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 8.1K GitHub stars and 2.2K GitHub forks. Here’s a link to SignalR's open source repository on GitHub

Top Alternatives to SignalR

SignalR alternatives & related posts

related Firebase posts

Tassanai Singprom
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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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.

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Pusher logo

Pusher

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Hosted APIs to build realtime apps with less code
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related Pusher posts

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

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Kirill Shirinkin
Kirill Shirinkin
Cloud and DevOps Consultant at mkdev · | 3 upvotes · 149.4K views
Shared insights
on
MattermostMattermostPusherPusherTwilioTwilio
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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related RabbitMQ posts

James Cunningham
James Cunningham
Operations Engineer at Sentry · | 18 upvotes · 898.8K views
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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
Shared insights
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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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PROS OF WEBRTC
CONS OF WEBRTC
    No cons available

    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

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    MQTT logo

    MQTT

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    A machine-to-machine Internet of Things connectivity protocol
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    related MQTT posts

    gRPC logo

    gRPC

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    A high performance, open-source universal RPC framework
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    related gRPC posts

    Shared insights
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    KafkaKafkagRPCgRPC
    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.

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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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    PROS OF WCF
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      CONS OF WCF
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        related Kafka posts

        Eric Colson
        Eric Colson
        Chief Algorithms Officer at Stitch Fix · | 19 upvotes · 1.4M 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
        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.

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