What is Arduino IDE and what are its top alternatives?
Arduino IDE is a popular integrated development environment used for programming Arduino boards. It provides a simple interface for writing and uploading code to the Arduino platform, making it easy for beginners to get started with their projects. However, Arduino IDE has limitations such as lack of advanced features, debugging capabilities, and limited support for other microcontrollers.
- PlatformIO: PlatformIO is an open-source ecosystem for IoT development with support for over 800 embedded boards. Key features include a unified project structure, built-in library manager, and advanced debugging tools. Pros include multi-platform support, extensive library compatibility, and seamless integration with popular IDEs. Cons include a steeper learning curve for beginners compared to Arduino IDE.
- Visual Studio Code: Visual Studio Code is a versatile code editor with extensions for Arduino development. It offers features such as IntelliSense code completion, built-in Git integration, and a powerful debugging environment. Pros include a customizable interface, support for multiple programming languages, and a large community of developers. Cons include the need to install additional extensions for Arduino development.
- Energia: Energia is an open-source electronics prototyping platform that supports different microcontrollers, including the MSP430 and Tiva C Series LaunchPad boards. Key features include a user-friendly interface, extensive library support, and compatibility with Arduino sketches. Pros include cross-platform support, active community forums, and support for various development boards. Cons include a smaller ecosystem compared to Arduino IDE.
- Mbed Studio: Mbed Studio is an IDE for Mbed OS development, offering features such as project management, code editing, and debugging capabilities. Pros include seamless integration with Mbed OS, built-in CMSIS-Pack support, and a collaborative online compiler. Cons include limited support for non-Mbed OS platforms and a specific focus on ARM microcontrollers.
- Arduino Web Editor: Arduino Web Editor is a cloud-based IDE that allows you to write and upload code directly from a web browser. Key features include automatic code syncing, access to a library of example projects, and collaboration tools for sharing code. Pros include platform independence, real-time code compilation, and easy access to a vast library of Arduino code. Cons include reliance on an internet connection and limited offline functionality.
- Atmel Studio: Atmel Studio is a professional IDE for Atmel microcontrollers, offering features such as advanced code editor, project management tools, and integrated debugger support. Pros include seamless integration with Atmel microcontrollers, extensive documentation, and a wide range of development tools. Cons include a complex setup process and a steep learning curve for beginners.
- Particle Workbench: Particle Workbench is an IDE for developing IoT applications with Particle devices, providing features like project templates, easy device setup, and cloud debugging capabilities. Pros include seamless integration with Particle hardware, cloud-based development environment, and a growing library of community projects. Cons include limited support for non-Particle devices and a focus on IoT applications.
- Ubidots: Ubidots is a cloud-based IoT development platform with a visual programming interface and integration with various hardware platforms, including Arduino. Key features include data visualization tools, device management capabilities, and real-time data monitoring. Pros include easy integration with Arduino projects, user-friendly dashboard creation, and scalable pricing options. Cons include limited support for advanced programming features compared to traditional IDEs.
- CircuitPython: CircuitPython is a beginner-friendly programming language for Adafruit boards, offering features like interactive REPL prompt, built-in libraries, and support for various hardware peripherals. Pros include easy setup process, extensive library support, and community-driven development. Cons include limited support for non-Adafruit boards and a specific focus on educational projects.
- MicroPython: MicroPython is a lightweight implementation of Python for microcontrollers, allowing you to write and execute Python scripts directly on the device. Key features include interactive REPL, unique syntax for hardware control, and support for various development boards. Pros include familiar Python syntax, easy code deployment, and a growing community of developers. Cons include limited library support compared to Arduino IDE and a specific focus on Python programming.
Top Alternatives to Arduino IDE
- PlatformIO
PlatformIO is an open source ecosystem for IoT development. Cross-platform build system and library manager. Continuous and IDE integration. Arduino and MBED compatible. Ready for Cloud compiling. ...
- uno
We built uno, a small tool similar to uniq (the UNIX CLI tool that removes duplicates) - but with fuzziness. uno considers two lines to be equal if their edit distance is less than a specified threshold, by default set to 30%. It reads from stdin and prints the deduplicated lines to stdout. ...
- 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. ...
- Visual Studio
Visual Studio is a suite of component-based software development tools and other technologies for building powerful, high-performance applications. ...
- 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 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. ...
- 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. ...
- Docker
The Docker Platform is the industry-leading container platform for continuous, high-velocity innovation, enabling organizations to seamlessly build and share any application — from legacy to what comes next — and securely run them anywhere ...
