What is Quarkus and what are its top alternatives?
Quarkus is a Kubernetes Native Java stack crafted for OpenJDK HotSpot and GraalVM, providing fast boot times, low memory usage, and easy scaling. Its key features include support for reactive programming, microservices architecture, and a developer-friendly ecosystem with extensions for common frameworks like Hibernate and Spring. However, Quarkus does have limitations when it comes to compatibility with certain Java APIs and libraries.
Micronaut: Micronaut is a modern, JVM-based, full-stack framework designed for building microservice and serverless applications. Key features include minimal memory usage, fast startup times, and support for GraalVM native images. Compared to Quarkus, Micronaut has a different programming model and may require a learning curve for developers.
Spring Boot: Spring Boot is a popular Java framework for creating stand-alone, production-grade Spring-based applications. It offers a wide range of features such as dependency injection, auto-configuration, and powerful monitoring tools. Compared to Quarkus, Spring Boot has a larger ecosystem and community support, but may not achieve the same level of performance optimization.
Helidon: Helidon is a collection of Java libraries for writing microservices designed to run on a variety of runtimes like GraalVM. It provides a reactive programming model, support for cloud-native features, and integration with popular frameworks like Eclipse MicroProfile. Compared to Quarkus, Helidon has a more lightweight architecture but may not offer as many extensions for common frameworks.
Dropwizard: Dropwizard is a Java framework for developing ops-friendly, high-performance, RESTful web services. It includes powerful metrics, request logging, and health checks to monitor application performance. Compared to Quarkus, Dropwizard is more focused on traditional web applications and may not provide the same level of support for cloud-native features.
Vert.x: Vert.x is a toolkit for building reactive applications on the JVM that are scalable, resilient, and responsive. It supports event-driven programming, asynchronous communication, and high concurrency. Compared to Quarkus, Vert.x offers a more lightweight runtime and is well-suited for high-performance, non-blocking applications.
Eclipse MicroProfile: Eclipse MicroProfile is a set of specifications for building microservices architecture with enterprise Java. It includes APIs for fault tolerance, health checks, and metrics monitoring. Compared to Quarkus, MicroProfile provides a standardized approach to developing cloud-native applications but may require integrating with additional frameworks for a full-stack solution.
Grizzly: Grizzly is a Java NIO framework that helps developers build scalable and efficient network applications. It offers features like non-blocking I/O, HTTP server support, and WebSocket capabilities. Compared to Quarkus, Grizzly is more focused on networking and may not provide as much support for full-stack development.
Ratpack: Ratpack is a set of Java libraries for building modern web applications with asynchronous and reactive programming paradigms. It provides a flexible, composable model for defining HTTP endpoints and handling requests. Compared to Quarkus, Ratpack emphasizes simplicity and performance but may require more manual configuration.
Apache Camel: Apache Camel is an open-source integration framework for connecting different systems by defining routing and mediation rules. It offers a wide range of components for integrating with various protocols, APIs, and data formats. Compared to Quarkus, Apache Camel is more focused on integration patterns and may not provide the same level of support for building standalone microservices.
Micronative: Micronative is a new player in the market that leverages GraalVM to build lightweight, standalone Java applications optimized for cloud-native environments. It offers features like modular extensions, observability tools, and automatic dependency resolution. Compared to Quarkus, Micronative aims to simplify the development of microservices and optimize resource usage for efficient scaling.
Top Alternatives to Quarkus
Spring BootSpring 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 Spring Boot applications need very little Spring configuration. ...
SpringA key element of Spring is infrastructural support at the application level: Spring focuses on the "plumbing" of enterprise applications so that teams can focus on application-level business logic, without unnecessary ties to specific deployment environments. ...
KubernetesKubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions. ...
Micronaut FrameworkIt is a modern, JVM-based, full-stack framework for building modular, easily testable microservice and serverless applications. It features a Dependency Injection and Aspect-Oriented Programming runtime that uses no reflection. ...
KnativeKnative provides a set of middleware components that are essential to build modern, source-centric, and container-based applications that can run anywhere: on premises, in the cloud, or even in a third-party data center ...
JavaJava is a programming language and computing platform first released by Sun Microsystems in 1995. There are lots of applications and websites that will not work unless you have Java installed, and more are created every day. Java is fast, secure, and reliable. From laptops to datacenters, game consoles to scientific supercomputers, cell phones to the Internet, Java is everywhere! ...
GraalVMAn ecosystem and shared runtime offering performance advantages not only to JVM-based languages such as Java, Scala, Groovy, and Kotlin, but also to programming languages as JavaScript, Ruby, Python, and R. Additionally, it enables the execution of native code on the JVM via LLVM front-end. ...
DropwizardDropwizard is a sneaky way of making fast Java web applications. Dropwizard pulls together stable, mature libraries from the Java ecosystem into a simple, light-weight package that lets you focus on getting things done. ...
