What is Netty and what are its top alternatives?
Netty is a high-performance, asynchronous event-driven networking framework for building network applications in Java. It provides an easy-to-use API that allows developers to write scalable and efficient networked applications. Key features of Netty include support for various protocols such as TCP, UDP, HTTP, and WebSocket, asynchronous IO model using NIO, and a flexible and extensible architecture. However, one limitation of Netty is its relatively steep learning curve for beginners.
Vert.x: Vert.x is a reactive toolkit for building reactive applications on the Java Virtual Machine. Key features include event-driven programming model, support for multiple programming languages, and a scalable and resilient architecture. Pros of Vert.x compared to Netty include a simpler programming model and better support for reactive programming, while a potential con is a smaller community compared to Netty.
Akka: Akka is a toolkit and runtime for building highly concurrent, distributed, and resilient applications in Java and Scala. Key features include actor-based concurrency model, fault tolerance, and location transparency. Pros of Akka compared to Netty include built-in support for clustering and distributed computing, while a potential con is a more complex programming model.
Reactor: Reactor is a reactive programming library for building non-blocking applications on the JVM. Key features include a flexible API, support for both synchronous and asynchronous programming, and integration with Spring Framework. Pros of Reactor compared to Netty include seamless integration with Spring ecosystem, while a potential con is a smaller community compared to Netty.
Undertow: Undertow is a lightweight, high-performance web server for building modern Java applications. Key features include non-blocking IO, support for HTTP/2 and WebSockets, and a flexible embeddable architecture. Pros of Undertow compared to Netty include simpler configuration and easier deployment, while a potential con is lack of support for other protocols like UDP.
Grizzly: Grizzly is a framework for building scalable, high-performance network applications in Java. Key features include a fast and efficient NIO engine, support for various protocols, and integration with Java EE technology. Pros of Grizzly compared to Netty include better integration with Java EE, while a potential con is a less active community compared to Netty.
Jetty: Jetty is a web server and servlet container for building Java web applications. Key features include support for HTTP/2, WebSocket, and Servlet 4.0, as well as a scalable and extensible architecture. Pros of Jetty compared to Netty include better support for web applications, while a potential con is a focus on web applications rather than general network programming.
Ratpack: Ratpack is a set of Java libraries for building modern, high-performance web applications. Key features include a reactive programming model, support for asynchronous and non-blocking IO, and integration with popular JVM languages. Pros of Ratpack compared to Netty include a focus on web application development, while a potential con is a learning curve for developers unfamiliar with reactive programming.
Spring WebFlux: Spring WebFlux is a reactive web framework from the Spring ecosystem for building non-blocking, event-driven applications. Key features include integration with Spring Boot, support for reactive streams, and a functional programming model. Pros of Spring WebFlux compared to Netty include seamless integration with Spring ecosystem and Spring Boot, while a potential con is a more opinionated programming model.
Gevent: Gevent is a coroutine-based networking library for Python that provides a high-level concurrency API. Key features include greenlet-based concurrency, asynchronous IO, and integration with popular Python libraries. Pros of Gevent compared to Netty include ease of use for Python developers, while a potential con is limited support for Java applications.
Twisted: Twisted is an event-driven networking engine for building networked applications in Python. Key features include support for various protocols, deferred execution model, and a rich set of modules for building different types of applications. Pros of Twisted compared to Netty include rich library ecosystem, while a potential con is a steeper learning curve for beginners in Python.
Top Alternatives to Netty
- Jetty
Jetty is used in a wide variety of projects and products, both in development and production. Jetty can be easily embedded in devices, tools, frameworks, application servers, and clusters. See the Jetty Powered page for more uses of Jetty. ...
- Mina
Mina works really fast because it's a deploy Bash script generator. It generates an entire procedure as a Bash script and runs it remotely in the server. Compare this to the likes of Vlad or Capistrano, where each command is run separately on their own SSH sessions. Mina only creates one SSH session per deploy, minimizing the SSH connection overhead. ...
- Apache Tomcat
Apache Tomcat powers numerous large-scale, mission-critical web applications across a diverse range of industries and organizations. ...
- Undertow
It is a flexible performant web server written in java, providing both blocking and non-blocking API’s based on NIO. It has a composition based architecture that allows you to build a web server by combining small single purpose handlers. The gives you the flexibility to choose between a full Java EE servlet 4.0 container, or a low level non-blocking handler, to anything in between. ...
