5); executor.execute(() -> { System.out.println("Task executed asynchronously"); }); 2. submit(Callable<T> task): Submits a task for execution and returns a Future representing the pending result of the task. Java ExecutorService executor = Executors.newSingleThreadExecutor(); Future<Integer> future = executor.submit(() -> { // Task logic return 42; }); 3. shutdown(): Initiates an orderly shutdown of the ExecutorService, allowing previously submitted tasks to execute before terminating. 4. shutdownNow(): Attempts to stop all actively executing tasks, halts the processing of waiting tasks, and returns a list of the tasks that were awaiting execution. Java List<Runnable> pendingTasks = executor.shutdownNow(); 5. awaitTermination(long timeout, TimeUnit unit): Blocks until all tasks have completed execution after a shutdown request, or the timeout occurs, or the current thread is interrupted, whichever happens first. Java boolean terminated = executor.awaitTermination(10, TimeUnit.SECONDS); if (terminated) { System.out.println("All tasks have completed execution"); } else { System.out.println("Timeout occurred before all tasks completed"); } 6. invokeAny(Collection<? extends Callable<T>> tasks): Executes the given tasks, returning the result of one that successfully completes. This method is useful when we have multiple tasks to run but we only care about the result of whichever one completes first. All other tasks are terminated. Java ExecutorService executor = Executors.newCachedThreadPool(); Set<Callable<String>> callables = new HashSet<>(); callables.add(() -> "Task 1"); callables.add(() -> "Task 2"); String result = executor.invokeAny(callables); System.out.println("Result: " + result); 7. invokeAll(Collection<? extends Callable<T>> tasks): Executes the given tasks, returning a list of Future objects representing their pending results. Java List<Callable<Integer>> tasks = Arrays.asList(() -> 1, () -> 2, () -> 3); List<Future<Integer>> futures = executor.invokeAll(tasks); for (Future<Integer> future : futures) { System.out.println("Result: " + future.get()); } Implementations The ExecutorService interface is typically implemented by various classes provided by the Java concurrency framework, such as ThreadPoolExecutor, ScheduledThreadPoolExecutor, and ForkJoinPool. Considerations Careful configuration of thread pool size to avoid underutilization or excessive resource consumption. Consider factors such as task submission rate, task priority, resource constraints, and the desired behavior in case of queue overflow. Choose the queue type that best meets your application's requirements for scalability, performance, and resource utilization. Proper handling of exceptions and task cancellation to ensure robustness and reliability. Understanding the concurrency semantics and potential thread safety issues in concurrent code. To create an instance of ExecutorService, we can pass ThreadFactory and task queue to be used while creating the pool. A ThreadFactory is an interface used to create new threads. It provides a way to encapsulate the logic for creating threads, allowing for customization of thread creation behavior. The primary purpose of a ThreadFactory is to decouple the thread creation process from the rest of the application logic, making it easier to manage and customize thread creation. It is preferred to pass custom Thread factory, as helps in setting thread prefix and priority if required. Java static final String prefix = "app.name.task"; ExecutorService executorService = Executors.newFixedThreadPool(5, () -> { Thread t = new Thread(r); t.setName(prefix + "-" + t.getId()); // Customize thread name if needed return t; }); TaskQueues When tasks are submitted to ExecutorService, if none of the threads in pool are available to process the tasks, they get stored in a queue, below are the different queue options to choose from. Unbounded Queue: An unbounded queue, such as LinkedBlockingQueue, has no fixed capacity and can grow dynamically to accommodate an unlimited number of tasks. It is suitable for scenarios where the task submission rate is unpredictable or where tasks need to be queued indefinitely without the risk of rejection due to queue overflow. However, keep in mind that unbounded queues can potentially lead to memory exhaustion if tasks are submitted at a faster rate than they can be processed. Bounded Queue: A bounded queue, such as ArrayBlockingQueue with a specified capacity, has a fixed size limit and can only hold a finite number of tasks. It is suitable for scenarios where resource constraints or backpressure mechanisms need to be enforced to prevent excessive memory usage or system overload. Tasks may be rejected or handled according to a specified rejection policy when the queue reaches its capacity. Priority Queue: A priority queue, such as PriorityBlockingQueue, orders tasks based on their priority or a specified comparator. It is suitable for scenarios where tasks have different levels of importance or urgency, and higher-priority tasks need to be processed before lower-priority ones. Priority queues ensure that tasks are executed in the order of their priority, regardless of their submission order. Synchronous Queue: A synchronous queue, such as SynchronousQueue, is a special type of queue that enables one-to-one task handoff between producer and consumer threads. It has a capacity of zero and requires both a producer and a consumer to be available simultaneously for task exchange to occur. Synchronous queues are suitable for scenarios where strict synchronization and coordination between threads are required, such as handoff between thread pools or bounded resource access. ScheduledThreadPool The ScheduledThreadPoolExecutor inherits thread pool management capabilities from ThreadPoolExecutor and provides functionalities for scheduling tasks to run after a given delay or periodically at defined intervals. Here's a detailed explanation: Runnable and Callable Tasks: You define tasks you want to schedule using these interfaces, similar to a regular ExecutorService. ScheduledFuture: This interface represents the result of a scheduled task submission. It allows checking the task's completion status, canceling the task before execution, and (for Callable tasks) retrieving the result upon completion. Scheduling Capabilities schedule(Runnable task, long delay, TimeUnit unit): Schedules a Runnable task to be executed after a specified delay in the given time unit (e.g., seconds, milliseconds). scheduleAtFixedRate(Runnable command, long initialDelay, long period, TimeUnit unit): Schedules a fixed-rate execution of a Runnable task. The task is first executed after the initialDelay, and subsequent executions occur with a constant period between them. scheduleWithFixedDelay(Runnable command, long initialDelay, long delay, TimeUnit unit): Schedules a fixed-delay execution of a Runnable task. Similar to scheduleAtFixedRate, but the delay is measured between the completion of the previous execution and the start of the next. Key Considerations Thread Pool Management: ScheduledThreadPoolExecutor maintains a fixed-sized thread pool by default. You can configure the pool size during object creation. Delayed Execution: Scheduled tasks are not guaranteed to execute precisely at the specified time. The actual execution time might be slightly different due to factors like thread availability and workload. Missed Executions: With fixed-rate scheduling, if the task execution time exceeds the period, subsequent executions might be skipped to maintain the fixed rate. Cancellation: You can cancel a scheduled task using the cancel method of the returned ScheduledFuture object. However, cancellation success depends on the task's state (not yet started, running, etc.). Java import java.util.concurrent.ScheduledExecutorService; import java.util.concurrent.Executors; import java.util.concurrent.TimeUnit; public class ScheduledThreadPoolExample { public static void main(String[] args) throws InterruptedException { // Create a ScheduledThreadPoolExecutor with 2 threads ScheduledExecutorService scheduler = Executors.newScheduledThreadPool(2); // Schedule a task with a 2-second delay Runnable task1 = () -> System.out.println("Executing task 1 after a delay"); scheduler.schedule(task1, 2, TimeUnit.SECONDS); // Schedule a task to run every 5 seconds with a fixed rate Runnable task2 = () -> System.out.println("Executing task 2 at fixed rate"); scheduler.scheduleAtFixedRate(task2, 1, 5, TimeUnit.SECONDS); // Schedule a task to run every 3 seconds with a fixed delay Runnable task3 = () -> System.out.println("Executing task 3 with fixed delay"); scheduler.scheduleWithFixedDelay(task3, 0, 3, TimeUnit.SECONDS); // Wait for some time to allow tasks to be executed Thread.sleep(15000); // Shutdown the scheduler scheduler.shutdown(); } } Shut Down ExecutorService Gracefully To efficiently shut down an ExecutorService, you can follow these steps: Call the shutdown() method to initiate the shutdown process. This method allows previously submitted tasks to execute before terminating but prevents the submission of new tasks. Call the shutdownNow() method if you want to force the ExecutorService to terminate immediately. This method attempts to stop all actively executing tasks, halts the processing of waiting tasks, and returns a list of the tasks that were awaiting execution but were never started. Await termination by calling the awaitTermination() method. This method blocks until all tasks have completed execution after a shutdown request, or the timeout occurs, or the current thread is interrupted, whichever happens first. Here's an example: Java ExecutorService executor = Executors.newFixedThreadPool(10); // Execute tasks using the executor // Shutdown the executor executor.shutdown(); try { // Wait for all tasks to complete or timeout after a certain period if (!executor.awaitTermination(60, TimeUnit.SECONDS)) { // If the timeout occurs, force shutdown executor.shutdownNow(); // Optionally, wait for the tasks to be forcefully terminated if (!executor.awaitTermination(60, TimeUnit.SECONDS)) { // Log a message indicating that some tasks failed to terminate } } } catch (InterruptedException ex) { // Log interruption exception executor.shutdownNow(); // Preserve interrupt status Thread.currentThread().interrupt(); } In summary, ExecutorService is a versatile framework that helps developers write efficient, scalable, and maintainable concurrent code.
