Alternatives to Axon logo

Alternatives to Axon

Kafka, JavaScript, Git, GitHub, and Python are the most popular alternatives and competitors to Axon.
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What is Axon and what are its top alternatives?

Axon is a popular event-driven microservices framework that helps developers build scalable and resilient applications. It provides features like command and query separation, event sourcing, and CQRS (Command Query Responsibility Segregation). However, while Axon offers powerful tools for dealing with complex systems, it can have a steep learning curve and may require significant effort to set up and configure.

  1. EventFlow: EventFlow is a lightweight CQRS+ES framework that simplifies building complex, scalable applications. It provides features like event sourcing, domain-driven design, and integration with popular transports like Kafka and RabbitMQ. Pros: Easy to use, extensive documentation. Cons: Less community support compared to Axon.
  2. Lagom: Lagom is a reactive microservices framework built on Akka and Play Framework. It offers CQRS and event sourcing, scalability, and resilience out of the box. Pros: Integrated publish-subscribe capabilities, seamless clustering. Cons: Steeper learning curve for beginners.
  3. Eventuate.io: Eventuate.io is an event sourcing and CQRS platform for microservices. It provides support for distributed data management, event-driven architectures, and distributed transactions. Pros: Out-of-the-box support for multiple programming languages. Cons: Limited community contributions.
  4. EventStore: EventStore is an open-source platform for event sourcing and stream processing. It offers features like pub/sub messaging, replicated event log, and multi-node clustering. Pros: Scalable and performant, great for real-time data processing. Cons: Complex setup and configuration.
  5. Spring Cloud Stream: Spring Cloud Stream is a framework for building event-driven microservices with Spring Boot. It provides support for message-driven architectures, event-driven programming, and seamless integration with messaging platforms like Kafka and RabbitMQ. Pros: Simplified development process, extensive Spring ecosystem. Cons: Limited support for advanced CQRS patterns.
  6. Apache Kafka: Apache Kafka is a distributed streaming platform that can be used for building event-driven applications. It offers features like fault tolerance, high-throughput messaging, and real-time processing of data streams. Pros: Scalable and reliable, great for data-intensive applications. Cons: Steeper learning curve, may require additional components for full CQRS support.
  7. Microsoft Orleans: Microsoft Orleans is a virtual actor model framework for building distributed systems. It provides support for building scalable, high-performance applications with built-in concurrency management and fault tolerance. Pros: Simplified programming model, automatic reconnection and recovery. Cons: Limited support for event sourcing and CQRS out of the box.
  8. Axeda: Axeda is an Industrial IoT platform that helps businesses connect and manage their devices. It provides features like device management, remote monitoring, and predictive maintenance capabilities. Pros: Industry-specific solutions, scalable infrastructure. Cons: Less focused on CQRS and event sourcing for application development.
  9. ReactiveX: ReactiveX is a library for composing asynchronous and event-based programs with observable sequences. It offers a functional programming paradigm for handling streams of data and events. Pros: Cross-platform support, wide range of language bindings. Cons: Not specifically tailored for building CQRS applications.
  10. AWS Step Functions: AWS Step Functions is a serverless orchestration service that helps developers manage workflows and automate tasks. It provides features like state machines, parallel execution, and error handling for building complex, event-driven applications. Pros: Fully managed service, seamless integration with AWS services. Cons: Limited support for advanced CQRS patterns, vendor lock-in risk.

Top Alternatives to Axon

  • Kafka
    Kafka

    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. ...

  • 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. ...

  • jQuery
    jQuery

    jQuery is a cross-platform JavaScript library designed to simplify the client-side scripting of HTML. ...

  • Node.js
    Node.js

    Node.js uses an event-driven, non-blocking I/O model that makes it lightweight and efficient, perfect for data-intensive real-time applications that run across distributed devices. ...

  • Visual Studio Code
    Visual Studio Code

    Build and debug modern web and cloud applications. Code is free and available on your favorite platform - Linux, Mac OSX, and Windows. ...

