Alternatives to Cesium logo

Alternatives to Cesium

three.js, Mapbox, ArcGIS, OpenLayers, and JavaScript are the most popular alternatives and competitors to Cesium.
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What is Cesium and what are its top alternatives?

it is used to create the leading web-based globe and map for visualizing dynamic data. We strive for the best possible performance, precision, visual quality, ease of use, platform support, and content.
Cesium is a tool in the Javascript Utilities & Libraries category of a tech stack.
Cesium is an open source tool with GitHub stars and GitHub forks. Here’s a link to Cesium's open source repository on GitHub

Top Alternatives to Cesium

  • three.js
    three.js

    It is a cross-browser JavaScript library and Application Programming Interface used to create and display animated 3D computer graphics in a web browser. ...

  • Mapbox
    Mapbox

    We make it possible to pin travel spots on Pinterest, find restaurants on Foursquare, and visualize data on GitHub. ...

  • ArcGIS
    ArcGIS

    It is a geographic information system for working with maps and geographic information. It is used for creating and using maps, compiling geographic data, analyzing mapped information, sharing and much more. ...

  • OpenLayers
    OpenLayers

    An opensource javascript library to load, display and render maps from multiple sources on web pages. ...

  • 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. ...

  • GitHub
    GitHub

    GitHub is the best place to share code with friends, co-workers, classmates, and complete strangers. Over three million people use GitHub to build amazing things together. ...

  • Python
    Python

    Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best. ...

Cesium alternatives & related posts

three.js logo

three.js

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A JavaScript 3D library
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PROS OF THREE.JS
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      related three.js posts

      Shared insights
      on
      BabylonJSBabylonJSthree.jsthree.jsUnityUnity

      We already have an existing 3d interactive application for windows, mac, and iOS devices and have planned to move that app to the web for high availability to different types of users. I have been searching for different options for it. Our existing application is made in Unity so we prefer to work on unity webgl but it also has its drawbacks. Other than that we are also thinking to change the tech stack to three.js or BabylonJS due to their high compatibility with the web ecosystem. I want to know which engine/library/framework we should use for the development of our 3d web application. Also with unity webgl, we want to develop all UI parts in web technologies only and will use the unity3d for 3d part only.

      Points that are very important to consider - 1. Memory optimization and allocation 2. Quality 3. Shaders 4. Materials 5. Lighting 6. Mesh editing, mesh creation at runtime 7. Ar 8. Vr 10. Support on different browsers including mobile browsers 11. Physics(gravity, collision, cloth simulation, etc.) 12. Initial load time 13. Speed and performance 14. Max vertices count. What happens when we load models exceeding max vertex count? 15. Development time 16. Learning curve (Unity3d we already working on) 17. Ease of use. What artists can do using any platform eg. in unity3d, artists can edit materials, set up lighting etc? 18. Future scope 19. Scalability 20. Integration with web ecosystem

      See more
      Shared insights
      on
      React VRReact VRthree.jsthree.jsReactReact

      I am about to create a React application that should show a 3-dimensional space where you can click and move.

      The goal is to make it accessible in the long run for VR. Important here is that it needs to be compatible with as many browsers as possible.

      I am wondering which would be a reasonable way to build this? A-Frame seems very popular but does not seem to be a good choice together with React. So the question is whether to go with plain three.js or to use one of the three.js-based Frameworks, e.g., React VR or react-three-fibre?

      I am new to VR. I am in the middle of an investigation and would appreciate the expertise of people who already gained experience in this field. I am happy to answer questions in detail if they are any. Thank you in advance.

      See more
      Mapbox logo

      Mapbox

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      Design and publish beautiful maps
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      PROS OF MAPBOX
      • 28
        Best mapping service outside of Google Maps
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        OpenStreetMap
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        Beautifully vectorable
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        Fluid user experience
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        Extensible
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        React/ RNative integration
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        3D Layers
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        Low Level API
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        Affordable
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        Great customer support
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        Custom themes
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        High data volume rendering
      CONS OF MAPBOX
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        related Mapbox posts

        Stephen Gheysens
        Lead Solutions Engineer at Inscribe · | 7 upvotes · 460.6K views

        Google Maps lets "property owners and their authorized representatives" upload indoor maps, but this appears to lack navigation ("wayfinding").

        MappedIn is a platform and has SDKs for building indoor mapping experiences (https://www.mappedin.com/) and ESRI ArcGIS also offers some indoor mapping tools (https://www.esri.com/en-us/arcgis/indoor-gis/overview). Finally, there used to be a company called LocusLabs that is now a part of Atrius and they were often integrated into airlines' apps to provide airport maps with wayfinding (https://atrius.com/solutions/personal-experiences/personal-wayfinder/).

