Alternatives to SignalR logo

Alternatives to SignalR

Firebase, Pusher, RabbitMQ, 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 8.4K GitHub stars and 2.3K GitHub forks. Here’s a link to SignalR's open source repository on GitHub

Top Alternatives to SignalR

  • Firebase

    Firebase

    Firebase is a cloud service designed to power real-time, collaborative applications. Simply add the Firebase library to your application to gain access to a shared data structure; any changes you make to that data are automatically synchronized with the Firebase cloud and with other clients within milliseconds. ...

  • Pusher

    Pusher

    Pusher is the category leader in delightful APIs for app developers building communication and collaboration features. ...

  • RabbitMQ

    RabbitMQ

    RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received. ...

  • WebRTC

    WebRTC

    It is a free, open project that enables web browsers with Real-Time Communications (RTC) capabilities via simple JavaScript APIs. The WebRTC components have been optimized to best serve this purpose. ...

  • MQTT

    MQTT

    It was designed as an extremely lightweight publish/subscribe messaging transport. It is useful for connections with remote locations where a small code footprint is required and/or network bandwidth is at a premium. ...

  • gRPC

    gRPC

    gRPC is a modern open source high performance RPC framework that can run in any environment. It can efficiently connect services in and across data centers with pluggable support for load balancing, tracing, health checking... ...

  • WCF

    WCF

    It is a framework for building service-oriented applications. Using this, you can send data as asynchronous messages from one service endpoint to another. A service endpoint can be part of a continuously available service hosted by IIS, or it can be a service hosted in an application. ...

  • Kafka

    Kafka

    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. ...

SignalR alternatives & related posts

Firebase logo

Firebase

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20.7K
1.9K
The Realtime App Platform
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PROS OF FIREBASE
  • 361
    Realtime backend made easy
  • 263
    Fast and responsive
  • 234
    Easy setup
  • 207
    Real-time
  • 186
    JSON
  • 127
    Free
  • 121
    Backed by google
  • 81
    Angular adaptor
  • 63
    Reliable
  • 36
    Great customer support
  • 26
    Great documentation
  • 23
    Real-time synchronization
  • 20
    Mobile friendly
  • 17
    Rapid prototyping
  • 12
    Great security
  • 11
    Automatic scaling
  • 10
    Freakingly awesome
  • 8
    Chat
  • 8
    Angularfire is an amazing addition!
  • 8
    Super fast development
  • 6
    Awesome next-gen backend
  • 6
    Ios adaptor
  • 5
    Built in user auth/oauth
  • 5
    Firebase hosting
  • 4
    Speed of light
  • 4
    Very easy to use
  • 3
    It's made development super fast
  • 3
    Great
  • 3
    Brilliant for startups
  • 2
    Great all-round functionality
  • 2
    Low battery consumption
  • 2
    I can quickly create static web apps with no backend
  • 2
    The concurrent updates create a great experience
  • 2
    JS Offline and Sync suport
  • 1
    Faster workflow
  • 1
    Large
  • 1
    Serverless
  • 1
    .net
  • 1
    Free SSL
  • 1
    Good Free Limits
  • 1
    Push notification
  • 1
    Easy to use
  • 1
    Easy Reactjs integration
CONS OF FIREBASE
  • 28
    Can become expensive
  • 15
    Scalability is not infinite
  • 14
    No open source, you depend on external company
  • 9
    Not Flexible Enough
  • 5
    Cant filter queries
  • 3
    Very unstable server
  • 2
    Too many errors
  • 2
    No Relational Data

related Firebase posts

Stephen Gheysens
Senior Solutions Engineer at Twilio · | 12 upvotes · 119.9K views

Hi Otensia! I'd definitely recommend using the skills you've already got and building with JavaScript is a smart way to go these days. Most platform services have JavaScript/Node SDKs or NPM packages, many serverless platforms support Node in case you need to write any backend logic, and JavaScript is incredibly popular - meaning it will be easy to hire for, should you ever need to.

My advice would be "don't reinvent the wheel". If you already have a skill set that will work well to solve the problem at hand, and you don't need it for any other projects, don't spend the time jumping into a new language. If you're looking for an excuse to learn something new, it would be better to invest that time in learning a new platform/tool that compliments your knowledge of JavaScript. For this project, I might recommend using Netlify, Vercel, or Google Firebase to quickly and easily deploy your web app. If you need to add user authentication, there are great examples out there for Firebase Authentication, Auth0, or even Magic (a newcomer on the Auth scene, but very user friendly). All of these services work very well with a JavaScript-based application.

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Tassanai Singprom

This is my stack in Application & Data

JavaScript PHP HTML5 jQuery Redis Amazon EC2 Ubuntu Sass Vue.js Firebase Laravel Lumen Amazon RDS GraphQL MariaDB

My Utilities Tools

Google Analytics Postman Elasticsearch

My Devops Tools

Git GitHub GitLab npm Visual Studio Code Kibana Sentry BrowserStack

My Business Tools

Slack

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Pusher logo

Pusher

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Hosted APIs to build realtime apps with less code
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PROS OF PUSHER
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    An easy way to give customers realtime features
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    Websockets
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    Simple
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    Easy to get started with
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    Free plan
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    Heroku Add-on
  • 11
    Easy and fast to configure and to understand
  • 9
    JSON
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    Azure Add-on
  • 5
    Support
  • 5
    Happy
  • 4
    Push notification
CONS OF PUSHER
  • 9
    Costly
  • 0
    Aa

related Pusher posts

Which messaging service (Pusher vs. PubNub vs. Google Cloud Pub/Sub) to use for IoT?

