What is Google App Engine and what are its top alternatives?
Google App Engine is a Platform as a Service (PaaS) offering from Google Cloud that allows developers to build and deploy applications easily on Google's infrastructure. It provides automatic scaling, built-in services like Datastore, and support for multiple programming languages. However, limitations include vendor lock-in, limited support for certain languages/frameworks, and potential performance issues with certain workloads.
Microsoft Azure App Service: Microsoft's PaaS offering that supports various programming languages and frameworks with features like automatic scaling, deployment slots, and integration with Azure services. Pros: Integration with other Azure services, supports multiple languages. Cons: May have a learning curve for beginners.
Amazon Elastic Beanstalk: AWS's PaaS offering that allows developers to deploy applications using various programming languages and containers. Pros: Easy deployment, integrates with other AWS services. Cons: May be complex for beginners, limited customizability compared to Google App Engine.
Heroku: A cloud platform that supports multiple programming languages and offers easy deployment of applications. Pros: Simplified deployment process, supports various add-ons. Cons: Limited control over infrastructure, may be expensive for certain workloads.
DigitalOcean App Platform: A platform that simplifies app deployment with features like managed infrastructure, scalability, and automatic SSL. Pros: Simple deployment process, transparent pricing. Cons: Limited language support compared to Google App Engine.
IBM Cloud Foundry: IBM's PaaS offering that supports various programming languages and provides autoscaling, logging, and monitoring features. Pros: Easy deployment process, integration with IBM Cloud services. Cons: Limited customization options, may be costly for certain workloads.
Oracle Cloud Application Container Service: Oracle's PaaS offering that supports running containerized applications with easy deployment and scalability features. Pros: Integration with Oracle Cloud services, supports containers. Cons: Limited language support, may not be as user-friendly as Google App Engine.
Red Hat OpenShift: A Kubernetes-based platform that offers container orchestration, scaling, and management features for deploying applications. Pros: Supports containers, customizable infrastructure. Cons: May have a learning curve for beginners, requires more configuration compared to Google App Engine.
Pivotal Cloud Foundry: A cloud-native platform that supports various programming languages and frameworks with features like auto-scaling, built-in services, and easy deployment. Pros: Simplified deployment process, supports multiple languages. Cons: May have licensing costs, limited customizability compared to Google App Engine.
Jelastic: A multi-cloud platform that supports DevOps automation, auto-scaling, containerization, and support for multiple programming languages and databases. Pros: Multi-cloud support, customizable infrastructure. Cons: May be complex for beginners, requires more management compared to Google App Engine.
Cloud Foundry: An open-source cloud platform that supports running applications in various languages and provides features like auto-scaling, service brokers, and container support. Pros: Open-source, customizable infrastructure. Cons: May require more setup and management compared to Google App Engine.
Top Alternatives to Google App Engine
- 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. ...
- DigitalOcean
We take the complexities out of cloud hosting by offering blazing fast, on-demand SSD cloud servers, straightforward pricing, a simple API, and an easy-to-use control panel. ...
- AWS Lambda
AWS Lambda is a compute service that runs your code in response to events and automatically manages the underlying compute resources for you. You can use AWS Lambda to extend other AWS services with custom logic, or create your own back-end services that operate at AWS scale, performance, and security. ...
- 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. ...
- AWS Elastic Beanstalk
Once you upload your application, Elastic Beanstalk automatically handles the deployment details of capacity provisioning, load balancing, auto-scaling, and application health monitoring. ...
- Amazon EC2
It is a web service that provides resizable compute capacity in the cloud. It is designed to make web-scale computing easier for developers. ...
- Azure App Service
Quickly build, deploy, and scale web apps created with popular frameworks .NET, .NET Core, Node.js, Java, PHP, Ruby, or Python, in containers or running on any operating system. Meet rigorous, enterprise-grade performance, security, and compliance requirements by using the fully managed platform for your operational and monitoring tasks. ...