Hello! My name is Roman Burdiuzha. I am a Cloud Architect, Co-Founder, and CTO at Gart Solutions. I have been working in the IT industry for 15 years, a significant part of which has been in management positions. Today I will tell you how I find specialists for my DevSecOps and AppSec teams, what I pay attention to, and how I communicate with job seekers who try to embellish their own achievements during interviews. Starting Point I may surprise some of you, but first of all, I look for employees not on job boards, but in communities, in general chats for IT specialists, and through acquaintances. This way you can find a person with already existing recommendations and make a basic assessment of how suitable he is for you. Not by his resume, but by his real reputation. And you can already know him because you are spinning in the same community. Building the Ideal DevSecOps and AppSec Team: My Hiring Criteria There are general chats in my city (and not only) for IT specialists, where you can simply write: "Guys, hello, I'm doing this and I'm looking for cool specialists to work with me." Then I send the requirements that are currently relevant to me. If all this is not possible, I use the classic options with job boards. Before inviting for an interview, I first pay attention to the following points from the resume and recommendations. Programming Experience I am sure that any security professional in DevSecOps and AppSec must know the code. Ideally, all security professionals should grow out of programmers. You may disagree with me, but DevSecOps and AppSec specialists should work with code to one degree or another, be it some YAML manifests, JSON, various scripts, or just a classic application written in Java, Go, and so on. It is very wrong when a security professional does not know the language in which he is looking for vulnerabilities. You can't look at one line that the scanner highlighted and say: "Yes, indeed, this line is exploitable in this case, or it's false." You need to know the whole project and its structure. If you are not a programmer, you simply will not understand this code. Taking Initiative I want my future employees to be proactive — I mean people who work hard enough, do big tasks, have ambitions, want to achieve, and spend a lot of time on specific tasks. I support people's desire to develop in their field, to advance in the community, and to look for interesting tasks and projects for themselves, including outside of work. And if the resume indicates the corresponding points, I will definitely highlight it as a plus. Work-Life Balance I also pay a lot of attention to this point and I always talk about it during the interview. The presence of hobbies and interests in a person indicates his ability to switch from work to something else, his versatility and not being fixated on one job. It doesn't have to be about active sports, hiking, walking, etc. The main thing is that a person's life has not only work but also life itself. This means that he will not burn out in a couple of years of non-stop work. The ability to rest and be distracted acts as a guarantee of long-term employment relationships. In my experience, there have only been a couple of cases when employees had only work in their lives and nothing more. But I consider them to be unique people. They have been working in this rhythm for a long time, do not burn out, and do not fall into depression. You need to have a certain stamina and character for this. But in 99% of cases, overwork and inability to rest are a guaranteed departure and burnout of the employee in 2-3 years. At the moment, he can do a lot, but I don't need to change people like gloves every couple of years. Education I graduated from postgraduate studies myself, and I think this is more a plus than a minus. You should check the availability of certificates and diplomas of education specified in the resume. Confirmation of qualifications through certificates can indicate the veracity of the declared competencies. It is not easy to study for five years, but at the same time, when you study, you are forced to think in the right direction, analyze complex situations, and develop something that has scientific novelty at present and can be used in the future with benefit for people. And here, in principle, it is the same: you combine common ideas with colleagues and create, for example, progressive DevOps, which allows you to further help people; in particular, in the security of the banking sector. References and Recommendations I ask the applicant to provide contacts of previous employers or colleagues who can give recommendations on his work. If a person worked in the field of information security, then there are usually mutual acquaintances with whom I also communicate and who can confirm his qualifications. What I Look for in an Interview Unfortunately, not all aspects can be clarified at the stage of reading the resume. The applicant may hide some things in order to present themselves in a more favorable light, but more often it is simply impossible to take into account all the points needed by the employer when compiling a resume. Through leading questions in a conversation with the applicant and his stories from previous jobs, I find out if the potential employee has the qualities listed below. Ability To Read It sounds funny, but in fact, it is not such a common quality. A person who can read and analyze can solve almost any problem. I am absolutely convinced of this because I have gone through it myself more than once. Now I try to look for information from many sources, I actively use the same ChatGPT and other similar services just to speed up the work. That is, the more information I push through myself, the more tasks I will solve, and, accordingly, I will be more successful. Sometimes I ask the candidate to find a solution to a complex problem online and provide him with material for analysis, I look at how quickly he can read and conduct a qualitative analysis of the provided article. Analytical Mind There are two processes: decomposition and composition. Programmers usually use the second part. They conduct compositional analysis, that is, they assemble some artifact from the code that is needed for further work. An information security analyst or security specialist uses decomposition. That is, on the contrary, it disassembles the artifact into its components and looks for vulnerabilities. If a programmer creates, then a security specialist disassembles. An analytical mind is needed in the part that is related to how someone else's code works. In the 90s, for example, we talked about disassembling if the code was written in assembler. That is, you have a binary file, and you need to understand how it works. And if you do not analyze all entry and exit points, all processes, and functions that the programmer has developed in this code, then you cannot be sure that the program works as intended. There can be many pitfalls and logical things related to the correct or incorrect operation of the program. For example, there is a function that can be passed a certain amount of data. The programmer can consider this function as some input numerical data that can be passed to it, or this data can be limited by some sequence or length. For example, we enter the card number. It seems like the card number has a certain length. But, at the same time, any analyst and you should understand that instead of a number there can be letters or special characters, and the length may not be the same as the programmer came up with. This also needs to be checked, and all hypotheses need to be analyzed, to look at everything much wider than what is embedded in the business logic and thinking of the programmer who wrote it all. How do you understand that the candidate has an analytical mind? All this is easily clarified at the stage of "talking" with the candidate. You can simply ask questions like: "There is a data sample for process X, which consists of 1000 parameters. You need to determine the most important 30. The analysis task will be solved by 3 groups of analysts. How will you divide these parameters to obtain high efficiency and reliability of the analysis?" Experience Working in a Critical Situation It is desirable that the applicant has experience working in a crunch; for example, if he worked with servers with some kind of large critical load and was on duty. Usually, these are night shifts, evening shifts, on a weekend, when you have to urgently raise and restore something. Such people are very valuable. They really know how to work and have personally gone through different "pains." They are ready to put out fires with you and, most importantly, are highly likely to be more careful than others. I worked for a company that had a lot of students without experience. They very often broke a lot of things, and after that, it was necessary to raise all this. This is, of course, partly a consequence of mentoring. You have to help, develop, and turn students into specialists, but this does not negate the "pain" of correcting mistakes. And until you go through all this with them, they do not become cool. If a person participated in these processes and had the strength and ability to raise and correct, this is very cool. You need to select and take such people for yourself because they clearly know how to work. How To Avoid Being Fooled by Job Seekers Job seekers may overstate their achievements, but this is fairly easy to verify. If a person has the necessary experience, you need to ask them practical questions that are difficult to answer without real experience. For example, I ask about the implementation of a particular practice from DevSecOps, that is, what orchestrator he worked in. In a few words, the applicant should write, for example, a job in which it was all performed, and what tool he used. You can even suggest some keys from this vulnerability scanner and ask what keys and in what aspect you would use to make everything work. Only a specialist who has worked with this can answer these questions. In my opinion, this is the best way to check a person. That is, you need to give small practical tasks that can be solved quickly. It happens that not all applicants have worked and are working with the same as me, and they may have more experience and knowledge. Then it makes sense to find some common questions and points of contact with which we worked together. For example, just list 20 things from the field of information security and ask what the applicant is familiar with, find common points of interest, and then go through them in detail. When an applicant brags about having developments in interviews, it is also better to ask specific questions. If a person tells without hesitation what he has implemented, you can additionally ask him some small details about each item and direction. For example, how did you implement SAST verification, and with what tools? If he tells in detail and, possibly, with some additional nuances related to the settings of a particular scanner, and this fits into the general concept, then the person lived by this and used what he is talking about. Wrapping Up These are all the points that I pay attention to when looking for new people. I hope this information will be useful both for my Team Lead colleagues and for job seekers who will know what qualities they need to develop to successfully pass the interview.
