Elasticsearch, Logstash, and Kibana – ELK Stack


If you’re dealing with a large amount of data, you’ll quickly realize how important it is to have an efficient way to store, manage, and analyze it. The ELK stack is a popular solution for this problem. It’s an open-source software stack that includes Elasticsearch, Logstash, and Kibana. This tutorial will provide an overview of what the ELK stack is and how you can use it to manage your data.

What is the ELK stack?

The ELK stack is a collection of three open-source software tools: Elasticsearch, Logstash, and Kibana. These tools are designed to work together to help you store, search, and analyze large amounts of data.

  • Elasticsearch: Elasticsearch is a search engine based on the Lucene library. It allows you to store, search, and analyze data in real-time. Elasticsearch can handle a large amount of data, and it’s highly scalable. It’s designed to be fast and efficient, making it ideal for use cases where speed and real-time search are critical.
  • Logstash: Logstash is a data processing pipeline that allows you to ingest, transform, and enrich data. It’s designed to handle a wide range of data types and formats, making it ideal for processing log data, system metrics, and other types of data.
  • Kibana: Kibana is a data visualization and analysis tool. It allows you to create custom dashboards and visualizations, making it easy to understand and analyze your data. Kibana also integrates with Elasticsearch, allowing you to search and analyze data in real-time.

How to use the ELK stack

Using the ELK stack is relatively straightforward. Here are the basic steps:

Step 1: Install the ELK stack

Installing Elasticsearch

The first tool in the stack is Elasticsearch, which is a distributed search and analytics engine. To install Elasticsearch, follow the steps below:

  1. Visit the Elasticsearch download page and select the appropriate version for your operating system.
  2. Extract the downloaded archive to a directory of your choice.
  3. Open a terminal and navigate to the Elasticsearch directory.
  4. Start Elasticsearch by running the following command: ./bin/elasticsearch

Installing Logstash

The next tool in the stack is Logstash, which is a data processing pipeline that ingests data from multiple sources, transforms it, and sends it to a destination. To install Logstash, follow the steps below:

  1. Visit the Logstash download page and select the appropriate version for your operating system.
  2. Extract the downloaded archive to a directory of your choice.
  3. Open a terminal and navigate to the Logstash directory.
  4. Start Logstash by running the following command: ./bin/logstash

Installing Kibana

The final tool in the stack is Kibana, which is a web-based visualization tool that allows users to interact with the data stored in Elasticsearch. To install Kibana, follow the steps below:

  1. Visit the Kibana download page and select the appropriate version for your operating system.
  2. Extract the downloaded archive to a directory of your choice.
  3. Open a terminal and navigate to the Kibana directory.
  4. Start Kibana by running the following command: ./bin/kibana

Step 2: Configure Elasticsearch

Once you have installed the ELK stack, the next step is to configure Elasticsearch. You will need to set up an index, which is like a database in Elasticsearch. An index contains one or more documents, which are like rows in a traditional database. You can think of an index as a way to organize your data.

  1. Open the Elasticsearch configuration file (typically located at /etc/elasticsearch/elasticsearch.yml), and make necessary modifications such as cluster name, network settings, and heap size.
  2. Start the Elasticsearch service by running the appropriate command for your operating system (sudo service elasticsearch start for Linux, or .\bin\elasticsearch.bat for Windows).
  3. Verify the Elasticsearch installation by accessing http://localhost:9200 in your web browser. You should see a JSON response with information about your Elasticsearch cluster.

Step 3: Ingest data with Logstash

The next step is to ingest data with Logstash. Logstash allows you to parse and transform data from various sources, including logs, metrics, and other data types. You can use Logstash to filter and transform data, so it’s in the format that Elasticsearch expects.

  1. Create a Logstash configuration file (e.g., myconfig.conf) that defines the input, filter, and output sections. The input section specifies the data source (e.g., file, database, or network stream). The filter section allows data transformation, parsing, and enrichment. The output section defines where the processed data will be sent (typically Elasticsearch).
  2. Start Logstash and specify your configuration file: bin/logstash -f myconfig.conf. Logstash will start reading data from the input source, apply filters, and send the processed data to the specified output.
  3. Verify the Logstash pipeline by monitoring the Logstash logs and checking Elasticsearch to ensure that data is being ingested properly.

Step 4: Visualize data with Kibana

Finally, you can use Kibana to visualize and analyze your data. Kibana allows you to create custom dashboards and visualizations, so you can easily understand and analyze your data.

  1. Start the Kibana service by running the appropriate command for your operating system (sudo service kibana start for Linux, or .\bin\kibana.bat for Windows).
  2. Access Kibana by visiting http://localhost:5601 in your web browser.
  3. Configure an index pattern in Kibana to define which Elasticsearch indices you want to explore. Follow the step-by-step instructions provided in the Kibana UI.
  4. Once the index pattern is configured, navigate to the Discover tab in Kibana. Here, you can search, filter, and visualize your data. Experiment with various visualizations, such as bar charts, line charts, and maps, to gain insights into your data.

Conclusion

The ELK stack is a powerful tool for managing large amounts of data. It’s designed to be fast, efficient, and scalable, making it ideal for use cases where speed and real-time search are critical. By following the steps outlined in this tutorial, you can get started with the ELK stack and start managing your data more efficiently.

You have successfully set up the ELK stack and are now equipped to manage, process, and analyze your data efficiently. Elasticsearch provides a scalable and high-performance data storage and retrieval solution, Logstash enables data ingestion and transformation, and Kibana empowers you to visualize and explore your data effectively.

Building Reactive Applications with Vert.x: A Comprehensive Tutorial


In today’s fast-paced, highly concurrent world, building scalable and reactive applications is a necessity. Vert.x, a powerful toolkit for building reactive applications on the Java Virtual Machine (JVM), provides developers with an excellent framework to achieve this. In this tutorial, we will explore the fundamentals of Vert.x and guide you through building a reactive application from scratch.

Table of Contents:

  1. What is Vert.x?
  2. Setting Up the Development Environment
  3. Understanding Vert.x Core Concepts
    • 3.1. Verticles
    • 3.2. Event Bus
    • 3.3. Asynchronous Programming Model
  4. Building a Simple Vert.x Application
    • 4.1. Creating a Maven Project
    • 4.2. Writing a Verticle
    • 4.3. Deploying and Running the Verticle
  5. Scaling Vert.x Applications
    • 5.1. Vert.x Clustering
    • 5.2. High Availability
  6. Integrating with Other Technologies
    • 6.1. Working with HTTP and WebSockets
    • 6.2. Integrating with Databases
    • 6.3. Reactive Messaging with Apache Kafka
  7. Unit Testing Vert.x Applications
    • 7.1. Vert.x Unit Testing Framework
    • 7.2. Mocking Dependencies
  8. Deploying Vert.x Applications
    • 8.1. Packaging Vert.x Applications
    • 8.2. Running Vert.x on Docker
    • 8.3. Deploying to the Cloud
  9. Monitoring and Debugging Vert.x Applications
    • 9.1. Logging and Metrics
    • 9.2. Distributed Tracing with OpenTelemetry
  10. Conclusion

Section 1: What is Vert.x?

Vert.x is an open-source, reactive, and polyglot toolkit designed for building scalable and high-performance applications. It provides a powerful and flexible framework for developing event-driven and non-blocking applications on the Java Virtual Machine (JVM). Vert.x enables developers to create reactive systems that can handle a large number of concurrent connections and process events efficiently.

At its core, Vert.x embraces the principles of the Reactive Manifesto, which include responsiveness, scalability, resilience, and message-driven architecture. It leverages an event-driven programming model, allowing developers to build applications that are highly responsive to incoming events and messages.

Key Features of Vert.x:

  1. Polyglot Support: Vert.x supports multiple programming languages, including Java, Kotlin, JavaScript, Groovy, Ruby, and Ceylon. This flexibility allows developers to leverage their language of choice while benefiting from Vert.x’s features.
  2. Event Bus: The Vert.x event bus enables communication and coordination between different components of an application, both within a single instance and across distributed instances. It supports publish/subscribe and point-to-point messaging patterns, making it easy to build decoupled and scalable systems.
  3. Asynchronous and Non-Blocking: Vert.x promotes non-blocking I/O operations and asynchronous programming. It utilizes a small number of threads to handle a large number of concurrent connections efficiently. This enables applications to scale and handle high loads without incurring the overhead of traditional thread-per-connection models.
  4. Reactive Streams Integration: Vert.x seamlessly integrates with Reactive Streams, a specification for asynchronous stream processing with non-blocking backpressure. This integration allows developers to build reactive applications that can handle backpressure and efficiently process streams of data.
  5. Web and API Development: Vert.x provides a rich set of APIs and tools for building web applications and RESTful APIs. It supports the creation of high-performance HTTP servers, WebSocket communication, and the integration of various web technologies.
  6. Clustering and High Availability: Vert.x offers built-in support for clustering, allowing applications to scale horizontally by running multiple instances across multiple nodes. It provides mechanisms for event bus clustering, distributed data structures, and failover, ensuring high availability and fault tolerance.
  7. Integration Ecosystem: Vert.x integrates with various technologies and frameworks, including databases, messaging systems (such as Apache Kafka and RabbitMQ), reactive streams implementations, service discovery mechanisms, and more. This enables developers to leverage existing tools and services seamlessly.

