Alternatives to Apollo logo

Alternatives to Apollo

Helios, GraphQL, Python, Relay Framework, and Heroku are the most popular alternatives and competitors to Apollo.
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What is Apollo and what are its top alternatives?

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.
Apollo is a tool in the Platform as a Service category of a tech stack.
Apollo is an open source tool with 12K GitHub stars and 1.8K GitHub forks. Here’s a link to Apollo's open source repository on GitHub

Top Alternatives to Apollo

  • Helios

    Helios

    Helios is a Docker orchestration platform for deploying and managing containers across an entire fleet of servers. Helios provides a HTTP API as well as a command-line client to interact with servers running your containers. ...

  • GraphQL

    GraphQL

    GraphQL is a data query language and runtime designed and used at Facebook to request and deliver data to mobile and web apps since 2012. ...

  • 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. ...

  • Relay Framework

    Relay Framework

    Never again communicate with your data store using an imperative API. Simply declare your data requirements using GraphQL and let Relay figure out how and when to fetch your data. ...

  • 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. ...

  • 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. ...

  • 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. ...

  • Red Hat OpenShift

    Red Hat OpenShift

    OpenShift is Red Hat's Cloud Computing Platform as a Service (PaaS) offering. OpenShift is an application platform in the cloud where application developers and teams can build, test, deploy, and run their applications. ...

Apollo alternatives & related posts

Helios logo

Helios

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60
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Docker container orchestration platform, by Spotify
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+ 1
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PROS OF HELIOS
    Be the first to leave a pro
    CONS OF HELIOS
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      related Helios posts

      GraphQL logo

      GraphQL

      22.2K
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      A data query language and runtime
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      PROS OF GRAPHQL
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        Schemas defined by the requests made by the user
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        Will replace RESTful interfaces
      • 59
        The future of API's
      • 47
        The future of databases
      • 12
        Self-documenting
      • 11
        Get many resources in a single request
      • 5
        Ask for what you need, get exactly that
      • 4
        Query Language
      • 3
        Evolve your API without versions
      • 3
        Fetch different resources in one request
      • 3
        Type system
      • 2
        GraphiQL
      • 2
        Ease of client creation
      • 2
        Easy setup
      • 1
        Good for apps that query at build time. (SSR/Gatsby)
      • 1
        Backed by Facebook
      • 1
        Easy to learn
      • 1
        "Open" document
      • 1
        Better versioning
      • 1
        Standard
      • 1
        1. Describe your data
      • 1
        Fast prototyping
      CONS OF GRAPHQL
      • 3
        Hard to migrate from GraphQL to another technology
      • 3
        More code to type.
      • 1
        Works just like any other API at runtime
      • 1
        Takes longer to build compared to schemaless.

      related GraphQL posts

      Shared insights
      on
      Node.jsNode.jsGraphQLGraphQLMongoDBMongoDB

      I just finished the very first version of my new hobby project: #MovieGeeks. It is a minimalist online movie catalog for you to save the movies you want to see and for rating the movies you already saw. This is just the beginning as I am planning to add more features on the lines of sharing and discovery

      For the #BackEnd I decided to use Node.js , GraphQL and MongoDB:

      1. Node.js has a huge community so it will always be a safe choice in terms of libraries and finding solutions to problems you may have

      2. GraphQL because I needed to improve my skills with it and because I was never comfortable with the usual REST approach. I believe GraphQL is a better option as it feels more natural to write apis, it improves the development velocity, by definition it fixes the over-fetching and under-fetching problem that is so common on REST apis, and on top of that, the community is getting bigger and bigger.

      3. MongoDB was my choice for the database as I already have a lot of experience working on it and because, despite of some bad reputation it has acquired in the last months, I still believe it is a powerful database for at least a very long list of use cases such as the one I needed for my website

      See more
      Nick Rockwell
      SVP, Engineering at Fastly · | 44 upvotes · 1.7M 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
      Python logo

