#Quarkus serverless For Java developers

[hawkstack]

20 project ideas for Red Hat OpenShift

1. OpenShift CI/CD Pipeline

Set up a Jenkins or Tekton pipeline on OpenShift to automate the build, test, and deployment process.

2. Multi-Cluster Management with ACM

Use Red Hat Advanced Cluster Management (ACM) to manage multiple OpenShift clusters across cloud and on-premise environments.

3. Microservices Deployment on OpenShift

Deploy a microservices-based application (e.g., e-commerce or banking) using OpenShift, Istio, and distributed tracing.

4. GitOps with ArgoCD

Implement a GitOps workflow for OpenShift applications using ArgoCD, ensuring declarative infrastructure management.

5. Serverless Application on OpenShift

Develop a serverless function using OpenShift Serverless (Knative) for event-driven architecture.

6. OpenShift Service Mesh (Istio) Implementation

Deploy Istio-based service mesh to manage inter-service communication, security, and observability.

7. Kubernetes Operators Development

Build and deploy a custom Kubernetes Operator using the Operator SDK for automating complex application deployments.

8. Database Deployment with OpenShift Pipelines

Automate the deployment of databases (PostgreSQL, MySQL, MongoDB) with OpenShift Pipelines and Helm charts.

9. Security Hardening in OpenShift

Implement OpenShift compliance and security best practices, including Pod Security Policies, RBAC, and Image Scanning.

10. OpenShift Logging and Monitoring Stack

Set up EFK (Elasticsearch, Fluentd, Kibana) or Loki for centralized logging and use Prometheus-Grafana for monitoring.

11. AI/ML Model Deployment on OpenShift

Deploy an AI/ML model using OpenShift AI (formerly Open Data Hub) for real-time inference with TensorFlow or PyTorch.

12. Cloud-Native CI/CD for Java Applications

Deploy a Spring Boot or Quarkus application on OpenShift with automated CI/CD using Tekton or Jenkins.

13. Disaster Recovery and Backup with Velero

Implement backup and restore strategies using Velero for OpenShift applications running on different cloud providers.

14. Multi-Tenancy on OpenShift

Configure OpenShift multi-tenancy with RBAC, namespaces, and resource quotas for multiple teams.

15. OpenShift Hybrid Cloud Deployment

Deploy an application across on-prem OpenShift and cloud-based OpenShift (AWS, Azure, GCP) using OpenShift Virtualization.

16. OpenShift and ServiceNow Integration

Automate IT operations by integrating OpenShift with ServiceNow for incident management and self-service automation.

17. Edge Computing with OpenShift

Deploy OpenShift at the edge to run lightweight workloads on remote locations, using Single Node OpenShift (SNO).

18. IoT Application on OpenShift

Build an IoT platform using Kafka on OpenShift for real-time data ingestion and processing.

19. API Management with 3scale on OpenShift

Deploy Red Hat 3scale API Management to control, secure, and analyze APIs on OpenShift.

20. Automating OpenShift Cluster Deployment

Use Ansible and Terraform to automate the deployment of OpenShift clusters and configure infrastructure as code (IaC).

For more details www.hawkstack.com 

#OpenShift #Kubernetes #DevOps #CloudNative #RedHat #GitOps #Microservices #CICD #Containers #HybridCloud #Automation  

[abarticles]

Quarkus framework and it's advantages

Quarkus is a framework similar to Spring Boot, but with an additional feature of delivering smaller artifacts with fast boot time, better resource utilization, and efficiency. It’s optimized for cloud, serverless, and containerized environments. The goal of Quarkus is to make java a leading platform in Kubernetes and serverless environment while offering developers to unified reactive and…

[sizzlenut]

Quarkus - a Kubernetes based framework

Quarkus is a Java framework tailored for deployment on Kubernetes. Key technology components surrounding it are OpenJDK HotSpot and GraalVM. The goal of Quarkus is to make Java a leading platform in Kubernetes and serverless environments while offering developers a unified reactive and imperative programming model to optimally address a wider range of distributed application architectures. Quarkus also offers near-instant scale-up and high-density utilisation in container orchestration platforms such as Kubernetes. Many more application instances can be run given the same hardware resources. After its initial debut, Quarkus underwent several enhancements over the next few months, culminating in a 1.0 release within the open source community in October 2019. As a new framework, Quarkus does not need to attempt to retrofit new patterns and principles into an existing codebase. Instead, it can focus on innovation.

