Tales of Training: Scaling CodeLabs with Swarm Mode and Docker-Compose

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Why does any "code lab workshop" or live demo are always such a challenge? A wise sysadmin once told me: “Get your hand dirty with the production to learn”. So I want to tell you a story of getting hand dirties, by creating a code lab environment considered as production. This story will show that we can build a reproducible environment for code-labs workshops, by using the Docker “tools”: the Engine, Swarm Mode, Docker-Compose, Moby, LinuxKit. Following the spirit of “Play With Docker”, but generalized at any service collection, this Codelab toolkit has been used on code-labs workshops of 120+ people. That path was not a free lunch, but the lessons learned will give you an idea on how a training environment can be efficiently done with Compose and Swarm Mode, by treating it as a “production” platform, tackling the plumbing “youth” limitations for the better of your use case. As a trainer, I never learned so much than building something to teach people someone else: this the story I want to tell you, the tale of using Docker as a tool of MASSIVE KNOWLEDGE SHARING, which is the root of growing our industry together.

Technology

Tales of
Training: Scaling
CodeLabs with
Swarm Mode
and Compose
Damien DUPORTAL
Training Engineer - CloudBees
@DamienDuportal - github.com/dduportal

Agenda
What is this talk about?
● Trench Story
● Drinking champagne
● Training / CodeLabs
● VM workload
● Container workload
● Orchestration
What is this talk NOT about?
● Toolings wars:
○ Kubernetes vs. Swarm
○ AWS vs. Azure
● Showing code (20 min only!)
○ Help yourself (GitHub):
http://bit.ly/2yoYG0v

Drinking my own champagne
Learn by TRYING:
● Experimentation never wastes
time
● Build something that works
and iterate

“CodeLab”?
● Part of a training (learning experience)
● Practical Exercise
● Using an isolated environment
● Dedicated to a single user
● Synonym of “workshop” or
“hands on lab”

CodeLab == Production
Constraints:
● Network
● Configuration diversity
● User Error (Learning process)
● Time boxed Sessions

CodeLab VM Content:
● Jenkins Training:
○ Store Git SCM (Gitea - Go)
○ Jenkins Master + Build Agents (JVMs)
○ Web Command Line (TTYDs - Python)
○ WebIDE (Codiad - PHP)
○ Store Artifacts (Artifactory - JVM)
○ Docker Registry (Go)

Challenges
● CodeLab defined by service list
○ Docker-compose
● Portability
○ Docker as blueprint
● Explicit definition
○ Definition as code

Step 1: VMs
Consul Servers
AWS EC2 VM
Host OS: Ubuntu
Docker & Compose
CodeLab Service Stack
Consul
AWS EC2 VM
Host OS: Ubuntu
Docker & Compose
CodeLab Service Stack
Consul
AWS EC2 VM
Host OS: Ubuntu
Docker & Compose
CodeLab Service Stack
Consul
AWS EC2 VM
Host OS: Ubuntu
Docker & Compose
CodeLab Service Stack
Consul
AWS EC2 VM
Host OS: Ubuntu
Docker & Compose
CodeLab Service Stack
Consul
AWS EC2 VM
Host OS: Ubuntu
Docker & Compose
CodeLab Service Stack
Consul
SSH Bastion
Reverse Proxies
Register into
Consul
End User
Trainer
Deploys with
Terraform
Load-Balancer
Reconfigure with
Consul-Template

Step 1: Take Aways
It works!
Easy to begin with and to operate
Cost (1 attendee per VM)
TTP (Time To Play)

Road to Native Orchestration
● Solving Costs Issue: More attendees per VM
● Solving TTP Issue:
○ Moving to container-based workload
(Look at PWD speed!)
○ Un-tie Infra from Services

Step 2.1: Provide Infra
SSH Bastion“Infra
Admin”
Deploys with
Terraform
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Manager (EC2 VM)
Swarm Manager (EC2 VM)
Swarm Manager (EC2 VM)

