Infrastructureascode slideshare-160331143725

INFRASTRUCTURE
as CODE
Running Microservices on AWS with
Docker, Terraform, and ECS

Why infrastructure-as-code
matters: a short story.

You are starting a
new project

I know, I’ll use Ruby on Rails!

> gem install rails
Fetching: i18n-0.7.0.gem (100%)
Fetching: json-1.8.3.gem (100%)
Building native extensions. This could take a while...
ERROR: Error installing rails:
ERROR: Failed to build gem native extension.
/usr/bin/ruby1.9.1 extconf.rb
creating Makefile
make
sh: 1: make: not found

Ah, I just need to install make

> sudo apt-get install make
...
Success!

> gem install rails
Fetching: nokogiri-1.6.7.2.gem (100%)
checking if the C compiler accepts ... yes
Building nokogiri using packaged libraries.
Using mini_portile version 2.0.0.rc2
checking for gzdopen() in -lz... no
zlib is missing; necessary for building libxml2
*** extconf.rb failed ***

Hmm. Time to visit StackOverflow.

> sudo apt-get install zlib1g-dev
...
Success!

> gem install rails
checking for gzdopen() in -lz... yes
checking for iconv... yes
Extracting libxml2-2.9.2.tar.gz into tmp/x86_64-pc-linux-
gnu/ports/libxml2/2.9.2... OK

nokogiri y u never install correctly?

(Spend 2 hours trying random
StackOverflow suggestions)

> gem install rails
...
Success!

> rails new my-project
> cd my-project
> rails start

> rails new my-project
> cd my-project
> rails start
/source/my-project/bin/spring:11:in `<top (required)>':
undefined method `path_separator' for Gem:Module
(NoMethodError)
from bin/rails:3:in `load'
from bin/rails:3:in `<main>'

Infrastructureascode slideshare-160331143725

Eventually, you get it working

Now you have to deploy your
Rails app in production

You use the AWS Console to
deploy an EC2 instance

> ssh ec2-user@ec2-12-34-56-78.compute-1.amazonaws.com
__| __|_ )
_| ( / Amazon Linux AMI
___|___|___|
[ec2-user@ip-172-31-61-204 ~]$ gem install rails

__| __|_ )
___|___|___|

Now you urgently have to update
all your Rails installs

> bundle update rails
checking for gzdopen() in -lz... yes
checking for iconv... yes
Extracting libxml2-2.9.2.tar.gz into tmp/x86_64-pc-linux-
gnu/ports/libxml2/2.9.2... OK

__| __|_ )
___|___|___|
The problem is that you’re
configuring servers manually

And that you’re deploying
infrastructure manually

A better alternative: infrastructure-
as-code

In this talk, we’ll go through a
real-world example:

We’ll configure & deploy two
microservices on Amazon ECS

With two infrastructure-as-code
tools: Docker and Terraform
TERRAFORM

I’m
Yevgeniy
Brikman
ybrikman.com

Co-founder of
Gruntwork
gruntwork.io

gruntwork.io
We offer DevOps
as a Service

gruntwork.io
And DevOps
as a Library

Author of
Hello,
Startup
hello-startup.net

And
Terraform:
Up & Running
terraformupandrunning.com

Slides and code from this talk:
ybrikman.com/speaking

1. Microservices
2. Docker
3. Terraform
4. ECS
5. Recap
Outline

Code is the enemy: the more you
have, the slower you go

Project Size
Lines of code
Bug Density
Bugs per thousand lines
of code
< 2K 0 – 25
2K – 6K 0 – 40
16K – 64K 0.5 – 50
64K – 512K 2 – 70
> 512K 4 – 100

As the code grows, the number of
bugs grows even faster

“Software
development doesn't
happen in a chart, an
IDE, or a design tool;
it happens in your
head.”