Arduino IDE alternatives & related posts
- Support for various Embedded Boards7
- It is the best5
- Integrates with VSCode and Atom3
- Device monitor filters1
- CI (continous integration)1
- CLI (command line tools1
related PlatformIO posts
related uno posts
Python
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- Scientific computing46
- Great documentation35
- Productivity29
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- Simple is better than complex24
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- Imperative19
- Very programmer and non-programmer friendly18
- Free18
- Powerfull language17
- Machine learning support17
- Fast and simple16
- Scripting14
- Explicit is better than implicit12
- Ease of development11
- Clear and easy and powerfull10
- Unlimited power9
- Import antigravity8
- It's lean and fun to code8
- Print "life is short, use python"7
- Python has great libraries for data processing7
- Rapid Prototyping6
- Readability counts6
- Now is better than never6
- Great for tooling6
- Flat is better than nested6
- Although practicality beats purity6
- I love snakes6
- High Documented language6
- There should be one-- and preferably only one --obvious6
- Fast coding and good for competitions6
- Web scraping5
- Lists, tuples, dictionaries5
- Great for analytics5
- Easy to setup and run smooth4
- Easy to learn and use4
- Plotting4
- Beautiful is better than ugly4
- Multiple Inheritence4
- Socially engaged community4
- Complex is better than complicated4
- CG industry needs4
- Simple and easy to learn4
- It is Very easy , simple and will you be love programmi3
- Flexible and easy3
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- If the implementation is easy to explain, it may be a g3
- If the implementation is hard to explain, it's a bad id3
- Special cases aren't special enough to break the rules3
- Pip install everything3
- List comprehensions3
- No cruft3
- Generators3
- Import this3
- Powerful language for AI3
- Can understand easily who are new to programming2
- Should START with this but not STICK with This2
- A-to-Z2
- Because of Netflix2
- Only one way to do it2
- Better outcome2
- Batteries included2
- Good for hacking2
- Securit2
- Procedural programming1
- Best friend for NLP1
- Slow1
- Automation friendly1
- Sexy af1
- Ni0
- Keep it simple0
- Powerful0
- Still divided between python 2 and python 353
- Performance impact28
- Poor syntax for anonymous functions26
- GIL22
- Package management is a mess19
- Too imperative-oriented14
- Hard to understand12
- Dynamic typing12
- Very slow12
- Indentations matter a lot8
- Not everything is expression8
- Incredibly slow7
- Explicit self parameter in methods7
- Requires C functions for dynamic modules6
- Poor DSL capabilities6
- No anonymous functions6
- Fake object-oriented programming5
- Threading5
- The "lisp style" whitespaces5
- Official documentation is unclear.5
- Hard to obfuscate5
- Circular import5
- Lack of Syntax Sugar leads to "the pyramid of doom"4
- The benevolent-dictator-for-life quit4
- Not suitable for autocomplete4
- Meta classes2
- Training wheels (forced indentation)1
related Python posts
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
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
Visual Studio
- Intellisense, ui305
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- JetBrains plugins (ReSharper etc.) work sufficiently OK5
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- I develop UWP apps and Intellisense is super useful1
- Cross platform development1
- The Power and Easiness to Do anything in any.. language1
- Available for Mac and Windows1
- Bulky16
- Made by Microsoft14
- Sometimes you need to restart to finish an update6
- Too much size for disk3
- Only avalible on Windows3
related Visual Studio posts
I use TypeScript because it greatly simplify my refactoring efforts. I regularly re-validate my assumption about application architecture, and strictness of types allow me write make changes safely using just Visual Studio tooling. Integration with existing JavaScript libraries very simple and fast. If I have no time, I could just use any
type as output of JS module. When I have more time, I could just submit PR to DefinitelyTyped and it would be quickly accepted. Overall it gives less ambiguity for my code.
.NET Core is #free, #cross-platform, and #opensource. A developer platform for building all types of apps ( #web apps #mobile #games #machinelearning #AI and #Desktop ).
Developers have chosen .NET for:
Productive: Combined with the extensive class libraries, common APIs, multi-language support, and the powerful tooling provided by the Visual Studio family ( Visual Studio and Visual Studio Code ), .NET is the most productive platform for developers.
Any app: From mobile applications running on iOS, Android and Windows, to Enterprise server applications running on Windows Server and Linux, or high-scale microservices running in the cloud, .NET provides a solution for you.
Performance: .NET is fast. Really fast! The popular TechEmpower benchmark compares web application frameworks with tasks like JSON serialization, database access, and server side template rendering - .NET performs faster than any other popular framework.
- Distributed version control system1.4K
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- Fast959
- Open source845
- Better than svn726
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- Simple306
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- Easy to use232
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- Most wide-spread VSC13
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- Efficient5
- Just awesome4
- Easy branching and merging3
- Github integration3
- Compatible2
- Possible to lose history and commits2
- Flexible2
- Team Integration1
- Easy1
- Light1
- Fast, scalable, distributed revision control system1
- Rebase supported natively; reflog; access to plumbing1
- Flexible, easy, Safe, and fast1
- CLI is great, but the GUI tools are awesome1
- It's what you do1
- Phinx0
- Hard to learn16
- Inconsistent command line interface11
- Easy to lose uncommitted work9
- Worst documentation ever possibly made8
- Awful merge handling5
- Unexistent preventive security flows3
- Rebase hell3
- Ironically even die-hard supporters screw up badly2
- When --force is disabled, cannot rebase2
- Doesn't scale for big data1
related Git posts
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.
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.
