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About me

Let me introduce myself

A bit about me

I am Dhananjaya Naidu and I’m just like you; a java lover. I feel proud to say that I am a Java Developer and currently located in Bangalore, India.

I enjoy coding in full Java/J2ee stack (Spring, JSF, Hibernate, Struts, Servlets, JSP) and Web Technologies (HTML, CSS, JavaScript, JQuery).

I love to play Cricket, Kabaddi, Valley Ball and any Outdoor Sports. I love the nature and like to travel new places.


Dhananjaya Naidu

Personal info

Dhananjaya Naidu Reddi

Be good, Do good !!!

Birthday: 19 Jun 1988
Website: www.rdnaidu.com
E-mail: hello@rdnaidu.com

Skills & Interests

Java & J2ee


My latest writings

Monday 11 December 2023

Introduction to Generative AI

What is Generative AI ?

Generative AI is a branch of artificial intelligence that focuses on creating new content or data from scratch, such as images, text, music, or speech. In this tutorial, I will introduce you to some of the basic concepts and techniques of generative AI, and show you how to use them to create your own content.

The main idea behind generative AI is to use a model that learns from a large dataset of existing content, and then generates new content that is similar but not identical to the original. For example, you can use generative AI to create realistic faces of people who do not exist, or write captions for images that describe what is happening in them.

There are different types of generative models, but one of the most popular and powerful ones is called a generative adversarial network (GAN). A GAN consists of two models: a generator and a discriminator. The generator tries to create new content that looks real, while the discriminator tries to distinguish between real and fake content. The generator and the discriminator compete with each other, and in the process, they both improve their skills.

To use a GAN, you need to have a large dataset of real content that you want to imitate, such as images of faces or text of articles. You also need to define the structure and parameters of the generator and the discriminator, which are usually neural networks. Then, you need to train the GAN by feeding it batches of real and fake content, and updating the weights of the models based on their performance. The training process can take a long time, depending on the size and complexity of the dataset and the models.

Once the GAN is trained, you can use the generator to create new content by giving it some random input, such as noise or a seed word. The generator will then output new content that resembles the real content, but with some variations and creativity. You can also use the discriminator to evaluate how realistic the generated content is, by giving it a score between 0 and 1.

Generative AI is a fascinating and rapidly evolving field that has many applications and challenges. It can be used for entertainment, education, research, art, and more. However, it also raises ethical and social issues, such as privacy, authenticity, bias, and responsibility. Therefore, it is important to use generative AI with caution and respect, and to be aware of its limitations and implications.

Some of the applications of Generative AI are:

  • Content creation: Generative AI can help writers, artists, designers, musicians, and other creative professionals to produce original and diverse content, such as novels, poems, paintings, logos, songs, etc. Generative AI can also assist with editing, enhancing, or optimizing the content.
  • Data augmentation: Generative AI can help researchers and developers to generate synthetic data that can be used to train or test machine learning models, such as images, text, speech, etc. This can help overcome the challenges of data scarcity, privacy, or quality.
  • Simulation and modeling: Generative AI can help scientists and engineers to simulate complex phenomena or systems that are difficult to observe or measure in reality, such as weather, climate, traffic, biology, etc. Generative AI can also help with creating realistic and interactive virtual environments for gaming, education, or entertainment.
  • Personalization and recommendation: Generative AI can help businesses and consumers to tailor products or services to their preferences or needs, such as fashion, music, travel, etc. Generative AI can also help with providing relevant and diverse recommendations or suggestions based on user behavior or feedback.
  • Anomaly detection and security: Generative AI can help organizations and individuals to detect and prevent abnormal or malicious activities or events, such as fraud, cyberattacks, spam, etc. Generative AI can also help with generating secure and robust encryption or authentication methods.

