Alternatives to Google Cloud Functions logo

Alternatives to Google Cloud Functions

AWS Lambda, Google App Engine, Azure Functions, Firebase, and Heroku are the most popular alternatives and competitors to Google Cloud Functions.
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What is Google Cloud Functions and what are its top alternatives?

Google Cloud Functions is a serverless platform that allows developers to build, deploy, and scale applications without managing infrastructure. Key features include automatic scaling, pay-as-you-go pricing, support for multiple programming languages, and integration with other Google Cloud services. However, Google Cloud Functions has limitations such as limited execution time (9 minutes), lack of support for custom runtimes, and slower cold start times.

  1. AWS Lambda: AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. Key features include automatic scaling, support for multiple languages, seamless integration with other AWS services, and flexible pricing options. Pros: Large community support, extensive documentation. Cons: Learning curve for beginners.

  2. Azure Functions: Azure Functions is a serverless compute service that enables you to run event-triggered code without having to manage infrastructure. Key features include support for multiple languages, pay-as-you-go pricing, seamless integration with Azure services, and monitoring and debugging tools. Pros: Tight integration with other Azure services, enterprise-ready. Cons: Less flexible pricing compared to competitors.

  3. IBM Cloud Functions: IBM Cloud Functions is a serverless platform that allows you to execute code in response to events without managing servers. Key features include support for multiple programming languages, seamless integration with other IBM Cloud services, and auto-scaling capabilities. Pros: Strong security features, enterprise-grade support. Cons: Limited third-party integrations compared to competitors.

  4. Firebase Cloud Functions: Firebase Cloud Functions is a serverless framework by Google that integrates with Firebase and Google Cloud services. Key features include real-time integration with Firebase database and Cloud Storage, support for multiple languages, and ease of use for mobile and web app developers. Pros: Tight integration with Firebase services, seamless deployment process. Cons: Limited scalability options compared to other cloud providers.

  5. Oracle Functions: Oracle Functions is a serverless platform that allows developers to build, deploy, and run applications without managing infrastructure. Key features include support for multiple languages, seamless integration with Oracle Cloud services, and customizable scaling options. Pros: Strong security and compliance features, robust monitoring tools. Cons: Limited third-party integrations compared to other cloud providers.

  6. Kubeless: Kubeless is a serverless framework built on Kubernetes that allows you to deploy functions as Kubernetes resources. Key features include support for multiple runtimes, event triggers, auto-scaling, and seamless integration with Kubernetes ecosystem. Pros: Flexibility to use any language/runtime, easy deployment process. Cons: Requires Kubernetes knowledge, less user-friendly than managed serverless platforms.

  7. OpenWhisk: Apache OpenWhisk is an open-source serverless platform that enables you to execute functions in response to events. Key features include support for multiple programming languages, event-driven architecture, auto-scaling, and extensibility through custom triggers and actions. Pros: Open-source community support, customizable architecture. Cons: Steeper learning curve, limited documentation compared to commercial offerings.

  8. Serverless Framework: Serverless Framework is an open-source tool that simplifies the deployment of serverless applications across multiple cloud providers. Key features include support for multiple cloud providers, configuration management, monitoring tools, and plugin ecosystem. Pros: Vendor-agnostic, easy to use for multi-cloud deployments. Cons: Some features limited to paid version, requires managing infrastructure configurations.

  9. IronFunctions: IronFunctions is an open-source serverless platform that allows developers to run functions in any language on any environment. Key features include multi-language support, docker-based execution, event-driven architecture, and support for multiple cloud providers. Pros: Flexibility to run functions on-premises or in the cloud, open-source community support. Cons: Limited commercial support compared to managed serverless platforms.

  10. Tencent Cloud Function: Tencent Cloud Function is a serverless compute service that enables you to run event-triggered code without provisioning or managing servers. Key features include support for multiple programming languages, seamless integration with Tencent Cloud services, pay-as-you-go pricing, and auto-scaling capabilities. Pros: Strong presence in Asia-Pacific region, competitive pricing. Cons: Limited global availability compared to other cloud providers.