Java applications are called WORA (Write Once Run Anywhere). This means a programmer can develop Java code on one system and can expect it to run on any other Java-enabled system without any adjustment. This is all possible because of JVM. The Java VM or Java Virtual Machine resides on the RAM. During execution, using the class loader the class files are brought on the RAM. The BYTE code is verified for any security breaches. Next, the execution engine will convert the Bytecode into Native machine code. 

Traditional Java stacks were engineered for monolithic applications with long start-up times and large memory requirements in a world where the cloud, containers, and Kubernetes did not exist. Java frameworks needed to evolve to meet the needs of this new world. 

Quarkus was created to enable Java developers to create applications for a modern, cloud-native world. Quarkus is a Kubernetes-native Java framework tailored for GraalVM and HotSpot, crafted from best-of-breed Java libraries and standards. The goal is to make Java the leading platform in Kubernetes and serverless environments while offering developers a framework to address a wider range of distributed application architectures. Quarkus was built from the ground up for Kubernetes making it easy to deploy applications without having to understand all of the complexities of the platform. Quarkus allows developers to automatically generate Kubernetes resources including building and deploying container images without having to manually create YAML files. Quarkus provides a cohesive, fun to use, full-stack framework by leveraging a growing list of hundreds of best-of-breed libraries that you love and use. All wired on a standard backbone.

One of the major productivity problems that face most Java developers is traditional Java development workflow. For most web developers this will generally be:

Write Code → Compile → Deploy → Refresh Browser → Repeat

This can be a major drain on productivity, as the compile + redeploy cycle can often take up to a minute or more. Quarkus aims to solve this problem with its Live Coding feature. When running in development mode the workflow is simply:

Write Code → Refresh Browser → Repeat

RESULTS

The above figure shows us the docker stats of the two containers, one running without (app-access) Quarkus the other with (app-access-jars) respectively. We can see the the docker container running with Quarkus takes up less CPU and memory utilisation.

We can see that the throughput with Quarkus is almost double than that without Quarkus. The more the throughput the better; throughput signifies the number of requests that can be sent per second.

CONCLUSION

As claimed by the Quarkus developer, Red Hat, we were able to see some difference in the response time and memory imprint taken by the API though not as gigantic difference as claimed by Red Hat.

[kaizencb]

Quarkus Serverless Functions: Quarkus is an open-source Java framework that solves traditional frameworks' weaknesses, including heavy memory consumption and issues scaling in container environments.

Visit our website:- www.kaizenbootcamp.com

#training #java #technology #tech #software #programming #development #code #webdevelopment #c #developer #web #computer #webdeveloper #javascript #programmer #computerscience #data #freshers #fresherjobs

[reportwire]

Java Serverless Functions With Quarkus Quick Start

Java Serverless Functions With Quarkus Quick Start

Are you looking for the shortest path or cheatsheet to bring your Java application into a serverless platform based on Kubernetes? Perhaps you don’t have enough time to stand up relevant infrastructure and configure settings for both the application and the platform. This article is a guide to developing Java serverless functions using a Quarkus quick start in the Developer Sandbox for Red Hat…

[datamattsson]

Got Quarkus?

[divergentsl]

Quarkus is an open-source Java framework that solves traditional frameworks' weaknesses, including heavy memory consumption and issues scaling in container environments. With Quarkus, Java developers can use familiar technology to build cloud-native microservices and serverless functions.

