Alternatives to Amazon Neptune logo

Alternatives to Amazon Neptune

Neo4j, GraphQL, OrientDB, JanusGraph, and Dgraph are the most popular alternatives and competitors to Amazon Neptune.
59
171
+ 1
15

What is Amazon Neptune and what are its top alternatives?

Amazon Neptune is a fully managed graph database service that enables customers to build and run applications that work with highly connected datasets. It supports popular graph models Property Graph and W3C's RDF, and provides features like ACID transactions, TinkerPop Gremlin, SPARQL, and more. However, customers have reported limitations such as the high cost associated with large datasets and the complexity of managing the service.

  1. Neo4j: Neo4j is a popular graph database management system that offers high performance, ACID transactions, and a flexible data model with powerful querying capabilities using the Cypher query language. Pros include strong community support, ease of use, and scalability. Cons may include high cost for enterprise features.

  2. OrientDB: OrientDB is a multi-model database management system that combines the power of graphs with document, key/value, object-oriented, and reactive models. Key features include distributed architecture, SQL support, indexing, and multi-master replication. Pros include flexibility in data models, while cons may include a learning curve for newcomers.

  3. ArangoDB: ArangoDB is a multi-model database management system that supports graphs, documents, and key/value models. It offers powerful graph traversal, ACID transactions, and a flexible query language (AQL). Pros include multi-model support and performance, while cons may include a less mature ecosystem compared to other options.

  4. TigerGraph: TigerGraph is a distributed graph database platform that delivers real-time deep link analytics. It offers parallel loading, querying, and computation for fast data processing. Pros include speed and scalability, while cons may include limited community support.

  5. JanusGraph: JanusGraph is an open-source, distributed graph database that supports various storage backend options such as Apache Cassandra, HBase, Google Cloud Bigtable, and more. It offers features like ACID transactions, global secondary indexing, and geo-spatial search. Pros include scalability and flexibility, while cons may include complexity in setup and configuration.

Top Alternatives to Amazon Neptune

  • Neo4j
    Neo4j

    Neo4j stores data in nodes connected by directed, typed relationships with properties on both, also known as a Property Graph. It is a high performance graph store with all the features expected of a mature and robust database, like a friendly query language and ACID transactions. ...

  • 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. ...

  • OrientDB
    OrientDB

    It is an open source NoSQL database management system written in Java. It is a Multi-model database, supporting graph, document, key/value, and object models, but the relationships are managed as in graph databases with direct connections between records. ...

  • JanusGraph
    JanusGraph

    It is a scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster. It is a transactional database that can support thousands of concurrent users executing complex graph traversals in real time. ...

  • Dgraph
    Dgraph

    Dgraph's goal is to provide Google production level scale and throughput, with low enough latency to be serving real time user queries, over terabytes of structured data. Dgraph supports GraphQL-like query syntax, and responds in JSON and Protocol Buffers over GRPC and HTTP. ...

  • 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. ...

Amazon Neptune alternatives & related posts

Neo4j logo

Neo4j

1.2K
1.4K
352
The world’s leading Graph Database
1.2K
1.4K
+ 1
352
PROS OF NEO4J
  • 70
    Cypher – graph query language
  • 61
    Great graphdb
  • 33
    Open source
  • 31
    Rest api
  • 27
    High-Performance Native API
  • 23
    ACID
  • 21
    Easy setup
  • 17
    Great support
  • 11
    Clustering
  • 9
    Hot Backups
  • 8
    Great Web Admin UI
  • 7
    Powerful, flexible data model
  • 7
    Mature
  • 6
    Embeddable
  • 5
    Easy to Use and Model
  • 4
    Best Graphdb
  • 4
    Highly-available
  • 2
    It's awesome, I wanted to try it
  • 2
    Great onboarding process
  • 2
    Great query language and built in data browser
  • 2
    Used by Crunchbase
CONS OF NEO4J
  • 9
    Comparably slow
  • 4
    Can't store a vertex as JSON
  • 1
    Doesn't have a managed cloud service at low cost

related Neo4j posts

Shared insights
on
Neo4jNeo4jKafkaKafkaMySQLMySQL

Hello Stackshare. I'm currently doing some research on real-time reporting and analytics architectures. We have a use case where 1million+ records of users, 4million+ activities, and messages that we want to report against. The start was to present it directly from MySQL, which didn't go well and puts a heavy load on the database. Anybody can suggest something where we feed the data and can report in realtime? Read some articles about ElasticSearch and Kafka https://medium.com/@D11Engg/building-scalable-real-time-analytics-alerting-and-anomaly-detection-architecture-at-dream11-e20edec91d33 EDIT: also considering Neo4j