Axon alternatives & related posts

Kafka logo

Kafka

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PROS OF KAFKA
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    High-throughput
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    Distributed
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    Scalable
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    High-Performance
  • 66
    Durable
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    Publish-Subscribe
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    Simple-to-use
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    Open source
  • 12
    Written in Scala and java. Runs on JVM
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    Message broker + Streaming system
  • 4
    KSQL
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    Avro schema integration
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    Robust
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    Suport Multiple clients
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    Extremely good parallelism constructs
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    Partioned, replayable log
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    Simple publisher / multi-subscriber model
  • 1
    Fun
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    Flexible
CONS OF KAFKA
  • 32
    Non-Java clients are second-class citizens
  • 29
    Needs Zookeeper
  • 9
    Operational difficulties
  • 5
    Terrible Packaging

related Kafka posts

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.

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Ashish Singh
Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 3M views

To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.

We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.

Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

#BigData #AWS #DataScience #DataEngineering

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JavaScript logo

JavaScript

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PROS OF JAVASCRIPT
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    Can be used on frontend/backend
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    Light weight
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    Flexible
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    You can't get a device today that doesn't run js
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    Non-blocking i/o
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    Ubiquitousness
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    Expressive
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    Extended functionality to web pages
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    Relatively easy language
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    Executed on the client side
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    Relatively fast to the end user
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    Pure Javascript
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    Functional programming
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    Async
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    Full-stack
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    Setup is easy
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    Future Language of The Web
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    Its everywhere
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    Because I love functions
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    JavaScript is the New PHP
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    Like it or not, JS is part of the web standard
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    Expansive community
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    Everyone use it
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    Can be used in backend, frontend and DB
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    Easy
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    Most Popular Language in the World
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    Powerful
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    Can be used both as frontend and backend as well
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    For the good parts
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    No need to use PHP
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    Easy to hire developers
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    Agile, packages simple to use
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    Love-hate relationship
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    Photoshop has 3 JS runtimes built in
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    Evolution of C
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    It's fun
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    Hard not to use
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    Versitile
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    Its fun and fast
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    Nice
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    Popularized Class-Less Architecture & Lambdas
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    Supports lambdas and closures
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    It let's me use Babel & Typescript
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    Can be used on frontend/backend/Mobile/create PRO Ui
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    1.6K Can be used on frontend/backend
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    Client side JS uses the visitors CPU to save Server Res
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    Easy to make something
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    Clojurescript
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    Promise relationship
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    Stockholm Syndrome
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    Function expressions are useful for callbacks
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    Scope manipulation
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    Everywhere
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    Client processing
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    What to add
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    Because it is so simple and lightweight
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    Only Programming language on browser
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    Test
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    Test2
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    Not the best
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    Easy to understand
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CONS OF JAVASCRIPT
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    A constant moving target, too much churn
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    Horribly inconsistent
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    Javascript is the New PHP
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    No ability to monitor memory utilitization
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    Shows Zero output in case of ANY error
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    Thinks strange results are better than errors
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    Can be ugly
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    No GitHub
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    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

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Git logo

Git

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    Distributed version control system
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    Efficient branching and merging
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    Fast
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    Open source
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    Better than svn
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    Great command-line application
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    Simple
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    Free
  • 232
    Easy to use
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    Does not require server
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    Distributed
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    Small & Fast
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    Feature based workflow
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    Staging Area
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    Most wide-spread VSC
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    Role-based codelines
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    Disposable Experimentation
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    Frictionless Context Switching
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    Data Assurance
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    Efficient
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    Just awesome
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    Github integration
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    Easy branching and merging
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    Compatible
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    Flexible
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    Possible to lose history and commits
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    Rebase supported natively; reflog; access to plumbing
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    Light
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    Team Integration
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    Fast, scalable, distributed revision control system
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    Easy
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    Flexible, easy, Safe, and fast
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    CLI is great, but the GUI tools are awesome
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    It's what you do
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    Phinx
CONS OF GIT
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    Hard to learn
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    Inconsistent command line interface
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    Easy to lose uncommitted work
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    Worst documentation ever possibly made
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    Awful merge handling
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    Unexistent preventive security flows
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    Rebase hell
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    When --force is disabled, cannot rebase
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    Ironically even die-hard supporters screw up badly
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    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.
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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.

