Java frameworks to create a cloud-native world

These lightweight Java frameworks combine cloud-native flexibility and Java’s proven engineering to make it easy to write responsive code that can be deployed anywhere.

The Java programming language has been around for more than a decade. It and its bytecode are now used in embedded chips and large server farms. Java’s combination of a solid virtual machine and large number of libraries makes it a fertile environment for creating code that runs everywhere.

Java’s biggest problem is in the server world, where it often has to manage connections from millions, if not thousands of users. Java tools were the first to create server-side applications that required business logic. It was relatively simple to build strong web applications using Java frameworks such as J2EE, Hibernate and Spring.

This technology was a success until JavaScript and Node.js were introduced. Node.js received a lot of attention and developers started migrating to the JavaScript environment. Two reasons were common: The first was that developers loved the possibility to run the same code both on the server or from a browser client. Due to their reactive design, Node.js servers delivered a significantly faster throughput.

To compete, the Java ecosystem was adapted. Some developers used tools such as Google Web Toolkit to translate JavaScript into Java. Next, they tried to speed up Java on servers. The server’s Java frameworks were limited in that each request received was assigned its own thread. Although this was an efficient way to organize outgoing and incoming data, it was very taxing. The overhead of creating a thread can be thousands of bytes, which could limit how many users each server can handle. Node.js utilized a different model to allow it to handle many more users with less overhead.

Java developers have introduced innovations from Node.js into the Java stack, especially cloud-native Java frameworks, in recent years. These frameworks mimic Node.js’s approach. They support lightweight functions that can run on cloud machines, and can stop and start quickly. These frameworks do not require additional libraries and can be deployed quickly on the smallest server instances. Cloud-native Java frameworks can support microservices that can be started and stopped independently. For the fastest builds and installations, they are typically shipped in containers such as Docker or Podman.

There are many options for modern Java developers who want a cloud-native experience. The ideal cloud-native Java framework takes advantage of the Java platform’s extensive experience and third-party libraries, adapting them to run in the cloud and making them more efficient. These are eight Java frameworks that were built from the ground up to support cloud-native development, deployment, and maintenance.

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Micronaut

Micronaut was created by Java frameworks Spring and Grails. It combines the best features of these Java frameworks, such as dependency injection and flexible configuration. However, it also removes the large memory footprint and slow startup that make them less attractive for microservice development. Annotations were carefully designed to provide sufficient information for dependency injections, without the need for memory-filling reflections like older frameworks. Micronaut’s configuration is more complete at compile time, which makes the code lighter and faster.

This framework supports a wide range of JVM-based languages, including Java, Kotlin and Groovy, and can be used to run them across different clouds. The pre-defined configuration files make it easy to deploy the server or serverless function on all major clouds. There are also well-written documentation pages that cover all major database connections.

Micronaut’s developers want to be able to work together in a team environment. To make unit testing easier, Micronaut includes an HttpClient integration. These tests are often easier and more thorough than those required for dynamic frameworks. Again, this is due to the effort done at compile.

Quarkus

Quarkus is a great resource for developers who want to learn how to mix imperative and reactive code. Quarkus started by anticipating cloud-native use cases and then created a framework with examples to support them. It is easy to roll the result into a container, and then deploy it in a Kubernetes Cluster.

Particular attention was paid to the speed of Kubernetes clusters’ boot times. This feature is ideal for functions that run intermittently, as they can be kept cold until invoked.

The project’s goal is to extend and embrace many of the Java community’s existing standards and libraries. JAX-RS annotations, for example, define REST endpoints. Eclipse MicroProfile is used to configure the configuration. Quarkus’s developers also integrated over 50 standard libraries. This means that you may recognize certain design patterns.

The Quarkus framework can be used for many services. Quarkus’ developers have been encouraging the use of the RESTeasy Reactive model since Quarkus 2.8. This is the default option for new projects, but it doesn’t mean you have to use it. RESTeasy Reactive provides a non-blocking structure with patterns that are simpler. Instead of assigning one thread for each request, a group of non-blocking threads handles all I/O and invokes code as needed.