Choosing the right backend technology for fintech development involves a detailed look at Java and Scala. Both languages bring distinct advantages to the table, and for professionals working in the fintech industry, understanding these nuances is crucial. There is no arguing Java is a true cornerstone in software development — stable, boasting comprehensive libraries and a vast ecosystem. Many of us — me included! — relied on it for years, and today Java is the backbone of countless financial systems. Scala, in many respects a more modern language, suggests an interesting blend of object-oriented and functional programming, proud of a syntax that reduces boilerplate code and boosts developer productivity. For teams searching to introduce functional programming concepts without stepping away from the JVM ecosystem, Scala is an intriguing option. Our discussion will cover the essential aspects that matter most in fintech backend development: ecosystem and libraries, concurrency, real-time processing, maintainability, and JVM interoperability. Let's analyze, side by side, how Java and Scala perform in the fast-paced, demanding world of fintech backend development, focusing on the concrete benefits and limitations each language presents. Ecosystem and Libraries for Fintech When deciding between Java and Scala for your fintech backend, your major concern will be the richness of their ecosystems and the availability of domain-specific libraries. Java accumulated an impressive array of libraries and frameworks that have become go-to resources for fintech projects. One example is Spring Boot – a real workhorse for setting up microservices, packed with features covering everything from securing transactions to managing data. There’s also Apache Kafka, pretty much the gold standard for managing event streams effectively. But what stands out about Java's ecosystem isn't just the sheer volume of tools but also the community backing them. A vast network of experienced Java developers means you’re never far from finding a solution or best practice advice, honed through years of real-world application. This kind of support network is simply invaluable. Scala, while newer on the scene, brings forward-thinking libraries and tools that are particularly well-suited to the challenges of modern fintech development. Akka, with its toolkit for crafting highly concurrent and resilient message-driven apps, fits perfectly with the needs of high-load financial systems. Alpakka, part of the Reactive Streams ecosystem, further extends Scala's capabilities, facilitating integration with a wide range of messaging systems and data stores. The language’s functional programming capabilities, combined with its interoperability with Java, allow teams to gradually adopt new paradigms without a complete overhaul. On the other hand, one significant challenge that fintech companies might face when adopting Scala is the relative scarcity of experienced Scala developers compared to Java developers. The smaller community size can make it difficult to find developers with deep experience in Scala, especially those who are adept at leveraging its advanced features in a fintech context. This scarcity can lead to higher recruitment costs and potentially longer project timelines, one of the factors to consider when deciding between Java and Scala. While Scala presents compelling advantages to fintech companies interested in building scalable, distributed systems, Java is still a strong contender. The choice between these languages will require you to carefully assess your project's needs, weighing specific pros and cons of the two paradigms. With this in mind, let’s compare some fundamental aspects of these two remarkable languages. Concurrency and Real-Time Processing In fintech, where handling multiple transactions swiftly and safely is the daily bread, a language’s concurrency models are of particular interest. Let’s see what Java and Scala offer us in this regard. Java and Concurrency in Fintech Initially, Java offered threads and locks – a straightforward but sometimes cumbersome way to manage concurrency. However, Java 8 introduced CompletableFuture, which marked a dramatic leap to straightforward asynchronous programming. CompletableFuture provides developers with a promise-like mechanism that can be completed at a later stage, making it ideal for fintech applications that require high throughput and low latency. Let’s consider a scenario where you need to fetch exchange rates from different services concurrently and then combine them to execute a transaction: Java CompletableFuture<Double> fetchUSDExchangeRate = CompletableFuture.supplyAsync(() -> { return exchangeService.getRate("USD"); }); CompletableFuture<Double> fetchEURExchangeRate = CompletableFuture.supplyAsync(() -> { return exchangeService.getRate("EUR"); }); fetchUSDExchangeRate .thenCombine(fetchEURExchangeRate, (usd, eur) -> { return processTransaction(usd, eur); }) .thenAccept(result -> System.out.println("Transaction Result: " + result)) .exceptionally(e -> { System.out.println("Error processing transaction: " + e.getMessage()); return null; }); In this snippet, supplyAsync initiates asynchronous tasks to fetch exchange rates. thenCombine waits for both rates before executing a transaction, ensuring that operations dependent on multiple external services can proceed smoothly. The exceptionally method provides a way to handle any errors that occur during execution, a crucial feature for maintaining robustness in financial operations. Scala and Concurrency With Akka Transitioning from Java to Scala’s actor model via Akka provides a stark contrast in handling concurrency. Akka actors, elegant yet efficient, are especially well-suited for the demands of fintech applications; they were designed to be lightweight and can be instantiated in the millions. They also bring fault tolerance through supervision strategies, ensuring the system remains responsive even when parts of it fail. Consider the previous example of fetching exchange rates and processing a transaction. Here’s how you can apply the actor model in Scala: Scala import akka.actor.Actor import akka.actor.ActorSystem import akka.actor.Props import akka.pattern.ask import akka.util.Timeout import scala.concurrent.duration._ import scala.concurrent.Future case class FetchRate(currency: String) case class RateResponse(rate: Double) case class ProcessTransaction(rate1: Double, rate2: Double) class ExchangeServiceActor extends Actor { def receive = { case FetchRate(currency) => sender() ! RateResponse(exchangeService.getRate(currency)) } } class TransactionActor extends Actor { implicit val timeout: Timeout = Timeout(5 seconds) def receive = { case ProcessTransaction(rate1, rate2) => val result = processTransaction(rate1, rate2) println(s"Transaction Result: $result") } } val system = ActorSystem("FintechSystem") val exchangeServiceActor = system.actorOf(Props[ExchangeServiceActor], "exchangeService") val transactionActor = system.actorOf(Props[TransactionActor], "transactionProcessor") implicit val timeout: Timeout = Timeout(5 seconds) import system.dispatcher // for the implicit ExecutionContext val usdRateFuture = (exchangeServiceActor ? FetchRate("USD")).mapTo[RateResponse] val eurRateFuture = (exchangeServiceActor ? FetchRate("EUR")).mapTo[RateResponse] val transactionResult = for { usdRate <- usdRateFuture eurRate <- eurRateFuture } yield transactionActor ! ProcessTransaction(usdRate.rate, eurRate.rate) Here, ExchangeServiceActor fetches currency rates asynchronously, while TransactionActor processes the transaction. The use of the ask pattern (?) allows us to send messages and receive futures in response, which we can then compose or combine as needed. This pattern elegantly handles the concurrency and asynchronicity inherent in fetching rates and processing transactions, without the direct management of threads. The actor model, by design, encapsulates state and behavior, making the codebase cleaner and easier to maintain. Fintech applications, with their demand for fault tolerance and quick scalability, are one of the major beneficiaries of Scala’s Akka framework. Code Readability and Maintainability in Fintech Java's syntax is known for its verbosity, which, applied to fintech, translates to clarity. Each line of code, while longer, is self-explanatory, making it easier for new team members to understand the business logic and the flow of the application. This characteristic is beneficial in environments where maintaining and auditing code is as crucial as writing it, given the regulatory scrutiny fintech applications often face. On the other hand, while Scala's more concise syntax reduces boilerplate and can lead to a tighter, more elegant codebase, it also introduces a significant challenge. The flexibility and variety of Scala can often result in different developers solving the same problem in multiple ways, creating what can be described as a "Babylon" within the project. This variability, while showcasing Scala's expressive power, can make it more difficult to maintain consistent coding standards and ensure code quality and understandability, especially in the highly regulated environment of fintech. This steepens the learning curve, especially for developers not familiar with functional programming paradigms. Consider a simple operation in a fintech application, such as validating a transaction against a set of rules. In Java, this might involve several explicit steps, each clearly laid out: Java public boolean validateTransaction(Transaction transaction) { if (transaction.getAmount() <= 0) { return false; } if (!knownCurrencies.contains(transaction.getCurrency())) { return false; } // Additional validation rules here return true; } The challenger, Scala, boasts a more concise syntax by virtue of its functional programming capabilities. This conciseness helps dramatically reduce the boilerplate code, making the codebase tighter and easier to maintain. Despite the challenge of maintaining a uniform standard across a team I mentioned above, the brevity of Scala code can be a significant asset, though it requires a steeper learning curve, especially for developers not familiar with functional programming paradigms. The same transaction validation in Scala might look significantly shorter, leveraging pattern matching and list comprehensions: Scala def validateTransaction(transaction: Transaction): Boolean = transaction match { case Transaction(amount, currency, _) if amount > 0 && knownCurrencies.contains(currency) => true case _ => false } JVM Interoperability and Legacy Integration A critical factor in choosing a backend technology for fintech applications is how well it integrates with existing systems. Many financial institutions rely on extensive legacy systems that are critical to their operations. Java’s and Scala’s paths to interoperability and integration within the JVM ecosystem have their unique advantages here. Java's long history and widespread use in the financial industry mean that most legacy systems in fintech are built using Java or compatible with Java. This compatibility facilitates seamless integration of new developments with existing systems. Java's stability and backward compatibility are key assets when updating or extending legacy systems, minimizing disruptions, and ensuring continuous operation. For instance, integrating a new Java-based service into an existing system can be as straightforward as: Java // Java service to be integrated with a legacy system public class NewJavaService { public String processData(String input) { // Process data return "Processed: " + input; } } This simplicity in integration is a significant advantage for Java, reducing the time and effort required to enhance or expand legacy systems with new functionalities. Scala's interoperability with Java is one of its standout features, allowing Scala to use Java libraries directly and vice versa. This interoperability means that financial institutions can adopt Scala for new projects or modules without abandoning their existing Java codebase. Scala can act as a bridge to more modern, functional programming paradigms while maintaining compatibility with the JVM ecosystem. For example, calling a Scala object from Java might look like this: Scala // Scala object object ScalaService { def processData(input: String): String = { // Process data s"Processed: $input" } } Scala // Java class calling Scala object public class JavaCaller { public static void main(String[] args) { String result = ScalaService.processData("Sample input"); System.out.println(result); } } This cross-language interoperability is particularly beneficial in fintech, where leveraging existing investments while adopting new technologies is often a strategic priority. Scala offers a path to modernize applications with functional programming concepts without a complete system overhaul. Conclusion It certainly is no revelation that the two languages have their strengths and difficulties. Java stands out for its robust ecosystem and libraries, offering a tried-and-tested path for developing fintech applications. Its traditional concurrency models and frameworks provide a solid foundation for building reliable and scalable systems. Moreover, Java's verbose syntax promotes clarity and maintainability, essential in the highly regulated fintech sector. Finally, Java's widespread adoption makes integration with existing systems and legacy code seamless Scala, on the other hand, will be your weapon of choice if you want to streamline your development process with a more expressive syntax and a robust concurrency management model. It’s particularly appealing for projects aiming for high scalability and resilience, without stepping completely away from the Java universe. This makes Scala a strategic choice for evolving your tech stack, introducing functional programming benefits while keeping the door open to Java's realm. So — no, there is no and probably never will be a definitive, final answer to this question. You will always have to balance the immediate needs of your project with long-term tech strategy. Do you build on the solid, familiar ground that Java offers, or do you step into Scala's territory, with its promise of modernized approaches and efficiency gains? In fintech, where innovation must meet reliability head-on, understanding the nuances of Java and Scala will equip you to make an informed decision that aligns with both your immediate project needs and your strategic goals for the future.
