What is Cloud Foundry and what are its top alternatives?
Cloud Foundry is an open-source platform as a service (PaaS) that enables developers to build, test, deploy, and scale applications quickly and easily. Key features include support for multiple programming languages, automation of application lifecycle management, easy integration with various services, and high availability. However, some limitations of Cloud Foundry include its complexity for beginners, limited control over underlying infrastructure, and potential performance issues with certain workloads.
Kubernetes: Kubernetes is a container orchestration platform that allows for automating deployment, scaling, and management of containerized applications. Key features include support for hybrid and multi-cloud environments, extensibility through a wide range of plugins, and efficient resource utilization. Pros of Kubernetes compared to Cloud Foundry include greater flexibility and control over infrastructure, while cons include a steeper learning curve and more configuration required.
OpenShift: OpenShift is an enterprise Kubernetes platform developed by Red Hat. Key features include built-in security and compliance, developer-friendly tools, and support for both self-managed and fully managed deployments. Pros of OpenShift compared to Cloud Foundry include better integration with Red Hat technologies, while cons include higher costs for enterprise support.
Docker Swarm: Docker Swarm is a container orchestration tool that enables users to cluster together multiple Docker hosts. Key features include simplicity of setup and use, seamless integration with Docker containers, and high performance. Pros of Docker Swarm compared to Cloud Foundry include lightweight architecture and no external dependencies, while cons include less flexibility and scalability compared to Kubernetes.
Rancher: Rancher is an open-source platform for managing Kubernetes clusters across any cloud provider. Key features include centralized management of multiple clusters, intuitive user interface, and extensive support for various Kubernetes distributions. Pros of Rancher compared to Cloud Foundry include ease of use for Kubernetes management, while cons include potential limitations in advanced Kubernetes configurations.
AWS Elastic Beanstalk: AWS Elastic Beanstalk is a platform as a service offering from Amazon Web Services that simplifies the deployment of web applications in the cloud. Key features include easy scalability, automatic load balancing, and support for multiple programming languages and frameworks. Pros of AWS Elastic Beanstalk compared to Cloud Foundry include seamless integration with other AWS services, while cons include vendor lock-in to the AWS ecosystem.
Heroku: Heroku is a cloud platform that enables developers to build, deploy, and scale applications with ease. Key features include support for various programming languages, a simple and intuitive platform interface, and easy integration with third-party services. Pros of Heroku compared to Cloud Foundry include faster application deployment and user-friendly interface, while cons include limited control over underlying infrastructure and potential scalability issues.
Google Kubernetes Engine (GKE): Google Kubernetes Engine is a managed Kubernetes service provided by Google Cloud. Key features include seamless integration with other Google Cloud services, auto-scaling capabilities, and easy monitoring and logging tools. Pros of GKE compared to Cloud Foundry include strong integration with Google Cloud ecosystem, while cons include potential higher costs compared to self-managed Kubernetes clusters.
Azure Kubernetes Service (AKS): Azure Kubernetes Service is a managed Kubernetes offering from Microsoft Azure. Key features include native integration with other Azure services, automated provisioning and scaling, and strong security features. Pros of AKS compared to Cloud Foundry include deep integration with Azure ecosystem, while cons include potential limitations in customization compared to self-managed Kubernetes.
Pivotal Platform: Pivotal Platform is a cloud-native platform offered by Pivotal Software, with features that focus on developer productivity, operational efficiency, and security. Key features include support for agile development practices, automated application deployment and scaling, and proactive monitoring and troubleshooting. Pros of Pivotal Platform compared to Cloud Foundry include strong developer support and guidance, while cons include potential higher costs for enterprise users.
IBM Cloud Foundry: IBM Cloud Foundry is a platform as a service offering from IBM that enables developers to build, deploy, and scale applications in the cloud. Key features include support for multiple programming languages, self-service deployment, and integration with other IBM cloud services. Pros of IBM Cloud Foundry compared to Cloud Foundry include seamless integration with IBM cloud ecosystem, while cons include potential limitations in customization compared to other PaaS offerings.
Top Alternatives to Cloud Foundry
- Red Hat OpenShift
OpenShift is Red Hat's Cloud Computing Platform as a Service (PaaS) offering. OpenShift is an application platform in the cloud where application developers and teams can build, test, deploy, and run their applications. ...
- 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 ...
- Kubernetes
Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions. ...
- OpenStack
OpenStack is a cloud operating system that controls large pools of compute, storage, and networking resources throughout a datacenter, all managed through a dashboard that gives administrators control while empowering their users to provision resources through a web interface. ...