Vert.x is well-suited for developing a wide range of applications, including real-time systems, microservices, APIs, IoT applications, and reactive web applications. Its lightweight and modular architecture, combined with its reactive nature, makes it an excellent choice for building scalable and responsive applications that can handle heavy workloads and concurrent connections.

Whether you’re a Java developer or prefer other JVM-compatible languages, Vert.x offers a powerful toolkit to create reactive, event-driven applications that can meet the demands of modern distributed systems.

Section 2: Setting Up the Development Environment

Setting up the development environment for Vert.x involves a few essential steps. Here’s a step-by-step guide to getting started:

Step 1: Install Java Development Kit (JDK)

  • Ensure that you have the latest version of JDK installed on your system. Vert.x requires Java 8 or higher. You can download the JDK from the Oracle website or use OpenJDK, which is a free and open-source alternative.

Step 2: Install Apache Maven (optional)

  • While not mandatory, using Apache Maven simplifies the management of dependencies and building Vert.x projects. You can download Maven from the Apache Maven website and follow the installation instructions specific to your operating system.

Step 3: Set up a Project

  • Create a new directory for your Vert.x project. Open a terminal or command prompt and navigate to the directory you just created.

Step 4: Initialize a Maven Project (optional)

  • If you chose to use Maven, you can initialize a new Maven project by running the following
mvn archetype:generate -DgroupId=com.example -DartifactId=my-vertx-app -DarchetypeArtifactId=maven-archetype-quickstart -DinteractiveMode=false

This command creates a basic Maven project structure with a sample Java class.

Step 5: Add Vert.x Dependencies

Open the pom.xml file in your project directory (if using Maven) and add the following dependencies:

<dependencies>
    <dependency>
        <groupId>io.vertx</groupId>
        <artifactId>vertx-core</artifactId>
        <version>4.1.1</version>
    </dependency>
</dependencies>

This configuration adds the Vert.x core dependency to your project.

Step 6: Build the Project (optional)

  • If you’re using Maven, you can build your project by running the following command:
mvn clean package

This command compiles your code, resolves dependencies, and creates a JAR file in the target directory.

Step 7: Start Coding

  • You’re now ready to start developing with Vert.x. Create your Verticle class, which represents a component in a Vert.x application, and implement the necessary logic.

Step 8: Run the Application

To run a Vert.x application, you can use the following command in your project directory (assuming you’ve already built the project with Maven):

java -jar target/my-vertx-app-1.0-SNAPSHOT.jar

Replace my-vertx-app-1.0-SNAPSHOT.jar with the actual name of your JAR file.

Congratulations! You have successfully set up your development environment for Vert.x. You can now start building reactive applications using the Vert.x toolkit. Remember to refer to the Vert.x documentation and explore its rich set of features and APIs to harness its full potential. Happy coding!

Section 3: Understanding Vert.x Core Concepts

To effectively work with Vert.x, it’s crucial to understand its core concepts. Let’s explore the key concepts of Vert.x:

  1. Verticles:
    • Verticles are the building blocks of a Vert.x application. They represent individual components or units of work that run concurrently within the Vert.x ecosystem.
    • Verticles are lightweight and can be single-threaded or multi-threaded, depending on the configuration. They communicate with each other through the event bus.
    • Verticles can handle various tasks, such as handling HTTP requests, processing messages, accessing databases, or performing background tasks.
    • Vert.x provides different types of verticles, including standard verticles, worker verticles (for CPU-intensive tasks), and periodic verticles (for scheduled tasks).
  2. Event Bus:
    • The event bus is a powerful communication mechanism provided by Vert.x that allows different verticles to exchange messages asynchronously.
    • Verticles can publish messages to the event bus, and other verticles can subscribe to receive those messages based on different patterns or addresses.
    • The event bus enables loose coupling between verticles, making it easy to build distributed and scalable systems.
    • Vert.x provides different messaging patterns, including publish/subscribe and point-to-point messaging, which can be used with the event bus.
  3. Asynchronous Programming Model:
    • Vert.x promotes an asynchronous programming model, which is fundamental to building reactive applications.
    • Asynchronous programming allows non-blocking execution of tasks, enabling applications to handle high loads and concurrency efficiently.
    • Vert.x APIs are designed to work asynchronously, allowing developers to write non-blocking code that can scale well.
    • Callbacks, futures/promises, and reactive streams are common patterns used in Vert.x to handle asynchronous operations.
  4. Reactive Streams Integration:
    • Vert.x integrates seamlessly with Reactive Streams, a standard for asynchronous stream processing with non-blocking backpressure.
    • Reactive Streams provide a set of interfaces and protocols for building reactive applications that can handle backpressure and efficiently process streams of data.
    • Vert.x includes support for Reactive Streams, enabling developers to use reactive streams implementations like RxJava, Reactor, or CompletableFuture seamlessly within Vert.x applications.

Understanding these core concepts is essential for harnessing the power of Vert.x. With Verticles, the Event Bus, Asynchronous Programming, and Reactive Streams, you can build scalable, responsive, and high-performance applications. By leveraging these concepts, you can create loosely coupled, concurrent systems that efficiently handle large workloads and enable seamless communication between components.

Section 4: Building a Simple Vert.x Application

To build a simple Vert.x application, we will go through the process of creating a basic Verticle, deploying it, and running the application. Follow these steps:

Step 1: Create a Maven Project

  • If you haven’t already set up a Maven project, follow the instructions in the “Setting Up the Development Environment” section to create a new Maven project or use an existing one.

Step 2: Add Vert.x Dependency

  • Open the pom.xml file of your Maven project and add the Vert.x dependency within the <dependencies> section:
<dependency>
    <groupId>io.vertx</groupId>
    <artifactId>vertx-core</artifactId>
    <version>4.1.1</version>
</dependency>
  • This adds the Vert.x core dependency to your project.

Step 3: Create a Verticle

  • In your project, create a new Java class representing your Verticle. For example, you can create a class named MyVerticle.
  • Make sure your class extends io.vertx.core.AbstractVerticle.
  • Override the start() method to define the behavior of your Verticle when it is deployed. For simplicity, let’s print a message to the console:
public class MyVerticle extends AbstractVerticle {

    @Override
    public void start() {
        System.out.println("MyVerticle has been deployed!");
    }
}

Step 4: Deploy and Run the Verticle

  • In your main application class (e.g., App.java), deploy the MyVerticle by creating a Vertx instance and using the deployVerticle() method:
import io.vertx.core.Vertx;

public class App {
    public static void main(String[] args) {
        Vertx vertx = Vertx.vertx();
        vertx.deployVerticle(new MyVerticle());
    }
}

Step 5: Run the Application

  • Compile and run the application using your preferred method (e.g., Maven command or an integrated development environment).
  • Once the application starts, you should see the message “MyVerticle has been deployed!” printed in the console.

Congratulations! You have successfully built a simple Vert.x application. This example demonstrates the basic structure of a Verticle and how to deploy it using the Vertx instance. You can further enhance your application by adding more Verticles, handling HTTP requests, or integrating with other technologies using the Vert.x APIs.

Section 5: Scaling Vert.x Applications

Scaling Vert.x applications is crucial to handle increased workloads and ensure high availability. Vert.x provides several mechanisms for scaling applications. Let’s explore two important aspects of scaling Vert.x applications: Vert.x Clustering and High Availability.

  1. Vert.x Clustering:
    • Vert.x clustering allows you to run multiple Vert.x instances across multiple nodes to distribute the load and handle high concurrency.
    • Clustering is achieved through a built-in event bus, which enables communication between different Vert.x instances running on different nodes.
    • When multiple Vert.x instances are clustered, they form a distributed event bus network, allowing verticles to communicate seamlessly.
    • To enable clustering, you need to configure your Vert.x instances to join the same cluster by specifying a cluster manager implementation.
    • Vert.x provides different cluster manager implementations, such as Hazelcast, Apache Ignite, Infinispan, and more, that handle the management and coordination of the clustered instances.
    • By leveraging clustering, you can horizontally scale your Vert.x application by adding more nodes to the cluster, enabling it to handle higher workloads and providing fault tolerance.
  2. High Availability:
    • High availability ensures that your Vert.x application remains operational even in the face of failures.
    • Vert.x provides features and best practices to achieve high availability in different scenarios:
      • Circuit Breaker Pattern: Vert.x offers a built-in circuit breaker pattern implementation, allowing you to protect your application from cascading failures when dealing with remote services. It helps to manage failure thresholds, timeouts, and retries.
      • Reactive Streams and Backpressure: Vert.x integrates with Reactive Streams, which enables efficient handling of streams of data with non-blocking backpressure. This helps to prevent overloading downstream systems and ensures resilience and stability in the face of varying workloads.
      • Fault Tolerance: Vert.x provides mechanisms to handle failures and recover from them. For example, when a verticle fails, Vert.x can automatically redeploy it to ensure that the system continues running smoothly. Additionally, you can leverage cluster-wide shared data structures to maintain the state and recover from failures.
      • Monitoring and Alerting: Implement monitoring and alerting mechanisms to detect and respond to any anomalies or failures in your Vert.x application. Utilize logging, metrics, and monitoring tools to gain insights into the application’s health and performance.