In the landscape of software development, efficiently processing large datasets has become paramount, especially with the advent of multicore processors. The Java Stream interface provided a leap forward by enabling sequential and parallel operations on collections. However, fully exploiting modern processors' capabilities while retaining the Stream API’s simplicity posed a challenge. Responding to this, I created an open-source library aimed at experimenting with a new method of parallelizing stream operations. This library diverges from traditional batching methods by processing each stream element in its own virtual thread, offering a more refined level of parallelism. In this article, I will talk about the library and its design. It is more detail than you need simply to use the library. The library is available on GitHub and also as a dependency in Maven Central. <dependency> <groupId>com.github.verhas</groupId> <artifactId>vtstream</artifactId> <version>1.0.1</version> </dependency> Check out the actual version number on the Maven Central site or on GitHub. This article is based on the version 1.0.1 of the library. Parallel Computing Parallel computing is not a new thing. It has been around for decades. The first computers were executing tasks in batches, hence in a serial way, but soon the idea of time-sharing came into the picture. The first time-sharing computer system was installed in 1961 at the Massachusetts Institute of Technology (MIT). This system, known as the Compatible Time-Sharing System (CTSS), allowed multiple users to log into a mainframe computer simultaneously, working in what appeared to be a private session. CTSS was a groundbreaking development in computer science, laying the foundation for modern operating systems and computing environments that support multitasking and multi-user operations. This was not a parallel computing system, per se. CTSS was designed to run on a single mainframe computer, the IBM 7094, at MIT. It has one CPU, thus the code was executed serially. Today we have multicore processors and multiple processors in a single computer. I edit this article on a computer that has 10 processor cores. To execute tasks concurrently, there are two plus-one approaches: Define the algorithm in a concurrent way; for example, reactive programming. Define the algorithm the good old sequential way and let some program decide on the concurrency. Mix the two. When we’re programming some reactive algorithm or defined streams as in Java 8 stream, we help the application execute the tasks concurrently. We define small parts and their interdependence so that the environment can decide which parts can be executed concurrently. The actual execution is done by the framework and when we are using Virtual threads, or Threads (perhaps processes) The difference is in the scheduler: who makes the decision about which processor should execute which task the next moment. In the case of threads or processes, the executor is the operating system. The difference between thread and process execution is that threads belonging to the same process share the same memory space. Processes have their own memory space. Similarly, virtual threads belonging to the same operating system thread share the same stack. Transitioning from processes to virtual threads, we encounter a reduction in shared resources and, consequently, overhead. This makes virtual threads significantly less costly compared to traditional threads. While a machine might support thousands of threads and processes, it can accommodate millions of virtual threads. In defining a task with streams, you are essentially outlining a series of operations to be performed on multiple elements. The decision to execute these operations concurrently rests with the framework, which may or may not choose to do so. However, Stream in Java serves as a high-level interface, offering us the flexibility to implement a version that facilitates concurrent execution of tasks. Implementing Streams in Threads The library contains two primary classes located in the main directory, namely: ThreadedStream Command ThreadedStream is the class responsible for implementing the Stream interface. public class ThreadedStream<T> implements Stream<T> { The Command class encompasses nested classes that implement functionality for stream operations. public static class Filter<T> extends Command<T, T> { public static class AnyMatch<T> extends Command<T, T> { public static class FindFirst<T> extends Command<T, T> { public static class FindAny<T> extends Command<T, T> { public static class NoOp<T> extends Command<T, T> { public static class Distinct<T> extends Command<T, T> { public static class Skip<T> extends Command<T, T> { public static class Peek<T> extends Command<T, T> { public static class Map<T, R> extends Command<T, R> { All the mentioned operators are intermediaries. The terminal operators are implemented within the ThreadedStream class, which converts the threaded stream into a regular stream before invoking the terminal operator on this stream. An example of this approach is the implementation of the collect method. @Override public <R> R collect(Supplier<R> supplier, BiConsumer<R, ? super T> accumulator, BiConsumer<R, R> combiner) { return toStream().collect(supplier, accumulator, combiner); } The source of the elements is also a stream, which means that the threading functionality is layered atop the existing stream implementation. This setup allows for the utilization of streams both as data sources and as destinations for processed data. Threading occurs in the interim, facilitating the parallel execution of intermediary commands. Therefore, the core of the implementation—and its most intriguing aspect—lies in the construction of the structure and its subsequent execution. We will first examine the structure of the stream data and then explore how the class executes operations utilizing virtual threads. Stream Data Structure The ThreadedStream class maintains its data through the following member variables: private final Command<Object, T> command; private final ThreadedStream<?> downstream; private final Stream<?> source; private long limit = -1; private boolean chained = false; command represents the Command object to be executed on the data. It might be a no-operation (NoOp) command or null if there is no specific command to execute. downstream variable points to the preceding ThreadedStream in the processing chain. A ThreadedStream retrieves data either from the immediate downstream stream, if available, or directly from the source if it’s the initial in the chain. source is the initial data stream. It remains defined even when a downstream is specified, in which scenario the source for both streams remains identical. limit specifies the maximum number of elements this stream is configured to process. Implementing a limit requires a workaround, as stream element processing starts immediately rather than being "pulled" by the terminal operation. Consequently, infinite streams cannot feed into a ThreadedStream. chained is a boolean flag indicating whether the stream is part of a processing chain. When true, it signifies that there is a subsequent stream dependent on this one’s output, preventing execution in cases of processing forks. This mechanism mirrors the approach found in JVM’s standard stream implementations. Stream Build The stream data structure is constructed dynamically as intermediary operations are chained together. The initiation of this process begins with the creation of a starting element, achieved by invoking the static method threaded on the ThreadedStream class. An exemplary line from the unit tests illustrates this initiation: final var k = ThreadedStream.threaded(Stream.of(1, 2, 3)); This line demonstrates the creation of a ThreadedStream instance named k, initialized with a source stream consisting of the elements 1, 2, and 3. The threaded method serves as the entry point for transforming a regular stream into a ThreadedStream, setting the stage for further operations that can leverage virtual threads for concurrent execution. When an intermediary operation is appended, it results in the creation of a new ThreadedStream instance. This new instance designates the preceding ThreadedStream as its downstream. Moreover, the source stream for this newly formed ThreadedStream remains identical to the source stream of its predecessor. This design ensures a seamless flow of data through the chain of operations, facilitating efficient processing in a concurrent environment. For example, when we call: final var t = k.map(x -> x * 2); The map method is called, which is: public <R> ThreadedStream<R> map(Function<? super T, ? extends