If you want to use generative AI in your project, you need to consider the following steps:

  • Define your goal: What kind of content or data do you want to generate? What is the purpose or use case of your project? What are the requirements or constraints of your project?
  • Collect your data: What kind of data do you need to train or test your generative AI model? How much data do you need? How can you obtain or create your data? How can you preprocess or clean your data?
  • Choose your model: What kind of generative AI technique do you want to use? How can you design or implement your model? What are the parameters or hyperparameters of your model? How can you evaluate or optimize your model?
  • Generate your output: How can you run or deploy your generative AI model? How can you generate or sample your output? How can you postprocess or refine your output? How can you measure or improve the quality or diversity of your output?

I hope this tutorial gave you a brief overview of generative AI and how to use it. If you want to learn more about generative AI and try it yourself, you can check out some of these resources:

- https://www.tensorflow.org/tutorials/generative
- https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html
- https://github.com/NVIDIA/DALI
- https://openai.com/blog/dall-e/

Thank you for reading!

Friday 11 August 2023

Microservice Architecture Design Patterns

Here I am mentioning some common microservice architecture design patterns:

1. Service Decomposition: 

Break down your application into smaller, independently deployable services that handle specific business functions.

2. API Gateway: 

Implement a single entry point for clients to interact with multiple services, managing routing, load balancing, and authentication.

3. Service Discovery: 

Use a service registry to help services find each other and communicate dynamically in a changing environment.

4. Load Balancing: 

Distribute incoming requests across multiple instances of a service to improve scalability and reliability.

5. Database per Service: 

Assign each microservice its own database to ensure data isolation and independence.

6. Event Sourcing: 

Store changes to an application's state as a sequence of events, allowing for easy audit trails, versioning, and rebuilding state.

7. CQRS (Command Query Responsibility Segregation): 

Separate read and write operations into different services, optimizing for performance and scalability.

8. Asynchronous Communication: 

Use message queues or publish-subscribe systems to enable asynchronous communication between services.

9. Saga Pattern: 

Manage distributed transactions across multiple services by breaking them down into a sequence of smaller steps with compensating actions.

10. Containerization: 

Use technologies like Docker to package applications and dependencies into isolated containers for consistent deployment.

11. Orchestration vs. Choreography: 

Decide between a central orchestrator or decentralized choreography for coordinating interactions between services.

12. Fault Tolerance and Resilience: 

Design services to handle failures gracefully by implementing retries, circuit breakers, and fallback mechanisms.

13. Auto-scaling: 

Automatically adjust the number of instances based on demand to optimize resource utilization.

14. Authentication and Authorization: 

Implement security measures like OAuth, JWT, or API tokens to control access to services and resources.

15. Observability and Monitoring: 

Set up logging, monitoring, and tracing to gain insights into the health and performance of your microservices.

These are just a few of the many design patterns you can apply when building a microservice architecture. Remember that the choice of patterns depends on the specific needs of your application and the challenges you're addressing.

Thursday 27 July 2023

Examples of converting Java 7 code to Java 8 code

Converting a for loop to a stream

In Java 7, you would use a for loop to iterate over a collection of elements. In Java 8, you can use a stream to do the same thing. Here is an example:
// Java 7
for (String element : collection) {
  // do something with element

// Java 8
collection.stream().forEach(element -> {
  // do something with element

Converting an if statement to a filter

In Java 7, you would use an if statement to filter a collection of elements. In Java 8, you can use a filter to do the same thing. Here is an example:
// Java 7
List<String> filteredList = new ArrayList<>();
for (String element : collection) {
  if (element.length() > 5) {

// Java 8  
List<String> filteredList = collection.stream().filter(element ->
  element.length() > 5).collect(Collectors.toList());

Converting a map to a reduce

In Java 7, you would use a map to create a new collection of elements. In Java 8, you can use a reduce to do the same thing. Here is an example:
// Java 7
Map<String, Integer> map = new HashMap<>();
for (String element : collection) {
  map.put(element, 1);

// Java 8 
Map<String, Integer> map =
  collection.stream().collect(Collectors.toMap(element -> element, element -> 1));
I hope this helps!