Top Alternatives to Google Cloud Functions

  • AWS Lambda
    AWS Lambda

    AWS Lambda is a compute service that runs your code in response to events and automatically manages the underlying compute resources for you. You can use AWS Lambda to extend other AWS services with custom logic, or create your own back-end services that operate at AWS scale, performance, and security. ...

  • Google App Engine
    Google App Engine

    Google has a reputation for highly reliable, high performance infrastructure. With App Engine you can take advantage of the 10 years of knowledge Google has in running massively scalable, performance driven systems. App Engine applications are easy to build, easy to maintain, and easy to scale as your traffic and data storage needs grow. ...

  • Azure Functions
    Azure Functions

    Azure Functions is an event driven, compute-on-demand experience that extends the existing Azure application platform with capabilities to implement code triggered by events occurring in virtually any Azure or 3rd party service as well as on-premises systems. ...

  • 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. ...

  • Heroku
    Heroku

    Heroku is a cloud application platform – a new way of building and deploying web apps. Heroku lets app developers spend 100% of their time on their application code, not managing servers, deployment, ongoing operations, or scaling. ...

  • Knative
    Knative

    Knative provides a set of middleware components that are essential to build modern, source-centric, and container-based applications that can run anywhere: on premises, in the cloud, or even in a third-party data center ...

  • JavaScript
    JavaScript

    JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles. ...

  • Git
    Git

    Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency. ...

Google Cloud Functions alternatives & related posts

AWS Lambda logo

AWS Lambda

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Automatically run code in response to modifications to objects in Amazon S3 buckets, messages in Kinesis streams, or...
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PROS OF AWS LAMBDA
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    No infrastructure
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    Cheap
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    Quick
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    Stateless
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    No deploy, no server, great sleep
  • 12
    AWS Lambda went down taking many sites with it
  • 6
    Event Driven Governance
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    Extensive API
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    Auto scale and cost effective
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    Easy to deploy
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    VPC Support
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CONS OF AWS LAMBDA
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    Compute time limited
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    Can't execute PHP w/o significant effort

related AWS Lambda posts

Jeyabalaji Subramanian

Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

Based on the above criteria, we selected the following tools to perform the end to end data replication:

We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

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Tim Nolet

Heroku Docker GitHub Node.js hapi Vue.js AWS Lambda Amazon S3 PostgreSQL Knex.js Checkly is a fairly young company and we're still working hard to find the correct mix of product features, price and audience.

We are focussed on tech B2B, but I always wanted to serve solo developers too. So I decided to make a $7 plan.

Why $7? Simply put, it seems to be a sweet spot for tech companies: Heroku, Docker, Github, Appoptics (Librato) all offer $7 plans. They must have done a ton of research into this, so why not piggy back that and try it out.

Enough biz talk, onto tech. The challenges were:

  • Slice of a portion of the functionality so a $7 plan is still profitable. We call this the "plan limits"
  • Update API and back end services to handle and enforce plan limits.
  • Update the UI to kindly state plan limits are in effect on some part of the UI.
  • Update the pricing page to reflect all changes.
  • Keep the actual processing backend, storage and API's as untouched as possible.

In essence, we went from strictly volume based pricing to value based pricing. Here come the technical steps & decisions we made to get there.

  1. We updated our PostgreSQL schema so plans now have an array of "features". These are string constants that represent feature toggles.
  2. The Vue.js frontend reads these from the vuex store on login.
  3. Based on these values, the UI has simple v-if statements to either just show the feature or show a friendly "please upgrade" button.
  4. The hapi API has a hook on each relevant API endpoint that checks whether a user's plan has the feature enabled, or not.

Side note: We offer 10 SMS messages per month on the developer plan. However, we were not actually counting how many people were sending. We had to update our alerting daemon (that runs on Heroku and triggers SMS messages via AWS SNS) to actually bump a counter.

What we build is basically feature-toggling based on plan features. It is very extensible for future additions. Our scheduling and storage backend that actually runs users' monitoring requests (AWS Lambda) and stores the results (S3 and Postgres) has no knowledge of all of this and remained unchanged.