For any consultancy related to Java, ReactJS, and ReactNative please contact us: https://lnkd.in/gngwzm63

[digital-dynasty]

Java-Framework: Quarkus 1.4 erobert die Kommandozeile

Das aktuelle Release erlaubt das Ausführen von Quarkus-Anwendungen im CLI und wartet zudem mit einem neuen Framework für Serverless Computing auf. Read more www.heise.de/developer/meldung/…... www.digital-dynasty.net/de/teamblogs/…

http://www.digital-dynasty.net/de/teamblogs/java-framework-quarkus-1-4-erobert-die-kommandozeile

[itbeatsbookmarks]

(Via: Hacker News)

Today’s developers are expected to develop resilient and scalable distributed systems. Systems that are easy to patch in the face of security concerns and easy to do low-risk incremental upgrades. Systems that benefit from software reuse and innovation of the open source model. Achieving all of this for different languages, using a variety of application frameworks with embedded libraries is not possible.

Recently I’ve blogged about “Multi-Runtime Microservices Architecture” where I have explored the needs of distributed systems such as lifecycle management, advanced networking, resource binding, state abstraction and how these abstractions have been changing over the years. I also spoke about “The Evolution of Distributed Systems on Kubernetes” covering how Kubernetes Operators and the sidecar model are acting as the primary innovation mechanisms for delivering the same distributed system primitives.

On both occasions, the main takeaway is the prediction that the progression of software application architectures on Kubernetes moves towards the sidecar model managed by operators. Sidecars and operators could become a mainstream software distribution and consumption model and in some cases even replace software libraries and frameworks as we are used to.

The sidecar model allows the composition of applications written in different languages to deliver joint value, faster and without the runtime coupling. Let’s see a few concrete examples of sidecars and operators, and then we will explore how this new software composition paradigm could impact us.

Out-of-Process Smarts on the Rise

In Kubernetes, a sidecar is one of the core design patterns achieved easily by organizing multiple containers in a single Pod. The Pod construct ensures that the containers are always placed on the same node and can cooperate by interacting over networking, file system or other IPC methods. And operators allow the automation, management and integration of the sidecars with the rest of the platform. The sidecars represent a language-agnostic, scalable data plane offering distributed primitives to custom applications. And the operators represent their centralized management and control plane.

Let’s look at a few popular manifestations of the sidecar model.

Envoy

Service Meshes such as Istio, Consul, and others are using transparent service proxies such as Envoy for delivering enhanced networking capabilities for distributed systems. Envoy can improve security, it enables advanced traffic management, improves resilience, adds deep monitoring and tracing features. Not only that, it understands more and more Layer 7 protocols such as Redis, MongoDB, MySQL and most recently Kafka. It also added response caching capabilities and even WebAssembly support that will enable all kinds of custom plugins. Envoy is an example of how a transparent service proxy adds advanced networking capabilities to a distributed system without including them into the runtime of the distributed application components.

Skupper

In addition to the typical service mesh, there are also projects, such as Skupper, that ship application networking capabilities through an external agent. Skupper solves multicluster Kubernetes communication challenges through a Layer 7 virtual network and offers advanced routing and connectivity capabilities. But rather than embedding Skupper into the business service runtime, it runs an instance per Kubernetes namespace which acts as a shared sidecar.

Cloudstate

Cloudstate is another example of the sidecar model, but this time for providing stateful abstractions for the serverless development model. It offers stateful primitives over GRPC for EventSourcing, CQRS, Pub/Sub, Key/Value stores and other use cases. Again, it an example of sidecars and operators in action but this time for the serverless programming model.

Dapr

Dapr is a relatively young project started by Microsoft, and it is also using the sidecar model for providing developer-focused distributed system primitives. Dapr offers abstractions for state management, service invocation and fault handling, resource bindings, pub/sub, distributed tracing and others. Even though there is some overlap in the capabilities provided by Dapr and Service Mesh, both are very different in nature. Envoy with Istio is injected and runs transparently from the service and represents an operational tool. Dapr, on the other hand, has to be called explicitly from the application runtime over HTTP or gRPC and it is an explicit sidecar targeted for developers. It is a library for distributed primitives that is distributed and consumed as a sidecar, a model that may become very attractive for developers consuming distributed capabilities.

