Google Compute Engine vs Socket.IO: What are the differences?
What is Google Compute Engine? Run large-scale workloads on virtual machines hosted on Google's infrastructure. Google Compute Engine is a service that provides virtual machines that run on Google infrastructure. Google Compute Engine offers scale, performance, and value that allows you to easily launch large compute clusters on Google's infrastructure. There are no upfront investments and you can run up to thousands of virtual CPUs on a system that has been designed from the ground up to be fast, and to offer strong consistency of performance.
What is Socket.IO? Realtime application framework (Node.JS server). Socket.IO enables real-time bidirectional event-based communication. It works on every platform, browser or device, focusing equally on reliability and speed.
Google Compute Engine and Socket.IO are primarily classified as "Cloud Hosting" and "Realtime Backend / API" tools respectively.
Some of the features offered by Google Compute Engine are:
- High-performance virtual machines- Compute Engine’s Linux VMs are consistently performant, scalable, highly secure and reliable. Supported distros include Debian and CentOS. You can choose from micro-VMs to large instances.
- Powered by Google’s global network- Create large compute clusters that benefit from strong and consistent cross-machine bandwidth. Connect to machines in other data centers and to other Google services using Google’s private global fiber network.
- (Really) Pay for what you use- Google bills in minute-level increments (with a 10-minute minimum charge), so you don’t pay for unused computing time.
On the other hand, Socket.IO provides the following key features:
- Real-time analytics - Push data to clients that gets represented as real-time counters, charts or logs.
- Binary streaming - Starting in 1.0, it's possible to send any blob back and forth: image, audio, video.
- Instant messaging and chat - Socket.IO's "Hello world" is a chat app in just a few lines of code.
"Backed by google", "Easy to scale" and "High-performance virtual machines" are the key factors why developers consider Google Compute Engine; whereas "Real-time", "Event-based communication" and "Node.js" are the primary reasons why Socket.IO is favored.
Socket.IO is an open source tool with 46.9K GitHub stars and 8.54K GitHub forks. Here's a link to Socket.IO's open source repository on GitHub.
9GAG, Snapchat, and CircleCI are some of the popular companies that use Google Compute Engine, whereas Socket.IO is used by Rainist, PedidosYa, and Trello. Google Compute Engine has a broader approval, being mentioned in 592 company stacks & 428 developers stacks; compared to Socket.IO, which is listed in 560 company stacks and 395 developer stacks.
What is Google Compute Engine?
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CodeFactor being a #SAAS product, our goal was to run on a cloud-native infrastructure since day one. We wanted to stay product focused, rather than having to work on the infrastructure that supports the application. We needed a cloud-hosting provider that would be reliable, economical and most efficient for our product.
CodeFactor.io aims to provide an automated and frictionless code review service for software developers. That requires agility, instant provisioning, autoscaling, security, availability and compliance management features. We looked at the top three #IAAS providers that take up the majority of market share: Amazon's Amazon EC2 , Microsoft's Microsoft Azure, and Google Compute Engine.
AWS has been available since 2006 and has developed the most extensive services ant tools variety at a massive scale. Azure and GCP are about half the AWS age, but also satisfied our technical requirements.
It is worth noting that even though all three providers support Docker containerization services, GCP has the most robust offering due to their investments in Kubernetes. Also, if you are a Microsoft shop, and develop in .NET - Visual Studio Azure shines at integration there and all your existing .NET code works seamlessly on Azure. All three providers have serverless computing offerings (AWS Lambda, Azure Functions, and Google Cloud Functions). Additionally, all three providers have machine learning tools, but GCP appears to be the most developer-friendly, intuitive and complete when it comes to #Machinelearning and #AI.
The prices between providers are competitive across the board. For our requirements, AWS would have been the most expensive, GCP the least expensive and Azure was in the middle. Plus, if you #Autoscale frequently with large deltas, note that Azure and GCP have per minute billing, where AWS bills you per hour. We also applied for the #Startup programs with all three providers, and this is where Azure shined. While AWS and GCP for startups would have covered us for about one year of infrastructure costs, Azure Sponsorship would cover about two years of CodeFactor's hosting costs. Moreover, Azure Team was terrific - I felt that they wanted to work with us where for AWS and GCP we were just another startup.