Hope this helps anyone building out their SaaS and is in a similar situation.

See more
Google App Engine logo

Google App Engine

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Build web applications on the same scalable systems that power Google applications
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PROS OF GOOGLE APP ENGINE
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    Easy to deploy
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    Auto scaling
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    Good free plan
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    Easy management
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    Scalability
  • 35
    Low cost
  • 32
    Comprehensive set of features
  • 28
    All services in one place
  • 22
    Simple scaling
  • 19
    Quick and reliable cloud servers
  • 6
    Granular Billing
  • 5
    Easy to develop and unit test
  • 4
    Monitoring gives comprehensive set of key indicators
  • 3
    Really easy to quickly bring up a full stack
  • 3
    Create APIs quickly with cloud endpoints
  • 2
    Mostly up
  • 2
    No Ops
CONS OF GOOGLE APP ENGINE
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    Dmitry Mukhin

    Uploadcare has built an infinitely scalable infrastructure by leveraging AWS. Building on top of AWS allows us to process 350M daily requests for file uploads, manipulations, and deliveries. When we started in 2011 the only cloud alternative to AWS was Google App Engine which was a no-go for a rather complex solution we wanted to build. We also didn’t want to buy any hardware or use co-locations.

    Our stack handles receiving files, communicating with external file sources, managing file storage, managing user and file data, processing files, file caching and delivery, and managing user interface dashboards.

    At its core, Uploadcare runs on Python. The Europython 2011 conference in Florence really inspired us, coupled with the fact that it was general enough to solve all of our challenges informed this decision. Additionally we had prior experience working in Python.

    We chose to build the main application with Django because of its feature completeness and large footprint within the Python ecosystem.

    All the communications within our ecosystem occur via several HTTP APIs, Redis, Amazon S3, and Amazon DynamoDB. We decided on this architecture so that our our system could be scalable in terms of storage and database throughput. This way we only need Django running on top of our database cluster. We use PostgreSQL as our database because it is considered an industry standard when it comes to clustering and scaling.

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    Nick Rockwell
    SVP, Engineering at Fastly · | 12 upvotes · 425.6K views

    So, the shift from Amazon EC2 to Google App Engine and generally #AWS to #GCP was a long decision and in the end, it's one that we've taken with eyes open and that we reserve the right to modify at any time. And to be clear, we continue to do a lot of stuff with AWS. But, by default, the content of the decision was, for our consumer-facing products, we're going to use GCP first. And if there's some reason why we don't think that's going to work out great, then we'll happily use AWS. In practice, that hasn't really happened. We've been able to meet almost 100% of our needs in GCP.

    So it's basically mostly Google Kubernetes Engine , we're mostly running stuff on Kubernetes right now.

    #AWStoGCPmigration #cloudmigration #migration

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    Azure Functions logo

    Azure Functions

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    Listen and react to events across your stack
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    PROS OF AZURE FUNCTIONS
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      Pay only when invoked
    • 11
      Great developer experience for C#
    • 9
      Multiple languages supported
    • 7
      Great debugging support
    • 5
      Can be used as lightweight https service
    • 4
      Easy scalability
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      WebHooks
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      Costo
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      Event driven
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      Azure component events for Storage, services etc
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      Poor developer experience for C#
    CONS OF AZURE FUNCTIONS
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      No persistent (writable) file system available
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      Poor support for Linux environments
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      Sporadic server & language runtime issues
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      Not suited for long-running applications

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    Kestas Barzdaitis
    Entrepreneur & Engineer · | 16 upvotes · 765.6K views

    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.

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    REST API for SaaS application

    I'm currently developing an Azure Functions REST API with TypeScript, tsoa, Mongoose, and Typegoose that contains simple CRUD activities. It does the job and has type-safety as well as the ability to generate OpenAPI specs for me.

    However, as the app scales up, there are more duplicated codes (for similar operations - like CRUD in each different model). It's also becoming more complex because I need to implement a multi-tenancy SaaS for both the API and the database.

