What is CometD and what are its top alternatives?
Top Alternatives to CometD
It enables real-time bidirectional event-based communication. It works on every platform, browser or device, focusing equally on reliability and speed. ...
RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received. ...
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. ...
- Apache Camel
An open source Java framework that focuses on making integration easier and more accessible to developers. ...
The Atmosphere Framework contains client and server side components for building Asynchronous Web Applications. The majority of popular frameworks are either supporting Atmosphere or supported natively by the framework. The Atmosphere Framework supports all major Browsers and Servers. ...
SignalR allows bi-directional communication between server and client. Servers can now push content to connected clients instantly as it becomes available. SignalR supports Web Sockets, and falls back to other compatible techniques for older browsers. SignalR includes APIs for connection management (for instance, connect and disconnect events), grouping connections, and authorization. ...
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. ...
- Apache HTTP Server
The Apache HTTP Server is a powerful and flexible HTTP/1.1 compliant web server. Originally designed as a replacement for the NCSA HTTP Server, it has grown to be the most popular web server on the Internet. ...
CometD alternatives & related posts
- Event-based communication141
- Open source101
- Binary streaming26
- No internet dependency21
- Large community10
- Fallback to polling if WebSockets not supported9
- Push notification6
- Ease of access and setup5
- Bad documentation11
- Githubs that complement it are mostly deprecated4
- Doesn't work on React Native3
- Small community2
- Websocket Errors2
related Socket.IO posts
I use Socket.IO because the application has 2 frontend clients, which need to communicate in real-time. The backend-server handles the communication between these two clients via websockets. Socket.io is very easy to set up in Node.js and ExpressJS.
In the research project, the 1st client shows panoramic videos in a so called cave system (it is the VR setup of our research lab, which consists of three big screens, which are specially arranged, so the user experience the videos more immersive), the 2nd client controls the videos/locations of the 1st client.
We are starting to work on a web-based platform aiming to connect artists (clients) and professional freelancers (service providers). In-app, timeline-based, real-time communication between users (& storing it), file transfers, and push notifications are essential core features. We are considering using Node.js, ExpressJS, React, MongoDB stack with Socket.IO & Apollo, or maybe using Real-Time Database and functionalities of Firebase.
- It's fast and it works with good metrics/monitoring232
- Ease of configuration79
- I like the admin interface58
- Easy to set-up and start with50
- Standard protocols18
- Intuitive work through python18
- Written primarily in Erlang10
- Simply superb8
- Completeness of messaging patterns6
- Scales to 1 million messages per second3
- Better than most traditional queue based message broker2
- Supports MQTT2
- Supports AMQP2
- Inubit Integration1
- Delayed messages1
- Runs on Open Telecom Platform1
- High performance1
- Clear documentation with different scripting language1
- Great ui1
- Better routing system1
- Too complicated cluster/HA config and management9
- Needs Erlang runtime. Need ops good with Erlang runtime6
- Configuration must be done first, not by your code5
related RabbitMQ posts
As Sentry runs throughout the day, there are about 50 different offline tasks that we execute—anything from “process this event, pretty please” to “send all of these cool people some emails.” There are some that we execute once a day and some that execute thousands per second.
Managing this variety requires a reliably high-throughput message-passing technology. We use Celery's RabbitMQ implementation, and we stumbled upon a great feature called Federation that allows us to partition our task queue across any number of RabbitMQ servers and gives us the confidence that, if any single server gets backlogged, others will pitch in and distribute some of the backlogged tasks to their consumers.
Hi, I am building an enhanced web-conferencing app that will have a voice/video call, live chats, live notifications, live discussions, screen sharing, etc features. Ref: Zoom.
I need advise finalizing the tech stack for this app. I am considering below tech stack:
- Frontend: React
- Backend: Node.js
- Database: MongoDB
- IAAS: #AWS
- Containers & Orchestration: Docker / Kubernetes
- DevOps: GitLab, Terraform
- Brokers: Redis / RabbitMQ
I need advice at the platform level as to what could be considered to support concurrent video streaming seamlessly.
Also, please suggest what could be a better tech stack for my app?
#SAAS #VideoConferencing #WebAndVideoConferencing #zoom #stack
- Open source18
- Written in Scala and java. Runs on JVM11
- Message broker + Streaming system8
- Avro schema integration4
- Suport Multiple clients3
- Partioned, replayable log2
- Extremely good parallelism constructs1
- Simple publisher / multi-subscriber model1
- Non-Java clients are second-class citizens31
- Needs Zookeeper28
- Operational difficulties8
- Terrible Packaging3
related Kafka posts
The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.
Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).
At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.
For more info:
- Our Algorithms Tour: https://algorithms-tour.stitchfix.com/
- Our blog: https://multithreaded.stitchfix.com/blog/
- Careers: https://multithreaded.stitchfix.com/careers/
#DataScience #DataStack #Data
As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data—this is made HA with the use of Patroni and Consul.
We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.
- Based on Enterprise Integration Patterns5
- Has over 250 components4
- Free (open source)4
- Highly configurable4
- Open Source3
- Has great community2
related Apache Camel posts
- Open source2
related Atmosphere posts
- Supports .NET server29
- Fallback to SSE, forever frame, long polling15
- Open source8
- Ease of use7
- Requires jQuery2
- Expertise hard to get2
- Weak iOS and Android support1
- Big differences between ASP.NET and Core versions1
related SignalR posts
SignalR or gRPC are always sending and receiving data on the client-side (from browser to .exe and back to browser). And web application is used for graphical visualization of data to the user. There is no need for local .exe to send or interact with remote web API. Which architecture or framework do you suggest to use in this case?
- High-performance http server1.4K
- Easy to configure729
- Open source607
- Load balancer530
- Web server224
- Easy setup136
- Content caching30
- Web Accelerator21
- Reverse Proxy8
- Supports http/27
- The best of them7
- Great Community5
- Lots of Modules5
- Enterprise version5
- High perfomance proxy server4
- Reversy Proxy3
- Streaming media3
- Embedded Lua scripting3
- Streaming media delivery3
- Fast and easy to set up2
- Ingress controller1
- Virtual hosting1
- Along with Redis Cache its the Most superior1
- Narrow focus. Easy to configure. Fast1
- Advanced features require subscription8
related NGINX posts
Recently I have been working on an open source stack to help people consolidate their personal health data in a single database so that AI and analytics apps can be run against it to find personalized treatments. We chose to go with a #containerized approach leveraging Docker #containers with a local development environment setup with Docker Compose and nginx for container routing. For the production environment we chose to pull code from GitHub and build/push images using Jenkins and using Kubernetes to deploy to Amazon EC2.
We also implemented a dashboard app to handle user authentication/authorization, as well as a custom SSO server that runs on Heroku which allows experts to easily visit more than one instance without having to login repeatedly. The #Backend was implemented using my favorite #Stack which consists of FeathersJS on top of Node.js and ExpressJS with PostgreSQL as the main database. The #Frontend was implemented using React, Redux.js, Semantic UI React and the FeathersJS client. Though testing was light on this project, we chose to use AVA as well as ESLint to keep the codebase clean and consistent.
We switched to Traefik so we can use the REST API to dynamically configure subdomains and have the ability to redirect between multiple servers.
We still use nginx with a docker-compose to expose the traffic from our APIs and TCP microservices, but for managing routing to the internet Traefik does a much better job
The biggest win for naologic was the ability to set dynamic configurations without having to restart the server
- Web server478
- Most widely-used web server305
- Virtual hosting218
- Ssl support138
- Since 199644
- Proven over many years4
- Perfect Support1
- Many available modules0
- Many available modules0
- Hard to set up4
related Apache HTTP Server posts
We've been happy with nginx as part of our stack. As an open source web application that folks install on-premise, the configuration system for the webserver is pretty important to us. I have a few complaints (e.g. the configuration syntax for conditionals is a pain), but overall we've found it pretty easy to build a configurable set of options (see link) for how to run Zulip on nginx, both directly and with a remote reverse proxy in front of it, with a minimum of code duplication.
Certainly I've been a lot happier with it than I was working with Apache HTTP Server in past projects.
nginx or Apache HTTP Server that's the question. The best choice depends on what it needs to serve. In general, Nginx performs better with static content, where Apache and Nginx score roughly the same when it comes to dynamic content. Since most webpages and web-applications use both static and dynamic content, a combination of both platforms may be the best solution.
Since both webservers are easy to deploy and free to use, setting up a performance or feature comparison test is no big deal. This way you can see what solutions suits your application or content best. Don't forget to look at other aspects, like security, back-end compatibility (easy of integration) and manageability, as well.
A reasonably good comparison between the two can be found in the link below.