Step 2.2: Provide Services
SSH Bastion“Infra
Admin”
Deploys with
Docker
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Manager (EC2 VM)
Swarm Manager (EC2 VM)
Swarm Manager (EC2 VM)
Deploys with
Docker
Trainer
CodeLab Service Stack
Support Services
Support Services

Step 2.3: Benefits
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Manager (EC2 VM)
Swarm Manager (EC2 VM)
Swarm Manager (EC2 VM)
CodeLab Service Stack
Support Services
Support Services
End User

Step 2: Positive Side Effect
● Relying on Docker ecosystem
● Less side-kicks to maintain

Step 2: Nice problems to have
● CodeLab Scaling Pattern
○ (Deploy N times, NOT Scaling)
● Monitoring / Metrics
● Resource Management

CodeLab Scaling Pattern
● Compose and Swarm:
○ Define a stack of services
○ Scale each horizontally
● Concern here:
○ Deploy N times the same stack

Monitoring/Metrics
It is NOT a challenge
● Should have done it at Step 1
● Feedback loop
● A LOT of “Working Out
of the box” tools

Resource Management
● Resource sharing patterns:
○ VM workloads: 1 stack for 1 VM resources
○ Container workload: N stacks for
N VMs resources
● JVM pattern: Capping Memory (OOM)
● Need to measure and iterate
○ Automation

Step 2: Take Aways
● It still works!
● Easier to begin with and to operate
● Cost: 4x less (4 attendees per VM)
● TTP: ~10 s instead of ~10 min
● Maintaining: 5 tools instead of 8

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In this deck from the Stanford HPC Conference, Christian Kniep from Docker, Inc. gives a tutorial on linux containers. "This tutorial provides a detailed overview of the components needed to run containerized applications and explores how distributed HPC applications can be tackled. We’ll explain the concept of Linux Containers and describe the bits and pieces participants will explore following step-by-step examples. The workshop will introduce the predominant forms of orchestration in the industry; what problems they solve and how to approach the problem. Attendees will explore the benefits and drawbacks of orchestrators first hand with their own small exemplary stack deployments. Finally the workshop will introduce how HPC and Big Data workloads can be tackled on-top of these service-oriented clusters." Watch the video: https://youtu.be/LJinZpCTyk0 Learn more: http://www.docker.com/ and http://hpcadvisorycouncil.com Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter

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In this demos loaded talk we’ll explore the best practices to create a Docker image for a Java app (it’s 2019 and new comers such as Jib, CNCF buildpacks are interesting alternatives to Docker builds !) - and how to integrate best with the Kubernetes ecosystem : after explaining main Kubernetes objects and notions, we’ll discuss Helm charts and productivity tools such as Skaffold, Draft and Telepresence.

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One of the upsides of Microservices is the ability to deploy often,at arbitrary schedules, and independently of other services, instead of requiring synchronized deployments happening on a fixed time. But to really leverage this advantage, we need fast, efficient, and reliable deployment processes. That's one of the value propositions of Containers in general, and Docker in particular. Docker offers a new, lightweight approach to application portability.It can build applications using easy-to-write, repeatable, efficient recipes; then it can ship them across environments using a common container format; and it can run them within isolated namespaces which abstract the operating environment, independently of the distribution,versions, network setup, and other details of this environment. But Docker can do way more than deploy your apps. Docker also enables you to generalize Microservices principles and apply them on operational tasks like logging, remote access, backups, and troubleshooting.This decoupling results in independent, smaller, simpler moving parts.

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Today developers want to change the code, build and deploy often, even several times per day. New versions of software may need to be tested on different distributions, and with different configurations. Achieving this with Virtual Machines it’s possible, but it’s very resource and time consuming. Docker provides an incredibly good solution for this, in particular if combined with Continuous Integration tools like Jenkins and Configuration Management tools like Puppet. This presentation focuses on the opportunities to configure automatically Docker images, use Docker containers as disposable workers during your tests, and even running your Continuous Integration system inside Docker.

