AWS Elastic Beanstalk vs Docker

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AWS Elastic Beanstalk vs Docker: What are the differences?

What is AWS Elastic Beanstalk? Quickly deploy and manage applications in the AWS cloud. Once you upload your application, Elastic Beanstalk automatically handles the deployment details of capacity provisioning, load balancing, auto-scaling, and application health monitoring.

What is Docker? Enterprise Container Platform for High-Velocity Innovation. 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.

AWS Elastic Beanstalk and Docker are primarily classified as "Platform as a Service" and "Virtual Machine Platforms & Containers" tools respectively.

Some of the features offered by AWS Elastic Beanstalk are:

  • Elastic Beanstalk is built using familiar software stacks such as the Apache HTTP Server for Node.js, PHP and Python, Passenger for Ruby, IIS 7.5 for .NET, and Apache Tomcat for Java
  • There is no additional charge for Elastic Beanstalk - you pay only for the AWS resources needed to store and run your applications.
  • Easy to begin – Elastic Beanstalk is a quick and simple way to deploy your application to AWS. You simply use the AWS Management Console, Git deployment, or an integrated development environment (IDE) such as Eclipse or Visual Studio to upload your application

On the other hand, Docker provides the following key features:

  • Integrated developer tools
  • open, portable images
  • shareable, reusable apps

"Integrates with other aws services" is the top reason why over 74 developers like AWS Elastic Beanstalk, while over 816 developers mention "Rapid integration and build up" as the leading cause for choosing Docker.

Docker is an open source tool with 54K GitHub stars and 15.6K GitHub forks. Here's a link to Docker's open source repository on GitHub.

According to the StackShare community, Docker has a broader approval, being mentioned in 3527 company stacks & 3449 developers stacks; compared to AWS Elastic Beanstalk, which is listed in 374 company stacks and 119 developer stacks.

- No public GitHub repository available -

What is 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.

What is 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
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What are some alternatives to AWS Elastic Beanstalk and Docker?
Google App Engine
Google has a reputation for highly reliable, high performance infrastructure. With App Engine you can take advantage of the 10 years of knowledge Google has in running massively scalable, performance driven systems. App Engine applications are easy to build, easy to maintain, and easy to scale as your traffic and data storage needs grow.
AWS CodeDeploy
AWS CodeDeploy is a service that automates code deployments to Amazon EC2 instances. AWS CodeDeploy makes it easier for you to rapidly release new features, helps you avoid downtime during deployment, and handles the complexity of updating your applications.
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.
AWS CloudFormation
You can use AWS CloudFormation’s sample templates or create your own templates to describe the AWS resources, and any associated dependencies or runtime parameters, required to run your application. You don’t need to figure out the order in which AWS services need to be provisioned or the subtleties of how to make those dependencies work.
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.
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Decisions about AWS Elastic Beanstalk and Docker
Jerome Dalbert
Jerome Dalbert
Senior Backend Engineer at StackShare · | 7 upvotes · 17.8K views
atGratify CommerceGratify Commerce
AWS Elastic Beanstalk
AWS Elastic Beanstalk
Heroku
Heroku
Rails
Rails
#PaaS

When creating the web infrastructure for our start-up, I wanted to host our app on a PaaS to get started quickly.

A very popular one for Rails is Heroku, which I love for free hobby side projects, but never used professionally. On the other hand, I was very familiar with the AWS ecosystem, and since I was going to use some of its services anyways, I thought: why not go all in on it?

It turns out that Amazon offers a PaaS called AWS Elastic Beanstalk, which is basically like an “AWS Heroku”. It even comes with a similar command-line utility, called "eb”. While edge-case Rails problems are not as well documented as with Heroku, it was very satisfying to manage all our cloud services under the same AWS account. There are auto-scaling options for web and worker instances, which is a nice touch. Overall, it was reliable, and I would recommend it to anyone planning on heavily using AWS.

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Tymoteusz Paul
Tymoteusz Paul
Devops guy at X20X Development LTD · | 13 upvotes · 283.2K views
Amazon EC2
Amazon EC2
LXC
LXC
CircleCI
CircleCI
Docker
Docker
Git
Git
Vault
Vault
Apache Maven
Apache Maven
Slack
Slack
Jenkins
Jenkins
TeamCity
TeamCity
Logstash
Logstash
Kibana
Kibana
Elasticsearch
Elasticsearch
Ansible
Ansible
VirtualBox
VirtualBox
Vagrant
Vagrant

Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

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Webpack
Webpack
React
React
Google Compute Engine
Google Compute Engine
GitLab CI
GitLab CI
Docker Swarm
Docker Swarm
Docker
Docker
Node.js
Node.js
#DeploymentWorkflow

I have got a small radio service running on Node.js. Front end is written with React and packed with Webpack . I use Docker for my #DeploymentWorkflow along with Docker Swarm and GitLab CI on a single Google Compute Engine instance, which is also a runner itself. Pretty unscalable decision but it works great for tiny projects. The project is available on https://ch1ller.com

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Amazon ElastiCache
Amazon ElastiCache
Amazon Elasticsearch Service
Amazon Elasticsearch Service
AWS Elastic Load Balancing (ELB)
AWS Elastic Load Balancing (ELB)
Memcached
Memcached
Redis
Redis
Python
Python
AWS Lambda
AWS Lambda
Amazon RDS
Amazon RDS
Microsoft SQL Server
Microsoft SQL Server
MariaDB
MariaDB
Amazon RDS for PostgreSQL
Amazon RDS for PostgreSQL
Rails
Rails
Ruby
Ruby
Heroku
Heroku
AWS Elastic Beanstalk
AWS Elastic Beanstalk