      Python

      150.7K
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      A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
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      PROS OF PYTHON
      • 1.1K
        Great libraries
      • 933
        Readable code
      • 824
        Beautiful code
      • 771
        Rapid development
      • 674
        Large community
      • 420
        Open source
      • 380
        Elegant
      • 270
        Great community
      • 262
        Object oriented
      • 209
        Dynamic typing
      • 71
        Great standard library
      • 53
        Very fast
      • 50
        Functional programming
      • 37
        Scientific computing
      • 36
        Easy to learn
      • 31
        Great documentation
      • 25
        Matlab alternative
      • 23
        Productivity
      • 23
        Easy to read
      • 20
        Simple is better than complex
      • 18
        It's the way I think
      • 17
        Imperative
      • 15
        Very programmer and non-programmer friendly
      • 15
        Free
      • 14
        Powerfull language
      • 14
        Powerful
      • 13
        Fast and simple
      • 12
        Scripting
      • 11
        Machine learning support
      • 9
        Explicit is better than implicit
      • 8
        Ease of development
      • 8
        Unlimited power
      • 8
        Clear and easy and powerfull
      • 7
        Import antigravity
      • 6
        Print "life is short, use python"
      • 6
        It's lean and fun to code
      • 5
        Fast coding and good for competitions
      • 5
        Flat is better than nested
      • 5
        There should be one-- and preferably only one --obvious
      • 5
        Python has great libraries for data processing
      • 5
        High Documented language
      • 5
        I love snakes
      • 5
        Although practicality beats purity
      • 5
        Great for tooling
      • 4
        Readability counts
      • 3
        Plotting
      • 3
        CG industry needs
      • 3
        Beautiful is better than ugly
      • 3
        Complex is better than complicated
      • 3
        Great for analytics
      • 3
        Multiple Inheritence
      • 3
        Now is better than never
      • 3
        Lists, tuples, dictionaries
      • 3
        Rapid Prototyping
      • 3
        Socially engaged community
      • 2
        List comprehensions
      • 2
        Web scraping
      • 2
        Many types of collections
      • 2
        Ys
      • 2
        Easy to setup and run smooth
      • 2
        Generators
      • 2
        Special cases aren't special enough to break the rules
      • 2
        If the implementation is hard to explain, it's a bad id
      • 2
        If the implementation is easy to explain, it may be a g
      • 2
        Simple and easy to learn
      • 2
        Import this
      • 2
        No cruft
      • 2
        Easy to learn and use
      • 1
        Better outcome
      • 1
        It is Very easy , simple and will you be love programmi
      • 1
        Powerful language for AI
      • 1
        Should START with this but not STICK with This
      • 1
        Flexible and easy
      • 1
        Batteries included
      • 1
        Good
      • 1
        A-to-Z
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        Only one way to do it
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        Because of Netflix
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        Pip install everything
      • 0
        Powerful
      • 0
        Pro
      CONS OF PYTHON
      • 51
        Still divided between python 2 and python 3
      • 29
        Performance impact
      • 26
        Poor syntax for anonymous functions
      • 21
        GIL
      • 19
        Package management is a mess
      • 14
        Too imperative-oriented
      • 12
        Dynamic typing
      • 12
        Hard to understand
      • 10
        Very slow
      • 8
        Not everything is expression
      • 7
        Indentations matter a lot
      • 7
        Explicit self parameter in methods
      • 6
        No anonymous functions
      • 6
        Poor DSL capabilities
      • 6
        Incredibly slow
      • 6
        Requires C functions for dynamic modules
      • 5
        The "lisp style" whitespaces
      • 5
        Fake object-oriented programming
      • 5
        Hard to obfuscate
      • 5
        Threading
      • 4
        Circular import
      • 4
        The benevolent-dictator-for-life quit
      • 4
        Official documentation is unclear.
      • 4
        Lack of Syntax Sugar leads to "the pyramid of doom"
      • 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 · | 39 upvotes · 4.2M 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
      Nick Parsons
      Director of Developer Marketing at Stream · | 35 upvotes · 1.4M 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

      See more
      Relay Framework logo

      Relay Framework

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      A JavaScript Framework for Building Data-Driven React Applications, by Facebook
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      PROS OF RELAY FRAMEWORK
      • 1
        Relay Modern
      CONS OF RELAY FRAMEWORK
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        related Relay Framework posts

        Zach Holman

        Apollo is my favorite open source project.

        Two things you need to worry about when making a statement like that: is the tool good, and how is the tool being built?