Quarkus alternatives & related posts
Spring Boot
- Powerful and handy149
- Easy setup134
- Java128
- Spring90
- Fast85
- Extensible46
- Lots of "off the shelf" functionalities37
- Cloud Solid32
- Caches well26
- Productive24
- Many receipes around for obscure features24
- Modular23
- Integrations with most other Java frameworks23
- Spring ecosystem is great22
- Auto-configuration21
- Fast Performance With Microservices21
- Community18
- Easy setup, Community Support, Solid for ERP apps17
- One-stop shop15
- Easy to parallelize14
- Cross-platform14
- Easy setup, good for build erp systems, well documented13
- Powerful 3rd party libraries and frameworks13
- Easy setup, Git Integration12
- It's so easier to start a project on spring5
- Kotlin4
- Microservice and Reactive Programming1
- The ability to integrate with the open source ecosystem1
- Heavy weight23
- Annotation ceremony18
- Java13
- Many config files needed11
- Reactive5
- Excellent tools for cloud hosting, since 5.x4
- Java 😒😒1
related Spring Boot posts
We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.
To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas
To build #Webapps we decided to use Angular 2 with RxJS
#Devops - GitHub , Travis CI , Terraform , Docker , Serverless
Is learning Spring and Spring Boot for web apps back-end development is still relevant in 2021? Feel free to share your views with comparison to Django/Node.js/ ExpressJS or other frameworks.
Please share some good beginner resources to start learning about spring/spring boot framework to build the web apps.
Spring
- Java230
- Open source157
- Great community136
- Very powerful123
- Enterprise114
- Lot of great subprojects64
- Easy setup60
- Convention , configuration, done44
- Standard40
- Love the logic31
- Good documentation13
- Dependency injection11
- Stability11
- MVC9
- Easy6
- Makes the hard stuff fun & the easy stuff automatic3
- Strong typing3
- Code maintenance2
- Best practices2
- Maven2
- Great Desgin2
- Easy Integration with Spring Security2
- Integrations with most other Java frameworks2
- Java has more support and more libraries1
- Supports vast databases1
- Large ecosystem with seamless integration1
- OracleDb integration1
- Live project1
- Draws you into its own ecosystem and bloat15
- Verbose configuration3
- Poor documentation3
- Java3
- Java is more verbose language in compare to python2
related Spring posts
Is learning Spring and Spring Boot for web apps back-end development is still relevant in 2021? Feel free to share your views with comparison to Django/Node.js/ ExpressJS or other frameworks.
Please share some good beginner resources to start learning about spring/spring boot framework to build the web apps.
I am consulting for a company that wants to move its current CubeCart e-commerce site to another PHP based platform like PrestaShop or Magento. I was interested in alternatives that utilize Node.js as the primary platform. I currently don't know PHP, but I have done full stack dev with Java, Spring, Thymeleaf, etc.. I am just unsure that learning a set of technologies not commonly used makes sense. For example, in PrestaShop, I would need to work with JavaScript better and learn PHP, Twig, and Bootstrap. It seems more cumbersome than a Node JS system, where the language syntax stays the same for the full stack. I am looking for thoughts and advice on the relevance of PHP skillset into the future AND whether the Node based e-commerce open source options can compete with Magento or Prestashop.
Kubernetes
- Leading docker container management solution166
- Simple and powerful129
- Open source107
- Backed by google76
- The right abstractions58
- Scale services25
- Replication controller20
- Permission managment11
- Supports autoscaling9
- Simple8
- Cheap8
- Self-healing6
- Open, powerful, stable5
- Reliable5
- No cloud platform lock-in5
- Promotes modern/good infrascture practice5
- Scalable4
- Quick cloud setup4
- Custom and extensibility3
- Captain of Container Ship3
- Cloud Agnostic3
- Backed by Red Hat3
- Runs on azure3
- A self healing environment with rich metadata3
- Everything of CaaS2
- Gke2
- Golang2
- Easy setup2
- Expandable2
- Sfg2
- Steep learning curve16
- Poor workflow for development15
- Orchestrates only infrastructure8
- High resource requirements for on-prem clusters4
- Too heavy for simple systems2
- Additional vendor lock-in (Docker)1
- More moving parts to secure1
- Additional Technology Overhead1
related Kubernetes posts
How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:
Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.
Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:
https://eng.uber.com/distributed-tracing/
(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)
Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark
Our first experience with .NET core was when we developed our OSS feature management platform - Tweek (https://github.com/soluto/tweek). We wanted to create a solution that is able to run anywhere (super important for OSS), has excellent performance characteristics and can fit in a multi-container architecture. We decided to implement our rule engine processor in F# , our main service was implemented in C# and other components were built using JavaScript / TypeScript and Go.
Visual Studio Code worked really well for us as well, it worked well with all our polyglot services and the .Net core integration had great cross-platform developer experience (to be fair, F# was a bit trickier) - actually, each of our team members used a different OS (Ubuntu, macos, windows). Our production deployment ran for a time on Docker Swarm until we've decided to adopt Kubernetes with almost seamless migration process.