R> mapper) { return new ThreadedStream<>(new Command.Map<>(mapper), this); } It generates a new ThreadedStream object wherein the preceding ThreadedStream acts as the downstream. Additionally, the command field is populated with a new instance of the Command class, configured with the specified mapper function. This process effectively constructs a linked list composed of ThreadedStream objects. This linked structure comes into play during the execution phase, triggered by invoking one of the terminal operations on the stream. This method ensures that each ThreadedStream in the sequence can process data in a manner that supports concurrent execution, leveraging the capabilities of virtual threads for efficient data processing. It’s crucial to understand that the ThreadedStream class refrains from performing any operations on the data until a terminal operation is called. Once execution commences, it proceeds concurrently. To facilitate independent execution of these operations, ThreadedStream instances are designed to be immutable. They are instantiated during the setup phase and undergo a single mutation when they are linked together. During execution, these instances serve as a read-only data structure, guiding the flow of operation execution. This immutability ensures thread safety and consistency throughout concurrent processing, allowing for efficient and reliable stream handling. Stream Execution The commencement of stream execution is triggered by invoking a terminal operation. These terminal operations are executed by first transforming the threaded stream back into a conventional stream, upon which the terminal operation is then performed. The collect method serves as a prime example of this process, as previously mentioned. This method is emblematic of how terminal operations are seamlessly integrated within the ThreadedStream framework, bridging the gap between concurrent execution facilitated by virtual threads and the conventional stream processing model of Java. By converting the ThreadedStream into a standard Stream, it leverages the rich ecosystem of terminal operations already available in Java, ensuring compatibility and extending functionality with minimal overhead. @Override public <R> R collect(Supplier<R> supplier, BiConsumer<R, ? super T> accumulator, BiConsumer<R, R> combiner) { return toStream().collect(supplier, accumulator, combiner); } The toStream() method represents the core functionality of the library, marking the commencement of stream execution by initiating a new virtual thread for each element in the source stream. This method differentiates between ordered and unordered execution through two distinct implementations: toUnorderedStream() toOrderedStream() The choice between these methods is determined by the isParallel() status of the source stream. It’s worth noting that executing an ordered stream in parallel can be advantageous. Although the results may be produced out of order, parallel processing accelerates the operation. Ultimately, care must be taken to collect the results in a sequential manner, despite the unordered processing potentially yielding higher efficiency by allowing elements to be passed to the resulting stream as soon as they become available, eliminating the need to wait for the preceding elements. The implementation of toStream() is designed to minimize an unnecessary collection of elements. Elements are forwarded to the resulting stream immediately upon readiness in the case of unordered streams, and in sequence upon the readiness and previous element’s forwarding in ordered streams. In subsequent sections, we delve into the specifics of these two execution methodologies. Unordered Stream Execution Unordered execution promptly forwards results as they become prepared. This approach employs a concurrent list for result storage, facilitating simultaneous result deposition by threads and retrieval by the target stream, preventing excessive list growth. The iteration over the source stream initiates the creation of a new virtual thread for each element. When a limit is imposed, it’s applied directly to the source stream, diverging from traditional stream implementations where limit acts as a genuine intermediary operation. The implementation of the unordered stream execution is as follows: private Stream<T> toUnorderedStream() { final var result = Collections.synchronizedList(new LinkedList<Command.Result<T>>()); final AtomicInteger n = new AtomicInteger(0); final Stream<?> limitedSource = limit >= 0 ? source.limit(limit) : source; limitedSource.forEach( t -> { Thread.startVirtualThread(() -> result.add(calculate(t))); n.incrementAndGet(); }); return IntStream.range(0, n.get()) .mapToObj(i -> { while (result.isEmpty()) { Thread.yield(); } return result.removeFirst(); }) .filter(f -> !f.isDeleted()) .peek(r -> { if (r.exception() != null) { throw new ThreadExecutionException(r.exception()); } }) .map(Command.Result::result); } The counter n is utilized to tally the number of threads initiated. The resulting stream is constructed using this counter by mapping the numbers 0 to n-1 to the elements of the concurrent list as they become ready. If the list lacks elements at any point, the process pauses, awaiting the availability of the next element. This waiting mechanism is implemented within a loop that incorporates a yield call to prevent unnecessary CPU consumption by halting the loop’s execution until it’s necessary to proceed. This efficient use of resources ensures that the system remains responsive and minimizes the potential for performance degradation during the execution of parallel tasks. Ordered Stream Execution Ordered stream execution introduces a more nuanced approach compared to its unordered counterpart. It incorporates a local class named Task, designed specifically to await the readiness of a particular thread. Similar to the unordered execution, a concurrent list is utilized, but with a key distinction: the elements of this list are the tasks themselves, rather than the results. This list is populated by the code responsible for thread creation, rather than by the threads themselves. The presence of a fully populated list eliminates the need for a separate counter to track thread initiation. Consequently, the process transitions to sequentially waiting on each thread as dictated by their order in the list, thereby ensuring that each thread’s output is relayed to the target stream in a sequential manner. This method meticulously maintains the ordered integrity of the stream’s elements, despite the concurrent nature of their processing, by aligning the execution flow with the sequence of the original stream. private Stream<T> toOrderedStream() { class Task { Thread workerThread; volatile Command.Result<T> result; /** * Wait for the thread calculating the result of the task to be finished. This method is blocking. * @param task the task to wait for */ static void waitForResult(Task task) { try { task.workerThread.join(); } catch (InterruptedException e) { task.result = deleted(); } } } final var tasks = Collections.synchronizedList(new LinkedList<Task>()); final Stream<?> limitedSource = limit >= 0 ? source.limit(limit) : source; limitedSource.forEach( sourceItem -> { Task task = new Task(); tasks.add(task); task.workerThread = Thread.startVirtualThread(() -> task.result = calculate(sourceItem)); } ); return tasks.stream() .peek(Task::waitForResult) .map(f -> f.result) .peek(r -> { if (r.exception() != null) { throw new ThreadExecutionException(r.exception()); } } ) .filter(r -> !r.isDeleted()).map(Command.Result::result); } Summary and Takeaway Having explored an implementation that facilitates the parallel execution of stream operations, it’s noteworthy that this library is open source, offering you the flexibility to either utilize it as is or reference its design and implementation to craft your own version. The detailed exposition provided here aims to shed light on both the conceptual underpinnings and practical aspects of the library’s construction. However, it’s important to acknowledge that the library has not undergone extensive testing. It received a review from Istvan Kovacs, a figure with considerable expertise in concurrent programming. Despite this, his review does not serve as an absolute assurance of the library’s reliability and absence of bugs. Consequently, should you decide to integrate this library into your projects, it’s advised to proceed with caution and conduct thorough testing to ensure it meets your requirements and standards. The library is provided "as is," with the understanding that users adopt it at their own risk, underpinning the importance of due diligence in its deployment.