GitHub
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- Great for collaboration30
- It's fun24
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- Learn from others source code20
- Because: Git16
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- Uses GIT4
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- Free private repos2
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- Issues tracker2
- Self Hosted2
- IAM integration2
- Very Easy to Use2
- Easy to use2
- Leads the copycats2
- Free HTML hostings2
- Easy and efficient maintainance of the projects2
- Beautiful2
- Dasf1
- Profound1
- Owned by micrcosoft55
- Expensive for lone developers that want private repos38
- Relatively slow product/feature release cadence15
- API scoping could be better10
- Only 3 collaborators for private repos9
- Limited featureset for issue management4
- Does not have a graph for showing history like git lens3
- GitHub Packages does not support SNAPSHOT versions2
- No multilingual interface1
- Takes a long time to commit1
- Expensive1
related GitHub posts
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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
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Context: I wanted to create an end to end IoT data pipeline simulation in Google Cloud IoT Core and other GCP services. I never touched Terraform meaningfully until working on this project, and it's one of the best explorations in my development career. The documentation and syntax is incredibly human-readable and friendly. I'm used to building infrastructure through the google apis via Python , but I'm so glad past Sung did not make that decision. I was tempted to use Google Cloud Deployment Manager, but the templates were a bit convoluted by first impression. I'm glad past Sung did not make this decision either.
Solution: Leveraging Google Cloud Build Google Cloud Run Google Cloud Bigtable Google BigQuery Google Cloud Storage Google Compute Engine along with some other fun tools, I can deploy over 40 GCP resources using Terraform!
Check Out My Architecture: CLICK ME
Check out the GitHub repo attached
Visual Studio Code
- Powerful multilanguage IDE340
- Fast308
- Front-end develop out of the box193
- Support TypeScript IntelliSense158
- Very basic but free142
- Git integration126
- Intellisense106
- Faster than Atom78
- Better ui, easy plugins, and nice git integration53
- Great Refactoring Tools45
- Good Plugins44
- Terminal42
- Superb markdown support38
- Open Source36
- Extensions35
- Awesome UI26
- Large & up-to-date extension community26
- Powerful and fast24
- Portable22
- Best code editor18
- Best editor18
- Easy to get started with17
- Lots of extensions15
- Good for begginers15
- Crossplatform15
- Built on Electron15
- Extensions for everything14
- Open, cross-platform, fast, monthly updates14
- All Languages Support14
- Easy to use and learn13
- "fast, stable & easy to use"12
- Extensible12
- Ui design is great11
- Totally customizable11
- Git out of the box11
- Useful for begginer11
- Faster edit for slow computer11
- SSH support10
- Great community10
- Fast Startup10
- Works With Almost EveryThing You Need9
- Great language support9
- Powerful Debugger9
- It has terminal and there are lots of shortcuts in it9
- Can compile and run .py files8
- Python extension is fast8
- Features rich7
- Great document formater7
- He is not Michael6
- Extension Echosystem6
- She is not Rachel6
- Awesome multi cursor support6
- VSCode.pro Course makes it easy to learn5
- Language server client5
- SFTP Workspace5
- Very proffesional5
- Easy azure5
- Has better support and more extentions for debugging4
- Supports lots of operating systems4
- Excellent as git difftool and mergetool4
- Virtualenv integration4
- Better autocompletes than Atom3
- Has more than enough languages for any developer3
- 'batteries included'3
- More tools to integrate with vs3
- Emmet preinstalled3
- VS Code Server: Browser version of VS Code2
- CMake support with autocomplete2
- Microsoft2
- Customizable2
- Light2
- Big extension marketplace2
- Fast and ruby is built right in2
- File:///C:/Users/ydemi/Downloads/yuksel_demirkaya_webpa1
- Slow startup46
- Resource hog at times29
- Poor refactoring20
- Poor UI Designer13
- Weak Ui design tools11
- Poor autocomplete10
- Super Slow8
- Huge cpu usage with few installed extension8
- Microsoft sends telemetry data8
- Poor in PHP7
- It's MicroSoft6
- Poor in Python3
- No Built in Browser Preview3
- No color Intergrator3
- Very basic for java development and buggy at times3
- No built in live Preview3
- Electron3
- Bad Plugin Architecture2
- Powered by Electron2
- Terminal does not identify path vars sometimes1
- Slow C++ Language Server1
related Visual Studio Code posts
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...
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.
- Rapid integration and build up823
- Isolation692
- Open source521
- Testability and reproducibility505
- Lightweight460
- Standardization218
- Scalable185
- Upgrading / downgrading / application versions106
- Security88
- Private paas environments85
- Portability34
- Limit resource usage26
- Game changer17
- I love the way docker has changed virtualization16
- Fast14
- Concurrency12
- Docker's Compose tools8
- Easy setup6
- Fast and Portable6
- Because its fun5
- Makes shipping to production very simple4
- Highly useful3
- It's dope3
- Package the environment with the application2
- Super2
- Open source and highly configurable2
- Simplicity, isolation, resource effective2
- MacOS support FAKE2
- Its cool2
- Does a nice job hogging memory2
- Docker hub for the FTW2
- HIgh Throughput2
- Very easy to setup integrate and build2
- Asdfd0
- New versions == broken features8
- Unreliable networking6
- Documentation not always in sync6
- Moves quickly4
- Not Secure3
related Docker posts
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.
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.