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GitHub logo

GitHub

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Powerful collaboration, review, and code management for open source and private development projects
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PROS OF GITHUB
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    Great for team collaboration
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    Issue tracker
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    Just works
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    Github pages
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  • 62
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  • 60
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    Easy to find projects
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    Network effect
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    Extensive API
  • 43
    Organizations
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    Developer Profiles
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    Git Powered Wikis
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    Great for collaboration
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    Clean interface and good integrations
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    Community SDK involvement
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    Learn from others source code
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    Because: Git
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    It integrates directly with Azure
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    Standard in Open Source collab
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    CI Integration
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    Hands down best online Git service available
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    Uses GIT
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    Version Control
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    Simple but powerful
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    Unlimited Public Repos at no cost
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    Free HTML hosting
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    Security options
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    Ci
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    All in one development service
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    Free HTML hostings
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    Easy and efficient maintainance of the projects
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    Beautiful
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    Easy source control and everything is backed up
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    IAM integration
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    Very Easy to Use
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    Good tools support
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    Issues tracker
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    Never dethroned
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    Self Hosted
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    Dasf
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    Profound
CONS OF GITHUB
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    Owned by micrcosoft
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    Expensive for lone developers that want private repos
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    Relatively slow product/feature release cadence
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    API scoping could be better
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    Only 3 collaborators for private repos
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    Limited featureset for issue management
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    Does not have a graph for showing history like git lens
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    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
196.3K
6.9K
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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jQuery logo

jQuery

190.4K
67.1K
6.6K
The Write Less, Do More, JavaScript Library.
190.4K
67.1K
+ 1
6.6K
PROS OF JQUERY
  • 1.3K
    Cross-browser
  • 957
    Dom manipulation
  • 809
    Power
  • 660
    Open source
  • 610
    Plugins
  • 459
    Easy
  • 395
    Popular
  • 350
    Feature-rich
  • 281
    Html5
  • 227
    Light weight
  • 93
    Simple
  • 84
    Great community
  • 79
    CSS3 Compliant
  • 69
    Mobile friendly
  • 67
    Fast
  • 43
    Intuitive
  • 42
    Swiss Army knife for webdev
  • 35
    Huge Community
  • 11
    Easy to learn
  • 4
    Clean code
  • 3
    Because of Ajax request :)
  • 2
    Powerful
  • 2
    Nice
  • 2
    Just awesome
  • 2
    Used everywhere
  • 1
    Improves productivity
  • 1
    Javascript
  • 1
    Easy Setup
  • 1
    Open Source, Simple, Easy Setup
  • 1
    It Just Works
  • 1
    Industry acceptance
  • 1
    Allows great manipulation of HTML and CSS
  • 1
    Widely Used
  • 1
    I love jQuery
CONS OF JQUERY
  • 6
    Large size
  • 5
    Sometimes inconsistent API
  • 5
    Encourages DOM as primary data source
  • 2
    Live events is overly complex feature

related jQuery posts

Kir Shatrov
Engineering Lead at Shopify · | 22 upvotes · 2M views

The client-side stack of Shopify Admin has been a long journey. It started with HTML templates, jQuery and Prototype. We moved to Batman.js, our in-house Single-Page-Application framework (SPA), in 2013. Then, we re-evaluated our approach and moved back to statically rendered HTML and vanilla JavaScript. As the front-end ecosystem matured, we felt that it was time to rethink our approach again. Last year, we started working on moving Shopify Admin to React and TypeScript.

Many things have changed since the days of jQuery and Batman. JavaScript execution is much faster. We can easily render our apps on the server to do less work on the client, and the resources and tooling for developers are substantially better with React than we ever had with Batman.

#FrameworksFullStack #Languages

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Ganesa Vijayakumar
Full Stack Coder | Technical Lead · | 19 upvotes · 4.7M views

I'm planning to create a web application and also a mobile application to provide a very good shopping experience to the end customers. Shortly, my application will be aggregate the product details from difference sources and giving a clear picture to the user that when and where to buy that product with best in Quality and cost.

I have planned to develop this in many milestones for adding N number of features and I have picked my first part to complete the core part (aggregate the product details from different sources).

As per my work experience and knowledge, I have chosen the followings stacks to this mission.

UI: I would like to develop this application using React, React Router and React Native since I'm a little bit familiar on this and also most importantly these will help on developing both web and mobile apps. In addition, I'm gonna use the stacks JavaScript, jQuery, jQuery UI, jQuery Mobile, Bootstrap wherever required.

Service: I have planned to use Java as the main business layer language as I have 7+ years of experience on this I believe I can do better work using Java than other languages. In addition, I'm thinking to use the stacks Node.js.