TL; DR: Scrum Master Interview Questions on Creating Value With Scrum If you are looking to fill a position for a Scrum Master (or agile coach) in your organization, you may find the following 12th set of the Scrum Master interview questions useful to identify the right candidate. They are derived from my eighteen years of practical experience with XP as well as Scrum, serving both as Product Owner and Scrum Master as well as interviewing dozens of Scrum Master candidates on behalf of my clients. So far, this Scrum Master interview guide has been downloaded more than 27,000 times. Scrum Master Interview Questions: How We Organized Questions and Answers Scrum has proven time and again to be the most popular framework for software development. Given that software is eating the world, a seasoned Scrum Master is even nowadays, given the frosty economic climate of Spring 2024, in high demand. And that demand causes the market entry of new professionals from other project management branches, probably believing that reading one or two Scrum books will be sufficient, which makes any Scrum Master interview a challenging task. The Scrum Master Interview Questions ebook provides both questions as well as guidance on the range of suitable answers. These should allow an interviewer to dive deep into a candidate’s understanding of Scrum and her agile mindset. However, please note: The answers reflect the personal experience of the authors and may not be valid for every organization: what works for organization A may not work in organization B. There are no suitable multiple-choice questions to identify a candidate’s agile mindset, given the complexity of applying “Agile” to any organization. The authors share a holistic view of agile practices: Agility covers the whole arch from product vision (our grand idea on how to improve mankind’s fate) to product discovery (what to build) plus product delivery (how to build it). Creating Value as a Scrum Master The following questions and responses are designed to draw out a nuanced understanding of a candidate’s experience and skills in applying agile product development principles to improve customer value and economics of delivery and enhance predictability in various organizational contexts to address the current economic climate: Question 74: Resistant Industries How have you tailored Scrum practices to elevate customer value, particularly in industries resistant to Agile practices? Background: This question probes the candidate’s ability to adapt Scrum principles to sectors where Agile is not the norm, emphasizing customer-centric product development. It seeks insights into the candidate’s innovative application of Scrum to foster customer engagement and satisfaction, even in challenging environments. It is also an opportunity for the candidate to build confidence in the interview process and rapport with the interviewers. Acceptable Answer: An excellent response would detail a scenario where the candidate navigated resistance by demonstrating Agile’s benefits through small-scale pilot projects or workshops. They would probably even describe specific adjustments to Scrum events or artifacts to align with industry-specific constraints, culminating in enhanced customer feedback loops and ultimately leading to product features that directly addressed customer pain points. Question 75: Reducing Product Costs Please describe a scenario in which you significantly reduced production costs through strategic Scrum application without compromising the product’s quality. Background: This delves into the candidate’s proficiency in supporting the optimization of a team’s capacity allocation and streamlining workflows within the Scrum framework to cut costs. It’s about balancing maintaining high-quality standards and achieving cost effectiveness through Agile practices. Acceptable Answer: Look for a narrative where the candidate identifies wasteful practices or bottlenecks in the development process and implements targeted Scrum practices to address them. Examples include refining the Product Backlog to focus on high-impact features, improving cross-functional collaboration to reduce dependencies, or leveraging automated testing to speed up lead time while preserving quality standards. The answer should highlight the candidate’s analytical problem-solving approach and ability to help the team accept a cost-conscious entrepreneurial stance to solving customer problems without sacrificing quality. Question 76: Improving Predictability in a Volatile Market Please share an experience where you used Scrum to improve predictability in product delivery in a highly volatile market. Background: This question explores the candidate’s capability to use Scrum to enhance delivery predictability amidst market fluctuations. It’s about leveraging Agile’s flexibility to adapt to changing priorities while maintaining a steady pace of delivery. Acceptable Answer: The candidate should recount an instance where they utilized Scrum artifacts and events to better forecast delivery timelines in a shifting landscape. This example might involve adjusting Sprint lengths, prioritizing Product Backlog items more dynamically, or involving closer stakeholder engagement to reassess priorities during Sprint Reviews or other alignment-creating opportunities, for example, User Story Mapping sessions. The story should underscore their strategic thinking in balancing flexibility with predictability and their communication skills in setting realistic expectations with stakeholders. Question 77: Successfully Promoting Scrum Despite Skepticism How have you promoted the value of Scrum in organizations where the leadership and middle management met Agile practices with skepticism? Background: This question examines the candidate’s ability to champion Scrum in environments resistant to change. Such an environment requires a deep understanding of Agile principles and strong advocacy and education skills. Acceptable Answer: Successful candidates will describe a multifaceted strategy that includes educating leadership on Agile benefits, organizing interactive workshops to demystify Scrum practices, and securing quick wins to demonstrate value. They might also discuss establishing a community of practice to sustain Agile learning and sharing success stories to build momentum. The answer should reflect their perseverance, persuasive communication, and their role as a change agent. (Learn more about successful stakeholder communication tactics during transformations here.) Question 78: Effective Change Please describe your approach to conducting effective Sprint Retrospectives that drive continuous improvement. Background: The question probes the candidate’s techniques for facilitating Retrospectives that genuinely contribute to team growth and product enhancement. It seeks to understand how they ensure these events are productive, inclusive, and actionable. Acceptable Answer: A comprehensive response would outline a structured approach to Retrospectives, including preparation, facilitation, follow-up practices, and valuable enhancements to the framework, for example, embracing the idea of a directly responsible individual to drive change the team considers beneficial. The candidate might mention using a variety of formats to keep the sessions engaging, techniques to ensure all team members contribute, and strategies for prioritizing action items. They should emphasize their method for tracking improvements over time to ensure accountability and demonstrate the Retrospective’s impact on the team’s performance and morale. Again, this question allows the candidates to distinguish themselves in the core competence of any Scrum Master. Question 79: Balancing Demands with Principles Please explain how you’ve balanced stakeholder demands with Agile principles to help the Scrum team prioritize work effectively. Background: This question seeks insights into the candidate’s ability to support the Scrum team in general and the Product Owner in particular in navigating competing demands, aligning stakeholder expectations with Agile principles to focus the team’s efforts on the most impactful work from the customers’ perception and the organization’s perspective. Acceptable Answer: The candidate should provide an example of supporting the Product Owner by employing prioritization techniques, such as User Story Mapping, in collaboration with stakeholders to align on priorities that offer the most value, leading to the creation of valuable Product Goals and roadmaps in the process. They should highlight their negotiation skills, ability to facilitate consensus, and adeptness at transparent communication to manage expectations and maintain a sustainable pace for the team. Question 80: Boring Projects and Motivation How do you sustain team motivation and engagement in long-term projects with high levels of task repetition? Background: This question explores the candidate’s strategies for keeping the team engaged and motivated through the monotony of prolonged projects or repetitive tasks. While we all like to work on cutting-edge technology all the time, everyday operations often comprise work that we consider less glamorous yet grudgingly accept as valuable, too. The question gauges a candidate’s ability to uphold enthusiasm and maintain high performance in a potentially less motivating environment. Acceptable Answer: Expect the candidate to discuss innovative approaches like introducing gamification elements to mundane tasks, rotating roles within the team to provide fresh challenges, and setting up regular skill-enhancement workshops. They might also mention the importance of celebrating small wins, giving recognition, for example, Kudo cards, and ensuring that the team’s work aligns with individual growth goals. The response should underline their commitment to maintaining a positive and stimulating work environment, even under challenging circumstances. Question 81: Onboarding New Team Members Please describe your experience integrating a new team member into an established Scrum team, ensuring a seamless transition and maintaining team productivity. Background: This question assesses the candidate’s approach to onboarding new team members to minimize disruption and maximize integration speed. This approach is critical for maintaining an existing team’s cohesive and productive dynamics, acknowledging that Scrum teams will regularly change composition. Acceptable Answer: Look for answers detailing a structured and inclusive onboarding plan that includes, for example: Mentorship programs A buddy system Clear documentation of team norms and expectations, such as a working agreement and a Definition of Done Team activities Gradual immersion into the Scrum team’s projects through pair programming or shadowing The candidate should highlight the importance of fostering an inclusive team culture that welcomes questions and supports new members in their learning journey, ensuring they feel valued and part of the team from day one. Question 82: Conflict Resolution How do you approach conflict resolution within a Scrum team or between the team and stakeholders to ensure continued progress and collaboration? Background: Conflicts are inevitable in any team dynamic. This question probes the candidate’s skills in navigating and resolving disagreements in a way that strengthens the team and stakeholder relationships rather than undermining them. Acceptable Answer: The candidate should describe their ability to act as a neutral mediator, actively listen to understand all perspectives, and facilitate problem-solving sessions focusing on interests rather than positions. They might also discuss creating forums for open dialogue, such as conflict-themed Retrospectives, and the importance of fostering a culture of trust and psychological safety where conflicts can be aired constructively. The response should convey their adeptness at turning conflicts into opportunities for growth and deeper understanding. However, the candidate should also make clear that not all disputes among team members may be solvable and that, once all team-based options have been exhausted, the Scrum Master needs to ask for management support to bring the conflict to a conclusion. Question 83: Scaling Scrum? Please reflect on a time when scaling Scrum across multiple teams presented significant challenges. How did you address these challenges to ensure the organization’s success with its Agile transformation? Background: Scaling Agile practices is a complex endeavor that can highlight organizational impediments and resistance. This question delves into the candidate’s experience in successfully scaling Scrum, ensuring alignment and cohesion among multiple teams, and helping everyone see the value in a transformation. Acceptable Answer: This open question allows candidates to address their familiarity with frameworks like LeSS or Nexus or share their opinion on whether SAFe is useful. Moreover, at a philosophical level, it opens the discussion of whether “Agile” is scalable at all, given that most scaling frameworks apply more processes to the issue. Also, the objecting opinion points to the need to descale the organization by empowering those closest to the problems to decide within the given constraints and governance rules. The candidate should emphasize the importance of maintaining a shared vision and goals, creating communities of practice to share knowledge and best practices, and addressing cultural barriers to change. They should also reflect on the importance of executive sponsorship, the strategic engagement of key stakeholders to champion and support the scaling effort, and the necessity of a failure culture. How To Use The Scrum Master Interview Questions Scrum has always been a hands-on business, and to be successful in this, a candidate needs to have a passion for getting her hands dirty. While the basic rules are trivial, getting a group of individuals with different backgrounds, levels of engagement, and personal agendas to form and perform as a team is a complex task. (As always, you might say, when humans and communication are involved.) Moreover, the larger the organization is, the more management levels there are, the more likely failure is lurking around the corner. The questions are not necessarily suited to turning an inexperienced interviewer into an agile expert. But in the hands of a seasoned practitioner, they can help determine what candidate has worked in the agile trenches in the past.