The mind can only handle so
much complexity at once

One solution is to break the code
into microservices

In a monolith, you use function
calls within one process
moduleA.func()
moduleB.func() moduleC.func() moduleD.func()
moduleE.func()

http://service.a
http://service.b http://service.c http://service.d
http://service.e
With services, you pass messages
between processes

Advantages of services:
1. Isolation
2. Technology agnostic
3. Scalability

Disadvantages of services:
1. Operational overhead
2. Performance overhead
3. I/O, error handling
4. Backwards compatibility
5. Global changes, transactions,
referential integrity all very hard

For more info, see: Splitting Up a
Codebase into Microservices and
Artifacts

For this talk, we’ll use two
example microservices

require 'sinatra'
get "/" do
"Hello, World!"
end
A sinatra backend that returns
“Hello, World”

class ApplicationController < ActionController::Base
def index
url = URI.parse(backend_addr)
req = Net::HTTP::Get.new(url.to_s)
res = Net::HTTP.start(url.host, url.port) {|http|
http.request(req)
}
@text = res.body
end
end
A rails frontend that calls the
sinatra backend

<h1>Rails Frontend</h1>
<p>
Response from the backend: <strong><%= @text %></strong>
</p>
And renders the response as
HTML

Docker allows you to build and
run code in containers

Containers are like lightweight
Virtual Machines (VMs)

VMs virtualize the hardware and run an
entire guest OS on top of the host OS
VM
Hardware
Host OS
Host User Space
Virtual Machine
Virtualized
hardware
Guest OS
Guest User
Space
App
VM
Virtualized
hardware
Guest OS
Guest User
Space
App
VM
Virtualized
hardware
Guest OS
Guest User
Space
App

This provides good isolation, but lots of
CPU, memory, disk, & startup overhead
VM
Hardware
Host OS
Host User Space
Virtual Machine
Virtualized
hardware
Guest OS
Guest User
Space
App
VM
Virtualized
hardware
Guest OS
Guest User
Space
App
VM
Virtualized
hardware
Guest OS
Guest User
Space
App

Containers virtualize User Space (shared
memory, processes, mount, network)
Container
VM
Hardware
Host OS
Host User Space
Virtual Machine
Virtualized
hardware
Guest OS
Guest User
Space
App
Hardware
Host OS
Host User Space
Container Engine
Virtualized
User Space
VM
Virtualized
hardware
Guest OS
Guest User
Space
App
VM
Virtualized
hardware
Guest OS
Guest User
Space
App
App
Container
Virtualized
User Space
App
Container
Virtualized
User Space
App

Container
VM
Hardware
Host OS
Host User Space
Virtual Machine
Virtualized
hardware
Guest OS
Guest User
Space
App
Hardware
Host OS
Host User Space
Container Engine
Virtualized
User Space
VM
Virtualized
hardware
Guest OS
Guest User
Space
App
VM
Virtualized
hardware
Guest OS
Guest User
Space
App
App
Container
Virtualized
User Space
App
Container
Virtualized
User Space
App
Isolation isn’t as good but much less CPU,
memory, disk, startup overhead

> docker run –it ubuntu bash
root@12345:/# echo "I'm in $(cat /etc/issue)”
I'm in Ubuntu 14.04.4 LTS
Running Ubuntu in a Docker
container

> time docker run ubuntu echo "Hello, World"
Hello, World
real 0m0.183s
user 0m0.009s
sys 0m0.014s
Containers boot very quickly.
Easily run a dozen at once.

You can define a Docker image
as code in a Dockerfile

FROM gliderlabs/alpine:3.3
RUN apk --no-cache add ruby ruby-dev
RUN gem install sinatra --no-ri --no-rdoc
RUN mkdir -p /usr/src/app
COPY . /usr/src/app
WORKDIR /usr/src/app
EXPOSE 4567
CMD ["ruby", "app.rb"]
Here is the Dockerfile for the
Sinatra backend

FROM gliderlabs/alpine:3.3
RUN apk --no-cache add ruby ruby-dev
RUN gem install sinatra --no-ri --no-rdoc
COPY . /usr/src/app
EXPOSE 4567
CMD ["ruby", "app.rb"]
It specifies dependencies, code,
config, and how to run the app

> docker build -t brikis98/sinatra-backend .
Step 0 : FROM gliderlabs/alpine:3.3
---> 0a7e169bce21
(...)
Step 8 : CMD ruby app.rb
---> 2e243eba30ed
Successfully built 2e243eba30ed
Build the Docker image

> docker run -it -p 4567:4567 brikis98/sinatra-backend
INFO WEBrick 1.3.1
INFO ruby 2.2.4 (2015-12-16) [x86_64-linux-musl]
== Sinatra (v1.4.7) has taken the stage on 4567 for
development with backup from WEBrick
INFO WEBrick::HTTPServer#start: pid=1 port=4567
Run the Docker image

> docker push brikis98/sinatra-backend
The push refers to a repository [docker.io/brikis98/sinatra-
backend] (len: 1)
2e243eba30ed: Image successfully pushed
7e2e0c53e246: Image successfully pushed
919d9a73b500: Image successfully pushed
(...)
v1: digest: sha256:09f48ed773966ec7fe4558 size: 14319
You can share your images by
pushing them to Docker Hub

Now you can reuse the same
image in dev, stg, prod, etc

> docker pull rails:4.2.6
And you can reuse images created
by others.