- 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. ...
Google App Engine alternatives & related posts
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.
DigitalOcean
- Great value for money560
- Simple dashboard364
- Good pricing362
- Ssds300
- Nice ui250
- Easy configuration191
- Great documentation156
- Ssh access138
- Great community135
- Ubuntu24
- Docker13
- IPv6 support12
- Private networking10
- 99.99% uptime SLA8
- Simple API7
- Great tutorials7
- 55 Second Provisioning6
- One Click Applications5
- Dokku4
- LAMP4
- Debian4
- CoreOS4
- Node.js4
- 1Gb/sec Servers3
- Word Press3
- Mean3
- LEMP3
- Simple Control Panel3
- Ghost3
- Runs CoreOS2
- Quick and no nonsense service2
- Django2
- Good Tutorials2
- Speed2
- Ruby on Rails2
- GitLab2
- Hex Core machines with dedicated ECC Ram and RAID SSD s2
- CentOS1
- Spaces1
- KVM Virtualization1
- Amazing Hardware1
- Transfer Globally1
- Fedora1
- FreeBSD1
- Drupal1
- FreeBSD Amp1
- Magento1
- ownCloud1
- RedMine1
- My go to server provider1
- Ease and simplicity1
- Nice1
- Find it superfitting with my requirements (SSD, ssh.1
- Easy Setup1
- Cheap1
- Static IP1
- It's the easiest to get started for small projects1
- Automatic Backup1
- Great support1
- Quick and easy to set up1
- Servers on demand - literally1
- Reliability1
- Variety of services0
- Managed Kubernetes0
- No live support chat3
- Pricing3
related DigitalOcean posts
This week, we finally released NurseryPeople.com. In the end, I chose to provision our server on DigitalOcean. So far, I am SO happy with that decision. Although setting everything up was a challenge, and I learned a lot, DigitalOceans blogs helped in so many ways. I was able to set up nginx and the Laravel web app pretty smoothly. I am also using Buddy for deploying changes made in git, which is super awesome. All I have to do in order to deploy is push my code to my private repo, and buddy transfers everything over to DigitalOcean. So far, we haven't had any downtime and DigitalOceans prices are quite fair for the power under the hood.
Coming from a non-web development environment background, I was a bit lost a first and bewildered by all the varying tools and platforms, and spent much too long evaluating before eventualy deciding on Laravel as the main core of my development.
But as I started development with Laravel that lead me into discovering Vue.js for creating beautiful front-end components that were easy to configure and extend, so I decided to standardise on Vue.js for most of my front-end development.
During my search for additional Vue.js components, a chance comment in a @laravel forum , led me to discover Quasar Framework initially for it's wide range of in-built components ... but once, I realised that Quasar Framework allowed me to use the same codebase to create apps for SPA, PWA, iOS, Android, and Electron then I was hooked.
So, I'm now using mainly just Quasar Framework for all the front-end, with Laravel providing a backend API service to the Front-end apps.
I'm deploying this all to DigitalOcean droplets via service called Moss.sh which deploys my private GitHub repositories directly to DigitalOcean in realtime.
AWS Lambda
- No infrastructure129
- Cheap83
- Quick70
- Stateless59
- No deploy, no server, great sleep47
- AWS Lambda went down taking many sites with it12
- Event Driven Governance6
- Extensive API6
- Auto scale and cost effective6
- Easy to deploy6
- VPC Support5
- Integrated with various AWS services3
- Cant execute ruby or go7
- Compute time limited3
- Can't execute PHP w/o significant effort1
related AWS Lambda posts
Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.
We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient
Based on the above criteria, we selected the following tools to perform the end to end data replication:
We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.
We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.
In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.
Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.
In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!
Heroku Docker GitHub Node.js hapi Vue.js AWS Lambda Amazon S3 PostgreSQL Knex.js Checkly is a fairly young company and we're still working hard to find the correct mix of product features, price and audience.
