Alternatives to Jelastic logo

Alternatives to Jelastic

Google App Engine, Amazon EBS, DigitalOcean, Kubernetes, and Heroku are the most popular alternatives and competitors to Jelastic.
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What is Jelastic and what are its top alternatives?

Jelastic is a Multi-Cloud DevOps PaaS for ISVs, telcos, service providers and enterprises needing to speed up development, reduce cost of IT infrastructure, improve uptime and security.
Jelastic is a tool in the Platform as a Service category of a tech stack.

Top Alternatives to Jelastic

  • 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. ...

  • Amazon EBS

    Amazon EBS

    Amazon EBS volumes are network-attached, and persist independently from the life of an instance. Amazon EBS provides highly available, highly reliable, predictable storage volumes that can be attached to a running Amazon EC2 instance and exposed as a device within the instance. Amazon EBS is particularly suited for applications that require a database, file system, or access to raw block level storage. ...

  • DigitalOcean

    DigitalOcean

    We take the complexities out of cloud hosting by offering blazing fast, on-demand SSD cloud servers, straightforward pricing, a simple API, and an easy-to-use control panel. ...

  • Kubernetes

    Kubernetes

    Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions. ...

  • 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. ...

  • Cloud Foundry

    Cloud Foundry

    Cloud Foundry is an open platform as a service (PaaS) that provides a choice of clouds, developer frameworks, and application services. Cloud Foundry makes it faster and easier to build, test, deploy, and scale applications. ...

  • AWS Elastic Beanstalk

    AWS Elastic Beanstalk

    Once you upload your application, Elastic Beanstalk automatically handles the deployment details of capacity provisioning, load balancing, auto-scaling, and application health monitoring. ...

  • Apollo

    Apollo

    Build a universal GraphQL API on top of your existing REST APIs, so you can ship new application features fast without waiting on backend changes. ...

Jelastic alternatives & related posts

Google App Engine logo

Google App Engine

6.6K
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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
  • 143
    Easy to deploy
  • 108
    Auto scaling
  • 80
    Good free plan
  • 64
    Easy management
  • 58
    Scalability
  • 36
    Low cost
  • 33
    Comprehensive set of features
  • 29
    All services in one place
  • 23
    Simple scaling
  • 20
    Quick and reliable cloud servers
  • 5
    Granular Billing
  • 4
    Easy to develop and unit test
  • 3
    Monitoring gives comprehensive set of key indicators
  • 2
    Create APIs quickly with cloud endpoints
  • 2
    Really easy to quickly bring up a full stack
  • 1
    Mostly up
  • 1
    No Ops
CONS OF GOOGLE APP ENGINE
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    related Google App Engine posts

    Nick Rockwell
    SVP, Engineering at Fastly · | 11 upvotes · 240K 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

    See more
    Aliadoc Team

    In #Aliadoc, we're exploring the crowdfunding option to get traction before launch. We are building a SaaS platform for website design customization.

    For the Admin UI and website editor we use React and we're currently transitioning from a Create React App setup to a custom one because our needs have become more specific. We use CloudFlare as much as possible, it's a great service.

    For routing dynamic resources and proxy tasks to feed websites to the editor we leverage CloudFlare Workers for improved responsiveness. We use Firebase for our hosting needs and user authentication while also using several Cloud Functions for Firebase to interact with other services along with Google App Engine and Google Cloud Storage, but also the Real Time Database is on the radar for collaborative website editing.

    We generally hate configuration but honestly because of the stage of our project we lack resources for doing heavy sysops work. So we are basically just relying on Serverless technologies as much as we can to do all server side processing.

    Visual Studio Code definitively makes programming a much easier and enjoyable task, we just love it. We combine it with Bitbucket for our source code control needs.

    See more
    Amazon EBS logo

    Amazon EBS

    640
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    Block level storage volumes for use with Amazon EC2 instances.
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    PROS OF AMAZON EBS
    • 35
      Point-in-time snapshots
    • 27
      Data reliability
    • 19
      Configurable i/o performance
    CONS OF AMAZON EBS
      Be the first to leave a con

      related Amazon EBS posts

      We are looking for a centralised monitoring solution for our application deployed on Amazon EKS. We would like to monitor using metrics from Kubernetes, AWS services (NeptuneDB, AWS Elastic Load Balancing (ELB), Amazon EBS, Amazon S3, etc) and application microservice's custom metrics.