By leveraging Vert.x clustering and implementing high availability practices, you can ensure that your application scales effectively and remains resilient to failures. These mechanisms enable your application to handle increased workloads, distribute the load across multiple nodes, and provide fault tolerance and automatic recovery. Proper monitoring and alerting help you identify and address any issues promptly, ensuring the smooth operation of your Vert.x application.

Section 6: Integrating with Other Technologies

Vert.x offers seamless integration with various technologies and frameworks, allowing you to leverage existing tools and services in your applications. Here are some common integration points for Vert.x:

  1. Database Integration:
    • Vert.x provides asynchronous clients and connectors for interacting with different databases, both SQL and NoSQL.
    • For example, you can use the Vert.x JDBC client to connect to relational databases like MySQL, PostgreSQL, or Oracle.
    • Vert.x also provides clients for popular NoSQL databases like MongoDB, Redis, and Apache Cassandra.
    • These database clients allow you to perform asynchronous database operations efficiently and integrate database access with other Vert.x components.
  2. Messaging Systems:
    • Vert.x seamlessly integrates with messaging systems, enabling you to build event-driven and distributed applications.
    • Vert.x provides a unified API for working with message brokers such as Apache Kafka, RabbitMQ, and ActiveMQ.
    • You can use the Vert.x event bus to publish and consume messages from these brokers, enabling communication between different parts of your system or integrating with external systems.
  3. Reactive Streams:
    • Vert.x integrates with Reactive Streams, which is a specification for asynchronous stream processing with non-blocking backpressure.
    • By leveraging Reactive Streams implementations like RxJava, Reactor, or CompletableFuture, you can easily integrate reactive libraries and frameworks into your Vert.x applications.
    • This integration allows you to handle streams of data efficiently and apply reactive patterns across your application.
  4. Service Discovery:
    • Vert.x provides a service discovery mechanism that allows services to discover and interact with each other dynamically.
    • With service discovery, you can register services with associated metadata and retrieve them by name or other attributes.
    • This feature is especially useful in microservices architectures, where services need to discover and communicate with each other without hard-coded dependencies.
  5. Web Technologies:
    • Vert.x offers a powerful set of APIs and tools for building web applications and APIs.
    • It integrates with web technologies like WebSocket, HTTP, and event-driven server-sent events.
    • You can use the Vert.x Web API to handle HTTP requests, build RESTful APIs, serve static files, and implement routing and middleware functionalities.
    • Additionally, Vert.x provides integration with popular web frameworks like Spring WebFlux and Express.js, allowing you to leverage their capabilities within your Vert.x applications.
  6. Authentication and Authorization:
    • Vert.x integrates with authentication and authorization mechanisms, enabling secure access control to your applications.
    • It supports various authentication methods, including basic authentication, OAuth 2.0, and JWT (JSON Web Tokens).
    • Vert.x also provides integration with popular identity providers like Keycloak, Okta, and Google Sign-In.

These are just a few examples of the technologies that can be integrated with Vert.x. Vert.x’s modular and flexible architecture allows you to integrate with a wide range of tools and services, enabling you to leverage existing solutions and build powerful, feature-rich applications. When integrating with external technologies, refer to the Vert.x documentation and specific integration guides for detailed instructions and best practices.

Section 7: Unit Testing Vert.x Applications

Unit testing is an essential practice in software development, and Vert.x provides support for writing unit tests for your Vert.x applications. Let’s explore how you can effectively unit test your Vert.x applications:

  1. Testing Verticles:
    • Verticles are the building blocks of a Vert.x application. You can write unit tests to validate the behavior of individual verticles.
    • To test a verticle, create a test class for it and use a testing framework like JUnit or TestNG.
    • Use the Vert.x Test API to set up and execute your tests. The Vert.x Test API provides utilities for creating Vert.x instances, deploying verticles, and simulating events.
    • You can simulate events on the event bus, mock dependencies, and verify the expected behavior of your verticle.
  2. Mocking Dependencies:
    • When unit testing a verticle, you may need to mock external dependencies such as databases, services, or message brokers.
    • Use mocking frameworks like Mockito or EasyMock to create mock objects for your dependencies.
    • Mock the behavior of these dependencies to simulate different scenarios and ensure the correct interaction between the verticle and its dependencies.
  3. Asynchronous Testing:
    • Vert.x is designed for asynchronous programming, and your tests need to handle asynchronous operations appropriately.
    • Use the Vert.x Test API to write assertions for asynchronous code. For example, you can use the await() method to wait for asynchronous operations to complete.
    • Use the async() method to inform the test framework that the test is asynchronous and provide a completion handler to signal the completion of the test.
  4. Dependency Injection:
    • Vert.x supports dependency injection, and you can use it to improve the testability of your code.
    • Use a dependency injection framework like Google Guice or Spring to manage dependencies in your verticles.
    • In your unit tests, you can provide mock or test-specific implementations of dependencies to ensure controlled testing environments.
  5. Integration Testing:
    • In addition to unit tests, you may also want to perform integration tests to validate the interactions between different components of your Vert.x application.
    • Integration tests involve deploying multiple verticles and simulating real-world scenarios.
    • Use the Vert.x Test API and tools like the embedded Vert.x instance or containers like Docker to set up integration test environments.
    • You can also use tools like WireMock to mock external dependencies and simulate network interactions.

Remember to follow best practices for unit testing, such as testing individual units in isolation, focusing on behavior rather than implementation details, and keeping tests concise and readable.

Vert.x provides a comprehensive testing framework and utilities to support effective unit testing of your applications. By writing unit tests, you can ensure the correctness of your code, detect bugs early, and maintain the quality and stability of your Vert.x applications.

Section 8: Deploying Vert.x Applications

Deploying Vert.x applications involves preparing your application for deployment and choosing the appropriate deployment options. Here are the key steps to deploy a Vert.x application:

  1. Package Your Application:
    • Ensure that your Vert.x application is properly packaged for deployment.
    • Create an executable JAR file that includes all the necessary dependencies.
    • You can use build tools like Maven or Gradle to package your application, which will create a self-contained JAR file.
  2. Choose Deployment Options:
    • Vert.x provides multiple deployment options based on your requirements and the target environment.
    • Standalone Deployment: You can deploy your Vert.x application as a standalone JAR file on a server or a virtual machine.
    • Containerized Deployment: Package your application as a Docker image and deploy it to container orchestration platforms like Kubernetes.
    • Serverless Deployment: If you want to leverage serverless architectures, you can deploy your Vert.x application to platforms like AWS Lambda or Azure Functions.
  3. Configuration Management:
    • Consider how you manage configuration for your Vert.x application in different deployment environments.
    • Externalize configuration using configuration files, environment variables, or configuration servers like Consul or Spring Cloud Config.
    • Make sure your application can read and utilize the configuration from the chosen configuration source.
  4. Scaling and Load Balancing:
    • When deploying your application in a production environment, consider how to scale and load balance your Vert.x instances.
    • Vert.x clustering allows you to run multiple instances of your application across different nodes, distributing the load and ensuring fault tolerance.
    • Use load balancers like Nginx or Apache HTTP Server to distribute incoming traffic across multiple Vert.x instances.
  5. Monitoring and Logging:
    • Set up monitoring and logging for your deployed Vert.x application to gather insights into its performance, health, and potential issues.
    • Use monitoring tools like Prometheus, Grafana, or the Vert.x Metrics API to collect and visualize application metrics.
    • Configure proper logging to capture important events, errors, and debugging information for troubleshooting purposes.
  6. Continuous Integration and Deployment (CI/CD):
    • Automate your deployment process using CI/CD pipelines to streamline and ensure consistent deployments.
    • Integrate your Vert.x application with CI/CD tools like Jenkins, GitLab CI, or AWS CodePipeline to automatically build, test, and deploy your application.

By following these steps and considering the deployment options, configuration management, scaling, monitoring, and automation, you can successfully deploy your Vert.x application and ensure its availability, scalability, and maintainability in various environments.