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Serverless has gained a lot on popularity recently and changed the way we develop the applications. We no longer need to care about setting up and managing the servers, scalability and deployment is simplified. Serverless is very often referred to as the approach which will let you to shift focus to implementing business logic when writing the code. But where has the complexity moved to now? How performant is Java code in serverless solution? Is serverless good for complex solutions? What are the benefits? During my talk I’d like to answer those questions based on our experiences of working on serverless solution written fully in Java.

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Learn how to scale your Continuous Integration and Continuous Delivery environment using containers. The Kubernetes project provides a container orchestration solution that greatly simplifies app deployments in large clusters and you can use Jenkins and Kubernetes together to run jobs on-demand. Building and testing is a great use case for containers, both due to the dynamic and isolation aspects, but it increases complexity when scaling to multiple nodes and clusters. Jenkins is an example of an application that can take advantage of Kubernetes technology to run Continuous Integration and Continuous Delivery workloads. Jenkins and Kubernetes can be integrated to transparently use on demand containers to run build agents and jobs, and isolate job execution. It also supports CI/CD-as-code using Jenkins Pipelines and automated deployments to Kubernetes clusters. The presentation will allow a better understanding of how to use Jenkins on Kubernetes for container based, totally dynamic, large scale CI and CD.

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"Come costruire una Platform As A Service con Docker, Kubernetes Go e Java" by Massimiliano Dessì Per automatizzare la CI e la CD, durante sviluppo, test, in preproduzione e in produzione si utilizzano le tecniche chiamate attualmente DevOps, in locale con Vagrant oppure su una PAAS su cloud, privati o pubblici. Possiamo costruire una PAAS scalabile utilizzando solo Docker, Docker e Kubernetes oppure soluzioni già pronte come Openshift 3 (che sta sopra Docker e Kubernetes). Nella presentazione vedremo come avere questi tre tipi di PAAS con in più uno strato di orchestrazione in GO/Java e Ansible per automatizzare il comportamento in base ad eventi monitorati

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Raymond Arifianto, AccelByte and Mark Mandel, Google - We have been deploying containerized micro-services for our Game Backend Services for a while. Now we are tackling the challenge to scale up fleets of game dedicated servers in multiple regions, multiple data centers and multiple providers - some in bare metal, some in Cloud. So we leverage docker containerization to deploy Game Servers to achieve Portability, Fast Deployment and Predictability, enabling us to scale up to thousands of servers, on demand, without a sweat.

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Nicholas Dille, Haufe-Lexware + Docker Captain - Docker continues to be the standard tool for building container images. For more than a year Docker ships with BuildKit as an alternative image builder, providing advanced features for secret and cache management. These features help to make image builds faster and more secure. In this session, Docker Captain Nicholas Dille will teach you how to use Buildkit features to your advantage.

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Lukonde Mwila, Entelect - As the cloud-native approach to development and deployment becomes more prevalent, it's an exciting time for software engineers to be equipped on how to dockerize multi-container applications and deploy them to the cloud. In this talk, Lukonde Mwila, Software Engineer at Entelect, will cover the following topics: - Docker Compose - Containerizing an Nginx Server - Containerizing an React App - Containerizing an Node.JS App - Containerizing anMongoDB App - Runing Multi-Container App Locally - Creating a CI/CD Pipeline - Adding a build stage to test containers and push images to Docker Hub - Deploying Multi-Container App to AWS Elastic Beanstalk Lukonde will start by giving an overview of how Docker Compose works and how it makes it very easy and straightforward to startup multiple Docker containers at the same time and automatically connect them together with some form of networking. After that, Lukonde will take a hands on approach to containerize an Nginx server, a React app, a NodeJS app and a MongoDB instance to demonstrate the power of Docker Compose. He'll demonstrate usage of two Docker files for an application, one production grade and the other for local development and running of tests. Lastly, he'll demonstrate creating a CI/CD pipeline in AWS to build and test our Docker images before pushing them to Docker Hub or AWS ECR, and finally deploying our multi-container application AWS Elastic Beanstalk.