In summary, we were leaning towards GCP. GCP's advantages in containerization, automation toolset, #Devops mindset, and pricing were the driving factors there. Nevertheless, we could not say no to Azure's financial incentives and a strong sense of partnership and support throughout the process.
Bottom line is, IAAS offerings with AWS, Azure, and GCP are evolving fast. At CodeFactor, we aim to be platform agnostic where it is practical and retain the flexibility to cherry-pick the best products across providers.
Google Compute Engine Amazon Web Services OVH Microsoft Azure Go GitHub
Last week, we released a fresh new release of Komiser with support of multiple AWS accounts. Komiser support multiple AWS accounts through named profiles that are stored in the credentials files.
You can now analyze and identify potential cost savings on unlimited AWS environments (Production, Staging, Sandbox, etc) on one single dashboard.
Read the full story in the blog post.
Google Compute Engine Amazon Web Services Go Docker Material Design for Angular Microsoft Azure GitHub I’m super excited to annonce the release of Komiser:2.1.0 with beta support of Google Cloud Platform. You can now use one single open source tool to detect both AWS and GCP overspending.
Komiser allows you to analyze and manage #cloud cost, usage, #security, and governance in one place. Hence, detecting potential vulnerabilities that could put your cloud environment at risk.
It allows you also to control your usage and create visibility across all used services to achieve maximum cost-effectiveness and get a deep understanding of how you spend on the #AWS, #GCP and #Azure.
I use Google Compute Engine instances as flexible, reproducible infrastructure that scale with my data science tasks.
Between Google Cloud and Amazon Web Services, I chose Google Cloud for its intuitive UI. SSH within the browser is very convenient.
Related blog post with example usage: Running an IPython Notebook on Google Compute Engine from Chrome
I use Socket.IO because using HTTP requests for a real-time multiplayer game just blows! Even with websockets, I had to scrunch the data being transmitted down to a bare minimum, and do some cheap compression tricks so that I can send data in JSON format. Otherwise, I would have to resort to sending binary data. I may end up doing that anyway when the time comes that I need to scale.
How do I use it? Each client opens a socket connection at startup. The server keeps track of these connections, and sends each client the visible portion of the Playfield repeatedly. The clients render this information, while sending requests and commands to the server (join,turn,fire,thrust,bomb,viewport change,etc.) in response to the player's actions. The server uses that to make adjustments to the player's ship on the Playfield.
Where we have browser support (recent Chrome, Firefox, and Safari), we make a WebSocket connection so that the server can push changes made by other people down to browsers listening on the appropriate channels. We use a modified version* of the Socket.io client and server libraries that allows us to keep many thousands of open WebSockets on each of our servers at very little cost in terms of CPU or memory usage. So when anything happens to a board you’re watching, that action is published to our server processes and propagated to your watching browser with very minimal latency, usually well under a second.
- I use Google Compute Engine instances as flexible, reproducible infrastructure that scales with my data science tasks.
- Between Google Cloud and Amazon Web Services, I chose Google Cloud for its intuitive UI. SSH within the browser is very convenient.
- Related blog post with example usage: Running an IPython Notebook on Google Compute Engine from Chrome
Socket.IO has a decent community footprint, including integrations with popular JS frameworks, and has fallbacks to maintain an app's services if websockets are not available for some reason. Websockets are an important factor in most of the web-facing apps I build, to provide asynchronous two-way communication between the app and whatever server or data source it is connected to.
Another one that we're not using, yet. But have realtime data updates within our applications and the central API will be a great bit of functionality that gives our clients more control and keep them informed of changes and updates in their stores, in real time.
Socket.io is used as our current multiplayer engine. The existing engine is very simplistic and only utilizes the websocket+http fallback transports and serves as a generic world/zone/screen grouping mechanism for displaying users to each other.
Infrastructure for Google App Engine, Google Cloud Endpoints, Memcached, and Google Cloud SQL components, as well as Git repository and Jenkins CI server.