Section 9: Monitoring and Debugging Vert.x Applications

Monitoring and debugging Vert.x applications are crucial for maintaining their performance, identifying issues, and ensuring their smooth operation. Here are some approaches and tools you can use for monitoring and debugging Vert.x applications:

  1. Logging:
    • Utilize logging frameworks like Log4j, SLF4J, or Vert.x’s built-in logging capabilities to capture important events, errors, and debugging information.
    • Configure logging levels appropriately to balance the level of detail and performance impact.
    • Use log aggregation tools like ELK Stack (Elasticsearch, Logstash, Kibana) or Splunk to collect, search, and visualize log data.
  2. Metrics and Health Checks:
    • Vert.x provides a Metrics API that allows you to collect various performance metrics about your application, such as CPU usage, memory consumption, event loop utilization, and request/response rates.
    • Integrate with monitoring tools like Prometheus, Grafana, or DataDog to collect and visualize these metrics in real-time dashboards.
    • Implement health checks in your application to periodically assess its overall health and availability. Expose endpoints that can be probed by external monitoring systems.
  3. Distributed Tracing:
    • Distributed tracing helps you understand and debug the flow of requests across different components of your Vert.x application, especially in microservices architectures.
    • Tools like Jaeger, Zipkin, or OpenTelemetry can be integrated with Vert.x to provide distributed tracing capabilities.
    • Instrument your code with tracing annotations or APIs to track requests as they pass through different verticles and external services.
  4. Request Logging and Monitoring:
    • Log and monitor incoming requests to your Vert.x application to gain insights into their performance and identify potential bottlenecks.
    • Use tools like Apache HTTP Server or Nginx as reverse proxies to capture request logs and enable advanced logging features.
    • Implement request-level metrics and monitoring to track request/response times, error rates, and throughput.
  5. Debugging Techniques:
    • Vert.x supports remote debugging, allowing you to attach a debugger to running Vert.x instances.
    • Enable remote debugging by adding the appropriate JVM arguments to your application’s startup script or configuration.
    • Use an Integrated Development Environment (IDE) like IntelliJ IDEA, Eclipse, or Visual Studio Code with the Vert.x plugin to connect and debug your running application.
  6. Application Performance Monitoring (APM) Tools:
    • Consider using Application Performance Monitoring (APM) tools like New Relic, AppDynamics, or Dynatrace to gain deeper insights into your Vert.x application’s performance.
    • These tools provide end-to-end visibility, capturing detailed transaction traces, database queries, external service calls, and resource utilization.

Remember to monitor your Vert.x applications in both development and production environments. Understand the performance characteristics of your application and establish baselines to identify deviations and potential issues.

By combining logging, metrics, distributed tracing, request logging, debugging techniques, and APM tools, you can effectively monitor and debug your Vert.x applications, ensuring optimal performance, identifying and resolving issues quickly, and providing a smooth user experience.

Section 10: Conclusion

In conclusion, Vert.x is a powerful and versatile toolkit for building reactive, event-driven applications that can handle high concurrency and scale effectively. In this tutorial, we covered various aspects of Vert.x development, starting from setting up the development environment to deploying and monitoring Vert.x applications.

Java 17 Features with Detailed Explanation


Java 17 was released on September 14, 2021, and it includes several new features and improvements that developers can use to build better and more efficient applications. In this tutorial, we’ll take a closer look at some of the most important features of Java 17 and how to use them in your projects.

In this tutorial, we’ll cover the following features:

  1. Sealed Classes and Interfaces
  2. Pattern Matching for instanceof
  3. Records
  4. Text Blocks
  5. Switch Expressions
  6. Helpful NullPointerExceptions
  7. Foreign-Memory Access API (Incubator)
  8. Vector API (Incubator)
  9. Enhanced Pseudo-Random Number Generators
  10. Enhanced NUMA-Aware Memory Allocation for G1

1. Sealed Classes and Interfaces:

Sealed Classes and Interfaces, a new language feature that allows developers to restrict the inheritance hierarchy of a class or interface. Sealed classes and interfaces provide greater control over how classes and interfaces can be extended, improving the design of object-oriented systems and making them more secure and maintainable.

Sealed classes and interfaces are defined using the sealed keyword, which restricts the set of classes or interfaces that can extend or implement the sealed class or interface. This restricts the inheritance hierarchy, preventing unauthorized subclasses or interfaces from being created.

The syntax for defining a sealed class or interface is as follows:

public sealed class MyClass permits SubClass1, SubClass2, ... {
    // class definition
}

In this example, the sealed keyword is used to define the class MyClass as a sealed class, and the permits keyword is used to list the permitted subclasses SubClass1, SubClass2, and so on. This restricts the set of classes that can extend MyClass to the specified subclasses.

The same syntax applies to sealed interfaces, as shown in the following example:

public sealed interface MyInterface permits SubInterface1, SubInterface2, … {<br>// interface definition<br>}

In this example, the sealed keyword is used to define the interface MyInterface as a sealed interface, and the permits keyword is used to list the permitted subinterfaces SubInterface1, SubInterface2, and so on. This restricts the set of interfaces that can extend MyInterface to the specified subinterfaces.

Sealed classes and interfaces provide several benefits, including:

  • Improved design: Sealed classes and interfaces provide greater control over the inheritance hierarchy, improving the overall design of the system and making it easier to reason about.
  • Security: Sealed classes and interfaces prevent unauthorized subclasses or interfaces from being created, reducing the risk of security vulnerabilities.
  • Maintainability: Sealed classes and interfaces make it easier to maintain the system over time, as changes to the inheritance hierarchy can be made more safely and with greater confidence.

In summary, sealed classes and interfaces are a new language feature in Java 17 that allow developers to restrict the inheritance hierarchy of a class or interface. By providing greater control over the inheritance hierarchy, sealed classes and interfaces improve the design of object-oriented systems and make them more secure and maintainable.

2. Pattern Matching for instanceof

Pattern matching for instanceof is a new language feature in Java 17 that allows developers to write more concise and expressive code when checking the type of an object. With pattern matching for instanceof, developers can combine a type check with a type cast into a single expression, making the code more readable and less error-prone.

Prior to Java 17, developers would typically use an if statement to check the type of an object and then cast it to the appropriate type. For example:

if (myObject instanceof MyClass) {
    MyClass myClass = (MyClass) myObject;
    // use myClass
}

With pattern matching for instanceof, the above code can be simplified into a single expression:

if (myObject instanceof MyClass myClass) {<br>// use myClass<br>}

In this example, the type check and the cast are combined into a single expression. If myObject is an instance of MyClass, it will be cast to MyClass and assigned to the new variable myClass, which can be used within the if block.

Pattern matching for instanceof also supports the use of the else keyword to specify a default branch, as shown in the following example:

if (myObject instanceof MyClass myClass) {
    // use myClass
} else {
    // handle other types
}

In this example, if myObject is not an instance of MyClass, the code in the else block will be executed instead.

Pattern matching for instanceof provides several benefits, including:

  • Concise and expressive code: Pattern matching for instanceof allows developers to write more concise and expressive code, making it easier to read and understand.
  • Fewer errors: By combining the type check and the cast into a single expression, pattern matching for instanceof reduces the risk of errors that can arise from separate type checks and casts.
  • Improved performance: Pattern matching for instanceof can improve performance by reducing the number of unnecessary casts.

In summary, pattern matching for instanceof is a new language feature in Java 17 that allows developers to write more concise and expressive code when checking the type of an object. By combining the type check and the cast into a single expression, pattern matching for instanceof reduces the risk of errors and improves performance.

3. Records

Records is a new feature introduced in Java 16 and finalized in Java 17 that provides a concise and immutable way to declare classes whose main purpose is to hold data. Records are essentially classes that are designed to store data rather than represent objects with behavior.

In Java, classes are typically created to represent objects that have both data and behavior. However, sometimes we need to create classes that are only used to hold data without any additional behavior. In such cases, creating a traditional class with fields, getters, setters, equals, hashCode, and toString methods can be quite verbose and repetitive.

With records, the syntax is much simpler and more concise. A record is defined using the record keyword, followed by the class name, and then a list of properties within parentheses. Here’s an example of a record definition:

public record Person(String name, int age) {}

In this example, we’ve created a record called Person with two properties: name of type String and age of type int. Note that we didn’t need to explicitly declare constructors, getters, setters, or other methods, because they are automatically generated by the compiler.

With records, you can also add additional methods, such as custom constructors or instance methods. Here’s an example:

public record Person(String name, int age) {
    public Person {
        if (age < 0) {
            throw new IllegalArgumentException("Age cannot be negative");
        }
    }
    
    public String getName() {
        return name.toUpperCase();
    }
}

In this example, we’ve added a custom constructor that checks if the age is negative, and an instance method that returns the uppercase name.

Records also provide a compact and readable way to override the default equals, hashCode, and toString methods. For example, the following record definition:

public record Person(String name, int age) {
    @Override
    public String toString() {
        return name + " (" + age + ")";
    }
}

overrides the default toString method to return a string representation of the Person record.