- Akka
Akka is a toolkit and runtime for building highly concurrent, distributed, and resilient message-driven applications on the JVM. ...
- NGINX
nginx [engine x] is an HTTP and reverse proxy server, as well as a mail proxy server, written by Igor Sysoev. According to Netcraft nginx served or proxied 30.46% of the top million busiest sites in Jan 2018. ...
- Spring Boot
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 Spring Boot applications need very little Spring configuration. ...
- Node.js
Node.js uses an event-driven, non-blocking I/O model that makes it lightweight and efficient, perfect for data-intensive real-time applications that run across distributed devices. ...
Netty alternatives & related posts
Jetty
- Lightweight15
- Embeddable10
- Very fast10
- Very thin6
- Scalable6
- Student0
related Jetty posts
- Easy, fast and light weight6
- Reusable task2
- Ruby1
related Mina posts
Apache Tomcat
- Easy79
- Java72
- Popular49
- Spring web1
- Blocking - each http request block a thread3
- Easy to set up2
related Apache Tomcat posts
I need some advice to choose an engine for generation web pages from the Spring Boot app. Which technology is the best solution today? 1) JSP + JSTL 2) Apache FreeMarker 3) Thymeleaf Or you can suggest even other perspective tools. I am using Spring Boot, Spring Web, Spring Data, Spring Security, PostgreSQL, Apache Tomcat in my project. I have already tried to generate pages using jsp, jstl, and it went well. However, I had huge problems via carrying already created static pages, to jsp format, because of syntax. Thanks.
- Performance4
- Lower footprint1
- Smaller community1
- Less known1
related Undertow posts
- Great concurrency model32
- Fast17
- Actor Library12
- Open source10
- Resilient7
- Message driven5
- Scalable5
- Mixing futures with Akka tell is difficult3
- Closing of futures2
- No type safety2
- Very difficult to refactor1
- Typed actors still not stable1
related Akka posts
To solve the problem of scheduling and executing arbitrary tasks in its distributed infrastructure, PagerDuty created an open-source tool called Scheduler. Scheduler is written in Scala and uses Cassandra for task persistence. It also adds Apache Kafka to handle task queuing and partitioning, with Akka to structure the library’s concurrency.
The service’s logic schedules a task by passing it to the Scheduler’s Scala API, which serializes the task metadata and enqueues it into Kafka. Scheduler then consumes the tasks, and posts them to Cassandra to prevent data loss.
I decided to use Akka instead of Kafka streams because I have personal relationships at @Lightbend.
NGINX
- High-performance http server1.4K
- Performance894
- Easy to configure730
- Open source607
- Load balancer530
- Free289
- Scalability288
- Web server226
- Simplicity175
- Easy setup136
- Content caching30
- Web Accelerator21
- Capability15
- Fast14
- High-latency12
- Predictability12
- Reverse Proxy8
- Supports http/27
- The best of them7
- Great Community5
- Lots of Modules5
- Enterprise version5
- High perfomance proxy server4
- Embedded Lua scripting3
- Streaming media delivery3
- Streaming media3
- Reversy Proxy3
- Blash2
- GRPC-Web2
- Lightweight2
- Fast and easy to set up2
- Slim2
- saltstack2
- Virtual hosting1
- Narrow focus. Easy to configure. Fast1
- Along with Redis Cache its the Most superior1
- Ingress controller1
- Advanced features require subscription10
related NGINX posts
Our whole DevOps stack consists of the following tools:
- GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
- Respectively Git as revision control system
- SourceTree as Git GUI
- Visual Studio Code as IDE
- CircleCI for continuous integration (automatize development process)
- Prettier / TSLint / ESLint as code linter
- SonarQube as quality gate
- Docker as container management (incl. Docker Compose for multi-container application management)
- VirtualBox for operating system simulation tests
- Kubernetes as cluster management for docker containers
- Heroku for deploying in test environments
- nginx as web server (preferably used as facade server in production environment)
- SSLMate (using OpenSSL) for certificate management
- Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
- PostgreSQL as preferred database system
- Redis as preferred in-memory database/store (great for caching)
The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:
- Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
- Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
- Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
- Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
- Scalability: All-in-one framework for distributed systems.
- Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
We chose AWS because, at the time, it was really the only cloud provider to choose from.
