Top Java Frameworks to Use

The Java programming language has evolved a lot since its inception in the early ‘90s, and it’s now one of the most popular programming languages in use today, with mobile apps being written in Java from Apple to Google. And as new projects are created daily, Java frameworks have been popping up all over the place, many of which have already been embraced by thousands of programmers across the globe. But which ones are worth your time?

Top Java Frameworks to Use

1. Spring Boot

The current version is 2.0.1, released in August 2018. Spring Boot makes it easy to create stand-alone, production-grade Spring based Applications that you can just run. We take an opinionated view of the Spring platform and third-party libraries so you can get started with minimum fuss. Most users create applications with just one main class, and application configuration is typically handled via Spring Boot’s Properties abstraction, either via command line properties or YAML files.

An optional plugin system allows for advanced customization. By default, Spring Boot uses a Tomcat web server as its embedded container but other container implementations are available such as Jetty, WebSphere, Undertow and Apache HTTP Server. Spring Boot also has optional support for Jetty, Tomcat or Undertow as a base HTTP server with WebSocket and SockJS support out of the box on supported platforms.

2. Vert.x

Vert.x is a toolkit for building reactive applications on top of Java and other JVM languages, targeting distributed systems in general. It provides a lightweight runtime and an accompanying set of tools for deploying verticles (the processing units) as daemons or simply as long-running services.

The startup time of such applications is close to zero since there’s no need to wait for JVMs to load before serving requests; instead, their development cycle focuses more on deploying new versions rather than writing them from scratch.

The end result are fully extensible servers that can respond promptly to fluctuations in traffic; you don’t have control over how many concurrent instances will be running at any given time, but you can adjust that number based on observed demand with only minimal impact over server performance.

Both local clusters and cloud deployments are feasible when using Vert.x, though not required—there’s always a tradeoff between scalability versus resource usage, and similar tradeoffs exist between immutable deployment models versus event-driven architectures that rely heavily on configuration data changes. Regardless of which approach you choose, however, Vert.x makes sure your application stays responsive even when under intense loads.

3. Apache Spark

Apache Spark is a fast and general-purpose cluster computing system. The Spark programming model provides two high-level APIs: a DataSet API for manipulating structured data and a DataFrame API for working with distributed label-encoded data. A third low-level API, called RDDs (Resilient Distributed Datasets), can be used to build other high-level features like Machine Learning or Streaming. So, what are Java frameworks?

In essence, they are software that helps developers to write code in java easily and quickly. Java frameworks support using popular design patterns so you don’t have to figure them out yourself; it also helps provide prebuilt libraries that you can use instead of writing everything from scratch. And let’s not forget performance: frameworks make your code run faster than without them. Because of all these benefits, many developers prefer using frameworks overwriting everything from scratch by themselves .

4. Play

Play is built on Scala, a language created by Martin Odersky and his team at EPFL (Ecole Polytechnique Fédérale de Lausanne) in Switzerland. It is described as agnostic because it isn’t backed by any major company or product. It also boasts an impressive community, with over 2 million Java developers according to Twitter Counter. Many companies look to hire Java developers for outsourcing work.

The framework was officially released in October 2009 and continues growing in popularity. It’s been one of LinkedIn’s Top 10 fastest-growing tech companies for four years running. Despite being lightweight compared to other frameworks, its modular architecture makes it possible for applications based on Play! to be both easy-to-understand and quick-to-build while still providing maximum performance. These features have made it an especially attractive option for mobile app development.

That said, you don’t need to use Android Studio, Kotlin or Groovy if you want to use Play!, but if you’re planning on building Android apps then those tools should definitely come in handy.

5. Kafka Streams

Built on top of Kafka, Kafka Streams is a framework for easy development and execution of stream processing programs. It provides a simple API for defining stream processing jobs that offer both batch and streaming semantics. If you’re looking to build big data applications in Java, it’s one worth watching Java Frameworks 2022.

The first among Spark-related frameworks, Spark SQL was developed as an abstraction layer over Apache Spark’s Resilient Distributed Datasets (RDDs) functionality; thus, its interface also resembles other RDD operations. It supports basic functionalities such as structured storage and manipulation for rows as well as powerful transformations like JOINs using programmer-defined functions. This can make tasks related to building ETL or Machine Learning systems easier than with other frameworks.

6. Apache Flink

Apache Flink is an open-source framework designed for distributed stream and batch data processing. It is mostly used for executing programs written in Java and Scala, although some limited support exists for other JVM languages. Apache Flink has a strong heritage: it was created by engineers from Yahoo! Research and released as an open-source project in November 2013.

One of its main differentiators compared to similar frameworks is that Apache Flink uses only push-based operators; while pull-based operators are also supported, they are considered secondary. Although Flink runs on top of Hadoop YARN or Mesos, it can also run standalone with other cluster managers.

Apache Flink supports various standard languages like Java. It also supports specific libraries like CEP (computation expressions) and libraries commonly used in algorithmic trading systems. Although originally designed as a machine learning framework, it now supports common analytical use cases such as ETL (extract-transform-load), OLAP (online analytical processing), streaming data analytics, event streams management, and more.

Wrapping Up

Understanding Java frameworks is absolutely mandatory for software developers.  Each framework has its pros and cons, so choose wisely depending on your needs.

Also Read:

Tips to choose the best hybrid mobile app development framework


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