        I previously worked at Mapbox and while I believe that it's a great platform for building map-based experiences, they don't have any simple solutions for indoor wayfinding. If I were doing this for fun as a side-project and prioritized saving money over saving time, here is what I would do:

        • Create a graph-based dataset representing the walking paths around your university, where nodes/vertexes represent the intersections of paths, and edges represent paths (literally paths outside, hallways, short path segments that represent entering rooms). You could store this in a hosted graph-based database like Neo4j, Amazon Neptune , or Azure Cosmos DB (with its Gremlin API) and use built-in "shortest path" queries, or deploy a PostgreSQL service with pgRouting.

        • Add two properties to each edge: one property for the distance between its nodes (libraries like @turf/helpers will have a distance function if you have the latitude & longitude of each node), and another property estimating the walking time (based on the distance). Once you have these values saved in a graph-based format, you should be able to easily query and find the data representation of paths between two points.

        • At this point, you'd have the routing problem solved and it would come down to building a UI. Mapbox arguably leads the industry in developer tools for custom map experiences. You could convert your nodes/edges to GeoJSON, then either upload to Mapbox and create a Tileset to visualize the paths, or add the GeoJSON to the map on the fly.

        *You might be able to use open source routing tools like OSRM (https://github.com/Project-OSRM/osrm-backend/issues/6257) or Graphhopper (instead of a custom graph database implementation), but it would likely be more involved to maintain these services.

        See more

        Which will give a better map (better view, markers options, info window) in an Android OS app?

        Leaflet with Mapbox or Leaflet with OpenStreetMap?

        See more
        ArcGIS logo

        ArcGIS

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        A geographic information system for working with maps
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        PROS OF ARCGIS
        • 7
          Reponsive
        • 4
          A lot of widgets
        • 4
          Data driven vizualisation
        • 2
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        • 2
          3D
        • 1
          Easy API
        CONS OF ARCGIS
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          related ArcGIS posts

          Stephen Gheysens
          Lead Solutions Engineer at Inscribe · | 7 upvotes · 460.6K views

          Google Maps lets "property owners and their authorized representatives" upload indoor maps, but this appears to lack navigation ("wayfinding").

          MappedIn is a platform and has SDKs for building indoor mapping experiences (https://www.mappedin.com/) and ESRI ArcGIS also offers some indoor mapping tools (https://www.esri.com/en-us/arcgis/indoor-gis/overview). Finally, there used to be a company called LocusLabs that is now a part of Atrius and they were often integrated into airlines' apps to provide airport maps with wayfinding (https://atrius.com/solutions/personal-experiences/personal-wayfinder/).

          I previously worked at Mapbox and while I believe that it's a great platform for building map-based experiences, they don't have any simple solutions for indoor wayfinding. If I were doing this for fun as a side-project and prioritized saving money over saving time, here is what I would do:

          • Create a graph-based dataset representing the walking paths around your university, where nodes/vertexes represent the intersections of paths, and edges represent paths (literally paths outside, hallways, short path segments that represent entering rooms). You could store this in a hosted graph-based database like Neo4j, Amazon Neptune , or Azure Cosmos DB (with its Gremlin API) and use built-in "shortest path" queries, or deploy a PostgreSQL service with pgRouting.

          • Add two properties to each edge: one property for the distance between its nodes (libraries like @turf/helpers will have a distance function if you have the latitude & longitude of each node), and another property estimating the walking time (based on the distance). Once you have these values saved in a graph-based format, you should be able to easily query and find the data representation of paths between two points.

          • At this point, you'd have the routing problem solved and it would come down to building a UI. Mapbox arguably leads the industry in developer tools for custom map experiences. You could convert your nodes/edges to GeoJSON, then either upload to Mapbox and create a Tileset to visualize the paths, or add the GeoJSON to the map on the fly.

          *You might be able to use open source routing tools like OSRM (https://github.com/Project-OSRM/osrm-backend/issues/6257) or Graphhopper (instead of a custom graph database implementation), but it would likely be more involved to maintain these services.

          See more
          OpenLayers logo

          OpenLayers

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          A high-performance, feature-packed library for all your mapping needs
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          PROS OF OPENLAYERS
          • 15
            Flexibility
          • 11
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          • 8
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          • 4
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          • 4
            Strong community
          • 4
            Choice of map providers
          • 3
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          • 1
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          CONS OF OPENLAYERS
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            JavaScript logo

            JavaScript

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              Like it or not, JS is part of the web standard
            • 9
              Expansive community
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              Everyone use it
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              Can be used in backend, frontend and DB
            • 9
              Easy
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            • 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
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              Love-hate relationship
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              It's fun
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              It let's me use Babel & Typescript
            • 6
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            • 6
              1.6K Can be used on frontend/backend
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            • 0
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            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
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            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 · 10.9M 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

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            Git logo

            Git

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            • 1
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            CONS OF GIT
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              Rebase hell
            • 2
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            • 1
              Doesn't scale for big data

            related Git posts

            Simon Reymann
            Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.7M 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 · 8.7M 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
            GitHub logo