We tend to use their basic building blocks (EC2, ELB, Amazon S3, Amazon RDS) rather than vendor specific components like databases and queuing. We deliberately decided to do this to ensure we could provide multi-cloud support or potentially move to another cloud provider if the offering was better for our customers.
We’ve utilized c3.large nodes for both the Node.js deployment and then for the .NET Core deployment. Both sit as backends behind an nginx instance and are managed using scaling groups in Amazon EC2 sitting behind a standard AWS Elastic Load Balancing (ELB).
While we’re satisfied with AWS, we do review our decision each year and have looked at Azure and Google Cloud offerings.
#CloudHosting #WebServers #CloudStorage #LoadBalancerReverseProxy
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
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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.
Node.js
- Npm1.4K
- Javascript1.3K
- Great libraries1.1K
- High-performance1K
- Open source805
- Great for apis486
- Asynchronous477
- Great community424
- Great for realtime apps390
- Great for command line utilities296
- Websockets85
- Node Modules83
- Uber Simple69
- Great modularity59
- Allows us to reuse code in the frontend58
- Easy to start42
- Great for Data Streaming35
- Realtime32
- Awesome28
- Non blocking IO25
- Can be used as a proxy18
- High performance, open source, scalable17
- Non-blocking and modular16
- Easy and Fun15
- Easy and powerful14
- Future of BackEnd13
- Same lang as AngularJS13
- Fullstack12
- Fast11
- Scalability10
- Cross platform10
- Simple9
- Mean Stack8
- Great for webapps7
- Easy concurrency7
- Typescript6
- Fast, simple code and async6
- React6
- Friendly6
- Control everything5
- Its amazingly fast and scalable5
- Easy to use and fast and goes well with JSONdb's5
- Scalable5
- Great speed5
- Fast development5
- It's fast4
- Easy to use4
- Isomorphic coolness4
- Great community3
- Not Python3
- Sooper easy for the Backend connectivity3
- TypeScript Support3
- Blazing fast3
- Performant and fast prototyping3
- Easy to learn3
- Easy3
- Scales, fast, simple, great community, npm, express3
- One language, end-to-end3
- Less boilerplate code3
- Npm i ape-updating2
- Event Driven2
- Lovely2
- Creat for apis1
- Node0
- Bound to a single CPU46
- New framework every day45
- Lots of terrible examples on the internet40
- Asynchronous programming is the worst33
- Callback24
- Javascript19
- Dependency hell11
- Dependency based on GitHub11
- Low computational power10
- Very very Slow7
- Can block whole server easily7
- Callback functions may not fire on expected sequence7
- Breaking updates4
- Unstable4
- Unneeded over complication3
- No standard approach3
- Bad transitive dependency management1
- Can't read server session1
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I just finished the very first version of my new hobby project: #MovieGeeks. It is a minimalist online movie catalog for you to save the movies you want to see and for rating the movies you already saw. This is just the beginning as I am planning to add more features on the lines of sharing and discovery
For the #BackEnd I decided to use Node.js , GraphQL and MongoDB:
Node.js has a huge community so it will always be a safe choice in terms of libraries and finding solutions to problems you may have
GraphQL because I needed to improve my skills with it and because I was never comfortable with the usual REST approach. I believe GraphQL is a better option as it feels more natural to write apis, it improves the development velocity, by definition it fixes the over-fetching and under-fetching problem that is so common on REST apis, and on top of that, the community is getting bigger and bigger.
MongoDB was my choice for the database as I already have a lot of experience working on it and because, despite of some bad reputation it has acquired in the last months, I still believe it is a powerful database for at least a very long list of use cases such as the one I needed for my website
Needs advice on code coverage tool in Node.js/ExpressJS with External API Testing Framework
Hello community,
I have a web application with the backend developed using Node.js and Express.js. The backend server is in one directory, and I have a separate API testing framework, made using SuperTest, Mocha, and Chai, in another directory. The testing framework pings the API, retrieves responses, and performs validations.
I'm currently looking for a code coverage tool that can accurately measure the code coverage of my backend code when triggered by the API testing framework. I've tried using Istanbul and NYC with instrumented code, but the results are not as expected.
Could you please recommend a reliable code coverage tool or suggest an approach to effectively measure the code coverage of my Node.js/Express.js backend code in this setup?