      We are expected to use around 80 microservices (not replicas). I think a total of 200-250 microservices will be there in the system with 10-12 slave nodes.

      We tried Prometheus but it looks like maintenance is a big issue. We need to manage scaling, maintaining the storage, and dealing with multiple exporters and Grafana. I felt this itself needs few dedicated resources (at least 2-3 people) to manage. Not sure if I am thinking in the correct direction. Please confirm.

      You mentioned Datadog and Sysdig charges per host. Does it charge per slave node?

      See more
      DigitalOcean logo

      DigitalOcean

      12.4K
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      Deploy an SSD cloud server in less than 55 seconds with a dedicated IP and root access.
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      PROS OF DIGITALOCEAN
      • 557
        Great value for money
      • 363
        Simple dashboard
      • 355
        Good pricing
      • 300
        Ssds
      • 248
        Nice ui
      • 192
        Easy configuration
      • 155
        Great documentation
      • 137
        Ssh access
      • 134
        Great community
      • 26
        Ubuntu
      • 12
        IPv6 support
      • 12
        Docker
      • 10
        Private networking
      • 7
        Great tutorials
      • 7
        Simple API
      • 7
        99.99% uptime SLA
      • 6
        55 Second Provisioning
      • 5
        One Click Applications
      • 4
        Node.js
      • 4
        CoreOS
      • 4
        LAMP
      • 4
        Dokku
      • 4
        Debian
      • 3
        Ghost
      • 3
        1Gb/sec Servers
      • 3
        Simple Control Panel
      • 3
        LEMP
      • 3
        Word Press
      • 2
        Runs CoreOS
      • 2
        Mean
      • 2
        Speed
      • 2
        GitLab
      • 2
        Django
      • 2
        Quick and no nonsense service
      • 2
        Good Tutorials
      • 2
        Ruby on Rails
      • 2
        Hex Core machines with dedicated ECC Ram and RAID SSD s
      • 1
        Spaces
      • 1
        My go to server provider
      • 1
        Ease and simplicity
      • 1
        Nice
      • 1
        Find it superfitting with my requirements (SSD, ssh.
      • 1
        Easy Setup
      • 1
        Transfer Globally
      • 1
        Drupal
      • 1
        FreeBSD Amp
      • 1
        Amazing Hardware
      • 1
        Magento
      • 1
        KVM Virtualization
      • 1
        ownCloud
      • 1
        RedMine
      • 1
        CentOS
      • 1
        Fedora
      • 1
        FreeBSD
      • 1
        Cheap
      • 1
        Static IP
      • 1
        It's the easiest to get started for small projects
      • 1
        Automatic Backup
      • 1
        Great support
      • 1
        Quick and easy to set up
      • 1
        Servers on demand - literally
      • 1
        Reliability
      • 0
        Variety of services
      • 0
        Managed Kubernetes
      CONS OF DIGITALOCEAN
      • 2
        Pricing
      • 2
        No live support chat

      related DigitalOcean posts

      I am going to build a backend which will serve my React site. It will need to interact with a PostgreSQL database where it will store and read users and create and use JSON Web Token for authenticating HTTP requests. I know EF core has good migration tooling, can Go provide the same or better? I am a one man team and I'll be hosting this either on Heroku or DigitalOcean.

      See more
      Rajat Jain
      Devops Engineer at Aurochssoftware · | 1 upvote · 159.2K views

      Building my skill set to become Devops Engineer-Tool chain: Amazon EC2, Amazon S3, Bitbucket, GitLab, PyCharm, Ubuntu, DigitalOcean, Docker, Git

      IT engineer with more than 6 months of experience in startups with focus on DevOps, Cloud infrastructure & Testing (QA). I had set up CI process, monitoring and infrastructure on dev/test (lower) environments