Thursday 29 June 2023

Learning Java8 Stream API

If you are preparing for a Java interview, then this article would be most useful. Since, after the continuous upgradation of Java versions, the interview questions also increased. Here, I have listed the most commonly asked Java 8 Stream API interview questions and answers.

The contents of the article include Interview questions on the following topics with examples:

Stream API

Stream API is one of the hot topics to be chosen by the interviewer in interviews. Let us read and know the most used concepts of stream API questions. Let us see some of the questions and answers to Stream API of Java 8.

1. What is stream API?

  • Java 8 provides a new additional package called java.util.stream This package consists of classes, interfaces and enums to allow functional-style operations on the elements.
  • We can use stream API using the java.util.stream package.
  • We can use a stream to filter, collect, print and convert from one data structure to another.
  • Stream APIs do not store elements. It is functional. Operations performed using stream do not modify its source.

2. What is the difference between Collection and Stream?

  • The main difference between a Collection and Stream is that Collection contains its elements but Stream doesn’t.
  • The stream works on a view where elements are stored by collection or array, but unlike other views, any change made on the stream does not reflect the original collection.

3. What is an Intermediate operation in Stream API?

  • Intermediate operations return a stream as the output and intermediate operations are not executed until a terminal operation is invoked on the stream. This is called lazy evaluation
  • Intermediate operations of stream API process the current data and then return the new stream.
  • When an intermediate operation is executed, it only creates a new stream.

Example: map(), limit(), filter(), skip(), flatMap(), sorted(), distinct(), peek()

4. What is a Terminal operation in Stream API?

  • As the name suggests — terminal means the last operation in the Stream pipeline. Terminal operation traverses the stream and produces a result or a collection but not a new stream.
  • Terminal operations produce the results of the stream after all the intermediate operations are applied, and we can no longer use the stream once the terminal operation is performed. forEach()
  • A stream pipeline consists of a source ( Collection, array, function or I/O channel) it will invoke an intermediate operation in the pipeline lastly the terminal operation is performed which makes the stream pipeline consumed and marked closed.
  • We can have only one terminal operation at the end of the pipeline. If any operation is performed on a closed stream, it will result in java.lang.IllegalStateException; stream has already been operated upon or closed.


  • collect()
  • forEach()
  • forEachOrdered()
  • findAny()
  • findFirst()
  • toArray()
  • reduce()
  • count()
  • min()
  • max()
  • anyMatch()
  • allMatch()
  • noneMatch()

5. What does the map() function do? Why do you use it?

  • The map() function performs map functional operation in java. This means it can transform one type of object into others.
  • Example: consider we have a list of Strings and want to convert a List of Integer, we can use map() by applying a function to convert String to integer eg: parseInt() to map() and it will apply that to all elements of the list and give a list of Integer.

6. What does the filter() method do? When do you use it?

  • The filter method is used to filter elements that satisfy a certain condition that is specified using a Predicate function.
  • A predicate function is nothing but a function that takes an Object and returns a boolean.
  • For example, if you have a List of Integer and you want a list of even integers

7. What does the flatMap() method do?

  • The flatMap() function is an extension of the map function. Apart from transferring one object into another, it also flattens it.
  • Example: Consider you have a list of list data and you want to combine all elements of lists into just one list. In this case, we can use flatMap()

8. What is predicate Interface in Java stream?

  • A Predicate is a functional interface that represents a function, which takes an Object and returns a boolean.
  • It is used in several Stream methods like filter(), which uses Predicate to filter unwanted elements.