See more
Stephen Gheysens
Lead Solutions Engineer at Inscribe · | 7 upvotes · 460.8K 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
GraphQL logo

GraphQL

33.3K
27.4K
310
A data query language and runtime
33.3K
27.4K
+ 1
310
PROS OF GRAPHQL
  • 75
    Schemas defined by the requests made by the user
  • 63
    Will replace RESTful interfaces
  • 62
    The future of API's
  • 49
    The future of databases
  • 13
    Self-documenting
  • 12
    Get many resources in a single request
  • 6
    Query Language
  • 6
    Ask for what you need, get exactly that
  • 3
    Fetch different resources in one request
  • 3
    Type system
  • 3
    Evolve your API without versions
  • 2
    Ease of client creation
  • 2
    GraphiQL
  • 2
    Easy setup
  • 1
    "Open" document
  • 1
    Fast prototyping
  • 1
    Supports subscription
  • 1
    Standard
  • 1
    Good for apps that query at build time. (SSR/Gatsby)
  • 1
    1. Describe your data
  • 1
    Better versioning
  • 1
    Backed by Facebook
  • 1
    Easy to learn
CONS OF GRAPHQL
  • 4
    Hard to migrate from GraphQL to another technology
  • 4
    More code to type.
  • 2
    Takes longer to build compared to schemaless.
  • 1
    No support for caching
  • 1
    All the pros sound like NFT pitches
  • 1
    No support for streaming
  • 1
    Works just like any other API at runtime
  • 1
    N+1 fetch problem
  • 1
    No built in security

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 · | 46 upvotes · 3.5M 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
OrientDB logo

OrientDB

75
107
14
An open source NoSQL database management system
75
107
+ 1
14
PROS OF ORIENTDB
  • 4
    Great graphdb
  • 2
    Great support
  • 2
    Open source
  • 1
    Multi-Model/Paradigm
  • 1
    ACID
  • 1
    Highly-available
  • 1
    Performance
  • 1
    Embeddable
  • 1
    Rest api
CONS OF ORIENTDB
  • 4
    Unstable

related OrientDB posts

We have an in-house build experiment management system. We produce samples as input to the next step, which then could produce 1 sample(1-1) and many samples (1 - many). There are many steps like this. So far, we are tracking genealogy (limited tracking) in the MySQL database, which is becoming hard to trace back to the original material or sample(I can give more details if required). So, we are considering a Graph database. I am requesting advice from the experts.

  1. Is a graph database the right choice, or can we manage with RDBMS?
  2. If RDBMS, which RDMS, which feature, or which approach could make this manageable or sustainable
  3. If Graph database(Neo4j, OrientDB, Azure Cosmos DB, Amazon Neptune, ArangoDB), which one is good, and what are the best practices?

I am sorry that this might be a loaded question.

See more
JanusGraph logo

JanusGraph

42
95
0
Open-source, distributed graph database
42
95
+ 1
0
PROS OF JANUSGRAPH
    Be the first to leave a pro
    CONS OF JANUSGRAPH
      Be the first to leave a con

      related JanusGraph posts

      Dgraph logo

      Dgraph

      124
      219
      9
      Fast, Distributed Graph DB
      124
      219
      + 1
      9
      PROS OF DGRAPH
      • 3
        Graphql as a query language is nice if you like apollo
      • 2
        Easy set up
      • 2
        Low learning curve
      • 1
        Open Source
      • 1
        High Performance
      CONS OF DGRAPH
        Be the first to leave a con

