Alternatives to WCF logo

Alternatives to WCF

RabbitMQ, REST, gRPC, Firebase, and Kafka are the most popular alternatives and competitors to WCF.
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What is WCF and what are its top alternatives?

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.
WCF is a tool in the Message Queue category of a tech stack.

Top Alternatives to WCF

  • RabbitMQ

    RabbitMQ

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

  • REST

    REST

    An architectural style for developing web services. A distributed system framework that uses Web protocols and technologies. ...

  • 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... ...

  • 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. ...

  • Kafka

    Kafka

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

  • Socket.IO

    Socket.IO

    It enables real-time bidirectional event-based communication. It works on every platform, browser or device, focusing equally on reliability and speed. ...

  • Amazon SQS

    Amazon SQS

    Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use. ...

  • Celery

    Celery

    Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well. ...

WCF alternatives & related posts

RabbitMQ logo

RabbitMQ

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Open source multiprotocol messaging broker
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PROS OF RABBITMQ
  • 229
    It's fast and it works with good metrics/monitoring
  • 79
    Ease of configuration
  • 58
    I like the admin interface
  • 50
    Easy to set-up and start with
  • 20
    Durable
  • 18
    Intuitive work through python
  • 18
    Standard protocols
  • 10
    Written primarily in Erlang
  • 8
    Simply superb
  • 6
    Completeness of messaging patterns
  • 3
    Scales to 1 million messages per second
  • 3
    Reliable
  • 2
    Better than most traditional queue based message broker
  • 2
    Distributed
  • 2
    Supports AMQP
  • 1
    Inubit Integration
  • 1
    Supports MQTT
  • 1
    Runs on Open Telecom Platform
  • 1
    High performance
  • 1
    Reliability
  • 1
    Clusterable
  • 1
    Clear documentation with different scripting language
  • 1
    Great ui
  • 1
    Better routing system
  • 1
    Delayed messages
CONS OF RABBITMQ
  • 9
    Too complicated cluster/HA config and management
  • 6
    Needs Erlang runtime. Need ops good with Erlang runtime
  • 5
    Configuration must be done first, not by your code
  • 4
    Slow

related RabbitMQ posts

James Cunningham
Operations Engineer at Sentry · | 18 upvotes · 1.3M views
Shared insights
on
CeleryCeleryRabbitMQRabbitMQ
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

See more
Yogesh Bhondekar
Co-Founder at weconnect.chat · | 15 upvotes · 97.8K views

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

See more
REST logo

REST

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A software architectural style
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PROS OF REST
  • 2
    Popularity
CONS OF REST
    Be the first to leave a con

    related REST posts

    gRPC logo

    gRPC

    854
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    47
    A high performance, open-source universal RPC framework
    854
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    PROS OF GRPC
    • 20
      Higth performance
    • 10
      The future of API
    • 10
      Easy setup
    • 4
      Contract-based
    • 3
      Polyglot
    CONS OF GRPC
      Be the first to leave a con

      related gRPC posts

      Shared insights
      on
      gRPCgRPCSignalRSignalR.NET.NET

      We need to interact from several different Web applications (remote) to a client-side application (.exe in .NET Framework, Windows.Console under our controlled environment). From the web applications, we need to send and receive data and invoke methods to client-side .exe on javascript events like users onclick. SignalR is one of the .Net alternatives to do that, but it adds overhead for what we need. Is it better to add SignalR at both client-side application and remote web application, or use gRPC as it sounds lightest and is multilingual?

      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?

      See more
      Shared insights
      on
      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.

      See more
      Firebase logo

      Firebase

      28.1K
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      The Realtime App Platform
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      PROS OF FIREBASE
      • 362
        Realtime backend made easy
      • 265
        Fast and responsive
      • 235
        Easy setup
      • 209
        Real-time
      • 187
        JSON
      • 130
        Free
      • 122
        Backed by google
      • 82
        Angular adaptor
      • 65
        Reliable
      • 36
        Great customer support
      • 27
        Great documentation
      • 23
        Real-time synchronization
      • 21
        Mobile friendly
      • 18
        Rapid prototyping
      • 13
        Great security
      • 12
        Automatic scaling
      • 11
        Freakingly awesome
      • 8
        Angularfire is an amazing addition!
      • 8
        Super fast development
      • 8
        Chat
      • 6
        Ios adaptor
      • 6
        Awesome next-gen backend
      • 5
        Built in user auth/oauth
      • 5
        Firebase hosting
      • 4
        Speed of light
      • 4
        Very easy to use
      • 3
        Great
      • 3
        Brilliant for startups
      • 3
        It's made development super fast
      • 2
        JS Offline and Sync suport
      • 2
        I can quickly create static web apps with no backend
      • 2
        Great all-round functionality
      • 2
        The concurrent updates create a great experience
      • 2
        Low battery consumption
      • 1
        Easy Reactjs integration
      • 1
        Push notification
      • 1
        Cloud functions
      • 1
        Easy to use
      • 1
        Faster workflow
      • 1
        Large
      • 1
        Serverless
      • 1
        .net
      • 1
        Good Free Limits
      • 1
        Free SSL
      • 1
        Free hosting
      • 1
        Free authentication solution
      • 1
        CDN & cache out of the box
      CONS OF FIREBASE
      • 29
        Can become expensive
      • 15
        No open source, you depend on external company
      • 15
        Scalability is not infinite
      • 9
        Not Flexible Enough
      • 5
        Cant filter queries
      • 3
        Very unstable server
      • 2
        Too many errors
      • 2
        No Relational Data
      • 1
        No offline sync

      related Firebase posts

      Stephen Gheysens
      Senior Solutions Engineer at Twilio · | 14 upvotes · 368.6K 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.