    So I chose to implement a repository pattern, and I have a "feeling" that .NET and C# will make development easier because, unlike TypeScript, it includes native support for Dependency Injection and great things like LINQ.

    It wouldn't take much effort to migrate because I can easily translate interfaces and basic CRUD operations to C#. So, I'm looking for advice on whether it's worth converting from TypeScript to.NET.

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    Firebase logo

    Firebase

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    PROS OF FIREBASE
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      Easy setup
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      Real-time
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      JSON
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      Free
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      Backed by google
    • 83
      Angular adaptor
    • 68
      Reliable
    • 36
      Great customer support
    • 32
      Great documentation
    • 25
      Real-time synchronization
    • 21
      Mobile friendly
    • 18
      Rapid prototyping
    • 14
      Great security
    • 12
      Automatic scaling
    • 11
      Freakingly awesome
    • 8
      Chat
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      Angularfire is an amazing addition!
    • 8
      Super fast development
    • 6
      Built in user auth/oauth
    • 6
      Firebase hosting
    • 6
      Ios adaptor
    • 6
      Awesome next-gen backend
    • 4
      Speed of light
    • 4
      Very easy to use
    • 3
      Great
    • 3
      It's made development super fast
    • 3
      Brilliant for startups
    • 2
      Free hosting
    • 2
      Cloud functions
    • 2
      JS Offline and Sync suport
    • 2
      Low battery consumption
    • 2
      .net
    • 2
      The concurrent updates create a great experience
    • 2
      Push notification
    • 2
      I can quickly create static web apps with no backend
    • 2
      Great all-round functionality
    • 2
      Free authentication solution
    • 1
      Easy Reactjs integration
    • 1
      Google's support
    • 1
      Free SSL
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      CDN & cache out of the box
    • 1
      Easy to use
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      Large
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      Faster workflow
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      Serverless
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      Good Free Limits
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    CONS OF FIREBASE
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      Very unstable server
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      No Relational Data
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      Too many errors
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      No offline sync

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    Johnny Bell

    I was building a personal project that I needed to store items in a real time database. I am more comfortable with my Frontend skills than my backend so I didn't want to spend time building out anything in Ruby or Go.

    I stumbled on Firebase by #Google, and it was really all I needed. It had realtime data, an area for storing file uploads and best of all for the amount of data I needed it was free!

    I built out my application using tools I was familiar with, React for the framework, Redux.js to manage my state across components, and styled-components for the styling.

    Now as this was a project I was just working on in my free time for fun I didn't really want to pay for hosting. I did some research and I found Netlify. I had actually seen them at #ReactRally the year before and deployed a Gatsby site to Netlify already.

    Netlify was very easy to setup and link to my GitHub account you select a repo and pretty much with very little configuration you have a live site that will deploy every time you push to master.

    With the selection of these tools I was able to build out my application, connect it to a realtime database, and deploy to a live environment all with $0 spent.

    If you're looking to build out a small app I suggest giving these tools a go as you can get your idea out into the real world for absolutely no cost.

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    Collins Ogbuzuru
    Front-end dev at Evolve credit · | 16 upvotes · 13.1K views

    Your tech stack is solid for building a real-time messaging project.

    React and React Native are excellent choices for the frontend, especially if you want to have both web and mobile versions of your application share code.

    ExpressJS is an unopinionated framework that affords you the flexibility to use it's features at your term, which is a good start. However, I would recommend you explore Sails.js as well. Sails.js is built on top of Express.js and it provides additional features out of the box, especially the Websocket integration that your project requires.

    Don't forget to set up Graphql codegen, this would improve your dev experience (Add Typescript, if you can too).

    I don't know much about databases but you might want to consider using NO-SQL. I used Firebase real-time db and aws dynamo db on a few of my personal projects and I love they're easy to work with and offer more flexibility for a chat application.