Camel K

Apache Camel is a mature integration library that rediscovers itself on Kubernetes. Its subproject Camel K uses heavily the operator model to improve the developer experience and integrate deeply with the Kubernetes platform. While Camel K does not rely on a sidecar, through its CLI and operator it is able to reuse the same application container and execute any local code modification in a remote Kubernetes cluster in less than a second. This is another example of developer-targeted software consumption through the operator model.

More to Come

And these are only some of the pioneer projects exploring various approaches through sidecars and operators. There is more work being done to reduce the networking overhead introduced by container-based distributed architectures such as the data plane development kit (DPDK), which is a userspace application that bypasses the layers of the Linux kernel networking stack and access directly to the network hardware. There is work in the Kubernetes project to create sidecar containers with more granular lifecycle guarantees. There are new Java projects based on GraalVM implementation such as Quarkus that reduce the resource consumption and application startup time which makes more workloads attractive for sidecars. All of these innovations will make the side-car model more attractive and enable the creation of even more such projects.

Sidecars providing distributed systems primitives

I’d not be surprised to see projects coming up around more specific use cases such as stateful orchestration of long-running processes such as Business Process Model and Notation (BPMN) engines in sidecars. Job schedulers in sidecars. Stateless integration engines i.e. Enterprise Integration Patterns implementations in sidecars. Data abstractions and data federation engines in sidecars. OAuth2/OpenID proxy in sidecars. Scalable database connection pools for serverless workloads in sidecars. Application networks as sidecars, etc. But why would software vendors and developers switch to this model? Let’s see a few of the benefits it provides.

Runtimes with Control Planes over Libraries

If you are a software vendor today, probably you have already considered offering your software to potential users as an API or a SaaS-based solution. This is the fastest software consumption model and a no-brainer to offer, when possible. Depending on the nature of the software you may be also distributing your software as a library or a runtime framework. Maybe it is time to consider if it can be offered as a container with an operator too. This mechanism of distributing software and the resulting architecture has some very unique benefits that the library mechanism cannot offer.

Supporting Polyglot Consumers

By offering libraries to be consumable through open protocols and standards, you open them up for all programming languages. A library that runs as a sidecar and consumable over HTTP, using a text format such as JSON does not require any specific client runtime library. Even when gRPC and Protobuf are used for low-latency and high-performance interactions, it is still easier to generate such clients than including third party custom libraries in the application runtime and implement certain interfaces.

Application Architecture Agnostic

The explicit sidecar architecture (as opposed to the transparent one) is a way of software capability consumption as a separate runtime behind a developer-focused API. It is an orthogonal feature that can be added to any application whether that is monolithic, microservices, functions-based, actor-based or anything in between. It can sit next to a monolith in a less dynamic environment, or next to every microservice in a dynamic cloud-based environment. It is trivial to create sidecars on Kubernetes, and doable on many other software orchestration platforms too.

Tolerant to Release Impedance Mismatch

Business logic is always custom and developed in house. Distributed system primitives are well-known commodity features, and consumed off-the-shelf as either platform features or runtime libraries. You might be consuming software for state abstractions, messaging clients, networking resiliency and monitoring libraries, etc. from third-party open source projects or companies. And these third party entities have their release cycles, critical fixes, CVE patches that impact your software release cycles too. When third party libraries are consumed as a separate runtime (sidecar), the upgrade process is simpler as it is behind an API and it is not coupled with your application runtime. The release impedance mismatch between your team and the consumed 3rd party libraries vendors becomes easier to manage.

Control Plane Included Mentality

When a feature is consumed as a library, it is included in your application runtime and it becomes your responsibility to understand how it works, how to configure, monitor, tune and upgrade. That is because the language runtimes (such as the JVM) and the runtime frameworks (such as Spring Boot or application servers) dictate how a third-party library can be included, configured, monitored and upgraded. When a software capability is consumed as a separate runtime (such as a sidecar or standalone container) it comes with its own control plane in the form of a Kubernetes operator.