        related Dgraph posts

        JavaScript logo

        JavaScript

        352.8K
        268.5K
        8.1K
        Lightweight, interpreted, object-oriented language with first-class functions
        352.8K
        268.5K
        + 1
        8.1K
        PROS OF JAVASCRIPT
        • 1.7K
          Can be used on frontend/backend
        • 1.5K
          It's everywhere
        • 1.2K
          Lots of great frameworks
        • 897
          Fast
        • 745
          Light weight
        • 425
          Flexible
        • 392
          You can't get a device today that doesn't run js
        • 286
          Non-blocking i/o
        • 237
          Ubiquitousness
        • 191
          Expressive
        • 55
          Extended functionality to web pages
        • 49
          Relatively easy language
        • 46
          Executed on the client side
        • 30
          Relatively fast to the end user
        • 25
          Pure Javascript
        • 21
          Functional programming
        • 15
          Async
        • 13
          Full-stack
        • 12
          Setup is easy
        • 12
          Future Language of The Web
        • 12
          Its everywhere
        • 11
          Because I love functions
        • 11
          JavaScript is the New PHP
        • 10
          Like it or not, JS is part of the web standard
        • 9
          Expansive community
        • 9
          Everyone use it
        • 9
          Can be used in backend, frontend and DB
        • 9
          Easy
        • 8
          Most Popular Language in the World
        • 8
          Powerful
        • 8
          Can be used both as frontend and backend as well
        • 8
          For the good parts
        • 8
          No need to use PHP
        • 8
          Easy to hire developers
        • 7
          Agile, packages simple to use
        • 7
          Love-hate relationship
        • 7
          Photoshop has 3 JS runtimes built in
        • 7
          Evolution of C
        • 7
          It's fun
        • 7
          Hard not to use
        • 7
          Versitile
        • 7
          Its fun and fast
        • 7
          Nice
        • 7
          Popularized Class-Less Architecture & Lambdas
        • 7
          Supports lambdas and closures
        • 6
          It let's me use Babel & Typescript
        • 6
          Can be used on frontend/backend/Mobile/create PRO Ui
        • 6
          1.6K Can be used on frontend/backend
        • 6
          Client side JS uses the visitors CPU to save Server Res
        • 6
          Easy to make something
        • 5
          Clojurescript
        • 5
          Promise relationship
        • 5
          Stockholm Syndrome
        • 5
          Function expressions are useful for callbacks
        • 5
          Scope manipulation
        • 5
          Everywhere
        • 5
          Client processing
        • 5
          What to add
        • 4
          Because it is so simple and lightweight
        • 4
          Only Programming language on browser
        • 1
          Test
        • 1
          Hard to learn
        • 1
          Test2
        • 1
          Not the best
        • 1
          Easy to understand
        • 1
          Subskill #4
        • 1
          Easy to learn
        • 0
          Hard 彤
        CONS OF JAVASCRIPT
        • 22
          A constant moving target, too much churn
        • 20
          Horribly inconsistent
        • 15
          Javascript is the New PHP
        • 9
          No ability to monitor memory utilitization
        • 8
          Shows Zero output in case of ANY error
        • 7
          Thinks strange results are better than errors
        • 6
          Can be ugly
        • 3
          No GitHub
        • 2
          Slow