            GitHub

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              All in one development service
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              IAM integration
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              Good tools support
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              Issues tracker
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              Owned by micrcosoft
            • 38
              Expensive for lone developers that want private repos
            • 15
              Relatively slow product/feature release cadence
            • 10
              API scoping could be better
            • 9
              Only 3 collaborators for private repos
            • 4
              Limited featureset for issue management
            • 3
              Does not have a graph for showing history like git lens
            • 2
              GitHub Packages does not support SNAPSHOT versions
            • 1
              No multilingual interface
            • 1
              Takes a long time to commit
            • 1
              Expensive

            related GitHub posts

            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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            Russel Werner
            Lead Engineer at StackShare · | 32 upvotes · 2.5M 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

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            Python logo

            Python

            240.7K
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            A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
            240.7K
            196.3K
            + 1
            6.9K
            PROS OF PYTHON
            • 1.2K
              Great libraries
            • 961
              Readable code
            • 846
              Beautiful code
            • 787
              Rapid development
            • 689
              Large community
            • 435
              Open source
            • 393
              Elegant
            • 282
              Great community
            • 272
              Object oriented
            • 220
              Dynamic typing
            • 77
              Great standard library
            • 59
              Very fast
            • 55
              Functional programming
            • 49
              Easy to learn
            • 45
              Scientific computing
            • 35
              Great documentation
            • 29
              Productivity
            • 28
              Easy to read
            • 28
              Matlab alternative
            • 23
              Simple is better than complex
            • 20
              It's the way I think
            • 19
              Imperative
            • 18
              Free
            • 18
              Very programmer and non-programmer friendly
            • 17
              Powerfull language
            • 17
              Machine learning support
            • 16
              Fast and simple
            • 14
              Scripting
            • 12
              Explicit is better than implicit
            • 11
              Ease of development
            • 10
              Clear and easy and powerfull
            • 9
              Unlimited power
            • 8
              It's lean and fun to code
            • 8
              Import antigravity
            • 7
              Print "life is short, use python"
            • 7
              Python has great libraries for data processing
            • 6
              Although practicality beats purity
            • 6
              Flat is better than nested
            • 6
              Great for tooling
            • 6
              Rapid Prototyping
            • 6
              Readability counts
            • 6
              High Documented language
            • 6
              I love snakes
            • 6
              Fast coding and good for competitions
            • 6
              There should be one-- and preferably only one --obvious
            • 6
              Now is better than never
            • 5
              Great for analytics
            • 5
              Lists, tuples, dictionaries
            • 4
              Easy to learn and use
            • 4
              Simple and easy to learn
            • 4
              Easy to setup and run smooth
            • 4
              Web scraping
            • 4
              CG industry needs
            • 4
              Socially engaged community
            • 4
              Complex is better than complicated
            • 4
              Multiple Inheritence
            • 4
              Beautiful is better than ugly
            • 4
              Plotting
            • 3
              If the implementation is hard to explain, it's a bad id
            • 3
              Special cases aren't special enough to break the rules
            • 3
              Pip install everything
            • 3
              List comprehensions
            • 3
              No cruft
            • 3
              Generators
            • 3
              Import this
            • 3
              It is Very easy , simple and will you be love programmi
            • 3
              Many types of collections
            • 3
              If the implementation is easy to explain, it may be a g
            • 2
              Batteries included
            • 2
              Should START with this but not STICK with This
            • 2
              Powerful language for AI
            • 2
              Can understand easily who are new to programming
            • 2
              Flexible and easy
            • 2
              Good for hacking
            • 2
              A-to-Z
            • 2
              Because of Netflix
            • 2
              Only one way to do it
            • 2
              Better outcome
            • 1
              Sexy af
            • 1
              Slow
            • 1
              Securit
            • 0
              Ni
            • 0
              Powerful
            CONS OF PYTHON
            • 53
              Still divided between python 2 and python 3
            • 28
              Performance impact
            • 26
              Poor syntax for anonymous functions
            • 22
              GIL
            • 19
              Package management is a mess
            • 14
              Too imperative-oriented
            • 12
              Hard to understand
            • 12
              Dynamic typing
            • 12
              Very slow
            • 8
              Indentations matter a lot
            • 8
              Not everything is expression
            • 7
              Incredibly slow
            • 7
              Explicit self parameter in methods
            • 6
              Requires C functions for dynamic modules
            • 6
              Poor DSL capabilities
            • 6
              No anonymous functions
            • 5
              Fake object-oriented programming
            • 5
              Threading
            • 5
              The "lisp style" whitespaces
            • 5
              Official documentation is unclear.
            • 5
              Hard to obfuscate
            • 5
              Circular import
            • 4
              Lack of Syntax Sugar leads to "the pyramid of doom"
            • 4
              The benevolent-dictator-for-life quit
            • 4
              Not suitable for autocomplete
            • 2
              Meta classes
            • 1
              Training wheels (forced indentation)

            related Python posts

            Conor Myhrvold
            Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 10.9M 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

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            Nick Parsons
            Building cool things on the internet 🛠️ at Stream · | 35 upvotes · 3.9M views

            Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.

            We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)

            We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.

            Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.

            #FrameworksFullStack #Languages

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