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Kevin Jones, NGNIX - NGINX is one of the most popular images on Docker Hub and has been at the forefront of the web since the early 2000's. In this talk we will discuss how and why NGINX's lightweight and powerful architecture makes it a very popular choice for securing containerized applications as a sidecar reverse proxy within containers. We will highlight important aspects of application security that NGINX can help with, such as TLS, HTTP, AuthN, AuthZ and traffic control.

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Kathleen Juell, Digital Ocean - Containers are an essential part of today's microservice ecosystem, as they allow developers and operators to maintain standards of reliability and reproducibility in fast-paced deployment scenarios. And while there are best practices that extend across stacks in containerized environments, there are also things that make each stack distinct, starting with the application image itself. This talk will dive into some of these particularities, both at the image and service level, while also covering general best practices for building and running Node applications with database backends using Docker and Compose.

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Jeff Hajewski, Salesforce - There is a wealth of information on building deep learning models with PyTorch or TensorFlow. Anyone interested in building a deep learning model is only a quick search away from a number of clear and well written tutorials that will take them from zero knowledge to having a working image classifier. But what happens when you need to deploy these models in a production setting? At Salesforce, we use TensorFlow models to help us provide customers with insights into their data, and we do this as close to real-time as possible. Designing these systems in a scalable manner requires overcoming a number of design challenges, but the core component is Docker. Docker enables us to design highly scalable systems by allowing us to focus on service interactions, rather than how our services will interact with the hardware. Docker is also at the core of our test infrastructure, allowing developers and data scientists to build and test the system in an end to end manner on their local machines. While some of this may sound complex, the core message is simplicity - Docker allows us to focus on the aspects of the system that matter, greatly simplifying our lives.

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James Fuller, webcomposite s.r.o. - Curl is the venerable (yet very modern) 'swiss army knife' command line tool and library for transferring data with URLs. Recently we (the Curl team) decided to build a release for Docker Hub. This talk will outline our current development workflow with respect to the docker image and provide insights on what it takes to build a docker image for mass public consumption. We are also keen to learn from users and other developers how we might improve and enhance the official curl docker image.

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Fabian Stäber, Instana - In recent years, we saw a great paradigm shift in software engineering away from static monolithic applications towards dynamic distributed horizontally scalable architectures. Docker is one of the key technologies enabling this development. This shift poses a lot of new challenges for application monitoring, ranging from practical issues (need for automation) to technical challenges (Docker networking) to organizational topics (blurring line between software engineers and operations) to fundamental questions (define what is an application). In this talk we show how Docker changed the way we do monitoring, how modern application monitoring systems work, and what future developments we expect.

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Clemente Biondo, Engineering Ingegneria Informatica - When the COVID 19 pandemic started, Engineering Ingegneria Informatica Group (1.25 billion euros of revenues, 65 offices around the world, 12.000 employees) was forced to put their digital transformation to the test in order to maintain operational continuity. In this session, Clemente Biondo, the Tech Lead of the Information Systems Department, will share how his company is reacting to this unforeseeable scenario and how Docker-driven digital transformation had paved the path for work to continue remotely. Clemente will discuss learnings moving from colocated teams, manual approaches, email based-business processes, and a monolithic application to a mature DevOps culture characterized by a distributed autonomous workforce and a continuous deployment process that deploys backward-compatible Docker containerized microservices into hybrid multi cloud datacenters an average of twice a day with zero-downtime. He will detail how they use Docker to unify dev, test and production environments, and as an efficient and automated mechanism for deploying applications. Lastly, Clemente shares how, in our darkest hour, he and others are working to shine their brightest light.