We are focussed on tech B2B, but I always wanted to serve solo developers too. So I decided to make a $7 plan.
Why $7? Simply put, it seems to be a sweet spot for tech companies: Heroku, Docker, Github, Appoptics (Librato) all offer $7 plans. They must have done a ton of research into this, so why not piggy back that and try it out.
Enough biz talk, onto tech. The challenges were:
- Slice of a portion of the functionality so a $7 plan is still profitable. We call this the "plan limits"
- Update API and back end services to handle and enforce plan limits.
- Update the UI to kindly state plan limits are in effect on some part of the UI.
- Update the pricing page to reflect all changes.
- Keep the actual processing backend, storage and API's as untouched as possible.
In essence, we went from strictly volume based pricing to value based pricing. Here come the technical steps & decisions we made to get there.
- We updated our PostgreSQL schema so plans now have an array of "features". These are string constants that represent feature toggles.
- The Vue.js frontend reads these from the vuex store on login.
- Based on these values, the UI has simple
v-if
statements to either just show the feature or show a friendly "please upgrade" button. - The hapi API has a hook on each relevant API endpoint that checks whether a user's plan has the feature enabled, or not.
Side note: We offer 10 SMS messages per month on the developer plan. However, we were not actually counting how many people were sending. We had to update our alerting daemon (that runs on Heroku and triggers SMS messages via AWS SNS) to actually bump a counter.
What we build is basically feature-toggling based on plan features. It is very extensible for future additions. Our scheduling and storage backend that actually runs users' monitoring requests (AWS Lambda) and stores the results (S3 and Postgres) has no knowledge of all of this and remained unchanged.
Hope this helps anyone building out their SaaS and is in a similar situation.
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
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
AWS Elastic Beanstalk
- Integrates with other aws services77
- Simple deployment65
- Fast44
- Painless28
- Free16
- Well-documented4
- Independend app container3
- Postgres hosting2
- Ability to be customized2
- Charges appear automatically after exceeding free quota2
- Lots of moving parts and config1
- Slow deployments0
related AWS Elastic Beanstalk posts
Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.
I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.
For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.
Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.
Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.
Future improvements / technology decisions included:
Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic
As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.
One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.
We initially started out with Heroku as our PaaS provider due to a desire to use it by our original developer for our Ruby on Rails application/website at the time. We were finding response times slow, it was painfully slow, sometimes taking 10 seconds to start loading the main page. Moving up to the next "compute" level was going to be very expensive.
We moved our site over to AWS Elastic Beanstalk , not only did response times on the site practically become instant, our cloud bill for the application was cut in half.
In database world we are currently using Amazon RDS for PostgreSQL also, we have both MariaDB and Microsoft SQL Server both hosted on Amazon RDS. The plan is to migrate to AWS Aurora Serverless for all 3 of those database systems.
Additional services we use for our public applications: AWS Lambda, Python, Redis, Memcached, AWS Elastic Load Balancing (ELB), Amazon Elasticsearch Service, Amazon ElastiCache
- Quick and reliable cloud servers647
- Scalability515
- Easy management393
- Low cost277
- Auto-scaling271
- Market leader89
- Backed by amazon80
- Reliable79
- Free tier67
- Easy management, scalability58
- Flexible13
- Easy to Start10
- Widely used9
- Web-scale9
- Elastic9
- Node.js API7
- Industry Standard5
- Lots of configuration options4
- GPU instances2
- Simpler to understand and learn1
- Extremely simple to use1
- Amazing for individuals1
- All the Open Source CLI tools you could want.1
- Ui could use a lot of work13
- High learning curve when compared to PaaS6
- Extremely poor CPU performance3
related Amazon EC2 posts
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
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.
- .Net Framework6
- Visual studio5
related Azure App Service posts
Easier setup and integration for PHP based applications. Azure App Service requires a lot of extra configuration, while AWS Elastic Beanstalk has most things set-up out of the box. On top of this, Azure is much more expensive.
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.