FROM rails:4.2.6
COPY . /usr/src/app
RUN bundle install
EXPOSE 3000
CMD ["rails", "start"]
The rails-frontend is built on top of
the official rails Docker image

No more insane install procedures!

rails_frontend:
image: brikis98/rails-frontend
ports:
- "3000:3000"
links:
- sinatra_backend:sinatra_backend
sinatra_backend:
image: brikis98/sinatra-backend
ports:
- "4567:4567"
Define your entire dev stack as
code with docker-compose

rails_frontend:
image: brikis98/rails-frontend
ports:
- "3000:3000"
links:
- sinatra_backend
sinatra_backend:
image: brikis98/sinatra-backend
ports:
- "4567:4567"
Docker links provide a simple
service discovery mechanism

> docker-compose up
Starting infrastructureascodetalk_sinatra_backend_1
Recreating infrastructureascodetalk_rails_frontend_1
sinatra_backend_1 | INFO WEBrick 1.3.1
sinatra_backend_1 | INFO ruby 2.2.4 (2015-12-16)
sinatra_backend_1 | Sinatra has taken the stage on 4567
rails_frontend_1 | INFO WEBrick 1.3.1
rails_frontend_1 | INFO ruby 2.3.0 (2015-12-25)
rails_frontend_1 | INFO WEBrick::HTTPServer#start: port=3000
Run your entire dev stack with one
command

Advantages of Docker:
1. Easy to create & share images
2. Images run the same way in all
environments (dev, test, prod)
3. Easily run the entire stack in dev
4. Minimal overhead
5. Better resource utilization

Disadvantages of Docker:
1. Maturity. Ecosystem developing
very fast, but still a ways to go
2. Tricky to manage persistent data in
a container
3. Tricky to pass secrets to containers

Terraform is a tool for
provisioning infrastructure

Terraform supports many
providers (cloud agnostic)

And many resources for each
provider

You define infrastructure as code
in Terraform templates

provider "aws" {
region = "us-east-1"
}
resource "aws_instance" "example" {
ami = "ami-408c7f28"
instance_type = "t2.micro"
}
This template creates a single EC2
instance in AWS

> terraform plan
+ aws_instance.example
ami: "" => "ami-408c7f28"
instance_type: "" => "t2.micro"
key_name: "" => "<computed>"
private_ip: "" => "<computed>"
public_ip: "" => "<computed>"
Plan: 1 to add, 0 to change, 0 to destroy.
Use the plan command to see
what you’re about to deploy

> terraform apply
aws_instance.example: Creating...
ami: "" => "ami-408c7f28"
instance_type: "" => "t2.micro"
key_name: "" => "<computed>"
private_ip: "" => "<computed>"
public_ip: "" => "<computed>”
aws_instance.example: Creation complete
Apply complete! Resources: 1 added, 0 changed, 0 destroyed.
Use the apply command to apply
the changes

Now our EC2 instance is running!

resource "aws_instance" "example" {
ami = "ami-408c7f28"
tags {
Name = "terraform-example"
}
}
Let’s give the EC2 instance a tag
with a readable name

> terraform plan
~ aws_instance.example
tags.#: "0" => "1"
tags.Name: "" => "terraform-example"
Use the plan command again to
verify your changes

> terraform apply
aws_instance.example: Refreshing state...
aws_instance.example: Modifying...
tags.#: "0" => "1"
tags.Name: "" => "terraform-example"
aws_instance.example: Modifications complete
Use the apply command again to
deploy those changes

Now our EC2 instance has a tag!

resource "aws_elb" "example" {
name = "example"
availability_zones = ["us-east-1a", "us-east-1b"]
instances = ["${aws_instance.example.id}"]
listener {
lb_port = 80
lb_protocol = "http"
instance_port = "${var.instance_port}"
instance_protocol = "http”
}
}
Let’s add an Elastic Load Balancer
(ELB).

name = "example"
listener {
lb_port = 80
instance_protocol = "http”
}
}
Terraform supports variables,
such as var.instance_port

name = "example"
listener {
lb_port = 80
instance_protocol = "http"
}
}
As well as dependencies like
aws_instance.example.id

name = "example"
listener {
lb_port = 80
}
}
It builds a dependency graph and
applies it in parallel.