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Kirill Shirinkin
Cloud and DevOps Consultant at mkdev · | 3 upvotes · 211.4K views
Shared insights
on
Mattermost
Pusher
Twilio
at

Recently we finished long research on chat tool for our students and mentors. In the end we picked Mattermost Team Edition as the cheapest and most feature complete option. We did consider building everything from scratch and use something like Pusher or Twilio on a backend, but then we would have to implement all the desktop and mobile clients and all the features oursevles. Mattermost gave us flexible API, lots of built in or easy to install integrations and future-proof feature set. We are still integrating it with our main platform but so far the team, existing mentors and students are very happy.

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RabbitMQ logo

RabbitMQ

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Open source multiprotocol messaging broker
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PROS OF RABBITMQ
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    It's fast and it works with good metrics/monitoring
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    Ease of configuration
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    I like the admin interface
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    Easy to set-up and start with
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    Durable
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    Intuitive work through python
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    Standard protocols
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    Written primarily in Erlang
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    Simply superb
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    Completeness of messaging patterns
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    Reliable
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    Scales to 1 million messages per second
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    Distributed
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    Supports AMQP
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    Better than most traditional queue based message broker
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    High performance
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    Reliability
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    Clusterable
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    Inubit Integration
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    Clear documentation with different scripting language
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    Great ui
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    Runs on Open Telecom Platform
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    Better routing system
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    Supports MQTT
CONS OF RABBITMQ
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    Too complicated cluster/HA config and management
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    Needs Erlang runtime. Need ops good with Erlang runtime
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    Configuration must be done first, not by your code
  • 4
    Slow

related RabbitMQ posts

James Cunningham
Operations Engineer at Sentry · | 18 upvotes · 1.2M views
Shared insights
on
Celery
RabbitMQ
at

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
Shared insights
on
RabbitMQ
Python
Redis
at

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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PROS OF WEBRTC
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    OpenSource
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    No Download
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    You can write anything around it, because it's a protoc
CONS OF WEBRTC
    Be the first to leave a con

    related WebRTC posts

    Hello. So, I wanted to make a decision on whether to use WebRTC or Amazon Chime for a conference call (meeting). My plan is to build an app with features like video broadcasting, and the ability for all the participants to talk and chat. I have used Agora's web SDK for video broadcasting, and Socket.IO for chat features. As I read the comparison between Amazon Chime and WebRTC, it further intrigues me on what I should use given my scenario? Is there any way that so many related technologies could be a hindrance to the other? Any advice would be appreciated. Thanks. Ritwik Neema

    See more
    joseph zeiad

    I am trying to implement video calling in a React Native app through Amazon Kinesis. But I was unlucky to find anything related to this on the web. Do you have any example code I can use? or any tutorial? If not, how easy is it to bridge the native library to RN? And what should I use WebRTC or Amazon Chime?? Thanks

    See more
    MQTT logo

    MQTT

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    A machine-to-machine Internet of Things connectivity protocol
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    PROS OF MQTT
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      Varying levels of Quality of Service to fit a range of
    • 1
      Very easy to configure and use with open source tools
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      Lightweight with a relatively small data footprint
    CONS OF MQTT
    • 1
      Easy to configure in an unsecure manner

    related MQTT posts

    gRPC logo

    gRPC

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    A high performance, open-source universal RPC framework
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    PROS OF GRPC
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      Higth performance
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      Easy setup
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      The future of API
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      Contract-based
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      Polyglot
    CONS OF GRPC
      Be the first to leave a con

      related gRPC posts

      Shared insights
      on
      Kafka
      gRPC
      at

      By mid-2015, Uber’s rider growth coupled with its cadence of releasing new services, like Eats and Freight, was pressuring the infrastructure. To allow the decoupling of consumption from production, and to add an abstraction layer between users, developers, and infrastructure, Uber built Catalyst, a serverless internal service mesh.

      Uber decided to build their own severless solution, rather that using something like AWS Lambda, speed for its global production environments as well as introspectability.

      See more
      WCF logo

      WCF

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      A runtime and a set of APIs for building connected, service-oriented applications
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      PROS OF WCF
      • 1
        Classes
      CONS OF WCF
        Be the first to leave a con

        related WCF posts

        Kafka logo

        Kafka

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        Distributed, fault tolerant, high throughput pub-sub messaging system
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        PROS OF KAFKA
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          High-throughput
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          Distributed
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          Scalable
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          High-Performance
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          Durable
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          Publish-Subscribe
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          Simple-to-use
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          Open source
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          Written in Scala and java. Runs on JVM
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          Message broker + Streaming system
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          Avro schema integration
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          Suport Multiple clients
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          Robust
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          KSQL
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          Partioned, replayable log
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          Fun
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          Extremely good parallelism constructs
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          Simple publisher / multi-subscriber model
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          Flexible
        CONS OF KAFKA
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          Non-Java clients are second-class citizens
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          Needs Zookeeper
        • 7
          Operational difficulties
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          Terrible Packaging

        related Kafka posts

        Eric Colson
        Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 1.8M views

        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:

        #DataScience #DataStack #Data

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        John Kodumal

        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.

        See more