9. What does the peek() method do in Stream API?

  • The peek() method of the Stream class allows us to see through a Stream pipeline.
  • The peek() method returns a stream consisting of the elements of the stream after performing the provided action on each element. This is useful when we want to print values after each intermediate operation
  • We can peek through each step and print meaningful messages on the console. It is generally used for debugging issues related to lambda expression and Stream processing.
public class Main {
public static void main(String[] args) {
final List<Integer> list = new ArrayList<>(Arrays.asList(1, 2, 3, 4, 5));

final List<Integer> ans = list.stream()
.filter(value -> value % 2 == 0)
.peek(value -> System.out.println("Filtered " + value))
.map(value -> value * 10)


Filtered 2
Filtered 4
[20, 40]

10. What is the difference between map() and flatMap() of Stream API?

The key difference between map() vs flatMap() in Java 8:

  • The function you pass to the map() operation returns a single value.
  • The function you pass to flatMap() operation returns a Stream of value.
  • The flatMap() is a combination of map and flat operation.
  • The map() is used for transformation only, but flatMap() is used for both transformation and flattening.

Example for map():

Example for flatMap():

11. Can we convert an Array into a Stream?

  • Yes, you can use Java to transform an array into a stream.
  • The Stream class provides a factory method to make a Stream from an array, such as Stream.of(T…), which accepts a variable parameter, also we can supply an array to it.


12. What is the difference between Stream API and Collection API?

13. What are the Stateful and stateless intermediate operations of Stream API?

  • Stateful operations are skip(), distinct(),limit() and sorted(). Rest all other stream operations are stateless.
  • When an operation requires retaining the information of the elements it has processed so far to process the current element then it is a stateful operation.
  • Example: Distinct operation requires keeping track of all the values it has processed so far, based on this information only it can decide whether the current value is unique or it has been processed before and accordingly either will add the current value to the new stream(which is the output of the distinct operation) or neglect the value and not add it to the new stream.

14. What is the difference between Stream’s findFirst() and findAny()?

  • As the name suggests, the findFirst() method is used to find the first element from the stream whereas the findAny() method is used to find any element from the stream.
  • The findFirst() is pre-deterministic whereas the findAny() is non-deterministic. In programming, Deterministic means the output is based on the input or initial state of the system.

15. What is a parallel stream? How to convert the list into a parallel stream?

  • One of the prominent features of Java 8 is Java Parallel Stream. It is meant for utilizing the various cores of the processor.
  • By default, all stream operations are sequential in Java unless explicitly specified as parallel. Parallel streams are created in Java in two ways.
  1. Calling the parallel() method on a sequential stream.
  2. Calling parallelStream() method on a collection.
  • Parallel streams are useful when we have many independent tasks that can be processed simultaneously to minimize the running time of the program.
  • All the Java code will usually have only one processing stream, where it is sequentially executed. But by using parallel streams, one can separate the Java code into more than one stream, which is executed in parallel on their separate cores, and the result is the combination of the individual results.
  • The order in which they are executed is not in our control. Hence, it is suggested to use a parallel stream when the order of execution of individual items does not affect the final result.


parallel(): parallel() method is called on the existing sequential stream to make it parallel.

public class Main {
public static void main(String[] args) {
Stream<Integer> stream = Stream.of(1, 2, 3, 4, 5);



parallelStream(): parallelStream() is called on Java collections like ListSet, etc to make it a parallel stream.

public class Main {
public static void main(String[] args) {
List<Integer> list = new ArrayList<>(Arrays.asList(1, 2, 3, 4, 5));



Important Points to Remember About Java Streams

  1. Streams are not data structures but rather a sequence of elements that can be processed in a pipeline of operations.
  2. Streams support two types of operations: intermediate and terminal. Intermediate operations transform or filter the stream, while terminal operations produce a result or side effect.
  3. Streams are designed to be lazy, meaning that the elements are processed on-demand as the terminal operation is executed.
  4. It’s important to close streams that are opened from I/O channels or resources by using the close() method or by utilizing try-with-resources to ensure proper resource management.
  5. Java streams are not suitable for all scenarios. In some cases, traditional iteration using loops may be more appropriate and efficient.

Happy Reading ☺


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