In summary, records are a new feature in Java 16/17 that provide a concise and immutable way to declare classes whose main purpose is to hold data. They simplify the creation of classes that are only used to hold data without any additional behavior, and provide automatic generation of constructors, getters, setters, equals, hashCode, and toString methods. With records, you can also add additional methods and override default methods in a compact and readable way.

4. Text Blocks

Text blocks provide a more readable way to declare multi-line strings in Java 17. Text blocks can contain line breaks and other whitespace characters without requiring special escape sequences.

String html = """
    <html>
        <head>
            <title>Hello, world!</title>
        </head>
        <body>
            <h1>Hello, world!</h1>
        </body>
    </html>
""";

In this example, the html string contains an HTML document declared using a text block. The text block starts with """ and ends with """, and the document is indented for readability.

Here’s an example that demonstrates how to use placeholders and expressions inside text blocks:

String name = "Alice";
int age = 30;
String message = """
                 Hello, ${name}!
                 
                 You are ${age} years old.
                 
                 Your age in dog years is ${age * 7}.
                 """;
System.out.println(message);

In this example, we define two variables (name and age) and use them inside a text block to create a message. The ${expression} syntax is used to include the values of the variables inside the message, and we also include an expression (age * 7) to calculate the age in dog years.

Text blocks can also be used with other features in Java, such as switch expressions and lambda expressions. For example, you can use a text block inside a switch expression to define a case label:

String day = "Monday";
String message = switch (day) {
    case "Monday", "Tuesday", "Wednesday", "Thursday", "Friday" -> """
                                                                   It's a weekday.
                                                                   
                                                                   Time to go to work.
                                                                   """;
    case "Saturday", "Sunday" -> """
                                 It's the weekend.
                                 
                                 Time to relax and have fun!
                                 """;
    default -> """
               Invalid day.
               
               Please enter a valid day of the week.
               """;
};
System.out.println(message);

In this example, we use a text block to define the message for each case label in the switch expression. This makes the code easier to read and maintain, and reduces the amount of boilerplate code that is required.

Overall, text blocks are a useful feature that can make Java code more concise and readable, especially in cases where you need to write multiline strings or include formatting whitespace.

5. Switch Expressions

Switch expressions are a new feature introduced in Java 17 that provide a more concise and expressive syntax for switch statements. Switch statements are commonly used in Java to evaluate a single value and perform different actions based on different cases. Prior to Java 17, switch statements could only be used to execute a block of code, but with switch expressions, you can now assign the result of the switch statement to a variable.

The syntax for switch expressions is similar to the syntax for switch statements, with a few differences. In switch expressions, the cases are defined using the -> operator instead of the : operator, and the switch expression returns a value instead of executing a block of code.

Here’s an example that demonstrates how to use switch expressions in Java 17:

String day = "Monday";
String result = switch (day) {
    case "Monday", "Tuesday", "Wednesday", "Thursday", "Friday" -> "Weekday";
    case "Saturday", "Sunday" -> "Weekend";
    default -> "Invalid day";
};
System.out.println(result); // Output: Weekday

In this example, we first define a string variable day with the value “Monday”. We then use a switch expression to evaluate the value of day and assign the result to a string variable called result. The switch expression has two cases: one for weekdays and one for weekends. If the value of day matches one of the weekdays, the switch expression will return the string “Weekday”, and if it matches one of the weekends, it will return the string “Weekend”. If day does not match any of the defined cases, the switch expression will return the string “Invalid day”.

One of the benefits of switch expressions is that they can make code more concise and easier to read. They can also reduce the amount of code you need to write in some cases. For example, consider the following code snippet that uses a switch statement to perform an action based on the value of a variable:

int value = 10;
switch (value) {
    case 1:
        System.out.println("One");
        break;
    case 2:
        System.out.println("Two");
        break;
    case 3:
        System.out.println("Three");
        break;
    default:
        System.out.println("Unknown");
        break;
}

With switch expressions, you can write the same code in a more concise way:

int value = 10;
String result = switch (value) {
    case 1 -> "One";
    case 2 -> "Two";
    case 3 -> "Three";
    default -> "Unknown";
};
System.out.println(result); // Output: Unknown

Switch expressions can be especially useful in situations where you need to perform a switch statement and assign the result to a variable, or when you need to perform complex operations based on the value of a variable.

6. Helpful NullPointerExceptions

Helpful NullPointerExceptions aims to provide more detailed information about null pointer exceptions (NPEs) at runtime. The goal of this feature is to make it easier for developers to identify the source of null pointer exceptions and fix them more quickly.

In previous versions of Java, when a null pointer exception occurred, the error message provided limited information about where the exception occurred and which variable was null. This made it difficult for developers to debug their code and find the root cause of the problem.

With the new Helpful NullPointerExceptions feature, the error message now includes additional details that can help developers identify the source of the problem. For example, the error message might now include information about the method or line number where the exception occurred, as well as the name of the variable that was null.

Here’s an example of how the error message for a null pointer exception might look with the Helpful NullPointerExceptions feature enabled:

Exception in thread "main" java.lang.NullPointerException: Cannot invoke "String.length()" because "s" is null
	at com.example.MyClass.myMethod(MyClass.java:10)

In this example, the error message includes the name of the method (myMethod) where the exception occurred, as well as the line number (10) and the name of the variable that was null (s).

To enable the Helpful NullPointerExceptions feature, you can use the -XX:+ShowCodeDetailsInExceptionMessages option when running your Java application. This option is only available in JDK 17 and later versions.

Overall, the Helpful NullPointerExceptions feature is a useful addition to Java that can make it easier for developers to debug their code and find and fix null pointer exceptions more quickly. By providing more detailed error messages, developers can spend less time searching for the source of the problem and more time fixing it.

7. Foreign-Memory Access API (Incubator)

Foreign-Memory Access API, which provides a way for Java developers to directly access and manipulate memory outside of the Java heap. This API is designed for use cases where high-performance access to memory is required, such as in graphics processing, machine learning, and database systems.

The Foreign-Memory Access API allows developers to create and manage direct buffers that are backed by native memory. These buffers can be used to read and write data directly to and from the memory, without going through the Java heap. This can significantly improve the performance of memory-intensive operations, as it avoids the overhead of copying data between the Java heap and native memory.

To use the Foreign-Memory Access API, you first need to create a memory segment that represents the native memory. This can be done using the MemorySegment class, which provides methods for allocating, deallocating, and accessing memory segments. Once you have a memory segment, you can create a direct buffer that is backed by the segment using the MemorySegment.asByteBuffer() method. This buffer can be used to read and write data to and from the memory segment, as you would with any other byte buffer.

Here’s an example of how to use the Foreign-Memory Access API to allocate a memory segment and create a direct buffer:

import jdk.incubator.foreign.*;
public class MemoryExample {
    public static void main(String[] args) {
        // Allocate a memory segment of 1024 bytes
        MemorySegment segment = MemorySegment.allocateNative(1024);
        // Create a direct buffer backed by the memory segment
        ByteBuffer buffer = segment.asByteBuffer();
        // Write some data to the buffer
        buffer.putInt(0, 123);
        buffer.putDouble(4, 3.14);
        // Read the data back from the buffer
        int i = buffer.getInt(0);
        double d = buffer.getDouble(4);
        // Print the values
        System.out.println("i = " + i);
        System.out.println("d = " + d);
        // Deallocate the memory segment
        segment.close();
    }
}

In this example, we first allocate a memory segment of 1024 bytes using the MemorySegment.allocateNative() method. We then create a direct buffer backed by the memory segment using the MemorySegment.asByteBuffer() method. We write some data to the buffer using the putInt() and putDouble() methods, and then read the data back using the getInt() and getDouble() methods. Finally, we deallocate the memory segment using the close() method.

Note that the Foreign-Memory Access API is an incubating feature in Java 17, which means that it is still under development and subject to change in future releases. It should only be used in production environments with caution and after thorough testing.

8. Vector API (Incubator)

Vector API provides a set of vectorized operations for working with SIMD (Single Instruction Multiple Data) instructions on modern CPU architectures. This API is designed for use cases where high-performance processing of large data sets is required, such as in scientific computing, machine learning, and graphics processing.

The Vector API allows developers to perform arithmetic and logical operations on vectors of data in a way that takes advantage of SIMD instructions, which can perform multiple calculations in parallel. This can significantly improve the performance of certain types of computations, as it reduces the number of instructions that need to be executed and maximizes the use of available CPU resources.

To use the Vector API, you first need to create a vector using one of the factory methods provided by the API. These factory methods create vectors of a specific type (such as IntVector or FloatVector) and with a specific size (such as 128 bits or 256 bits). Once you have a vector, you can perform various operations on it, such as addition, subtraction, multiplication, and comparison.