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    Heroku logo

    Heroku

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    Build, deliver, monitor and scale web apps and APIs with a trail blazing developer experience.
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      Easy setup
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      Add-ons for almost everything
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      Beginner friendly
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      Better for startups
    • 133
      Low learning curve
    • 48
      Postgres hosting
    • 41
      Easy to add collaborators
    • 30
      Faster development
    • 24
      Awesome documentation
    • 19
      Simple rollback
    • 19
      Focus on product, not deployment
    • 15
      Natural companion for rails development
    • 15
      Easy integration
    • 12
      Great customer support
    • 8
      GitHub integration
    • 6
      Painless & well documented
    • 6
      No-ops
    • 4
      I love that they make it free to launch a side project
    • 4
      Free
    • 3
      Great UI
    • 3
      Just works
    • 2
      PostgreSQL forking and following
    • 2
      MySQL extension
    • 1
      Security
    • 1
      Able to host stuff good like Discord Bot
    • 0
      Sec
    CONS OF HEROKU
    • 27
      Super expensive
    • 9
      Not a whole lot of flexibility
    • 7
      No usable MySQL option
    • 7
      Storage
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      Low performance on free tier
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      24/7 support is $1,000 per month

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    Russel Werner
    Lead Engineer at StackShare · | 32 upvotes · 2.2M views

    StackShare Feed is built entirely with React, Glamorous, and Apollo. One of our objectives with the public launch of the Feed was to enable a Server-side rendered (SSR) experience for our organic search traffic. When you visit the StackShare Feed, and you aren't logged in, you are delivered the Trending feed experience. We use an in-house Node.js rendering microservice to generate this HTML. This microservice needs to run and serve requests independent of our Rails web app. Up until recently, we had a mono-repo with our Rails and React code living happily together and all served from the same web process. In order to deploy our SSR app into a Heroku environment, we needed to split out our front-end application into a separate repo in GitHub. The driving factor in this decision was mostly due to limitations imposed by Heroku specifically with how processes can't communicate with each other. A new SSR app was created in Heroku and linked directly to the frontend repo so it stays in-sync with changes.

    Related to this, we need a way to "deploy" our frontend changes to various server environments without building & releasing the entire Ruby application. We built a hybrid Amazon S3 Amazon CloudFront solution to host our Webpack bundles. A new CircleCI script builds the bundles and uploads them to S3. The final step in our rollout is to update some keys in Redis so our Rails app knows which bundles to serve. The result of these efforts were significant. Our frontend team now moves independently of our backend team, our build & release process takes only a few minutes, we are now using an edge CDN to serve JS assets, and we have pre-rendered React pages!

    #StackDecisionsLaunch #SSR #Microservices #FrontEndRepoSplit

    See more
    Simon Reymann
    Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.3M views

    Our whole DevOps stack consists of the following tools:

    • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
    • Respectively Git as revision control system
    • SourceTree as Git GUI
    • Visual Studio Code as IDE
    • CircleCI for continuous integration (automatize development process)
    • Prettier / TSLint / ESLint as code linter
    • SonarQube as quality gate
    • Docker as container management (incl. Docker Compose for multi-container application management)
    • VirtualBox for operating system simulation tests
    • Kubernetes as cluster management for docker containers
    • Heroku for deploying in test environments
    • nginx as web server (preferably used as facade server in production environment)
    • SSLMate (using OpenSSL) for certificate management
    • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
    • PostgreSQL as preferred database system
    • Redis as preferred in-memory database/store (great for caching)

    The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

    • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
    • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
    • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
    • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
    • Scalability: All-in-one framework for distributed systems.
    • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
    See more
    Knative logo

    Knative

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    PROS OF KNATIVE
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      On top of Kubernetes
    CONS OF KNATIVE
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      related Knative posts

      Currently been using an older version of OpenFaaS, but the new version now requires payment for things we did on the older version. Been looking for alternatives to OpenFaas that have Kafka integrations, and scale to 0 capabilities.