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Chris Lauer, NOAA Space Weather Prediction Center - This is the story of how adopting a containerized workflow changed the way our small software team works at NOAA’s Space Weather Prediction Center. Our old architecture, a big ball of mud shared-database integration, just wasn’t cutting it - it was killing our agility. Over the past two years, our small team has adopted a microservice style architecture, using Docker with docker-compose and environment files as our deployment strategy for all new development. We’ve discovered the joys of using containers for identical dev, staging, and production environments. We work closely with scientists: much of the code we’re running has complicated and conflicting library dependencies. Docker captures these beautifully - we’ve even had some success teaching our scientists to use it! I’ll share what we’ve learned, some of the persistent challenges we face, and one place we really got it wrong. This talk builds off of a popular hallway track from DockerCon 2019.

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Brian Christner, 56k + Docker Captain - In this session, we will unlock the full potential of using Microsoft Visual Studio Code (VS Code) and Docker Desktop to turn you into a Docker Power User. When we expand and utilize the VS Code Docker plugin, we can take our projects and Docker skills to the next level. In addition to using VS Code, we streamline our Docker Desktop development workflow with less context switching and built-in shortcuts. You will learn how to bootstrap new projects, quickly write Dockerfiles utilizing templates, build, run, and interact with containers all from VS Code.

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Ashish Sharma, SS&C Eze - SS&C Eze provides various products in the stock market domain. We spent the last couple of years building Eclipse which is an investment suite born in cloud. The journey so far has been very interesting. The very first version of the product were a bunch of monolithic windows services and deployed using Octopus tool. We successfully managed to bring all the monolithic problem to the cloud and created a nightmare for ourselves. We then started applying microservices architecture principles and started breaking the monolithic into small services. Very soon we realized that we need a better packaging/deployment tool. Docker looked like a magical solution to our problem. Since its adoption, It has not only solved the deployment problem for us but has made a deep impact on different aspects of SDLC. It allowed us to use heterogeneous technology stacks, simplified development environment setup, simplified our testing strategy, improved our speed of delivery, and made our developers more productive. In this talk I would like to share our experience of using Docker and its positive impact on our SDLC.

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Ara Pulido, Datadog - Container technologies, although not new, have increased their popularity in the past few years, with container orchestrators allowing companies around the world to adopt these technologies to help them ship and scale microservices with precision and velocity. Kubernetes is currently the most popular container orchestration platform, and while many organizations are migrating their workloads to it, Kubernetes is still relatively immature. New corner cases, errors, and quirks are regularly discovered as users push the boundaries of size and scale. When Datadog adopted Kubernetes we discovered some of these boundaries the hard way, and we continuously challenge and modify our infrastructure decisions in order to fit our use case. Join me in this talk for our story on what we learned while we scaled our Kubernetes clusters, the contributions to Kubernetes we made along the way, and how you can apply those learnings when growing your Kubernetes clusters from a handful to hundreds or thousands of nodes.

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Andy Clemenko, StackRox - One underutilized, and amazing, thing about the docker image scheme is labels. Labels are a built in way to document all aspects about the image itself. Think about all the information that the tags inside your clothing carry. If you care to look you can find out everything about the garment. All that information can be very valuable. Now think about how we can leverage labels to carry similar information. We can even use the labels to contain Docker Compose or even Kubernetes Yaml. We can even include labels into the CI/CD process making things more secure and smoother. Come find out some fun techniques on how to leverage labels to do some fun and amazing things.

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Patrick Deloulay, Micro Focus - Micro Focus started their digital transformation 3 years ago, moving the entire portfolio into hundreds of container images. Leveraging Docker Hub as our primary registry service, we will cover how we ended up building a simple but secure push/pull model to publish and deliver our premium assets to our customers and partners to both meet the high agility of our DevOps teams while greatly simplifying the deployment of our applications.