Database and ORM: I'm gonna pick MySQL as DB and Hibernate as ORM since I have a piece of good knowledge and also work experience on this combination.

Search Engine: I need to deal with a large amount of product data and it's in-detailed info to provide enough details to end user at the same time I need to focus on the performance area too. so I have decided to use Solr as a search engine for product search and suggestions. In addition, I'm thinking to replace Solr by Elasticsearch once explored/reviewed enough about Elasticsearch.

Host: As of now, my plan to complete the application with decent features first and deploy it in a free hosting environment like Docker and Heroku and then once it is stable then I have planned to use the AWS products Amazon S3, EC2, Amazon RDS and Amazon Route 53. I'm not sure about Microsoft Azure that what is the specialty in it than Heroku and Amazon EC2 Container Service. Anyhow, I will do explore these once again and pick the best suite one for my requirement once I reached this level.

Build and Repositories: I have decided to choose Apache Maven and Git as these are my favorites and also so popular on respectively build and repositories.

Additional Utilities :) - I would like to choose Codacy for code review as their Startup plan will be very helpful to this application. I'm already experienced with Google CheckStyle and SonarQube even I'm looking something on Codacy.

Happy Coding! Suggestions are welcome! :)

Thanks, Ganesa

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Node.js logo

Node.js

185.3K
157.3K
8.5K
A platform built on Chrome's JavaScript runtime for easily building fast, scalable network applications
185.3K
157.3K
+ 1
8.5K
PROS OF NODE.JS
  • 1.4K
    Npm
  • 1.3K
    Javascript
  • 1.1K
    Great libraries
  • 1K
    High-performance
  • 805
    Open source
  • 486
    Great for apis
  • 477
    Asynchronous
  • 423
    Great community
  • 390
    Great for realtime apps
  • 296
    Great for command line utilities
  • 84
    Websockets
  • 83
    Node Modules
  • 69
    Uber Simple
  • 59
    Great modularity
  • 58
    Allows us to reuse code in the frontend
  • 42
    Easy to start
  • 35
    Great for Data Streaming
  • 32
    Realtime
  • 28
    Awesome
  • 25
    Non blocking IO
  • 18
    Can be used as a proxy
  • 17
    High performance, open source, scalable
  • 16
    Non-blocking and modular
  • 15
    Easy and Fun
  • 14
    Easy and powerful
  • 13
    Future of BackEnd
  • 13
    Same lang as AngularJS
  • 12
    Fullstack
  • 11
    Fast
  • 10
    Scalability
  • 10
    Cross platform
  • 9
    Simple
  • 8
    Mean Stack
  • 7
    Great for webapps
  • 7
    Easy concurrency
  • 6
    Typescript
  • 6
    Fast, simple code and async
  • 6
    React
  • 6
    Friendly
  • 5
    Control everything
  • 5
    Its amazingly fast and scalable
  • 5
    Easy to use and fast and goes well with JSONdb's
  • 5
    Scalable
  • 5
    Great speed
  • 5
    Fast development
  • 4
    It's fast
  • 4
    Easy to use
  • 4
    Isomorphic coolness
  • 3
    Great community
  • 3
    Not Python
  • 3
    Sooper easy for the Backend connectivity
  • 3
    TypeScript Support
  • 3
    Blazing fast
  • 3
    Performant and fast prototyping
  • 3
    Easy to learn
  • 3
    Easy
  • 3
    Scales, fast, simple, great community, npm, express
  • 3
    One language, end-to-end
  • 3
    Less boilerplate code
  • 2
    Npm i ape-updating
  • 2
    Event Driven
  • 2
    Lovely
  • 1
    Creat for apis
  • 0
    Node
CONS OF NODE.JS
  • 46
    Bound to a single CPU
  • 45
    New framework every day
  • 40
    Lots of terrible examples on the internet
  • 33
    Asynchronous programming is the worst
  • 24
    Callback
  • 19
    Javascript
  • 11
    Dependency hell
  • 11
    Dependency based on GitHub
  • 10
    Low computational power
  • 7
    Very very Slow
  • 7
    Can block whole server easily
  • 7
    Callback functions may not fire on expected sequence
  • 4
    Breaking updates
  • 4
    Unstable
  • 3
    Unneeded over complication
  • 3
    No standard approach
  • 1
    Bad transitive dependency management
  • 1
    Can't read server session

related Node.js 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

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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.