      looked at Apache OpenWhisk, but we run on RKE2, and my initial install of Openwhisk appears to be too out of date to support RKE2 and missing images from docker.io. So now looking at Knative. What are your thoughts? We need support to be able to process functions about 10k a min, which can vary on time of execution, between ms and mins. So looking for horizontal scaling that can be controlled by other metrics, than just cpu and ram utilization, but more so, for example if the wait is over 5 scale out.. Issue with older openfaas, was scaling on RKE2 was not working great, for example, I could get it to scale from 5 to 20 pods, but only 12 of them would ever have data, but my backlog would have 100k's of files waiting.. So even though it scaled up, it was as if the distribution of work was only being married to specific pods. If I killed the pods that had no work, they come up again with no work, if I killed one with work, then another pod would scale up and another pod would start to get work. And On occasion with hours, it would reset down to the original deployment allotment of pods, and never scale up again, until I go into Kubernetes and tell it to add more pods.

      So hoping to find a solution that doesn't require as much triage, to work with scaling, as points in time we are at higher volume and other points of time could be no volume.

      See more
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      JavaScript

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      Lightweight, interpreted, object-oriented language with first-class functions
      351.3K
      267.5K
      + 1
      8.1K
      PROS OF JAVASCRIPT
      • 1.7K
        Can be used on frontend/backend
      • 1.5K
        It's everywhere
      • 1.2K
        Lots of great frameworks
      • 897
        Fast
      • 745
        Light weight
      • 425
        Flexible
      • 392
        You can't get a device today that doesn't run js
      • 286
        Non-blocking i/o
      • 237
        Ubiquitousness
      • 191
        Expressive
      • 55
        Extended functionality to web pages
      • 49
        Relatively easy language
      • 46
        Executed on the client side
      • 30
        Relatively fast to the end user
      • 25
        Pure Javascript
      • 21
        Functional programming
      • 15
        Async
      • 13
        Full-stack
      • 12
        Setup is easy
      • 12
        Future Language of The Web
      • 12
        Its everywhere
      • 11
        Because I love functions
      • 11
        JavaScript is the New PHP
      • 10
        Like it or not, JS is part of the web standard
      • 9
        Expansive community
      • 9
        Everyone use it
      • 9
        Can be used in backend, frontend and DB
      • 9
        Easy
      • 8
        Most Popular Language in the World
      • 8
        Powerful
      • 8
        Can be used both as frontend and backend as well
      • 8
        For the good parts
      • 8
        No need to use PHP
      • 8
        Easy to hire developers
      • 7
        Agile, packages simple to use
      • 7
        Love-hate relationship
      • 7
        Photoshop has 3 JS runtimes built in
      • 7
        Evolution of C
      • 7
        It's fun
      • 7
        Hard not to use
      • 7
        Versitile
      • 7
        Its fun and fast
      • 7
        Nice
      • 7
        Popularized Class-Less Architecture & Lambdas
      • 7
        Supports lambdas and closures
      • 6
        It let's me use Babel & Typescript
      • 6
        Can be used on frontend/backend/Mobile/create PRO Ui
      • 6
        1.6K Can be used on frontend/backend
      • 6
        Client side JS uses the visitors CPU to save Server Res
      • 6
        Easy to make something
      • 5
        Clojurescript
      • 5
        Promise relationship
      • 5
        Stockholm Syndrome
      • 5
        Function expressions are useful for callbacks
      • 5
        Scope manipulation
      • 5
        Everywhere
      • 5
        Client processing
      • 5
        What to add
      • 4
        Because it is so simple and lightweight
      • 4
        Only Programming language on browser
      • 1
        Test
      • 1
        Hard to learn
      • 1
        Test2
      • 1
        Not the best
      • 1
        Easy to understand
      • 1
        Subskill #4
      • 1
        Easy to learn
      • 0
        Hard 彤
      CONS OF JAVASCRIPT
      • 22
        A constant moving target, too much churn
      • 20
        Horribly inconsistent
      • 15
        Javascript is the New PHP
      • 9
        No ability to monitor memory utilitization
      • 8
        Shows Zero output in case of ANY error
      • 7
        Thinks strange results are better than errors
      • 6
        Can be ugly
      • 3
        No GitHub
      • 2
        Slow

      related JavaScript posts

      Zach Holman

      Oof. I have truly hated JavaScript for a long time. Like, for over twenty years now. Like, since the Clinton administration. It's always been a nightmare to deal with all of the aspects of that silly language.