Here’s an example of how to use the Vector API to perform a vectorized addition operation:

In this example, we first create two vectors of four floats each using the FloatVector.fromArray() method. We then add the two vectors together using the add() method and store the result in a third vector. Finally, we print the result.

import jdk.incubator.vector.*;
public class VectorExample {
    public static void main(String[] args) {
        // Create two vectors of four floats each
        FloatVector a = FloatVector.fromArray(VectorSpecies_128.F_128, new float[]{1, 2, 3, 4});
        FloatVector b = FloatVector.fromArray(VectorSpecies_128.F_128, new float[]{5, 6, 7, 8});
        // Add the two vectors together
        FloatVector c = a.add(b);
        // Print the result
        System.out.println("c = " + c);
    }
}

Note that the Vector API is an incubating feature in Java 17, which means that it is still under development and subject to change in future releases. It should only be used in production environments with caution and after thorough testing. Additionally, the Vector API requires hardware support for SIMD instructions, which may not be available on all systems.

9. Enhanced Pseudo-Random Number Generators

Java 17 introduces enhancements to the existing Pseudo-Random Number Generators (PRNG) in the java.util.random package. These enhancements provide developers with more flexibility and control over the generation of random numbers, as well as improved security.

The enhancements include three new algorithms, new methods for generating random bytes and random integers, and improvements to the existing SplittableRandom class.

New PRNG Algorithms

Java 17 introduces three new PRNG algorithms:

  • LXM
  • PCG64
  • Xoshiro

These algorithms provide different trade-offs between performance and randomness, and allow developers to choose the one that best fits their specific use case.

New Methods for Generating Random Bytes and Integers

Java 17 also introduces new methods in the java.util.random package for generating random bytes and random integers. These methods include:

  • RandomGenerator.nextInt(int bound) and RandomGenerator.nextLong(long bound): These methods generate random integers and longs respectively within the specified range.
  • RandomGenerator.nextBytes(byte[] bytes): This method generates random bytes and fills them into the specified array.

These new methods provide more convenience and flexibility to developers, making it easier to generate random numbers with specific characteristics.

Improvements to SplittableRandom

Java 17 also introduces improvements to the SplittableRandom class, which provides a way to generate repeatable sequences of random numbers. The improvements include:

  • A new split() method that returns a new instance of the SplittableRandom class with a different seed, allowing for the generation of independent sequences of random numbers.
  • Improved performance for generating large numbers of random numbers in parallel.

These improvements make the SplittableRandom class more useful for applications that require large amounts of random data, such as Monte Carlo simulations and statistical analysis.

The enhancements to the Pseudo-Random Number Generators in Java 17 provide developers with more flexibility and control over the generation of random numbers, as well as improved security. With the introduction of new algorithms and methods, and improvements to the SplittableRandom class, Java 17 makes it easier to generate random numbers with specific characteristics, and to generate large amounts of random data efficiently.

10. Enhanced NUMA-Aware Memory Allocation for G1

Java 17 introduces an enhancement to the Garbage-First Garbage Collector (G1) that improves its ability to allocate memory in a Non-Uniform Memory Access (NUMA) architecture. This enhancement is designed to improve the performance of applications running on NUMA systems, which are increasingly common in modern high-performance computing environments.

In NUMA architectures, memory is distributed across multiple nodes, each with its own local memory and access latency. Applications running on these systems can experience performance degradation if memory allocation is not optimized to take into account the NUMA topology.

The enhanced NUMA-aware memory allocation in G1 improves performance by allocating memory in a way that takes into account the NUMA topology of the system. Specifically, it attempts to allocate memory on the local node whenever possible, reducing the need for remote memory accesses that can result in increased latency and reduced performance.

The enhanced allocation strategy works by first identifying the NUMA topology of the system and then using that information to allocate memory in a way that maximizes locality. The strategy also takes into account the current state of the system, such as the availability of free memory and the current load on each node, to ensure that allocations are made in an efficient and effective manner.

To enable NUMA-aware memory allocation in G1, developers can set the -XX:+UseNUMA flag when running their application. This flag tells the JVM to use the enhanced allocation strategy, which can result in improved performance on NUMA architectures.

In addition to the -XX:+UseNUMA flag, developers can also use the -XX:NumAProximityPolicy flag to control the proximity policy used by G1 when allocating memory. The default policy is compact, which attempts to allocate memory on the closest node first. Other policies, such as scatter and balance, are also available, allowing developers to fine-tune the allocation strategy to meet the specific needs of their application.

In summary, the enhanced NUMA-aware memory allocation in G1 in Java 17 provides a valuable tool for developers working with applications running on NUMA architectures. By taking into account the NUMA topology of the system, G1 can allocate memory in a way that maximizes locality and minimizes remote memory accesses, resulting in improved performance and reduced latency.

Apache Kafka vs Apache Flink


Apache Kafka and Apache Flink are two popular open-source tools that can be used for real-time data streaming and processing. While they share some similarities, there are also significant differences between them. In this blog tutorial, we will compare Apache Kafka and Apache Flink to help you understand which tool may be best suited for your needs.

What is Apache Kafka?

Apache Kafka is a distributed streaming platform that is designed to handle high-volume data streams in real-time. Kafka is a publish-subscribe messaging system that allows data producers to send data to a central broker, which then distributes the data to data consumers. Kafka is designed to be scalable, fault-tolerant, and durable, and it can handle large volumes of data without sacrificing performance.

What is Apache Flink?

Apache Flink is an open-source, distributed stream processing framework that is designed to process large amounts of data in real-time. Flink uses a stream processing model, which means that it processes data as it comes in, rather than waiting for all the data to arrive before processing it. Flink is designed to be fault-tolerant and scalable, and it can handle both batch and stream processing workloads.

Comparison of Apache Kafka and Apache Flink Here are some of the key differences between Apache Kafka and Apache Flink:

  1. Data processing model Apache Kafka is primarily a messaging system that is used for data transport and storage. While Kafka does provide some basic processing capabilities, its primary focus is on data transport. Apache Flink, on the other hand, is a full-fledged stream processing framework that is designed for data processing.
  2. Processing speed Apache Kafka is designed to handle high-volume data streams in real-time, but it does not provide any built-in processing capabilities. Apache Flink, on the other hand, is designed specifically for real-time data processing, and it can process data as it comes in, without waiting for all the data to arrive.
  3. Fault tolerance Both Apache Kafka and Apache Flink are designed to be fault-tolerant. Apache Kafka uses replication to ensure that data is not lost if a broker fails, while Apache Flink uses checkpointing to ensure that data is not lost if a task fails.
  4. Scalability Both Apache Kafka and Apache Flink are designed to be scalable. Apache Kafka can be scaled horizontally by adding more brokers to the cluster, while Apache Flink can be scaled horizontally by adding more nodes to the cluster.
  5. Use cases Apache Kafka is commonly used for data transport and storage in real-time applications, such as log aggregation, metrics collection, and messaging. Apache Flink is commonly used for real-time data processing, such as stream analytics, fraud detection, and real-time recommendations.

Conclusion:

Apache Kafka and Apache Flink are both powerful tools that can be used for real-time data streaming and processing. Apache Kafka is primarily a messaging system that is used for data transport and storage, while Apache Flink is a full-fledged stream processing framework that is designed for data processing. Both tools are designed to be fault-tolerant and scalable, but they have different use cases. If you need a messaging system for data transport and storage, Apache Kafka may be the better choice. If you need a full-fledged stream processing framework for real-time data processing, Apache Flink may be the better choice.

Sort Employee Objects on Age


This program defines an Employee class with properties of name, id, and age, and implements the Comparable interface to enable sorting by age. The main method creates a list of 100 employee objects and sorts them based on age using the Collections.sort method. Finally, the sorted list of employees is printed to the console.

import java.util.ArrayList;
import java.util.Collections;
import java.util.Comparator;
import java.util.List;

public class Employee implements Comparable<Employee> {
    private String name;
    private String id;
    private int age;

    public Employee(String name, String id, int age) {
        this.name = name;
        this.id = id;
        this.age = age;
    }

    public String getName() {
        return name;
    }

    public String getId() {
        return id;
    }

    public int getAge() {
        return age;
    }

    @Override
    public int compareTo(Employee other) {
        return Integer.compare(this.age, other.age);
    }

    @Override
    public String toString() {
        return "Employee{" +
                "name='" + name + '\'' +
                ", id='" + id + '\'' +
                ", age=" + age +
                '}';
    }

    public static void main(String[] args) {
        // Create a list of 100 employee objects
        List<Employee> employees = new ArrayList<>();
        employees.add(new Employee("John", "1001", 25));
        employees.add(new Employee("Jane", "1002", 30));
        employees.add(new Employee("Bob", "1003", 28));
        // ... and so on for the other 97 employees

        // Sort the list of employees based on age (ascending order)
        Collections.sort(employees);
        System.out.println(employees);
    }
}

Apache Kafka – Java Producer & Consumer


Apache Kakfa is an opensource distributed event streaming platform which works based on publish/subscribe messaging system. That means, there would be a producer who publishes messages to Kafka and a consumer who reads messages from Kafka. In between, Kafka acts like a filesystem or database commit log.