        From a tool perspective... yeah, Apollo is great. I'm convinced that GraphQL is the way forward for me, and Apollo's just a great way to tackle it. Even beyond that, it just offers a good mentality to how you should build your database-backed app. I've used Relay in the past, back before they made a bunch of changes with Relay Modern (which all seem positive!), but switching to Apollo is just night-and-day. They've been doing better in the last 12 months or so at making smart abstractions in the React Apollo library, to the point where I'd just get these monster all-red pull requests where I can delete all my cruddy code and replace it with far fewer lines of their great abstractions.

        But from a build perspective... Apollo fares even better, I think. By this, I mean their project inertia, their progress, their ability to ship stable code — but still ship meaningful new functionality, too. They're not afraid to move their ideas in other directions (integrating with React Native, for example). Kills me to see projects that are just heads-down on their little world as the world passes them by, and so far... yeah, Apollo's been on top of it.

        Anyway, big fan. It's really changed how I write frontend code, and I feel hella confident while working with it.

        See more
        Heroku logo

        Heroku

        19.9K
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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
        • 704
          Easy deployment
        • 460
          Free for side projects
        • 374
          Huge time-saver
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          Simple scaling
        • 261
          Low devops skills required
        • 190
          Easy setup
        • 174
          Add-ons for almost everything
        • 154
          Beginner friendly
        • 150
          Better for startups
        • 133
          Low learning curve
        • 48
          Postgres hosting
        • 41
          Easy to add collaborators
        • 30
          Faster development
        • 24
          Awesome documentation
        • 19
          Focus on product, not deployment
        • 19
          Simple rollback
        • 15
          Natural companion for rails development
        • 15
          Easy integration
        • 12
          Great customer support
        • 8
          GitHub integration
        • 6
          No-ops
        • 6
          Painless & well documented
        • 4
          Free
        • 4
          I love that they make it free to launch a side project
        • 3
          Just works
        • 3
          Great UI
        • 2
          PostgreSQL forking and following
        • 2
          MySQL extension
        • 1
          Able to host stuff good like Discord Bot
        • 0
          Sec
        • 0
          Security
        CONS OF HEROKU
        • 23
          Super expensive
        • 6
          Not a whole lot of flexibility
        • 5
          No usable MySQL option
        • 5
          Storage
        • 4
          Low performance on free tier
        • 1
          24/7 support is $1,000 per month

        related Heroku posts

        Russel Werner
        Lead Engineer at StackShare · | 29 upvotes · 1.5M 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

        See more
        Simon Reymann
        Senior Fullstack Developer at QUANTUSflow Software GmbH · | 28 upvotes · 3.2M 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
        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
        • 144
          Easy to deploy
        • 108
          Auto scaling
        • 80
          Good free plan
        • 64
          Easy management
        • 58
          Scalability
        • 35
          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 · 282.1K 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
          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
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            Integrates with other aws services
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            Simple deployment
          • 44
            Fast
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            Painless
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            Free
          • 3
            Independend app container
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            Well-documented
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            Ability to be customized
          • 2
            Postgres hosting
          CONS OF AWS ELASTIC BEANSTALK
          • 2
            Charges appear automatically after exceeding free quota
          • 1
            Lots of moving parts and config
          • 0
            Slow deployments

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          Julien DeFrance
          Principal Software Engineer at Tophatter · | 16 upvotes · 2.4M 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
          Red Hat OpenShift logo

          Red Hat OpenShift

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          Red Hat's free Platform as a Service (PaaS) for hosting Java, PHP, Ruby, Python, Node.js, and Perl apps
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          PROS OF RED HAT OPENSHIFT
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            Easier than Heroku for a WordPress blog
          • 6
            PostgreSQL support
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            Good balance between Heroku and AWS for flexibility
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            Free, Easy Setup, Lot of Gear or D.I.Y Gear
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            Great Support
          • 2
            Overly complicated and over engineered in majority of e
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            Golang support
          • 2
            Its free and offer custom domain usage
          • 1
            Meteor support
          • 1
            Easy setup and great customer support
          • 1
            High Security
          • 1
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          • 1
            because it is easy to manage
          • 1
            Logging & Metrics
          • 1
            Autoscaling at a good price point
          • 1
            Great free plan with excellent support
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            This is the only free one among the three as of today
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          Conor Myhrvold
          Tech Brand Mgr, Office of CTO at Uber · | 39 upvotes · 4.2M 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)

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