- Terraform
With Terraform, you describe your complete infrastructure as code, even as it spans multiple service providers. Your servers may come from AWS, your DNS may come from CloudFlare, and your database may come from Heroku. Terraform will build all these resources across all these providers in parallel. ...
- Heroku
Heroku is a cloud application platform – a new way of building and deploying web apps. Heroku lets app developers spend 100% of their time on their application code, not managing servers, deployment, ongoing operations, or scaling. ...
- NGINX
nginx [engine x] is an HTTP and reverse proxy server, as well as a mail proxy server, written by Igor Sysoev. According to Netcraft nginx served or proxied 30.46% of the top million busiest sites in Jan 2018. ...
- Apache HTTP Server
The Apache HTTP Server is a powerful and flexible HTTP/1.1 compliant web server. Originally designed as a replacement for the NCSA HTTP Server, it has grown to be the most popular web server on the Internet. ...
Cloud Foundry alternatives & related posts
Red Hat OpenShift
- Good free plan99
- Open Source63
- Easy setup47
- Nodejs support43
- Well documented42
- Custom domains32
- Mongodb support28
- Clean and simple architecture27
- PHP support25
- Customizable environments21
- Ability to run CRON jobs11
- Easier than Heroku for a WordPress blog9
- Easy deployment8
- PostgreSQL support7
- Autoscaling7
- Good balance between Heroku and AWS for flexibility7
- Free, Easy Setup, Lot of Gear or D.I.Y Gear5
- Shell access to gears4
- Great Support3
- High Security3
- Logging & Metrics3
- Cloud Agnostic2
- Runs Anywhere - AWS, GCP, Azure2
- No credit card needed2
- Because it is easy to manage2
- Secure2
- Meteor support2
- Overly complicated and over engineered in majority of e2
- Golang support2
- Its free and offer custom domain usage2
- Autoscaling at a good price point1
- Easy setup and great customer support1
- MultiCloud1
- Great free plan with excellent support1
- This is the only free one among the three as of today1
- Decisions are made for you, limiting your options2
- License cost2
- Behind, sometimes severely, the upstreams1
related Red Hat OpenShift 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
We use Kubernetes because we decided to migrate to a hosted cluster (not AWS) and still be able to scale our clusters up and down depending on load. By wrapping it with OpenShift we are now able to easily adapt to demand but also able to separate concerns into separate Pods depending on use-cases we have.
- 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.
Kubernetes
- Leading docker container management solution166
- Simple and powerful129
- Open source107
- Backed by google76
- The right abstractions58
- Scale services25
- Replication controller20
- Permission managment11
- Supports autoscaling9
- Simple8
- Cheap8
- Self-healing6
- Open, powerful, stable5
- Reliable5
- No cloud platform lock-in5
- Promotes modern/good infrascture practice5
- Scalable4
- Quick cloud setup4
- Custom and extensibility3
- Captain of Container Ship3
- Cloud Agnostic3
- Backed by Red Hat3
- Runs on azure3
- A self healing environment with rich metadata3
- Everything of CaaS2
- Gke2
- Golang2
- Easy setup2
- Expandable2
- Sfg2
- Steep learning curve16
- Poor workflow for development15
- Orchestrates only infrastructure8
- High resource requirements for on-prem clusters4
- Too heavy for simple systems2
- Additional vendor lock-in (Docker)1
- More moving parts to secure1
- Additional Technology Overhead1
related Kubernetes 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
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...
- Private cloud60
- Avoid vendor lock-in39
- Flexible in use23
- Industry leader7
- Robust architecture5
- Supported by many companies in top5004
related OpenStack posts
Terraform
- Infrastructure as code121
- Declarative syntax73
- Planning45
- Simple28
- Parallelism24
- Well-documented8
- Cloud agnostic8
- It's like coding your infrastructure in simple English6
- Immutable infrastructure6
- Platform agnostic5
- Extendable4
- Automation4
- Automates infrastructure deployments4
- Portability4
- Lightweight2
- Scales to hundreds of hosts2
- Doesn't have full support to GKE1
related Terraform posts
![Google Cloud IoT Core](https://img.stackshare.io/service/7702/cloud-iot-core.png)
![Terraform](https://img.stackshare.io/service/1276/default_2316907c4199f912e2ed79cbdb99025c9e5e2665.png)
![Python](https://img.stackshare.io/service/993/pUBY5pVj.png)
![Google Cloud Deployment Manager](https://img.stackshare.io/service/6810/deployment-manager-3.png)
![Google Cloud Build](https://img.stackshare.io/service/9309/PoHJY3K8_400x400.jpg)
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
![Heroku](https://img.stackshare.io/service/133/3wgIDj3j.png)
![Kubernetes](https://img.stackshare.io/service/1885/21_d3cvM.png)
![Google Kubernetes Engine](https://img.stackshare.io/service/1587/s01TMTGn.png)
![Google Cloud SQL for PostgreSQL](https://img.stackshare.io/service/6671/sql.png)
![PostgreSQL](https://img.stackshare.io/service/1028/ASOhU5xJ.png)
We recently moved our main applications from Heroku to Kubernetes . The 3 main driving factors behind the switch were scalability (database size limits), security (the inability to set up PostgreSQL instances in private networks), and costs (GCP is cheaper for raw computing resources).