Quarkus can be deployed in a variety of ways. Although it is “container-first”, it can also run on bare metal. Funqy, a built-in configuration option, simplifies the creation of functions that are accepted by AWS Lambda and Azure Functions.

Spring Cloud Functions

Because Spring has been the basis for many projects over the past two decades, Java developers are familiar with it. Spring’s developers decided to make a new version, which is better suited for cloud deployment and other roles. Spring Cloud Functions functions can be redeployed easily to perform a range of tasks, such as stream processing, background work, and web services.

Spring Cloud Functions continues many of the same philosophical traditions that Spring has pioneered. This framework supports either a reactive or an imperative style of cloud functions.

The project’s main goal is to support a broad range of options. The adapters can be used to integrate the functions into AWS Lambda and Microsoft Azure, Apache OpenWhisk and Google Cloud Platform. You can also find adapters for major streaming platforms like Apache Kafka and Solace as well as standalone Spring Cloud Stream. You can focus on the development of the functions instead of worrying about packaging and deployment.

Spring Cloud Functions’ development team worked hard to address many of the challenges and pitfalls of cloud deployment. Spring Cloud Skipper is a tool that allows you to manage multiple cloud deployments. Spring Cloud Sleuth allows you to trace data flows and help with debugging. Spring Cloud Security handles many of the tasks required to secure an application so that the proper people can perform the functions. There are many subprojects.

This project provides a solid foundation for the distribution of business applications across a range of platforms. Your application logic can be encapsulated in a Cloud Function POJO and placed into many roles.

Vert.x

Vert.x was created by the creators to make it a fast framework. They simplified the event loop and optimized the connection with the database. Vert.x uses a single event loop, similar to Node.js. This allows it to handle multiple connections at once. It uses Java’s threading model for processing events with multiple threads within a pool. If they are available, it may also run on multiple cores.

It is designed to make it easier to create the pipeline that will process an event stream. To avoid messy code and layered callbacks, it borrows constructs such as promises and futures. Asynchronous options allow for clean code that is easy to read and contains simple method invocation chains as events move along the event bus.

Vert.x’s development team is not rigid about their vision. Vert.x is not a framework, but a toolkit. Modularity allows you to pick and choose the features that you want and build an architecture that suits your application. Kotlin’s Coroutines support is available for programmers who prefer an imperative structure to a reactive one.

This project is part of the Eclipse ecosystem. There are many options and versions available to give you a lot of flexibility. For example, the Vert.x app generator will generate Java or Kotlin code, with many dependencies such as API support or template engines.

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Java Graph Tutorial – How To Implement Graph Data Structure

This Comprehensive Java Graph Tutorial Explains Graph Data Structure in detail. It includes how to Create, Implement, Represent & Traverse Graphs in Java:

A graph data structure mainly represents a network connecting various points. These points are termed as vertices and the links connecting these vertices are called ‘Edges’. So a graph g is defined as a set of vertices V and edges E that connect these vertices.

Graphs are mostly used to represent various networks like computer networks, social networks, etc. They can also be used to represent various dependencies in software or architectures. These dependency graphs are very useful in analyzing the software and also at times debugging it.

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Java Graph Data Structure

1. Different Variants Of Graph

• Directed Graph
• Weighted Graph

How To Create A Graph?

2. Graph Representation In Java

• Adjacency Matrix
• Adjacency List

Java Graph Data Structure

Given below is a graph having five vertices {A,B,C,D,E} and edges given by {{AB},{AC},{AD},{BD},{CE},{ED}}. As the edges do not show any directions, this graph is known as ‘undirected graph’.

Apart from the undirected graph shown above, there are several variants of the graph in Java.

Let’s discuss these variants in detail.

Different Variants Of Graph

The following are some of the variants of the graph.

#1) Directed Graph

A directed graph or digraph is a graph data structure in which the edges have a specific direction. They originate from one vertex and culminate into another vertex.

In the above diagram, there is an edge from vertex A to vertex B. But note that A to B is not the same as B to A like in an undirected graph unless there is an edge specified from B to A.