      See more
      Kubernetes logo

      Kubernetes

      30.4K
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      Manage a cluster of Linux containers as a single system to accelerate Dev and simplify Ops
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      PROS OF KUBERNETES
      • 151
        Leading docker container management solution
      • 121
        Simple and powerful
      • 95
        Open source
      • 70
        Backed by google
      • 55
        The right abstractions
      • 24
        Scale services
      • 16
        Replication controller
      • 9
        Permission managment
      • 6
        Simple
      • 5
        Supports autoscaling
      • 5
        Cheap
      • 3
        Promotes modern/good infrascture practice
      • 3
        No cloud platform lock-in
      • 3
        Self-healing
      • 3
        Open, powerful, stable
      • 3
        Scalable
      • 3
        Reliable
      • 2
        A self healing environment with rich metadata
      • 2
        Captain of Container Ship
      • 2
        Quick cloud setup
      • 1
        Custom and extensibility
      • 1
        Expandable
      • 1
        Easy setup
      • 1
        Gke
      • 1
        Golang
      • 1
        Backed by Red Hat
      • 1
        Everything of CaaS
      • 1
        Runs on azure
      • 1
        Cloud Agnostic
      • 1
        Sfg
      CONS OF KUBERNETES
      • 13
        Poor workflow for development
      • 10
        Steep learning curve
      • 4
        Orchestrates only infrastructure
      • 2
        High resource requirements for on-prem clusters

      related Kubernetes posts

      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 37 upvotes · 3.5M 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
      Yshay Yaacobi

      Our first experience with .NET core was when we developed our OSS feature management platform - Tweek (https://github.com/soluto/tweek). We wanted to create a solution that is able to run anywhere (super important for OSS), has excellent performance characteristics and can fit in a multi-container architecture. We decided to implement our rule engine processor in F# , our main service was implemented in C# and other components were built using JavaScript / TypeScript and Go.

      Visual Studio Code worked really well for us as well, it worked well with all our polyglot services and the .Net core integration had great cross-platform developer experience (to be fair, F# was a bit trickier) - actually, each of our team members used a different OS (Ubuntu, macos, windows). Our production deployment ran for a time on Docker Swarm until we've decided to adopt Kubernetes with almost seamless migration process.

      After our positive experience of running .Net core workloads in containers and developing Tweek's .Net services on non-windows machines, C# had gained back some of its popularity (originally lost to Node.js), and other teams have been using it for developing microservices, k8s sidecars (like https://github.com/Soluto/airbag), cli tools, serverless functions and other projects...

      See more
      Heroku logo

      Heroku

      17K
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      Build, deliver, monitor and scale web apps and APIs with a trail blazing developer experience.
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      PROS OF HEROKU
      • 703
        Easy deployment
      • 460
        Free for side projects
      • 374
        Huge time-saver
      • 348
        Simple scaling
      • 261
        Low devops skills required
      • 189
        Easy setup
      • 174
        Add-ons for almost everything
      • 153
        Beginner friendly
      • 149
        Better for startups
      • 133
        Low learning curve
      • 47
        Postgres hosting
      • 41
        Easy to add collaborators
      • 30
        Faster development
      • 24
        Awesome documentation
      • 19
        Simple rollback
      • 18
        Focus on product, not deployment
      • 15
        Easy integration
      • 15
        Natural companion for rails development
      • 11
        Great customer support
      • 7
        GitHub integration
      • 6
        No-ops
      • 5
        Painless & well documented
      • 3
        Just works
      • 3
        Free
      • 2
        PostgreSQL forking and following
      • 2
        I love that they make it free to launch a side project
      • 2
        Great UI
      • 2
        MySQL extension
      CONS OF HEROKU
      • 22
        Super expensive
      • 6
        No usable MySQL option
      • 6
        Not a whole lot of flexibility
      • 5
        Storage
      • 4
        Low performance on free tier

      related Heroku posts

      Russel Werner
      Lead Engineer at StackShare · | 29 upvotes · 1.3M 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 · | 28 upvotes · 2.2M 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
      Cloud Foundry logo

      Cloud Foundry

      140
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      4
      Deploy and scale applications in seconds on your choice of private or public cloud
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      PROS OF CLOUD FOUNDRY
      • 1
        Perfectly aligned with springboot
      • 1
        Free distributed tracing (zipkin)
      • 1
        Application health management
      • 1
        Free service discovery (Eureka)
      CONS OF CLOUD FOUNDRY
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        related Cloud Foundry posts