Alternative Text: This comic depicts an interaction between two characters and is split into four panes. In the upper left pane, Character 1 enters the scene with a slightly agitated expression and comments to Character 2, "Your PR makes SQL injection possible!" Character 2, who is typing away at their computer, responds happily, "Wow, that wasn't even my intention," as if Character 1 has paid them a compliment. In the upper right pane, Character 1, now with an increasingly agitated expression, says, "I mean, your code is vulnerable." Character 2, now standing and facing Character 1, is almost proudly embarrassed at what they take as positive feedback and replies, "Stop praising me, I get shy." In the lower-left pane, Character 1, now shown with sharp teeth and a scowl, points a finger at Character 2 and shouts clearly, "Vulnerable is bad!" Character 2 seems shocked at this statement, standing with their mouth and eyes wide open. In the lower right and final pane of the comic, Character 2, smiling once again, replies with the comment, "At least it can do SQL injection!" Character 1 stares back at Character 2 with a blank expression.
People initially became interested in blockchain several years ago after learning about it as a decentralized digital ledger. It supports transparency because no one can change information stored on it once added. People can also watch transactions as they happen, further enhancing visibility. But how does blockchain support the integrity of cloud-stored data? 3 Ways Blockchain Supports the Integrity of Cloud-Stored Data 1. Protecting and Facilitating the Sharing of Medical Records Technological advancements have undoubtedly improved the ease of sharing medical records between providers. When patients go to new healthcare facilities, all involved parties can easily see those individuals’ histories, treatments, test results, and more. Such records keep everyone updated about what’s happened to patients, which significantly reduces the likelihood of redundancies and confusion that could extend a health management timeline. Cloud computing has also accelerated information-sharing efforts within healthcare and other industries. It allows medical professionals to access and collaborate through scalable platforms. Many healthcare workers also appreciate how they can access cloud apps from anywhere. That convenience supports physicians who must travel for continuing medical education events, travel nurses, surgeons who split their time between multiple hospitals, and others who often work from numerous locations. However, despite these cloud computing benefits, a security-related downside is platforms use a centralized infrastructure to allow record sharing across users. That characteristic leaves cloud tools open to data breaches. In one case, researchers proposed addressing this shortcoming with a blockchain architecture to authenticate users and enable opportunities for sharing medical records securely. The group prioritized blockchain due to its immutability while seeking to create a system that allowed patients and their providers to share and store medical records securely. The researchers also wanted to design something that was not at risk of data loss or other failures. The researchers implemented so-called “special recognition keys” to identify medical-related specifics, such as identifying doctors, patients, and hospitals. When testing their system, some metrics studied included the time to complete a transaction and how well the communication-related attributes performed. The outcomes suggested the researchers’ approach worked far better than existing solutions. 2. Improving Access Control Data breaches can be costly, catastrophic events. Although there’s no single solution for preventing them, people can make meaningful progress by focusing on access control. One of the most convenient things about the cloud is it allows all authorized users to access content regardless of their location. However, as the number of people engaging with a cloud platform increases, so does the risk of compromised credentials that could allow hackers to enter networks and wreak havoc. Many corporate leaders have prioritized cloud-first strategies. That approach can strengthen cybersecurity because service providers have numerous security features to supplement internal measures. Additionally, cloud-based backup capabilities facilitate faster data recovery if cyberattacks occur. However, research suggests some access control practices used by cloud administrators have significant shortcomings that could make cyberattacks more likely. For example, one study about access management for cloud platforms found 49% of administrators store passwords in a spreadsheet. That’s a huge security risk for many reasons, but it also highlights the need for better password hygiene practices. Fortunately, the blockchain is well-positioned to solve this problem. In one example, researchers developed a blockchain system that uses attribute-based encryption technology to improve how cloud users access content. The setup also contains an audit contract that dynamically manages who can use the cloud and when. The team’s creation built a fine-grained and searchable system that maintained access control by strengthening cloud security and getting the desired results without excessive computing power. Results also showed this system increased storage capacity. When the group performed a security analysis on their blockchain creation, they found it stopped chosen-plaintext attacks and cybersecurity breaches based on guessed keywords. A theoretical examination and associated experiments suggested this tool worked better from a computing power and storage efficiency perspective than comparable alternatives. 3. Curbing Emerging Technologies’ Potential Threats Even as new technologies show tremendous progress and excite people about the future, some individuals specifically investigate how they could harm others through technological advancements. Developments associated with ChatGPT and other generative AI tools are excellent examples. Indeed, these chatbots can save people time by assisting them with tasks such as idea generation or outline creation. However, because these tools create believable-sounding paragraphs in seconds, some cybercriminals use generative artificial intelligence (genAI) chatbots to write phishing emails much faster than before. It’s easy to imagine the ramifications of a cybercriminal who writes a convincing phishing message and uses it to access someone’s cloud-stored information. ChatGPT runs on a cloud platform built by OpenAI, which created the chatbot. A lesser-known issue affecting data integrity is OpenAI uses interactions with the tool to train future versions of the algorithms. People can opt out of having their conversations become part of the training, but many people haven’t or don’t know the process for doing it. As workers eagerly tested ChatGPT and similar tools, some committed potential security breaches without realizing it. Consider if a web developer enters a proprietary code string into ChatGPT and asks the tool for help debugging it. That seemingly minor decision could result in sensitive information becoming part of training data and no longer being carefully protected by the developer’s employer. Some leaders quickly established rules for appropriate usage or banned generative AI tools to address these threats. A February 2024 study also showed some workers kept entering sensitive information when using ChatGPT despite knowing the associated risks. It’s still unclear how the blockchain will support data integrity for people using cloud-based generative AI tools, but many professionals are upbeat about the potential. Conclusion: Using Blockchain for Cloud Data Protection Entities ranging from government agencies to e-commerce stores use cloud platforms daily. These options are incredibly convenient because they eliminate geographical barriers and allow people to use them through an active internet connection anywhere in the world. However, many cloud tools store sensitive data, such as health records or payment details. Since cloud platforms hold such a wealth of information, hackers will likely continue targeting them. Although most cloud providers have built-in security features, cybercriminals continually seek ways to circumvent such protections. The examples here show why the blockchain is an excellent candidate for much-needed additional safeguards.
The essential mathematics for both Artificial intelligence (AI) and quantum computing are foundational to understanding and advancing these cutting-edge fields. In AI, concepts like linear algebra, calculus, probability theory, and optimization are pivotal for modeling data, training machine learning algorithms, and making predictions. Similarly, in quantum computing, these mathematical pillars are indispensable for representing quantum states, designing quantum algorithms, and analyzing quantum phenomena. Whether it's optimizing neural networks or harnessing the power of quantum superposition, a solid grasp of these mathematical principles is crucial for pushing the boundaries of artificial intelligence and quantum computing alike. Complex Numbers Complex numbers, which consist of a real and imaginary part (a+ib), and complex arithmetic and functions are fundamental to quantum mechanics. They allow for the representation of quantum states and the mathematical operations performed on them. In AI, complex numbers have also found applications in areas like neural networks and signal processing. A complex number Linear Algebra Linear algebra, including concepts like vectors, matrices, linear transformations, and eigenvalues/eigenvectors, is crucial for both quantum computing and many AI techniques. It provides the mathematical framework for representing and manipulating the states and operators in quantum systems, as well as the data structures and algorithms used in AI. Calculus and Optimization Calculus and optimization are crucial for training and tuning AI models, as well as for understanding the dynamics of quantum systems. The key concepts that need basic understanding are differentiation and integration, gradient-based optimization techniques, and variational methods. Additionally, a good understanding of convex optimization is an add-on in the context of optimization algorithms and loss minimization. Refer to Convex Optimization by Boyd and Vandenberghe. Mathematics for AI and Quantum Hilbert Spaces Quantum mechanics utilizes the mathematical structure of Hilbert spaces, which generalize the concepts of vectors and linear algebra to infinite dimensions. This allows for the representation of quantum states as vectors in a Hilbert space. Some AI models, such as those based on kernel methods, also make use of Hilbert space structures. Probability and Statistics Both quantum computing and AI rely heavily on probability theory and statistical methods. Quantum mechanics describes the probabilistic nature of measurements, while many AI algorithms, like Bayesian networks and reinforcement learning, are built on probabilistic foundations. Group Theory and Representation Theory Symmetry groups, unitary transformations, and irreducible representations are advanced mathematical concepts that are important for understanding the underlying structure of quantum systems and some quantum algorithms. Conclusion While the depth of understanding required may vary, a solid grasp of these core mathematical areas is essential for both advancing AI, including deep learning, and developing quantum computing technologies. The essential mathematics for both AI and quantum computing share several key concepts. Linear algebra serves as a cornerstone, enabling the representation of data and quantum states through vectors and matrices. Probability theory underpins both fields, facilitating the understanding of uncertainty in AI models and the probabilistic nature of quantum phenomena. Optimization techniques play a vital role in training machine learning models and optimizing quantum algorithms. Additionally, concepts from calculus provide the mathematical framework for gradient-based optimization and understanding quantum dynamics. Together, these mathematical foundations form the basis for advancing research and innovation in both AI and quantum computing domains.