A directed graph is cyclic if there is at least one path that has its first and last vertex as the same. In the above diagram, a path A->B->C->D->E->A forms a directed cycle or cyclic graph.

Conversely, a directed acyclic graph is a graph in which there is no directed cycle i.e. there is no path that forms a cycle.

#2) Weighted Graph

In a weighted graph, a weight is associated with each edge of the graph. The weight normally indicates the distance between the two vertices. The following diagram shows the weighted graph. As no directions are shown this is the undirected graph.

Note that a weighted graph can be directed or undirected.

How To Create A Graph?

Java does not provide a full-fledged implementation of the graph data structure. However, we can represent the graph programmatically using Collections in Java. We can also implement a graph using dynamic arrays like vectors.

Usually, we implement graphs in Java using HashMap collection. HashMap elements are in the form of key-value pairs. We can represent the graph adjacency list in a HashMap.

A most common way to create a graph is by using one of the representations of graphs like adjacency matrix or adjacency list. We will discuss these representations next and then implement the graph in Java using the adjacency list for which we will use ArrayList.

Graph Representation In Java

Graph representation means the approach or technique using which graph data is stored in the computer’s memory.

We have two main representations of graphs as shown below.

Adjacency Matrix

Adjacency Matrix is a linear representation of graphs. This matrix stores the mapping of vertices and edges of the graph. In the adjacency matrix, vertices of the graph represent rows and columns. This means if the graph has N vertices, then the adjacency matrix will have size NxN.

If V is a set of vertices of the graph then intersection Mij in the adjacency list = 1 means there is an edge existing between vertices i and j.

To better understand this concept clearly, let us prepare an adjacency Matrix for an undirected graph.

As seen from the above diagram, we see that for vertex A, the intersections AB and AE are set to 1 as there is an edge from A to B and A to E. Similarly intersection BA is set to 1, as this is an undirected graph and AB = BA. Similarly, we have set all the other intersections for which there is an edge to 1.

In case the graph is directed, the intersection Mij will be set to 1 only if there is a clear edge directed from Vi to Vj.

This is shown in the following illustration.

As we can see from the above diagram, there is an edge from A to B. So intersection AB is set to 1 but intersection BA is set to 0. This is because there is no edge directed from B to A.

Consider vertices E and D. We see that there are edges from E to D as well as D to E. Hence we have set both these intersections to 1 in the adjacency Matrix.

Now we move on to weighted graphs. As we know from the weighted graph, an integer also known as weight is associated with each edge. We represent this weight in the adjacency Matrix for the edge that exists. This weight is specified whenever there is an edge from one vertex to another instead of ‘1’.

This representation is shown below.

Adjacency List

Instead of representing a graph as an adjacency matrix which is sequential in nature, we can also use linked representation. This linked representation is known as the adjacency list. An adjacency list is nothing but a linked list and each node in the list represents a vertex.

The presence of an edge between two vertices is denoted by a pointer from the first vertex to the second. This adjacency list is maintained for each vertex in the graph.

When we have traversed all the adjacent nodes for a particular node, we store NULL in the next pointer field of the last node of the adjacency list.

Now we will use the above graphs that we used to represent the adjacency matrix to demonstrate the adjacency list.

The above figure shows the adjacency list for the undirected graph. We see that each vertex or node has its adjacency list.

In the case of the undirected graph, the total lengths of adjacency lists are usually twice the number of edges. In the above graph, the total number of edges is 6 and the total or sum of the length of all the adjacency lists is 12.

Now let’s prepare an adjacency list for the directed graph.

As seen from the above figure, in the directed graph the total length of the adjacency lists of the graph is equal to the number of edges in the graph. In the above graph, there are 9 edges and sum of the lengths of adjacency lists for this graph = 9.

Now let us consider the following weighted directed graph. Note that each edge of the weighted graph has a weight associated with it. So when we represent this graph with the adjacency list, we have to add a new field to each list node that will denote the weight of the edge.

The adjacency list for the weighted graph is shown below.

The above diagram shows the weighted graph and its adjacency list. Note that there is a new space in the adjacency list that denotes the weight of each node.

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