Alternatives to SignalR logo

Alternatives to SignalR

Firebase, RabbitMQ, Pusher, WebRTC, and MQTT are the most popular alternatives and competitors to SignalR.
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What is SignalR and what are its top alternatives?

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.
SignalR is a tool in the Realtime Backend / API category of a tech stack.
SignalR is an open source tool with 7.9K GitHub stars and 2.2K GitHub forks. Here’s a link to SignalR's open source repository on GitHub

SignalR alternatives & related posts

related Firebase posts

fontumi
fontumi
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Node.js
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Git
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Visual Studio Code
Visual Studio Code

Fontumi focuses on the development of telecommunications solutions. We have opted for technologies that allow agile development and great scalability.

Firebase and Node.js + FeathersJS are technologies that we have used on the server side. Vue.js is our main framework for clients.

Our latest products launched have been focused on the integration of AI systems for enriched conversations. Google Compute Engine , along with Dialogflow and Cloud Firestore have been important tools for this work.

Git + GitHub + Visual Studio Code is a killer stack.

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Aliadoc Team
Aliadoc Team
at aliadoc.com · | 5 upvotes · 128.1K views
atAliadocAliadoc
React
React
Create React App
Create React App
CloudFlare
CloudFlare
Firebase
Firebase
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Cloud Functions for Firebase
Google App Engine
Google App Engine
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Google Cloud Storage
Serverless
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Visual Studio Code
Visual Studio Code
Bitbucket
Bitbucket
#Aliadoc

In #Aliadoc, we're exploring the crowdfunding option to get traction before launch. We are building a SaaS platform for website design customization.

For the Admin UI and website editor we use React and we're currently transitioning from a Create React App setup to a custom one because our needs have become more specific. We use CloudFlare as much as possible, it's a great service.

For routing dynamic resources and proxy tasks to feed websites to the editor we leverage CloudFlare Workers for improved responsiveness. We use Firebase for our hosting needs and user authentication while also using several Cloud Functions for Firebase to interact with other services along with Google App Engine and Google Cloud Storage, but also the Real Time Database is on the radar for collaborative website editing.

We generally hate configuration but honestly because of the stage of our project we lack resources for doing heavy sysops work. So we are basically just relying on Serverless technologies as much as we can to do all server side processing.

Visual Studio Code definitively makes programming a much easier and enjoyable task, we just love it. We combine it with Bitbucket for our source code control needs.

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related RabbitMQ posts

James Cunningham
James Cunningham
Operations Engineer at Sentry · | 18 upvotes · 155K views
atSentrySentry
Celery
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RabbitMQ
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#MessageQueue

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.

#MessageQueue

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Tim Abbott
Tim Abbott
Founder at Zulip · | 10 upvotes · 93.7K views
atZulipZulip
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Python
Python
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We've been using RabbitMQ as Zulip's queuing system since we needed a queuing system. What I like about it is that it scales really well and has good libraries for a wide range of platforms, including our own Python. So aside from getting it running, we've had to put basically 0 effort into making it scale for our needs.

However, there's several things that could be better about it: * It's error messages are absolutely terrible; if ever one of our users ends up getting an error with RabbitMQ (even for simple things like a misconfigured hostname), they always end up needing to get help from the Zulip team, because the errors logs are just inscrutable. As an open source project, we've handled this issue by really carefully scripting the installation to be a failure-proof configuration (in this case, setting the RabbitMQ hostname to 127.0.0.1, so that no user-controlled configuration can break it). But it was a real pain to get there and the process of determining we needed to do that caused a significant amount of pain to folks installing Zulip. * The pika library for Python takes a lot of time to startup a RabbitMQ connection; this means that Zulip server restarts are more disruptive than would be ideal. * It's annoying that you need to run the rabbitmqctl management commands as root.

But overall, I like that it has clean, clear semanstics and high scalability, and haven't been tempted to do the work to migrate to something like Redis (which has its own downsides).

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WebRTC logo

WebRTC

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A free, open project that provides browsers and mobile applications with Real-Time Communications
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    MQTT logo

    MQTT

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    A machine-to-machine Internet of Things connectivity protocol
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      across_the_grid
      Full-stack web developer at Capmo GmbH · | 10 upvotes · 52.2K views
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      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.

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      JavaScript
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      I have always been interested in building a real-time multiplayer game engine that could be massively scalable, and recently I decided to start working on a MMO version of the classic "snake" game. I wanted the entire #Stack to be based on ES6 JavaScript so for the #Backend I chose to use FeathersJS with MongoDB for game/user data storage, Redis for distributed mutex and pub/sub, and Socket.IO for real-time communication. For the #Frontend I used React with Redux.js, the FeathersJS client as well as HTML5 canvas to render the view.

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      Google Cloud Pub/Sub logo

      Google Cloud Pub/Sub

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      Global service for real-time and reliable messaging and streaming data
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      NATS logo

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      WCF logo

      WCF

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      A runtime and a set of APIs for building connected, service-oriented applications
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        A realtime, open-source JavaScript back end from RethinkDB
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          Pulsar logo

          Pulsar

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          Marc Bollinger
          Marc Bollinger
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          Lumosity is home to the world's largest cognitive training database, a responsibility we take seriously. For most of the company's history, our analysis of user behavior and training data has been powered by an event stream--first a simple Node.js pub/sub app, then a heavyweight Ruby app with stronger durability. Both supported decent throughput and latency, but they lacked some major features supported by existing open-source alternatives: replaying existing messages (also lacking in most message queue-based solutions), scaling out many different readers for the same stream, the ability to leverage existing solutions for reading and writing, and possibly most importantly: the ability to hire someone externally who already had expertise.

          We ultimately migrated to Kafka in early- to mid-2016, citing both industry trends in companies we'd talked to with similar durability and throughput needs, the extremely strong documentation and community. We pored over Kyle Kingsbury's Jepsen post (https://aphyr.com/posts/293-jepsen-Kafka), as well as Jay Kreps' follow-up (http://blog.empathybox.com/post/62279088548/a-few-notes-on-kafka-and-jepsen), talked at length with Confluent folks and community members, and still wound up running parallel systems for quite a long time, but ultimately, we've been very, very happy. Understanding the internals and proper levers takes some commitment, but it's taken very little maintenance once configured. Since then, the Confluent Platform community has grown and grown; we've gone from doing most development using custom Scala consumers and producers to being 60/40 Kafka Streams/Connects.

          We originally looked into Storm / Heron , and we'd moved on from Redis pub/sub. Heron looks great, but we already had a programming model across services that was more akin to consuming a message consumers than required a topology of bolts, etc. Heron also had just come out while we were starting to migrate things, and the community momentum and direction of Kafka felt more substantial than the older Storm. If we were to start the process over again today, we might check out Pulsar , although the ecosystem is much younger.

          To find out more, read our 2017 engineering blog post about the migration!

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