Wireshark, the free, open-source packet sniffer and network protocol analyzer, has cemented itself as an indispensable tool in network troubleshooting, analysis, and security (on both sides). This article delves into the features, uses, and practical tips for harnessing the full potential of Wireshark, expanding on aspects that may have been glossed over in discussions or demonstrations. Whether you're a developer, security expert, or just curious about network operations, this guide will enhance your understanding of Wireshark and its applications. Introduction to Wireshark Wireshark was initially developed by Eric Rescorla and Gerald Combs, and designed to capture and analyze network packets in real-time. Its capabilities extend across various network interfaces and protocols, making it a versatile tool for anyone involved in networking. Unlike its command-line counterpart, tcpdump, Wireshark's graphical interface simplifies the analysis process, presenting data in a user-friendly "proto view" that organizes packets in a hierarchical structure. This facilitates quick identification of protocols, ports, and data flows. The key features of Wireshark are: Graphical User Interface (GUI): Eases the analysis of network packets compared to command-line tools Proto view: Displays packet data in a tree structure, simplifying protocol and port identification Compatibility: Supports a wide range of network interfaces and protocols Browser Network Monitors FireFox and Chrome contain a far superior network monitor tool built into them. It is superior because it is simpler to use and works with secure websites out of the box. If you can use the browser to debug the network traffic you should do that. In cases where your traffic requires low-level protocol information or is outside of the browser, Wireshark is the next best thing. Installation and Getting Started To begin with Wireshark, visit their official website for the download. The installation process is straightforward, but attention should be paid to the installation of command-line tools, which may require separate steps. Upon launching Wireshark, users are greeted with a selection of network interfaces as seen below. Choosing the correct interface, such as the loopback for local server debugging, is crucial for capturing relevant data. When debugging a Local Server (localhost), use the loopback interface. Remote servers will probably fit with the en0 network adapter. You can use the activity graph next to the network adapter to identify active interfaces for capture. Navigating Through Noise With Filters One of the challenges of using Wireshark is the overwhelming amount of data captured, including irrelevant "background noise" as seen in the following image. Wireshark addresses this with powerful display filters, allowing users to hone in on specific ports, protocols, or data types. For instance, filtering TCP traffic on port 8080 can significantly reduce unrelated data, making it easier to debug specific issues. Notice that there is a completion widget on top of the Wireshark UI that lets you find out the values more easily. In this case, we filter by port tcp.port == 8080 which is the port used typically in Java servers (e.g., Spring Boot/tomcat). But this isn't enough as HTTP is more concise. We can filter by protocol by adding http to the filter which narrows the view to HTTP requests and responses as shown in the following image. Deep Dive Into Data Analysis Wireshark excels in its ability to dissect and present network data in an accessible manner. For example, HTTP responses carrying JSON data are automatically parsed and displayed in a readable tree structure as seen below. This feature is invaluable for developers and analysts, providing insights into the data exchanged between clients and servers without manual decoding. Wireshark parses and displays JSON data within the packet analysis pane. It offers both hexadecimal and ASCII views for raw packet data. Beyond Basic Usage While Wireshark's basic functionalities cater to a wide range of networking tasks, its true strength lies in advanced features such as ethernet network analysis, HTTPS decryption, and debugging across devices. These tasks, however, may involve complex configuration steps and a deeper understanding of network protocols and security measures. There are two big challenges when working with Wireshark: HTTPS decryption: Decrypting HTTPS traffic requires additional configuration but offers visibility into secure communications. Device debugging: Wireshark can be used to troubleshoot network issues on various devices, requiring specific knowledge of network configurations. The Basics of HTTPS Encryption HTTPS uses the Transport Layer Security (TLS) or its predecessor, Secure Sockets Layer (SSL), to encrypt data. This encryption mechanism ensures that any data transferred between the web server and the browser remains confidential and untouched. The process involves a series of steps including handshake, data encryption, and data integrity checks. Decrypting HTTPS traffic is often necessary for developers and network administrators to troubleshoot communication errors, analyze application performance, or ensure that sensitive data is correctly encrypted before transmission. It's a powerful capability in diagnosing complex issues that cannot be resolved by simply inspecting unencrypted traffic or server logs. Methods for Decrypting HTTPS in Wireshark Important: Decrypting HTTPS traffic should only be done on networks and systems you own or have explicit permission to analyze. Unauthorized decryption of network traffic can violate privacy laws and ethical standards. Pre-Master Secret Key Logging One common method involves using the pre-master secret key to decrypt HTTPS traffic. Browsers like Firefox and Chrome can log the pre-master secret keys to a file when configured to do so. Wireshark can then use this file to decrypt the traffic: Configure the browser: Set an environment variable (SSLKEYLOGFILE) to specify a file where the browser will save the encryption keys. Capture traffic: Use Wireshark to capture the traffic as usual. Decrypt the traffic: Point Wireshark to the file with the pre-master secret keys (through Wireshark's preferences) to decrypt the captured HTTPS traffic. Using a Proxy Another approach involves routing traffic through a proxy server that decrypts HTTPS traffic and then re-encrypts it before sending it to the destination. This method might require setting up a dedicated decryption proxy that can handle the TLS encryption/decryption: Set up a decryption proxy: Tools like Mitmproxy or Burp Suite can act as an intermediary that decrypts and logs HTTPS traffic. Configure network to route through proxy: Ensure the client's network settings route traffic through the proxy. Inspect Traffic: Use the proxy's tools to inspect the decrypted traffic directly. Integrating tcpdump With Wireshark for Enhanced Network Analysis While Wireshark offers a graphical interface for analyzing network packets, there are scenarios where using it directly may not be feasible due to security policies or operational constraints. tcpdump, a powerful command-line packet analyzer, becomes invaluable in these situations, providing a flexible and less intrusive means of capturing network traffic. The Role of tcpdump in Network Troubleshooting tcpdump allows for the capture of network packets without a graphical user interface, making it ideal for use in environments with strict security requirements or limited resources. It operates under the principle of capturing network traffic to a file, which can then be analyzed at a later time or on a different machine using Wireshark. Key Scenarios for tcpdump Usage High-security environments: In places like banks or government institutions where running network sniffers might pose a security risk, tcpdump offers a less intrusive alternative. Remote servers: Debugging issues on a cloud server can be challenging with Wireshark due to the graphical interface; tcpdump captures can be transferred and analyzed locally. Security-conscious customers: Customers may be hesitant to allow third-party tools to run on their systems; tcpdump's command-line operation is often more palatable. Using tcpdump Effectively Capturing traffic with tcpdump involves specifying the network interface and an output file for the capture. This process is straightforward but powerful, allowing for detailed analysis of network interactions: Command syntax: The basic command structure for initiating a capture involves specifying the network interface (e.g., en0 for wireless connections) and the output file name. Execution: Once the command is run, tcpdump silently captures network packets. The capture continues until it's manually stopped, at which point the captured data can be saved to the specified file. Opening captures in Wireshark: The file generated by tcpdump can be opened in Wireshark for detailed analysis, utilizing Wireshark's advanced features for dissecting and understanding network traffic. The following shows the tcpdump command and its output: $ sudo tcpdump -i en0 -w output Password: tcpdump: listening on en, link-type EN10MB (Ethernet), capture size 262144 bytes ^C3845 packets captured 4189 packets received by filter 0 packets dropped by kernel Challenges and Considerations Identifying the correct network interface for capture on remote systems might require additional steps, such as using the ifconfig command to list available interfaces. This step is crucial for ensuring that relevant traffic is captured for analysis. Final Word Wireshark stands out as a powerful tool for network analysis, offering deep insights into network traffic and protocols. Whether it's for low-level networking work, security analysis, or application development, Wireshark's features and capabilities make it an essential tool in the tech arsenal. With practice and exploration, users can leverage Wireshark to uncover detailed information about their networks, troubleshoot complex issues, and secure their environments more effectively. Wireshark's blend of ease of use with profound analytical depth ensures it remains a go-to solution for networking professionals across the spectrum. Its continuous development and wide-ranging applicability underscore its position as a cornerstone in the field of network analysis. Combining tcpdump's capabilities for capturing network traffic with Wireshark's analytical prowess offers a comprehensive solution for network troubleshooting and analysis. This combination is particularly useful in environments where direct use of Wireshark is not possible or ideal. While both tools possess a steep learning curve due to their powerful and complex features, they collectively form an indispensable toolkit for network administrators, security professionals, and developers alike. This integrated approach not only addresses the challenges of capturing and analyzing network traffic in various operational contexts but also highlights the versatility and depth of tools available for understanding and securing modern networks. Videos Wireshark tcpdump
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