Aristoeu
commented
1 year ago SOLID
The Single-responsibility principle: "There should never be more than one reason for a class to change."[5] In other words, every class should have only one responsibility.[6] The Open–closed principle: "Software entities ... should be open for extension, but closed for modification."[7] The Liskov substitution principle: "Functions that use pointers or references to base classes must be able to use objects of derived classes without knowing it."[8] See also design by contract.[8] The Interface segregation principle: "Clients should not be forced to depend upon interfaces that they do not use."[9][4] The Dependency inversion principle: "Depend upon abstractions, [not] concretions."[10][4]
OOP
Abstraction, encapsulation, polymorphism, and inheritance are the four main theoretical principles of object-oriented programming. But Java also works with three further OOP concepts: association, aggregation, and composition.
Association
Association means the act of establishing a relationship between two unrelated classes. For example, when you declare two fields of different types (e.g. Car and Bicycle) within the same class and make them interact with each other, you have created an association.
Association in Java:
Two separate classes are associated through their objects The two classes are unrelated, each can exist without the other one Can be a one-to-one, one-to-many, many-to-one, or many-to-many relationship
Aggregation
Aggregation is a narrower kind of association. It occurs when there’s a one-way (HAS-A) relationship between the two classes we associate through their objects.
For example, every Passenger has a Car, but a Car doesn’t necessarily have a Passenger. When you declare the Passenger class, you can create a field of the Car type that shows which car the passenger belongs to. Then, when you instantiate a new Passenger object, you can access the data stored in the related Car as well.
Aggregation in Java:
One-directional association Represents a HAS-A relationship between two classes Only one class is dependent on the other
Composition
Composition is a stricter form of aggregation. It occurs when the two classes you associate are mutually dependent and can’t exist without each other.
For example, take a Car and an Engine class. A Car cannot run without an Engine, while an Engine also can’t function without being built into a Car. This kind of relationship between objects is also called a PART-OF relationship.
Composition in Java:
A restricted form of aggregation Represents a PART-OF relationship between two classes Both classes are dependent on each other If one class ceases to exist, the other can’t survive alone
Synthetic monitoring, also known as synthetic testing, is a method used in web monitoring to simulate end-user behavior and interactions with a website or web application. The goal is to test the performance, functionality, and availability of web services under controlled conditions.
In synthetic monitoring, automated scripts or bots are created to perform specific actions or workflows, such as logging in, clicking buttons, filling out forms, navigating through a site, etc. These actions are meant to mimic the behavior of real users. The scripts can run periodically or continuously to measure performance and detect any issues or downtime.
Here are a few key uses of synthetic monitoring:
Availability Monitoring: Synthetic monitoring can constantly check your website or web application to ensure it's up and running, and alert you when it's not available. Performance Monitoring: By simulating user interactions, synthetic monitoring can measure the response times and load times of different elements of your site, helping to identify bottlenecks and performance issues. Functional Testing: Synthetic monitoring can validate that various features and transactions on your website are working correctly, such as the checkout process on an e-commerce site. Benchmarking: Synthetic monitoring can be used to compare your site's performance against competitors or to measure performance before and after site changes or releases. It's worth noting that while synthetic monitoring provides valuable insights into system performance and functionality, it's best used in conjunction with real user monitoring (RUM) for a comprehensive view of user experience, as synthetic monitoring doesn't fully capture the diversity of user behavior, devices, browsers, and network conditions.
Azure DevOps Pipelines is a cloud-based continuous integration/continuous delivery (CI/CD) platform provided by Microsoft Azure. It helps in automating the build, testing, and deployment of applications, making the whole development process faster and more reliable.
Regarding synthetic monitoring, it can be integrated within your Azure DevOps Pipeline to continuously monitor the performance and availability of your applications in production, ensuring that any potential issues are detected and fixed as quickly as possible.
Here is a basic process on how you could set up synthetic monitoring in your Azure DevOps Pipeline:
Create Synthetic Tests: First, you'll need to create synthetic tests that simulate user interactions with your application. These tests can be created using various tools and scripting languages, depending on your needs and the nature of your application. Integrate Synthetic Tests into Your Pipeline: Once your synthetic tests are ready, you can integrate them into your Azure DevOps Pipeline. You can add a new task in your pipeline to run these tests after your application is deployed. Set up Alerts: With your synthetic tests running as part of your pipeline, you should also set up alerts for any test failures. Azure DevOps provides built-in features for sending notifications based on the results of pipeline tasks. Analyze Test Results: The results of your synthetic tests can be analyzed directly in Azure DevOps, or they can be exported to other tools for further analysis. This will give you insight into the performance and availability of your application in a production-like environment. Continuous Improvement: Use the results from synthetic monitoring to identify bottlenecks or issues, then iterate and improve your application. The goal is to enhance the user experience and maintain high availability and performance. Remember, synthetic monitoring with Azure DevOps Pipelines is just one part of a robust monitoring strategy. It's also essential to include other forms of monitoring, like real user monitoring (RUM), log analytics, and application performance monitoring (APM), to get a comprehensive view of your application's health and performance.
CI/CD stands for Continuous Integration and Continuous Delivery/Deployment. These terms refer to practices that are designed to make the process of developing and delivering software more consistent, efficient, and safe.