In this article we will discuss about writing a Kafka producer and consumer using Java with customized serialization and deserializations.

Kakfa Producer Application:

Producer is the one who publish the messages to Kafka topic. Topic is a partitioner in Kafka environment, it is very similar to a folder in a file system. In the below example program, messages are getting published to Kafka topic ‘kafka-message-count-topic‘.

package com.malliktalksjava.kafka.producer;

import java.util.Properties;
import java.util.concurrent.ExecutionException;

import com.malliktalksjava.kafka.constants.KafkaConstants;
import com.malliktalksjava.kafka.util.CustomPartitioner;
import org.apache.kafka.clients.producer.*;
import org.apache.kafka.common.serialization.LongSerializer;
import org.apache.kafka.common.serialization.StringSerializer;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

public class KafkaSampleProducer {

    static Logger log = LoggerFactory.getLogger(KafkaSampleProducer.class);

    public static void main(String[] args) {
        runProducer();
    }

    static void runProducer() {
        Producer<Long, String> producer = createProducer();

        for (int index = 0; index < KafkaConstants.MESSAGE_COUNT; index++) {
            ProducerRecord<Long, String> record = new ProducerRecord<Long, String>(KafkaConstants.TOPIC_NAME,
                    "This is record " + index);
            try {
                RecordMetadata metadata = producer.send(record).get();
                //log.info("Record sent with key " + index + " to partition " + metadata.partition() +
                 //       " with offset " + metadata.offset());
                System.out.println("Record sent with key " + index + " to partition " + metadata.partition() +
                        " with offset " + metadata.offset());
            } catch (ExecutionException e) {
                log.error("Error in sending record", e);
                e.printStackTrace();
            } catch (InterruptedException e) {
                log.error("Error in sending record", e);
                e.printStackTrace();
            }
        }
    }

    public static Producer<Long, String> createProducer() {
        Properties props = new Properties();
        props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, KafkaConstants.KAFKA_BROKERS);
        props.put(ProducerConfig.CLIENT_ID_CONFIG, KafkaConstants.CLIENT_ID);
        props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, LongSerializer.class.getName());
        props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());
        props.put(ProducerConfig.PARTITIONER_CLASS_CONFIG, CustomPartitioner.class.getName());
        return new KafkaProducer<>(props);
    }
}

Kakfa Consumer Program:

Consumer is the one who subscribe to Kafka topic to read the messages. There are different ways to read the messages from Kafka, below example polls the topic for every thousend milli seconds to fetch the messages from Kafka.

package com.malliktalksjava.kafka.consumer;

import java.util.Collections;
import java.util.Properties;

import com.malliktalksjava.kafka.constants.KafkaConstants;
import com.malliktalksjava.kafka.producer.KafkaSampleProducer;
import org.apache.kafka.clients.consumer.Consumer;
import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.apache.kafka.common.serialization.LongDeserializer;
import org.apache.kafka.common.serialization.StringDeserializer;

import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

public class KafkaSampleConsumer {
    static Logger log = LoggerFactory.getLogger(KafkaSampleProducer.class);

    public static void main(String[] args) {
        runConsumer();
    }

    static void runConsumer() {
        Consumer<Long, String> consumer = createConsumer();
        int noMessageFound = 0;
        while (true) {
            ConsumerRecords<Long, String> consumerRecords = consumer.poll(1000);
            // 1000 is the time in milliseconds consumer will wait if no record is found at broker.
            if (consumerRecords.count() == 0) {
                noMessageFound++;
                if (noMessageFound > KafkaConstants.MAX_NO_MESSAGE_FOUND_COUNT)
                    // If no message found count is reached to threshold exit loop.
                    break;
                else
                    continue;
            }

            //print each record.
            consumerRecords.forEach(record -> {
                System.out.println("Record Key " + record.key());
                System.out.println("Record value " + record.value());
                System.out.println("Record partition " + record.partition());
                System.out.println("Record offset " + record.offset());
            });

            // commits the offset of record to broker.
            consumer.commitAsync();
        }
        consumer.close();
    }

        public static Consumer<Long, String> createConsumer() {
            Properties props = new Properties();
            props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, KafkaConstants.KAFKA_BROKERS);
            props.put(ConsumerConfig.GROUP_ID_CONFIG, KafkaConstants.GROUP_ID_CONFIG);
            props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, LongDeserializer.class.getName());
            props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class.getName());
            props.put(ConsumerConfig.MAX_POLL_RECORDS_CONFIG, KafkaConstants.MAX_POLL_RECORDS);
            props.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, "false");
            props.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, KafkaConstants.OFFSET_RESET_EARLIER);

            Consumer<Long, String> consumer = new KafkaConsumer<>(props);
            consumer.subscribe(Collections.singletonList(KafkaConstants.TOPIC_NAME));
            return consumer;
        }
}

Messages will be published to a Kafka partition called Topic. A Kafka topic is sub-divided into units called partitions for fault tolerance and scalability.

Every Record in Kafka has key value pairs, while publishing messages key is optional. If you don’t pass the key, Kafka will assign its own key for each message. In Above example, ProducerRecord<Integer, String> is the message that published to Kafka has Integer type as key and String as value.

Message Model Class: Below model class is used to publish the object. Refer to below descriptions on how this class being used in the application.

package com.malliktalksjava.kafka.model;

import java.io.Serializable;

public class Message implements Serializable{

    private static final long serialVersionUID = 1L;

    private String id;
    private String name;

    public String getId() {
        return id;
    }

    public void setId(String id) {
        this.id = id;
    }

    public String getName() {
        return name;
    }

    public void setName(String name) {
        this.name = name;
    }
}

Constants class: All the constants related to this application have been placed into below class.

package com.malliktalksjava.kafka.constants;

public class KafkaConstants {

    public static String KAFKA_BROKERS = "localhost:9092";

    public static Integer MESSAGE_COUNT=100;

    public static String CLIENT_ID="client1";

    public static String TOPIC_NAME="kafka-message-count-topic";

    public static String GROUP_ID_CONFIG="consumerGroup1";

    public static String GROUP_ID_CONFIG_2 ="consumerGroup2";

    public static Integer MAX_NO_MESSAGE_FOUND_COUNT=100;

    public static String OFFSET_RESET_LATEST="latest";

    public static String OFFSET_RESET_EARLIER="earliest";

    public static Integer MAX_POLL_RECORDS=1;
}

Custom Serializer: Serializer is the class which converts java objects to write into disk. Below custom serializer is converting the Message object to JSON String. Serialized message will be placed into Kafka Topic, this message can’t be read until it is deserialized by the consumer.

package com.malliktalksjava.kafka.util;

import java.util.Map;
import com.malliktalksjava.kafka.model.Message;
import org.apache.kafka.common.serialization.Serializer;
import com.fasterxml.jackson.databind.ObjectMapper;

public class CustomSerializer implements Serializer<Message> {

    @Override
    public void configure(Map<String, ?> configs, boolean isKey) {

    }

    @Override
    public byte[] serialize(String topic, Message data) {
        byte[] retVal = null;
        ObjectMapper objectMapper = new ObjectMapper();
        try {
            retVal = objectMapper.writeValueAsString(data).getBytes();
        } catch (Exception exception) {
            System.out.println("Error in serializing object"+ data);
        }
        return retVal;
    }
    @Override
    public void close() {

    }
}

Custom Deserializer: Below custom deserializer, converts the serealized object coming from Kafka into Java object.

package com.malliktalksjava.kafka.util;

import java.util.Map;

import com.malliktalksjava.kafka.model.Message;
import org.apache.kafka.common.serialization.Deserializer;

import com.fasterxml.jackson.databind.ObjectMapper;

public class CustomObjectDeserializer implements Deserializer<Message> {

    @Override
    public void configure(Map<String, ?> configs, boolean isKey) {
    }

    @Override
    public Message deserialize(String topic, byte[] data) {
        ObjectMapper mapper = new ObjectMapper();
        Message object = null;
        try {
            object = mapper.readValue(data, Message.class);
        } catch (Exception exception) {
            System.out.println("Error in deserializing bytes "+ exception);
        }
        return object;
    }

    @Override
    public void close() {
    }
}

Custom Partitioner: If you would like to do any custom settings for Kafka, you can do that using the java code. Below is the sample custom partitioner created as part of this applicaiton.

package com.malliktalksjava.kafka.util;

import org.apache.kafka.clients.producer.Partitioner;
import org.apache.kafka.common.Cluster;
import java.util.Map;

public class CustomPartitioner implements Partitioner{

    private static final int PARTITION_COUNT=50;

    @Override
    public void configure(Map<String, ?> configs) {

    }

    @Override
    public int partition(String topic, Object key, byte[] keyBytes, Object value, byte[] valueBytes, Cluster cluster) {
        Integer keyInt=Integer.parseInt(key.toString());
        return keyInt % PARTITION_COUNT;
    }

    @Override
    public void close() {
    }
}

Here is the GIT Hub link for the program: https://github.com/mallikarjungunda/kafka-producer-consumer

Hope you liked the details, please share your feedback in comments.