      But wowza, things have changed. Tooling is just way, way better. I'm primarily web-oriented, and using React and Apollo together the past few years really opened my eyes to building rich apps. And I deeply apologize for using the phrase rich apps; I don't think I've ever said such Enterprisey words before.

      But yeah, things are different now. I still love Rails, and still use it for a lot of apps I build. But it's that silly rich apps phrase that's the problem. Users have way more comprehensive expectations than they did even five years ago, and the JS community does a good job at building tools and tech that tackle the problems of making heavy, complicated UI and frontend work.

      Obviously there's a lot of things happening here, so just saying "JavaScript isn't terrible" might encompass a huge amount of libraries and frameworks. But if you're like me, yeah, give things another shot- I'm somehow not hating on JavaScript anymore and... gulp... I kinda love it.

      See more
      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 10.1M views

      How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

      Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

      Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

      https://eng.uber.com/distributed-tracing/

      (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

      Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

      See more
      Git logo

      Git

      290.2K
      174.4K
      6.6K
      Fast, scalable, distributed revision control system
      290.2K
      174.4K
      + 1
      6.6K
      PROS OF GIT
      • 1.4K
        Distributed version control system
      • 1.1K
        Efficient branching and merging
      • 959
        Fast
      • 845
        Open source
      • 726
        Better than svn
      • 368
        Great command-line application
      • 306
        Simple
      • 291
        Free
      • 232
        Easy to use
      • 222
        Does not require server
      • 27
        Distributed
      • 22
        Small & Fast
      • 18
        Feature based workflow
      • 15
        Staging Area
      • 13
        Most wide-spread VSC
      • 11
        Role-based codelines
      • 11
        Disposable Experimentation
      • 7
        Frictionless Context Switching
      • 6
        Data Assurance
      • 5
        Efficient
      • 4
        Just awesome
      • 3
        Github integration
      • 3
        Easy branching and merging
      • 2
        Compatible
      • 2
        Flexible
      • 2
        Possible to lose history and commits
      • 1
        Rebase supported natively; reflog; access to plumbing
      • 1
        Light
      • 1
        Team Integration
      • 1
        Fast, scalable, distributed revision control system
      • 1
        Easy
      • 1
        Flexible, easy, Safe, and fast
      • 1
        CLI is great, but the GUI tools are awesome
      • 1
        It's what you do
      • 0
        Phinx
      CONS OF GIT
      • 16
        Hard to learn
      • 11
        Inconsistent command line interface
      • 9
        Easy to lose uncommitted work
      • 7
        Worst documentation ever possibly made
      • 5
        Awful merge handling
      • 3
        Unexistent preventive security flows
      • 3
        Rebase hell
      • 2
        When --force is disabled, cannot rebase
      • 2
        Ironically even die-hard supporters screw up badly
      • 1
        Doesn't scale for big data

      related Git posts

      Simon Reymann
      Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.3M views

      Our whole DevOps stack consists of the following tools:

      • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
      • Respectively Git as revision control system
      • SourceTree as Git GUI
      • Visual Studio Code as IDE
      • CircleCI for continuous integration (automatize development process)
      • Prettier / TSLint / ESLint as code linter
      • SonarQube as quality gate
      • Docker as container management (incl. Docker Compose for multi-container application management)
      • VirtualBox for operating system simulation tests
      • Kubernetes as cluster management for docker containers
      • Heroku for deploying in test environments
      • nginx as web server (preferably used as facade server in production environment)
      • SSLMate (using OpenSSL) for certificate management
      • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
      • PostgreSQL as preferred database system
      • Redis as preferred in-memory database/store (great for caching)

      The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

      • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
      • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
      • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
      • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
      • Scalability: All-in-one framework for distributed systems.
      • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
      See more
      Tymoteusz Paul
      Devops guy at X20X Development LTD · | 23 upvotes · 8.3M views

      Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

      It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

      I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

      We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

      If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

      The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

      Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

      See more