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Lukonde Mwila, Entelect As the cloud-native approach to development and deployment becomes more prevalent, it's an exciting time for software engineers to be equipped on how to dockerize multi-container applications and deploy them to the cloud. In this talk, Lukonde Mwila, Software Engineer at Entelect, will cover the following topics: - Docker Compose - Containerizing an Nginx Server - Containerizing an React App - Containerizing an Node.JS App - Containerizing anMongoDB App - Runing Multi-Container App Locally - Creating a CI/CD Pipeline - Adding a build stage to test containers and push images to Docker Hub - Deploying Multi-Container App to AWS Elastic Beanstalk Lukonde will start by giving an overview of how Docker Compose works and how it makes it very easy and straightforward to startup multiple Docker containers at the same time and automatically connect them together with some form of networking. After that, Lukonde will take a hands on approach to containerize an Nginx server, a React app, a NodeJS app and a MongoDB instance to demonstrate the power of Docker Compose. He'll demonstrate usage of two Docker files for an application, one production grade and the other for local development and running of tests. Lastly, he'll demonstrate creating a CI/CD pipeline in AWS to build and test our Docker images before pushing them to Docker Hub or AWS ECR, and finally deploying our multi-container application AWS Elastic Beanstalk.

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Elton Stoneman, Docker Captain + Container Consultant and Trainer How do you provide a SaaS offering when your product is a 10-year old Fortran app, currently built to run on Windows 10? With Docker and Kubernetes of course - and you can do it in a week (... to prototype level at least). In this session I'll walk through the processes and practicalities of taking an older Windows app, making it run in containers with Kubernetes, and then building a simple API wrapper to host the whole stack as a cloud-based SaaS product. There's a lot of technology here from a real world case study, and I'll focus on: - running Windows apps in Docker containers - building a .NET Core API which can run in Linux or Windows containers - running the stack in Kubernetes with Docker Desktop locally and AKS in the cloud - configuring AKS workloads in Azure to burst out to Azure Container Instances And there's a core theme to this session: Docker and Kubernetes are complex technologies, but they're the key to modern development. If you invest time learning them, they make projects like this simple, portable, fast and fun.

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This virtual meetup introduces the concepts and best practices of using Docker containers for software development for the Arm architecture across a variety of hardware systems. Using Docker Desktop on Windows or Mac, Amazon Web Services (AWS) A1 instances, and embedded Linux, we will demonstrate the latest Docker features to build, share, and run multi-architecture images with transparent support for Arm.

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1. Tales of Training: Scaling CodeLabs with Swarm Mode and Compose Damien DUPORTAL Training Engineer - CloudBees @DamienDuportal - github.com/dduportal

2. Agenda What is this talk about? ● Trench Story ● Drinking champagne ● Training / CodeLabs ● VM workload ● Container workload ● Orchestration What is this talk NOT about? ● Toolings wars: ○ Kubernetes vs. Swarm ○ AWS vs. Azure ● Showing code (20 min only!) ○ Help yourself (GitHub): http://bit.ly/2yoYG0v

3. Drinking my own champagne Learn by TRYING: ● Experimentation never wastes time ● Build something that works and iterate

4. “CodeLab”? ● Part of a training (learning experience) ● Practical Exercise ● Using an isolated environment ● Dedicated to a single user ● Synonym of “workshop” or “hands on lab”

5. CodeLab == Production Constraints: ● Network ● Configuration diversity ● User Error (Learning process) ● Time boxed Sessions

6. CodeLab VM Content: ● Jenkins Training: ○ Store Git SCM (Gitea - Go) ○ Jenkins Master + Build Agents (JVMs) ○ Web Command Line (TTYDs - Python) ○ WebIDE (Codiad - PHP) ○ Store Artifacts (Artifactory - JVM) ○ Docker Registry (Go)