That has a lot of benefits as the control plane understands the software it manages (the operand) and comes with all the necessary management intelligence that otherwise would be distributed as documentation and best practices. What’s more, operators also integrate deeply with Kubernetes and offer a unique blend of platform integration and operand management intelligence out-of-the-box. Operators are created by the same developers who are creating the operands, they understand the internals of the containerized features and know how to operate the best. Operators are executables SREs in containers, and the number of operators and their capabilities are increasing steadily with more operators and marketplaces coming up.

Software Distribution and Consumption in the Future

Software Distributed as Sidecars with Control Planes

Let’s say you are a software provider of a Java framework. You may distribute it as an archive or a Maven artifact. Maybe you have gone a step further and you distribute a container image. In either case, in today’s cloud-native world, that is not good enough. The users still have to know how to patch and upgrade a running application with zero downtime. They have to know what to backup and restore its state. They have to know how to configure their monitoring and alerting thresholds. They have to know how to detect and recover from complex failures. They have to know how to tune an application based on the current load profile.

In all of these and similar scenarios, intelligent control planes in the form of Kubernetes operators are the answer. An operator encapsulates platform and domain knowledge of an application in a declaratively configured component to manage the workload.

Sidecars and operators could become a mainstream software distribution and consumption model and in some cases even replace software libraries and frameworks as we are used to.

Let’s assume that you are providing a software library that is included in the consumer applications as a dependency. Maybe it is the client-side library of the backend framework described above. If it is in Java, for example, you may have certified it to run it on a JEE server, provided Spring Boot Starters, Builders, Factories, and other implementations that are all hidden behind a clean Java interface. You may have even backported it to .Net too.

With Kubernetes operators and sidecars all of that is hidden from the consumer. The factory classes are replaced by the operator, and the only configuration interface is a YAML file for the custom resource. The operator is then responsible for configuring the software and the platform so that users can consume it as an explicit sidecar, or a transparent proxy. In all cases, your application is available for consumption over remote API and fully integrated with the platform features and even other dependent operators. Let’s see how that happens.

Software Consumed over Remote APIs Rather than Embedded Libraries

One way to think about sidecars is similar to the composition over inheritance principle in OOP, but in a polyglot context. It is a different way of organizing the application responsibilities by composing capabilities from different processes rather than including them into a single application runtime as dependencies. When you consume software as a library, you instantiate a class, call its methods by passing some value objects. When you consume it as an out-of-process capability, you access a local process. In this model, methods are replaced with APIs, in-process methods invocation with HTTP or gRPC invocations, and value objects with something like CloudEvents. This is a change from application servers to Kubernetes as the distributed runtime. A change from language-specific interfaces, to remote APIs. From in-memory calls to HTTP, from value objects to CloudEvents, etc.

This requires software providers to distribute containers and controllers to operate them. To create IDEs that are capable of building and debugging multiple runtime services locally. CLIs for quickly deploying code changes into Kubernetes and configuring the control planes. Compilers that can decide what to compile in a custom application runtime, what capabilities to consume from a sidecar and what from the orchestration platform.

Software consumers and providers ecosystem

In the longer term, this will lead to the consolidation of standardized APIs that are used for the consumption of common primitives in sidecars. Rather than language-specific standards and APIs we will have polyglot APIs. For example, rather than Java Database Connectivity (JDBC) API, caching API for Java (JCache), Java Persistence API (JPA), we will have polyglot APIs over HTTP using something like CloudEvents. Sidecar centric APIs for messaging, caching, reliable networking, cron jobs and timer scheduling, resource bindings (connectors to other APIs, protocols), idempotency, SAGAs, etc. And all of these capabilities will be delivered with the management layer included in the form of operators and even wrapped with self-service UIs. The operators are key enablers here as they will make this even more distributed architecture easy to manage and self-operate on Kubernetes. The management interface of the operator is defined by the CustomResourceDefinition and represents another public-facing API that remains application-specific.

