What is Google Cloud Pub/Sub and what are its top alternatives?
Top Alternatives to Google Cloud Pub/Sub
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. ...
RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received. ...
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. ...
It enables real-time bidirectional event-based communication. It works on every platform, browser or device, focusing equally on reliability and speed. ...
Pusher is the category leader in delightful APIs for app developers building communication and collaboration features. ...
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. ...
It is a simple to use, blazing fast, and thoroughly tested WebSocket client and server implementation. ...
Unlike traditional enterprise messaging systems, NATS has an always-on dial tone that does whatever it takes to remain available. This forms a great base for building modern, reliable, and scalable cloud and distributed systems. ...
Google Cloud Pub/Sub alternatives & related posts
- Open source17
- Written in Scala and java. Runs on JVM11
- Message broker + Streaming system8
- Avro schema integration4
- Suport Multiple clients2
- Partioned, replayable log2
- Extremely good parallelism constructs1
- Simple publisher / multi-subscriber model1
- Non-Java clients are second-class citizens30
- 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.
- 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
- Realtime backend made easy369
- Fast and responsive268
- Easy setup240
- Backed by google126
- Angular adaptor82
- Great customer support35
- Great documentation31
- Real-time synchronization25
- Mobile friendly21
- Rapid prototyping18
- Great security14
- Automatic scaling12
- Freakingly awesome11
- Angularfire is an amazing addition!8
- Super fast development8
- Firebase hosting6
- Built in user auth/oauth6
- Awesome next-gen backend6
- Ios adaptor6
- Very easy to use4
- Speed of light4
- It's made development super fast3
- Brilliant for startups3
- JS Offline and Sync suport2
- Low battery consumption2
- Push notification2
- Free hosting2
- Cloud functions2
- The concurrent updates create a great experience2
- I can quickly create static web apps with no backend2
- Great all-round functionality2
- Free authentication solution2
- CDN & cache out of the box1
- Google's support1
- Simple and easy1
- Faster workflow1
- Free SSL1
- Easy Reactjs integration1
- Easy to use1
- Good Free Limits1
- Can become expensive31
- No open source, you depend on external company16
- Scalability is not infinite15
- Not Flexible Enough9
- Cant filter queries7
- Very unstable server3
- No Relational Data3
- Too many errors2
- No offline sync2
related Firebase posts
This is my stack in Application & Data
My Utilities Tools
Google Analytics Postman Elasticsearch
My Devops Tools
Git GitHub GitLab npm Visual Studio Code Kibana Sentry BrowserStack
My Business Tools
- 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.
- An easy way to give customers realtime features55
- Easy to get started with27
- Free plan25
- Heroku Add-on12
- Easy and fast to configure and to understand11
- Azure Add-on6
- Push notification4
related Pusher posts
Which messaging service (Pusher vs. PubNub vs. Google Cloud Pub/Sub) to use for IoT?
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.
- 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?
related ws posts
- Fastest pub-sub system out there22
- Rock solid16
- Easy to grasp11
- Easy, Fast, Secure4
- Robust Security Model2
- Persistence with Jetstream supported2
- No Order1
- No Persistence1