7. Challenges ● CodeLab defined by service list ○ Docker-compose ● Portability ○ Docker as blueprint ● Explicit definition ○ Definition as code

8. Step 1: VMs Consul Servers AWS EC2 VM Host OS: Ubuntu Docker & Compose CodeLab Service Stack Consul AWS EC2 VM Host OS: Ubuntu Docker & Compose CodeLab Service Stack Consul AWS EC2 VM Host OS: Ubuntu Docker & Compose CodeLab Service Stack Consul AWS EC2 VM Host OS: Ubuntu Docker & Compose CodeLab Service Stack Consul AWS EC2 VM Host OS: Ubuntu Docker & Compose CodeLab Service Stack Consul AWS EC2 VM Host OS: Ubuntu Docker & Compose CodeLab Service Stack Consul SSH Bastion Reverse Proxies Register into Consul End User Trainer Deploys with Terraform Load-Balancer Reconfigure with Consul-Template

9. Step 1: Take Aways It works! Easy to begin with and to operate Cost (1 attendee per VM) TTP (Time To Play)

10. Road to Native Orchestration ● Solving Costs Issue: More attendees per VM ● Solving TTP Issue: ○ Moving to container-based workload (Look at PWD speed!) ○ Un-tie Infra from Services

11. Step 2.1: Provide Infra SSH Bastion“Infra Admin” Deploys with Terraform Swarm Worker (EC2 VM) Swarm Worker (EC2 VM) Swarm Worker (EC2 VM) Swarm Worker (EC2 VM) Swarm Worker (EC2 VM) Swarm Worker (EC2 VM) Swarm Manager (EC2 VM) Swarm Manager (EC2 VM) Swarm Manager (EC2 VM)

12. Step 2.2: Provide Services SSH Bastion“Infra Admin” Deploys with Docker Swarm Worker (EC2 VM) Swarm Worker (EC2 VM) Swarm Worker (EC2 VM) Swarm Worker (EC2 VM) Swarm Worker (EC2 VM) Swarm Worker (EC2 VM) Swarm Manager (EC2 VM) Swarm Manager (EC2 VM) Swarm Manager (EC2 VM) Deploys with Docker Trainer CodeLab Service Stack Support Services Support Services

13. Step 2.3: Benefits Swarm Worker (EC2 VM) Swarm Worker (EC2 VM) Swarm Worker (EC2 VM) Swarm Worker (EC2 VM) Swarm Worker (EC2 VM) Swarm Worker (EC2 VM) Swarm Manager (EC2 VM) Swarm Manager (EC2 VM) Swarm Manager (EC2 VM) CodeLab Service Stack Support Services Support Services End User

14. Step 2: Positive Side Effect ● Relying on Docker ecosystem ● Less side-kicks to maintain

15. Step 2: Nice problems to have ● CodeLab Scaling Pattern ○ (Deploy N times, NOT Scaling) ● Monitoring / Metrics ● Resource Management

16. CodeLab Scaling Pattern ● Compose and Swarm: ○ Define a stack of services ○ Scale each horizontally ● Concern here: ○ Deploy N times the same stack

17. Monitoring/Metrics It is NOT a challenge ● Should have done it at Step 1 ● Feedback loop ● A LOT of “Working Out of the box” tools

18. Resource Management ● Resource sharing patterns: ○ VM workloads: 1 stack for 1 VM resources ○ Container workload: N stacks for N VMs resources ● JVM pattern: Capping Memory (OOM) ● Need to measure and iterate ○ Automation

19. Step 2: Take Aways ● It still works! ● Easier to begin with and to operate ● Cost: 4x less (4 attendees per VM) ● TTP: ~10 s instead of ~10 min ● Maintaining: 5 tools instead of 8

20. Thank you and enjoy DockerCon!

Tales of Training: Scaling CodeLabs with Swarm Mode and Docker-Compose

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Tales of Training: Scaling CodeLabs with Swarm Mode and Docker-Compose