This is a big shift in mentality to a different way of distributing and consuming software, driven by the speed of delivery and operability. It is a shift from a single runtime to multi runtime application architectures. It is a shift similar to what the hardware industry had to go through from single-core to multicore platforms when Moore’s law ended. It is a shift that is slowly happening by building all the elements of the puzzle: we have uniformly adopted and standardized containers, we have a de facto standard for orchestration through Kubernetes, possibly improved sidecars coming soon, rapid operators adoption, CloudEvents as a widely agreed standard, light runtimes such as Quarkus, etc. With the foundation in place, applications, productivity tools, practices, standardized APIs, and ecosystem will come too.

This post was originally published at ​The New Stack here.

[holytheoristtastemaker]

OCI Grails & Micronaut Product Lead and Principal Software Engineer, Graeme Rocher, published a report comparing the speeds of Micronaut, Quarkus, and Spring Boot on JDK 14. Which is the fastest and which has the lowest memory consumption? What do we want? Speed. Which is the fastest microservice framework? Quarkus, Micronaut, and Spring Boot are three modern frameworks for Java that share a similar overlap of features and capabilities. While all three services have their pros, cons, and unique use cases, they are often pitted against each other. Which is the fastest and which has the lowest memory consumption? Quarkus, Micronaut, and Spring Boot Let’s take a quick rundown of all three frameworks. Developed by Red Hat, Quarkus is a “supersonic subatomic Java”, which is not only fun to say, but a perfect description. It is a Kubernetes-native Java stack designed for OpenJDK HotSpot and GraalVM and includes the best Java libraries and standards. One of the pros of Quarkus is its speedy start-up time. Micronaut is a cloud-native JVM-based polyglot full-stack framework for building microservices and serverless applications. It features low memory consumption, no matter the size of your codebase. Check out the guide for Micronaut 2.0.0.M2 release. Spring Boot is an open source Java framework that makes it easy to create stand-alone production-grade Spring applications and microservices with embedded Tomcat, Jetty, or Undertow. Spring Boot apps require little configuration so they can “just run”. All of these frameworks claim speed, but only one can be the fastest. Putting them to the test OCI Grails & Micronaut Product Lead and Principal Software Engineer, Graeme Rocher published a report comparing the speeds of Micronaut, Quarkus, and Spring Boot on JDK 14. The test looks at Micronaut 2.0 M2, Quarkus 1.3.1, and Spring Boot 2.3 M2 on JDK 14 using a 2019 iMac Pro Xeon 8 Core. Here are the results of the benchmark test, taken from an average of 10 runs: Benchmark results. Source. The test confirms that Quarkus’ boot time is unmatched with a time to first response of 890ms. Spring is the best at compilation time with 1.33s for a ./mvn clean compile. (Graeme Rocher notes that this is because Spring does not perform any compilation-time processing.) However, as you can see from the table, in every other task, Micronaut takes the lead and has the lowest memory consumption after load test of the three. Graeme Rocher writes: The Quarkus team has made bold claims about the memory efficiency of Quarkus, so it was surprising to see such a disparity when actual tests were conducted that seem to disprove these claims. The Micronaut team and I are disappointed that we had to take it upon ourselves to perform these tests and publish the results, not as a simple opportunity to help others improve their processes and applications, but to respond to misinformation that could, theoretically, do the opposite. Source code In order to prevent reporting bias, the source code for the examples is available on GitHub for users to test out for themselves on their own machine. See the performance comparison video on YouTube. At the end of the day, the key takeaway here is that all three are quite fast. The Micronaut, Quarkus, and Spring frameworks all have great performance that will likely only continue to improve with future updates. Do your numbers differ? Which JVM framework do you prefer?

http://damianfallon.blogspot.com/2020/04/micronaut-benchmarks-faster-than.html