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Visual Studio Code logo

Visual Studio Code

175.4K
159.8K
2.3K
Build and debug modern web and cloud applications, by Microsoft
175.4K
159.8K
+ 1
2.3K
PROS OF VISUAL STUDIO CODE
  • 340
    Powerful multilanguage IDE
  • 308
    Fast
  • 193
    Front-end develop out of the box
  • 158
    Support TypeScript IntelliSense
  • 142
    Very basic but free
  • 126
    Git integration
  • 106
    Intellisense
  • 78
    Faster than Atom
  • 53
    Better ui, easy plugins, and nice git integration
  • 45
    Great Refactoring Tools
  • 44
    Good Plugins
  • 42
    Terminal
  • 38
    Superb markdown support
  • 36
    Open Source
  • 35
    Extensions
  • 26
    Awesome UI
  • 26
    Large & up-to-date extension community
  • 24
    Powerful and fast
  • 22
    Portable
  • 18
    Best editor
  • 18
    Best code editor
  • 17
    Easy to get started with
  • 15
    Lots of extensions
  • 15
    Good for begginers
  • 15
    Crossplatform
  • 15
    Built on Electron
  • 14
    Open, cross-platform, fast, monthly updates
  • 14
    Extensions for everything
  • 14
    All Languages Support
  • 13
    Easy to use and learn
  • 12
    Extensible
  • 12
    "fast, stable & easy to use"
  • 11
    Ui design is great
  • 11
    Useful for begginer
  • 11
    Totally customizable
  • 11
    Git out of the box
  • 11
    Faster edit for slow computer
  • 10
    SSH support
  • 10
    Great community
  • 10
    Fast Startup
  • 9
    Great language support
  • 9
    It has terminal and there are lots of shortcuts in it
  • 9
    Works With Almost EveryThing You Need
  • 9
    Powerful Debugger
  • 8
    Can compile and run .py files
  • 8
    Python extension is fast
  • 7
    Great document formater
  • 7
    Features rich
  • 6
    He is not Michael
  • 6
    Awesome multi cursor support
  • 6
    She is not Rachel
  • 6
    Extension Echosystem
  • 5
    VSCode.pro Course makes it easy to learn
  • 5
    SFTP Workspace
  • 5
    Very proffesional
  • 5
    Language server client
  • 5
    Easy azure
  • 4
    Has better support and more extentions for debugging
  • 4
    Supports lots of operating systems
  • 4
    Virtualenv integration
  • 4
    Excellent as git difftool and mergetool
  • 3
    Emmet preinstalled
  • 3
    More tools to integrate with vs
  • 3
    Has more than enough languages for any developer
  • 3
    Better autocompletes than Atom
  • 3
    'batteries included'
  • 2
    Microsoft
  • 2
    Light
  • 2
    Big extension marketplace
  • 2
    CMake support with autocomplete
  • 2
    Fast and ruby is built right in
  • 2
    VS Code Server: Browser version of VS Code
  • 2
    Customizable
CONS OF VISUAL STUDIO CODE
  • 46
    Slow startup
  • 29
    Resource hog at times
  • 20
    Poor refactoring
  • 13
    Poor UI Designer
  • 11
    Weak Ui design tools
  • 10
    Poor autocomplete
  • 8
    Super Slow
  • 8
    Huge cpu usage with few installed extension
  • 8
    Microsoft sends telemetry data
  • 7
    Poor in PHP
  • 6
    It's MicroSoft
  • 3
    Poor in Python
  • 3
    No Built in Browser Preview
  • 3
    No color Intergrator
  • 3
    Very basic for java development and buggy at times
  • 3
    No built in live Preview
  • 3
    Electron
  • 2
    Bad Plugin Architecture
  • 2
    Powered by Electron
  • 1
    Terminal does not identify path vars sometimes
  • 1
    Slow C++ Language Server

related Visual Studio Code posts

Vaibhav Taunk
Team Lead at Technovert · | 31 upvotes · 4M views

I am starting to become a full-stack developer, by choosing and learning .NET Core for API Development, Angular CLI / React for UI Development, MongoDB for database, as it a NoSQL DB and Flutter / React Native for Mobile App Development. Using Postman, Markdown and Visual Studio Code for development.

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Yshay Yaacobi

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

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

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

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