        AWS Elastic Beanstalk logo

        AWS Elastic Beanstalk

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        Quickly deploy and manage applications in the AWS cloud.
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        PROS OF AWS ELASTIC BEANSTALK
        • 77
          Integrates with other aws services
        • 65
          Simple deployment
        • 44
          Fast
        • 28
          Painless
        • 16
          Free
        • 3
          Independend app container
        • 3
          Well-documented
        • 2
          Postgres hosting
        • 2
          Ability to be customized
        CONS OF AWS ELASTIC BEANSTALK
        • 2
          Charges appear automatically after exceeding free quota
        • 0
          Slow deployments

        related AWS Elastic Beanstalk posts

        Julien DeFrance
        Principal Software Engineer at Tophatter · | 16 upvotes · 2.2M views

        Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

        I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

        For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

        Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

        Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

        Future improvements / technology decisions included:

        Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

        As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

        One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

        See more

        We initially started out with Heroku as our PaaS provider due to a desire to use it by our original developer for our Ruby on Rails application/website at the time. We were finding response times slow, it was painfully slow, sometimes taking 10 seconds to start loading the main page. Moving up to the next "compute" level was going to be very expensive.

        We moved our site over to AWS Elastic Beanstalk , not only did response times on the site practically become instant, our cloud bill for the application was cut in half.

        In database world we are currently using Amazon RDS for PostgreSQL also, we have both MariaDB and Microsoft SQL Server both hosted on Amazon RDS. The plan is to migrate to AWS Aurora Serverless for all 3 of those database systems.

        Additional services we use for our public applications: AWS Lambda, Python, Redis, Memcached, AWS Elastic Load Balancing (ELB), Amazon Elasticsearch Service, Amazon ElastiCache

        See more
        Apollo logo

        Apollo

        1.6K
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        16
        GraphQL server for Express, Connect, Hapi, Koa and more
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        PROS OF APOLLO
        • 11
          From the creators of Meteor
        • 2
          Great documentation
        • 2
          Real time if use subscription
        • 1
          Open source
        CONS OF APOLLO
          Be the first to leave a con

          related Apollo posts

          Nick Rockwell
          SVP, Engineering at Fastly · | 42 upvotes · 1.4M views

          When I joined NYT there was already broad dissatisfaction with the LAMP (Linux Apache HTTP Server MySQL PHP) Stack and the front end framework, in particular. So, I wasn't passing judgment on it. I mean, LAMP's fine, you can do good work in LAMP. It's a little dated at this point, but it's not ... I didn't want to rip it out for its own sake, but everyone else was like, "We don't like this, it's really inflexible." And I remember from being outside the company when that was called MIT FIVE when it had launched. And been observing it from the outside, and I was like, you guys took so long to do that and you did it so carefully, and yet you're not happy with your decisions. Why is that? That was more the impetus. If we're going to do this again, how are we going to do it in a way that we're gonna get a better result?

          So we're moving quickly away from LAMP, I would say. So, right now, the new front end is React based and using Apollo. And we've been in a long, protracted, gradual rollout of the core experiences.

          React is now talking to GraphQL as a primary API. There's a Node.js back end, to the front end, which is mainly for server-side rendering, as well.

          Behind there, the main repository for the GraphQL server is a big table repository, that we call Bodega because it's a convenience store. And that reads off of a Kafka pipeline.

          See more
          Adam Neary

          At Airbnb we use GraphQL Unions for a "Backend-Driven UI." We have built a system where a very dynamic page is constructed based on a query that will return an array of some set of possible “sections.” These sections are responsive and define the UI completely.

          The central file that manages this would be a generated file. Since the list of possible sections is quite large (~50 sections today for Search), it also presumes we have a sane mechanism for lazy-loading components with server rendering, which is a topic for another post. Suffice it to say, we do not need to package all possible sections in a massive bundle to account for everything up front.

          Each section component defines its own query fragment, colocated with the section’s component code. This is the general idea of Backend-Driven UI at Airbnb. It’s used in a number of places, including Search, Trip Planner, Host tools, and various landing pages. We use this as our starting point, and then in the demo show how to (1) make and update to an existing section, and (2) add a new section.

          While building your product, you want to be able to explore your schema, discovering field names and testing out potential queries on live development data. We achieve that today with GraphQL Playground, the work of our friends at #Prisma. The tools come standard with Apollo Server.

          #BackendDrivenUI

          See more