ReactJs basic application creation


React is one of the popular java script library used to build user interfaces in web applications. 

As per Reactjs.orgReact is a declarative, efficient, and flexible JavaScript library for building user interfaces. It lets you compose complex UIs from small and isolated pieces of code called “components”.

In this tutorial, we would like to understand on how to create a basic react application using command line. You need to have nodejs installed into your desktop or laptop to create reactjs based application.

Navigate to your workspace folder(folder where you want to create project) in command/terminal prompt. Type below command to create basic react application with the folder name as malliktalksjava.

npx create-react-app malliktalksjava

Move to react application folder using below command.

cd malliktalksjava/

Start the application using below command.

npm start

Access localhost URL, http://localhost:3000/. React basic application will be displayed as given below.

React JS_Hello World App

This basic application doesn’t handle back-end logic or databases; it just creates a front-end build pipeline, so you can use it with any back-end you want. But, it installs Babel and webpack behind the scenes, below is the purpose of Babel and webpack:

Babel is a JavaScript compiler and transpiler, used to convert one source code to other. You will be able to use the new ES6 features in your code where, babel converts it into plain old ES5 which can be run on all browsers.

Webpack is a module bundler, it takes dependent modules and compiles them to a single bundle file. You can use this bundle while developing apps using command line or, by configuring it using webpack.config file.

 

Apache Kafka – Environment Setup


Apache Kakfa is an opensource distributed event streaming platform which works based on publish/subscribe messaging system. That means, there would be a producer who publishes messages to Kafka and a consumer who reads messages from Kafka. In between, Kafka acts like a filesystem or database commit log.

In this post we will setup kafka local environment, create topic, publish and consume messages using console clients.

Step 1: Download latest version of Apache Kafka from Apache Kafka website: https://kafka.apache.org/downloads.

Extract the folder into your local and navigate to the folder in Terminal session (if Mac) or command line (if windows):

$ tar -xzf kafka_2.13-3.1.0.tgz 
$ cd kafka_2.13-3.1.0

Step 2: Run Kafka in your local:

Run zookeeper using the below command terminal/command line window 1:

# Start the ZooKeeper service
$ bin/zookeeper-server-start.sh config/zookeeper.properties

Run Kafka using the below command in another terminal or command line:

# Start the Kafka broker service
$ bin/kafka-server-start.sh config/server.properties

Note: You must have Java8 or above in your machine to run Kafka.

Once above two services are run successfully in local, you are set with running Kafka in your local machine.

Step 3: Create topic in Kafka to produce/consume the message in another terminal or command like. In below example, topic name is ‘order-details’ and kafka broker is running in my localhost 9092 port.

$ bin/kafka-topics.sh --create --topic order-details --bootstrap-server localhost:9092

If needed, use describe topic to understand more details about topic created above:

$ bin/kafka-topics.sh --describe --topic order-details --bootstrap-server localhost:9092

Output looks like below:
Topic: order-details	PartitionCount: 1	ReplicationFactor: 1	Configs: segment.bytes=1073741824
	Topic: order-details	Partition: 0	Leader: 0	Replicas: 0	Isr: 0

Step 4: Write events to topic

Run the console producer client to write a few events into your topic. By default, each line you enter will result in a separate event being written to the topic.

$ bin/kafka-console-producer.sh --topic order-details --bootstrap-server localhost:9092
Order 1 details
Order 2 details

Step 5: Read events from Kafka

Open another terminal session/command line and run the console consumer client to read the events you just created:

$ bin/kafka-console-consumer.sh --topic order-details --from-beginning --bootstrap-server localhost:9092
Order 1 details
Order 2 details

Conclusion:

By completing all the above steps, you learned about setting up kafka environment, creating topics, producing the messages using console producer and consming the message using console consumer.

Different ways of sorting an User Object


There are many ways to sort a java object but it is very hard to figure out which one is more efficient. Here is an example which describes different ways of executing sorting mechanism for User object based on age.

Try to run this application in you local machine to see which method is more efficient and good to use in our regular programming life.

package com.malliktalksjava.java8;

import java.util.ArrayList;
import java.util.Collections;
import java.util.Comparator;
import java.util.List;

import static java.util.stream.Collectors.toList;

public class SortingExampleUser {

    public static void main(String[] args) {
        List<User> userList = new ArrayList<>();
        userList.add(new User("Ram", 28));
        userList.add(new User("Raj", 35));
        userList.add(new User("Rakesh", 31));
        userList.add(new User("Peter", 30));
        userList.add(new User("John", 25));
        userList.add(new User("Sri", 55));

        long starttime = System.currentTimeMillis();
        System.out.println("sortListUsingCollections : " + sortListUsingCollections(userList));
        System.out.println("Time Taken in Millis : " + (System.currentTimeMillis() - starttime));

        long starttime2 = System.currentTimeMillis();
        System.out.println("sortListUsingCollections : " + sortListUsingStreams(userList));
        System.out.println("Time Taken in Millis  2: " + (System.currentTimeMillis() - starttime2));

        long starttime3 = System.currentTimeMillis();
        System.out.println("sortListUsingCollections : " + sortUsingLambda(userList));
        System.out.println("Time Taken in Millis  2: " + (System.currentTimeMillis() - starttime3));


    }


    //using Collections.sort
    private static List<User> sortListUsingCollections(List<User> list){

        Collections.sort(list, Comparator.comparingInt(User::getAge));
        //Collections.reverse(list); // un comment if for descending order

        return list;
    }

    //using streams and comparator
    private static List<User> sortListUsingStreams(List<User> list){

        return list.stream()
                .sorted(Comparator.comparingInt(User::getAge))
                //.sorted(Comparator.comparingInt(User::getAge).reversed()) //-- for reverse order uncomment this line and comment above line
                .collect(toList());
    }

    //using lambda expressions
    private static List<User> sortUsingLambda(List<User> list){

        return list.stream()
                .sorted((User user1, User user2) -> user1.getAge() > user2.getAge() ? 1: 0)
                //.sorted((User user1, User user2) -> user1.getAge() < user2.getAge() ? 1: 0) - uncomment if reverse order needed
                .collect(toList());

    }
}

class User{
    private String name;
    private int age;

    public User(String name, int age) {
        this.name = name;
        this.age = age;
    }

    public String getName() {
        return name;
    }

    public void setName(String name) {
        this.name = name;
    }

    public int getAge() {
        return age;
    }

    public void setAge(int age) {
        this.age = age;
    }

    @Override
    public String toString() {
        return "User{" +
                "name='" + name + '\'' +
                ", age=" + age +
                '}';
    }
}

Selenium Automation – Open URL in multiple browsers


This example shows how to open a url in multiple browsers for browser based testing using Selenium and WebdriverManager.

package seleniumProjects;

import org.openqa.selenium.WebDriver;
import org.openqa.selenium.chrome.ChromeDriver;
import org.openqa.selenium.edge.EdgeDriver;
import org.openqa.selenium.firefox.FirefoxDriver;
import org.openqa.selenium.safari.SafariDriver;

import io.github.bonigarcia.wdm.WebDriverManager;
import io.github.bonigarcia.wdm.config.DriverManagerType;

public class StartBrowser{
	
	static WebDriver driver = null;
	static String[] appList= {"chrome","firefox","edgedriver", "safari"};
	
	public static void main(String[] args) throws Exception {
		
		for(int i=0;i<appList.length;i++) {
		browserStart(appList[i],"http://google.com");
		Thread.sleep(5000);
		browserClose();
	
		}
	}
	

	public static void browserStart(String appName, String appUrl)
			throws InstantiationException, IllegalAccessException {

		if (appName.equals("chrome")) { //Run Chrome browser
			WebDriverManager.chromedriver().setup();
			driver = new ChromeDriver();
		} else if (appName.equals("firefox")) { //Run in Firefox broweser
			WebDriverManager.firefoxdriver().setup();
			driver = new FirefoxDriver();
		} else if (appName.equals("edgedriver")) { // Run in Edge browser
			WebDriverManager.edgedriver().setup();
			driver = new EdgeDriver();
		} else if (appName.equals("safari")) { //Run in Safari browser
            //For Safari browser, you need enable 
			//'Allow Remote Automation' under develop menu
			DriverManagerType safari = DriverManagerType.SAFARI;
			WebDriverManager.getInstance(safari).setup();
			driver = new SafariDriver();
		}

		driver.get(appUrl);
		driver.manage().window().maximize();

	}

	public static void browserClose() {
		driver.close();
	}
}