      See more
      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

      See more
      Kafka logo

      Kafka

      15.7K
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      Distributed, fault tolerant, high throughput pub-sub messaging system
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      PROS OF KAFKA
      • 122
        High-throughput
      • 116
        Distributed
      • 87
        Scalable
      • 81
        High-Performance
      • 65
        Durable
      • 36
        Publish-Subscribe
      • 19
        Simple-to-use
      • 15
        Open source
      • 10
        Written in Scala and java. Runs on JVM
      • 6
        Message broker + Streaming system
      • 4
        Avro schema integration
      • 2
        Suport Multiple clients
      • 2
        Robust
      • 2
        KSQL
      • 2
        Partioned, replayable log
      • 1
        Fun
      • 1
        Extremely good parallelism constructs
      • 1
        Simple publisher / multi-subscriber model
      • 1
        Flexible
      CONS OF KAFKA
      • 27
        Non-Java clients are second-class citizens
      • 26
        Needs Zookeeper
      • 7
        Operational difficulties
      • 2
        Terrible Packaging

      related Kafka posts

      Eric Colson
      Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 2.1M 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

      See more
      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
      Socket.IO logo

      Socket.IO

      9K
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      Realtime application framework (Node.JS server)
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      PROS OF SOCKET.IO
      • 214
        Real-time
      • 143
        Event-based communication
      • 142
        Node.js
      • 102
        Open source
      • 102
        WebSockets
      • 26
        Binary streaming
      • 22
        No internet dependency
      • 9
        Fallback to polling if WebSockets not supported
      • 8
        Large community
      • 5
        Ease of access and setup
      • 4
        Push notification
      CONS OF SOCKET.IO
      • 11
        Bad documentation
      • 4
        Githubs that complement it are mostly deprecated
      • 3
        Doesn't work on React Native
      • 2
        Websocket Errors
      • 2
        Small community

      related Socket.IO posts

      across_the_grid
      Full-stack web developer · | 10 upvotes · 338.6K views
      Shared insights
      on
      Socket.IOSocket.IONode.jsNode.jsExpressJSExpressJS

      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.

      See more

      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.

      See more
      Amazon SQS logo

      Amazon SQS

      1.9K
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      Fully managed message queuing service
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      PROS OF AMAZON SQS
      • 60
        Easy to use, reliable
      • 39
        Low cost
      • 27
        Simple
      • 13
        Doesn't need to maintain it
      • 8
        It is Serverless
      • 4
        Has a max message size (currently 256K)
      • 3
        Easy to configure with Terraform
      • 3
        Triggers Lambda
      • 3
        Delayed delivery upto 15 mins only
      • 3
        Delayed delivery upto 12 hours
      • 1
        JMS compliant
      • 1
        Support for retry and dead letter queue
      • 1
        D
      CONS OF AMAZON SQS
      • 2
        Has a max message size (currently 256K)
      • 2
        Proprietary
      • 2
        Difficult to configure
      • 1
        Has a maximum 15 minutes of delayed messages only

      related Amazon SQS posts

      Praveen Mooli
      Engineering Manager at Taylor and Francis · | 14 upvotes · 2M views

      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

      See more
      Tim Specht
      ‎Co-Founder and CTO at Dubsmash · | 14 upvotes · 622.5K views

      In order to accurately measure & track user behaviour on our platform we moved over quickly from the initial solution using Google Analytics to a custom-built one due to resource & pricing concerns we had.

      While this does sound complicated, it’s as easy as clients sending JSON blobs of events to Amazon Kinesis from where we use AWS Lambda & Amazon SQS to batch and process incoming events and then ingest them into Google BigQuery. Once events are stored in BigQuery (which usually only takes a second from the time the client sends the data until it’s available), we can use almost-standard-SQL to simply query for data while Google makes sure that, even with terabytes of data being scanned, query times stay in the range of seconds rather than hours. Before ingesting their data into the pipeline, our mobile clients are aggregating events internally and, once a certain threshold is reached or the app is going to the background, sending the events as a JSON blob into the stream.

      In the past we had workers running that continuously read from the stream and would validate and post-process the data and then enqueue them for other workers to write them to BigQuery. We went ahead and implemented the Lambda-based approach in such a way that Lambda functions would automatically be triggered for incoming records, pre-aggregate events, and write them back to SQS, from which we then read them, and persist the events to BigQuery. While this approach had a couple of bumps on the road, like re-triggering functions asynchronously to keep up with the stream and proper batch sizes, we finally managed to get it running in a reliable way and are very happy with this solution today.

      #ServerlessTaskProcessing #GeneralAnalytics #RealTimeDataProcessing #BigDataAsAService

      See more
      Celery logo

      Celery

      1.3K
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      Distributed task queue
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      265
      PROS OF CELERY
      • 94
        Task queue
      • 61
        Python integration
      • 37
        Django integration
      • 29
        Scheduled Task
      • 18
        Publish/subsribe
      • 6
        Easy to use
      • 6
        Various backend broker
      • 5
        Great community
      • 4
        Workflow
      • 4
        Free
      • 1
        Dynamic
      CONS OF CELERY
      • 4
        Sometimes loses tasks
      • 1
        Depends on broker

      related Celery posts

      James Cunningham
      Operations Engineer at Sentry · | 18 upvotes · 1.3M views
      Shared insights
      on
      CeleryCeleryRabbitMQRabbitMQ
      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

      See more
      Pulkit Sapra

      Hi! I am creating a scraping system in Django, which involves long running tasks between 1 minute & 1 Day. As I am new to Message Brokers and Task Queues, I need advice on which architecture to use for my system. ( Amazon SQS, RabbitMQ, or Celery). The system should be autoscalable using Kubernetes(K8) based on the number of pending tasks in the queue.

      See more