We prefer using managed services, so we are using Google Kubernetes Engine with Google Cloud SQL for PostgreSQL for our PostgreSQL databases and Google Cloud Memorystore for Redis . For our CI/CD pipeline, we are using CircleCI and Google Cloud Build to deploy applications managed with Helm . The new infrastructure is managed with Terraform .
Read the blog post to go more in depth.
Heroku
- Easy deployment703
- Free for side projects459
- Huge time-saver374
- Simple scaling348
- Low devops skills required261
- Easy setup190
- Add-ons for almost everything174
- Beginner friendly153
- Better for startups150
- Low learning curve133
- Postgres hosting48
- Easy to add collaborators41
- Faster development30
- Awesome documentation24
- Simple rollback19
- Focus on product, not deployment19
- Natural companion for rails development15
- Easy integration15
- Great customer support12
- GitHub integration8
- Painless & well documented6
- No-ops6
- I love that they make it free to launch a side project4
- Free4
- Great UI3
- Just works3
- PostgreSQL forking and following2
- MySQL extension2
- Security1
- Able to host stuff good like Discord Bot1
- Sec0
- Super expensive27
- Not a whole lot of flexibility9
- No usable MySQL option7
- Storage7
- Low performance on free tier5
- 24/7 support is $1,000 per month2
related Heroku posts
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
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.
NGINX
- High-performance http server1.4K
- Performance894
- Easy to configure730
- Open source607
- Load balancer530
- Free289
- Scalability288
- Web server226
- Simplicity175
- Easy setup136
- Content caching30
- Web Accelerator21
- Capability15
- Fast14
- High-latency12
- Predictability12
- Reverse Proxy8
- Supports http/27
- The best of them7
- Great Community5
- Lots of Modules5
- Enterprise version5
- High perfomance proxy server4
- Embedded Lua scripting3
- Streaming media delivery3
- Streaming media3
- Reversy Proxy3
- Blash2
- GRPC-Web2
- Lightweight2
- Fast and easy to set up2
- Slim2
- saltstack2
- Virtual hosting1
- Narrow focus. Easy to configure. Fast1
- Along with Redis Cache its the Most superior1
- Ingress controller1
- Advanced features require subscription10
related NGINX 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.
We chose AWS because, at the time, it was really the only cloud provider to choose from.
We tend to use their basic building blocks (EC2, ELB, Amazon S3, Amazon RDS) rather than vendor specific components like databases and queuing. We deliberately decided to do this to ensure we could provide multi-cloud support or potentially move to another cloud provider if the offering was better for our customers.
We’ve utilized c3.large nodes for both the Node.js deployment and then for the .NET Core deployment. Both sit as backends behind an nginx instance and are managed using scaling groups in Amazon EC2 sitting behind a standard AWS Elastic Load Balancing (ELB).
While we’re satisfied with AWS, we do review our decision each year and have looked at Azure and Google Cloud offerings.
#CloudHosting #WebServers #CloudStorage #LoadBalancerReverseProxy
Apache HTTP Server
- Web server479
- Most widely-used web server305
- Virtual hosting217
- Fast148
- Ssl support138
- Since 199644
- Asynchronous28
- Robust5
- Proven over many years4
- Mature2
- Perfomance2
- Perfect Support1
- Many available modules0
- Many available modules0
- Hard to set up4
related Apache HTTP Server posts
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.
We've been happy with nginx as part of our stack. As an open source web application that folks install on-premise, the configuration system for the webserver is pretty important to us. I have a few complaints (e.g. the configuration syntax for conditionals is a pain), but overall we've found it pretty easy to build a configurable set of options (see link) for how to run Zulip on nginx, both directly and with a remote reverse proxy in front of it, with a minimum of code duplication.
Certainly I've been a lot happier with it than I was working with Apache HTTP Server in past projects.