After our positive experience of running .Net core workloads in containers and developing Tweek's .Net services on non-windows machines, C# had gained back some of its popularity (originally lost to Node.js), and other teams have been using it for developing microservices, k8s sidecars (like https://github.com/Soluto/airbag), cli tools, serverless functions and other projects...
- Compilable to machine code12
- Tiny memory footprint8
- Open source7
- Almost instantaneous startup7
- Tiny compiled code size6
- High Escalability4
- Minimal overhead2
- Hasn't Servlet API2
- Simplified reactive programming2
- Serverless support1
- Jakarta EE1
- No hot reload3
related Micronaut Framework posts
- Portability5
- Autoscaling4
- Open source3
- Eventing3
- Secure Eventing3
- On top of Kubernetes3
related Knative posts
Currently been using an older version of OpenFaaS, but the new version now requires payment for things we did on the older version. Been looking for alternatives to OpenFaas that have Kafka integrations, and scale to 0 capabilities.
looked at Apache OpenWhisk, but we run on RKE2, and my initial install of Openwhisk appears to be too out of date to support RKE2 and missing images from docker.io. So now looking at Knative. What are your thoughts? We need support to be able to process functions about 10k a min, which can vary on time of execution, between ms and mins. So looking for horizontal scaling that can be controlled by other metrics, than just cpu and ram utilization, but more so, for example if the wait is over 5 scale out.. Issue with older openfaas, was scaling on RKE2 was not working great, for example, I could get it to scale from 5 to 20 pods, but only 12 of them would ever have data, but my backlog would have 100k's of files waiting.. So even though it scaled up, it was as if the distribution of work was only being married to specific pods. If I killed the pods that had no work, they come up again with no work, if I killed one with work, then another pod would scale up and another pod would start to get work. And On occasion with hours, it would reset down to the original deployment allotment of pods, and never scale up again, until I go into Kubernetes and tell it to add more pods.
So hoping to find a solution that doesn't require as much triage, to work with scaling, as points in time we are at higher volume and other points of time could be no volume.
Java
- Great libraries603
- Widely used446
- Excellent tooling401
- Huge amount of documentation available396
- Large pool of developers available334
- Open source208
- Excellent performance203
- Great development158
- Used for android150
- Vast array of 3rd party libraries148
- Compiled Language60
- Used for Web52
- Managed memory46
- High Performance46
- Native threads45
- Statically typed43
- Easy to read35
- Great Community33
- Reliable platform29
- Sturdy garbage collection24
- JVM compatibility24
- Cross Platform Enterprise Integration22
- Good amount of APIs20
- Universal platform20
- Great Support18
- Great ecosystem14
- Backward compatible11
- Lots of boilerplate11
- Everywhere10
- Excellent SDK - JDK9
- Cross-platform7
- It's Java7
- Static typing7
- Portability6
- Mature language thus stable systems6
- Better than Ruby6
- Long term language6
- Used for Android development5
- Clojure5
- Vast Collections Library5
- Best martial for design4
- Most developers favorite4
- Old tech4
- Testable3
- History3
- Javadoc3
- Stable platform, which many new languages depend on3
- Great Structure3
- Faster than python2
- Type Safe2
- Job0
- Verbosity33
- NullpointerException27
- Nightmare to Write17
- Overcomplexity is praised in community culture16
- Boiler plate code12
- Classpath hell prior to Java 98
- No REPL6
- No property4
- Code are too long3
- Non-intuitive generic implementation2
- There is not optional parameter2
- Floating-point errors2
- Java's too statically, stronglly, and strictly typed1
- Returning Wildcard Types1
- Terrbible compared to Python/Batch Perormence1
related Java posts
How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:
Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.
Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:
https://eng.uber.com/distributed-tracing/
(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)
Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark
When you think about test automation, it’s crucial to make it everyone’s responsibility (not just QA Engineers'). We started with Selenium and Java, but with our platform revolving around Ruby, Elixir and JavaScript, QA Engineers were left alone to automate tests. Cypress was the answer, as we could switch to JS and simply involve more people from day one. There's a downside too, as it meant testing on Chrome only, but that was "good enough" for us + if really needed we can always cover some specific cases in a different way.
related GraalVM posts
Dropwizard
- Quick and easy to get a new http service going27
- Health monitoring23
- Metrics integration20
- Easy setup20
- Good conventions18
- Good documentation14
- Lightweight14
- Java Powered13
- Good Testing frameworks10
- Java powered, lightweight7
- Simple5
- Scalable4
- Great performance, Good in prod3
- Open source2
- All in one-productive-production ready-makes life easy2
- Slightly more confusing dependencies2
- Not on ThoughtWorks radar since 20141
related Dropwizard posts
Grafana and Prometheus together, running on Kubernetes , is a powerful combination. These tools are cloud-native and offer a large community and easy integrations. At PayIt we're using exporting Java application metrics using a Dropwizard metrics exporter, and our Node.js services now use the prom-client npm library to serve metrics.
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