        related JavaScript posts

        Zach Holman

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

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

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

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

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

        See more
        Git logo

        Git

        291.8K
        175K
        6.6K
        Fast, scalable, distributed revision control system
        291.8K
        175K
        + 1
        6.6K
        PROS OF GIT
        • 1.4K
          Distributed version control system
        • 1.1K
          Efficient branching and merging
        • 959
          Fast
        • 845
          Open source
        • 726
          Better than svn
        • 368
          Great command-line application
        • 306
          Simple
        • 291
          Free
        • 232
          Easy to use
        • 222
          Does not require server
        • 27
          Distributed
        • 22
          Small & Fast
        • 18
          Feature based workflow
        • 15
          Staging Area
        • 13
          Most wide-spread VSC
        • 11
          Role-based codelines
        • 11
          Disposable Experimentation
        • 7
          Frictionless Context Switching
        • 6
          Data Assurance
        • 5
          Efficient
        • 4
          Just awesome
        • 3
          Github integration
        • 3
          Easy branching and merging
        • 2
          Compatible
        • 2
          Flexible
        • 2
          Possible to lose history and commits
        • 1
          Rebase supported natively; reflog; access to plumbing
        • 1
          Light
        • 1
          Team Integration
        • 1
          Fast, scalable, distributed revision control system
        • 1
          Easy
        • 1
          Flexible, easy, Safe, and fast
        • 1
          CLI is great, but the GUI tools are awesome
        • 1
          It's what you do
        • 0
          Phinx
        CONS OF GIT
        • 16
          Hard to learn
        • 11
          Inconsistent command line interface
        • 9
          Easy to lose uncommitted work
        • 7
          Worst documentation ever possibly made
        • 5
          Awful merge handling
        • 3
          Unexistent preventive security flows
        • 3
          Rebase hell
        • 2
          When --force is disabled, cannot rebase
        • 2
          Ironically even die-hard supporters screw up badly
        • 1
          Doesn't scale for big data

        related Git posts

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

        280.7K
        245K
        10.3K
        Powerful collaboration, review, and code management for open source and private development projects
        280.7K
        245K
        + 1
        10.3K
        PROS OF GITHUB
        • 1.8K
          Open source friendly
        • 1.5K
          Easy source control
        • 1.3K
          Nice UI
        • 1.1K
          Great for team collaboration
        • 867
          Easy setup
        • 504
          Issue tracker
        • 486
          Great community
        • 483
          Remote team collaboration
        • 451
          Great way to share
        • 442
          Pull request and features planning
        • 147
          Just works
        • 132
          Integrated in many tools
        • 121
          Free Public Repos
        • 116
          Github Gists
        • 112
          Github pages
        • 83
          Easy to find repos
        • 62
          Open source
        • 60
          It's free
        • 60
          Easy to find projects
        • 56
          Network effect
        • 49
          Extensive API
        • 43
          Organizations
        • 42
          Branching
        • 34
          Developer Profiles
        • 32
          Git Powered Wikis
        • 30
          Great for collaboration
        • 24
          It's fun
        • 23
          Clean interface and good integrations
        • 22
          Community SDK involvement
        • 20
          Learn from others source code
        • 16
          Because: Git
        • 14
          It integrates directly with Azure
        • 10
          Standard in Open Source collab
        • 10
          Newsfeed
        • 8
          It integrates directly with Hipchat
        • 8
          Fast
        • 8
          Beautiful user experience
        • 7
          Easy to discover new code libraries
        • 6
          Smooth integration
        • 6
          Cloud SCM
        • 6
          Nice API
        • 6
          Graphs
        • 6
          Integrations
        • 6
          It's awesome
        • 5
          Quick Onboarding
        • 5
          Reliable
        • 5
          Remarkable uptime
        • 5
          CI Integration
        • 5
          Hands down best online Git service available
        • 4
          Uses GIT
        • 4
          Version Control
        • 4
          Simple but powerful
        • 4
          Unlimited Public Repos at no cost
        • 4
          Free HTML hosting
        • 4
          Security options
        • 4
          Loved by developers
        • 4
          Easy to use and collaborate with others
        • 3
          Ci
        • 3
          IAM
        • 3
          Nice to use
        • 3
          Easy deployment via SSH
        • 2
          Easy to use
        • 2
          Leads the copycats
        • 2
          All in one development service
        • 2
          Free private repos
        • 2
          Free HTML hostings
        • 2
          Easy and efficient maintainance of the projects
        • 2
          Beautiful
        • 2
          Easy source control and everything is backed up
        • 2
          IAM integration
        • 2
          Very Easy to Use
        • 2
          Good tools support
        • 2
          Issues tracker
        • 2
          Never dethroned
        • 2
          Self Hosted
        • 1
          Dasf
        • 1
          Profound
        CONS OF GITHUB
        • 54
          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.

        See more
        Russel Werner
        Lead Engineer at StackShare · | 32 upvotes · 2.6M 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