Continuous Integration (CI): This is a development practice where developers integrate their code changes into a shared repository frequently, usually multiple times per day. Each integration is then automatically built and tested to catch bugs or issues early. The goal is to reduce integration problems and improve software quality. Continuous Delivery (CD): This is the logical extension of Continuous Integration, where the software changes that pass the automated tests are automatically deployed to a staging or production environment. The objective is to ensure that software is always in a deployable state. In some implementations, the CD could also stand for Continuous Deployment, which is similar to Continuous Delivery, but there's no manual intervention in the process. In Continuous Deployment, every change that passes all stages of your production pipeline is released to your customers.
The overall aim of CI/CD is to create a more agile development process, so that teams can improve and adjust their software at a faster pace and with less risk. Tools like Jenkins, Travis CI, GitLab CI/CD, and CircleCI are commonly used to implement these CI/CD practices.
Docker and Kubernetes are both important components in the world of containerization, but they serve different purposes and are typically used together rather than being alternatives to one another.
Here's a basic comparison:
Docker is an open-source platform that automates the deployment, scaling, and management of applications through containerization. A Docker container encapsulates an application along with all its dependencies in a single package, ensuring that it runs consistently on any infrastructure. Docker's primary benefit is that it enables developers to easily package, distribute, and run applications in isolated environments.
On the other hand, Kubernetes (K8s) is a container orchestration platform, also open-source, designed to automate the deployment, scaling, and management of containerized applications. It's typically used in environments where you are running multiple containers across multiple machines. Kubernetes organizes containers into "Pods", and can manage the scaling, load balancing, and network communication for those Pods.
In summary, Docker provides an easy and efficient way to containerize applications, while Kubernetes helps to manage those containers in a production setting. When combined, Docker and Kubernetes are a powerful toolset for deploying and managing complex, scalable applications
K8s is an abbreviation for Kubernetes, an open-source container orchestration platform that automates the deployment, scaling, and management of applications.
The name "Kubernetes" originates from Greek, meaning "helmsman" or "pilot", and, as its name suggests, it helps navigate and manage services within a containerized infrastructure.
Here's how Kubernetes (K8s) works:
Container Deployment: Kubernetes can deploy your application (packed into containers) onto a cluster of computers rather than a single machine, providing high availability and redundancy. Scaling: Kubernetes can automatically scale the number of containers up or down based on the usage of your application (auto-scaling), making it highly efficient in terms of resource usage. Load Balancing: Kubernetes can distribute network traffic among a fleet of containers, improving the overall performance of your application. Service Discovery and Networking: Kubernetes provides containers with their own IP addresses and a single DNS name and can load-balance across them. Health Checks and Self-Healing: Kubernetes can monitor the health of your containers and, if needed, restart failed containers, replace and reschedule containers when nodes die, and kill containers that don’t respond to your user-defined health check. Secrets and Configuration Management: Kubernetes lets you store and manage sensitive information, such as passwords, OAuth tokens, and SSH keys. Overall, Kubernetes provides a platform for automating deployment, scaling, and operations of application containers across clusters of hosts, providing a container-centric infrastructure.
I've recently been diving into the JVM, or Java Virtual Machine, which is an abstract computing machine that enables a computer to run a Java program.
The JVM plays a crucial role in the Java ecosystem. It is responsible for converting the Java bytecode into the machine language, creating an abstraction between the compiled Java code and the specific operating system running the program.
One of the major advantages of the JVM is its "Write Once, Run Anywhere" capability. Since the JVM interprets bytecode into machine-specific instructions, Java programs can be run on any device with a JVM, making Java highly portable.
Another thing I find fascinating about the JVM is its automatic garbage collection. This means that programmers don't need to manually manage the memory allocation and deallocation process, reducing the chance of memory leaks and other related bugs.
Furthermore, the JVM also offers features like JIT (Just-In-Time) compilation, which improves the performance of Java applications by compiling bytecode to native machine code at runtime, and multithreading, which allows concurrent execution of two or more parts of a program for maximum utilization of the CPU.
While learning about the JVM, I've also been intrigued by the different tools available for tuning and optimizing JVM performance. Understanding the inner workings of JVM helps in troubleshooting performance issues and writing efficient and scalable Java code.
Even though I am still exploring the depth of the JVM, this knowledge has already enhanced my Java development skills and I'm eager to apply it in real-world projects
Just-In-Time (JIT) compilation is a feature of the Java Virtual Machine (JVM) that significantly enhances performance. The JVM initially interprets Java bytecode, which is a general, platform-independent version of your code. However, interpreting bytecode is slower than running compiled native code.
This is where JIT compilation comes in. It selectively compiles bytecode into native machine code at runtime. Instead of interpreting the bytecode line by line every time a method is called, the JVM can directly execute the compiled native code, leading to a considerable speed boost.
The JVM employs a JIT compiler to perform this conversion just in time, as the name suggests. When the JVM identifies a method or block of bytecode that is executed frequently (a "hot spot"), it uses the JIT compiler to convert it into machine code.
JIT compilation combines the advantages of interpretation (platform independence) and static compilation (speed). It contributes to Java's famous "Write Once, Run Anywhere" capability, allowing developers to write their programs once and run them quickly on any device that has a JVM.