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

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Julien DeFrance
Julien DeFrance
Principal Software Engineer at Tophatter · | 16 upvotes · 394.8K views
atSmartZipSmartZip
Amazon DynamoDB
Amazon DynamoDB
Ruby
Ruby
Node.js
Node.js
AWS Lambda
AWS Lambda
New Relic
New Relic
Amazon Elasticsearch Service
Amazon Elasticsearch Service
Elasticsearch
Elasticsearch
Superset
Superset
Amazon Quicksight
Amazon Quicksight
Amazon Redshift
Amazon Redshift
Zapier
Zapier
Segment
Segment
Amazon CloudFront
Amazon CloudFront
Memcached
Memcached
Amazon ElastiCache
Amazon ElastiCache
Amazon RDS for Aurora
Amazon RDS for Aurora
MySQL
MySQL
Amazon RDS
Amazon RDS
Amazon S3
Amazon S3
Docker
Docker
Capistrano
Capistrano
AWS Elastic Beanstalk
AWS Elastic Beanstalk
Rails API
Rails API
Rails
Rails
Algolia
Algolia

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.

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AWS Elastic Beanstalk
AWS Elastic Beanstalk
Heroku
Heroku
uWSGI
uWSGI
Gunicorn
Gunicorn

I use Gunicorn because does one thing - it’s a WSGI HTTP server - and it does it well. Deploy it quickly and easily, and let the rest of your stack do what the rest of your stack does well, wherever that may be.

uWSGI “aims at developing a full stack for building hosting services” - if that’s a thing you need then ok, but I like the principle of doing one thing well, and I deploy to platforms like Heroku and AWS Elastic Beanstalk where the rest of the “hosting service” is provided and managed for me.

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Interest over time
Reviews of AWS Elastic Beanstalk and Docker
Avatar of gdi2290
Co-Founder and CTO at Tipe
Review ofDockerDocker

Docker is the new kid on the block disrupting virtualization nowadays. You're able to save up to 70% of your development cost on AWS (or any other cloud) switching to Docker. For example instead of paying for many small VMs you can spin up a large one with many Docker containers to drastically lower your cost. That alone is only one of the reasons why Docker is the future and it's not even the best feature: isolation, testa­bil­i­ty, re­pro­ducibil­i­ty, standardization, security, and upgrading / down­grad­ing / ap­pli­ca­tion versions to name a few. You can spin up 1000's of Docker containers on an ordinary Laptop, but you would have trouble spinning up 100's of VMs. If you haven't already checked out Docker you're missing out on a huge opportunity to join the movement that will change development/production environments forever

Review ofDockerDocker

The support for macOS is a fake.

I can't work with docker in macOS because de network and comunications with the container don't works correctly.

How developers use AWS Elastic Beanstalk and Docker
Avatar of ssshake
ssshake uses DockerDocker

Currently experimenting. The idea is to isolate any services where I'm not confident yet in their security/quality. The hope is that if there is an exploit in a given service that an attacker won't be able break out of the docker container and cause damage to my systems.

An example of a service I would isolate in a docker container would be a minecraft browser map application I use. I don't know who wrote it, I don't know who's vetting it, I don't know the source code. I would feel a lot better putting this in a container before I expose it to the internet.

I believe I will follow this process for anything that's not properly maintained (not in an trusted apt-repo or some other sort of confidence)

Avatar of AngeloR
AngeloR uses DockerDocker

We are testing out docker at the moment, building images from successful staging builds for all our APIs. Since we operate in a SOA (not quite microservices), developers have a dockerfile that they can run to build the entirety of our api infrastructure on their machines. We use the successful builds from staging to power these instances allowing them to do some more manual integration testing across systems.

Avatar of Yaakov Gesher
Yaakov Gesher uses DockerDocker

Each component of the app was launched in a separate container, so that they wouldn't have to share resources: the front end in one, the back end in another, a third for celery, a fourth for celery-beat, and a fifth for RabbitMQ. Actually, we ended up running four front-end containers and eight back-end, due to load constraints.

Avatar of sapslaj
sapslaj uses DockerDocker

Linux containers are so much more lightweight than VMs which is quite important for my limited budget. However, Docker has much more support and tooling for it unlike LXC, hence why I use it. rkt is interesting, although I will probably stick with Docker due to being more widespread.

Avatar of Packet
Packet uses DockerDocker

We are running primarily as a micro-services platform and Docker lets us iterate on these smaller units consistently from dev to staging to production. It is also integral to our continuous deployment system for rolling out or rolling back new features.

Avatar of ONLICAR
ONLICAR uses AWS Elastic BeanstalkAWS Elastic Beanstalk

Elastic Beanstalk gives us a managed platform for our front end servers to make sure that traffic is never overloading our servers and that deployments are always successful.

Avatar of Lumanu
Lumanu uses AWS Elastic BeanstalkAWS Elastic Beanstalk

Elastic Beanstalk manages our environments. We rely on it to manage rolling out new versions of services.

Avatar of Flux Work
Flux Work uses AWS Elastic BeanstalkAWS Elastic Beanstalk

Easy to get started. Essentially a package of several AWS products integrated for you.

Avatar of Daniel Pupius
Daniel Pupius uses AWS Elastic BeanstalkAWS Elastic Beanstalk

For convenience I use Elastic Beanstalk to host all my sites.

Avatar of Undisclosed, Do Not Contact or Spam Please
Undisclosed, Do Not Contact or Spam Please uses AWS Elastic BeanstalkAWS Elastic Beanstalk

All server-side deployments go to one of 5 EB environments.

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