After running apply, we have an ELB!

> terraform destroy
aws_instance.example: Refreshing state... (ID: i-f3d58c70)
aws_elb.example: Refreshing state... (ID: example)
aws_elb.example: Destroying...
aws_elb.example: Destruction complete
aws_instance.example: Destroying...
aws_instance.example: Destruction complete
Use the destroy command to
delete all your resources

For more info, check out The Comprehensive Guide
to Terraform

Advantages of Terraform:
1. Concise, readable syntax
2. Reusable code: inputs, outputs,
modules
3. Plan command!
4. Cloud agnostic
5. Very active development

Disadvantages of Terraform:
1. Maturity
2. Collaboration on Terraform state is
hard (but terragrunt makes it easier)
3. No rollback
4. Poor secrets management

EC2 Container Service (ECS) is a
way to run Docker on AWS

ECS Overview
EC2 Instance
ECS Cluster
ECS Scheduler
ECS Agent
ECS Tasks
ECS Task Definition
{
"cluster": "example",
"serviceName": ”foo",
"taskDefinition": "",
"desiredCount": 2
}
ECS Service Definition
{
"name": "example",
"image": "foo/example",
"cpu": 1024,
"memory": 2048,
"essential": true,
}

ECS Cluster: several servers
managed by ECS
EC2 Instance
ECS Cluster

Typically, the servers are in an
Auto Scaling Group
EC2 Instance
Auto Scaling Group

Which can automatically
relaunch failed servers
EC2 Instance
Auto Scaling Group

Each server must run the ECS
Agent
EC2 Instance
ECS Cluster
ECS Agent

ECS Task: Docker container(s)
to run, resources they need
EC2 Instance
ECS Cluster
ECS Agent
ECS Task Definition
{
"name": "example",
"cpu": 1024,
"memory": 2048,
"essential": true,
}

ECS Service: long-running ECS
Task & ELB settings
EC2 Instance
ECS Cluster
ECS Agent
ECS Task Definition
{
"name": "example",
"cpu": 1024,
"memory": 2048,
"essential": true,
}
{
"desiredCount": 2
}

ECS Scheduler: Deploys Tasks
across the ECS Cluster
EC2 Instance
ECS Cluster
ECS Agent
ECS Task Definition
{
"name": "example",
"cpu": 1024,
"memory": 2048,
"essential": true,
}
{
"desiredCount": 2
}
ECS Scheduler ECS Tasks

It will also automatically
redeploy failed Services
EC2 Instance
ECS Cluster
ECS Agent
ECS Task Definition
{
"name": "example",
"cpu": 1024,
"memory": 2048,
"essential": true,
}
{
"desiredCount": 2
}

You can associate an ALB or
ELB with each ECS service
EC2 Instance
ECS Cluster
ECS Agent
ECS Tasks
User

This lets you distribute traffic
across multiple ECS Tasks
EC2 Instance
ECS Cluster
ECS Agent
ECS Tasks
User

Which allows zero-downtime
deployment
EC2 Instance
ECS Cluster
ECS Agent
ECS TasksUser
v1
v1
v1 v2

As well as a simple form of
service discovery
EC2 Instance
ECS Cluster
ECS Agent
ECS Tasks

You can use CloudWatch
alarms to trigger auto scaling
EC2 Instance
ECS Cluster
ECS Agent
ECS Tasks
CloudWatch

You can scale up by running
more ECS Tasks
EC2 Instance
ECS Cluster
ECS Agent
ECS Tasks
CloudWatch

And by adding more EC2
Instances
EC2 Instance
ECS Cluster
ECS Agent
ECS Tasks
CloudWatch

And scale back down when load
is lower
EC2 Instance
ECS Cluster
ECS Agent
ECS Tasks
CloudWatch

Let’s deploy our microservices in
ECS using Terraform

Define the ECS Cluster as an
Auto Scaling Group (ASG)
EC2 Instance
ECS Cluster

resource "aws_ecs_cluster" "example_cluster" {
name = "example-cluster"
}
resource "aws_autoscaling_group" "ecs_cluster_instances" {
name = "ecs-cluster-instances"
min_size = 3
max_size = 3
launch_configuration =
"${aws_launch_configuration.ecs_instance.name}"
}

Ensure each server in the ASG
runs the ECS Agent
EC2 Instance
ECS Cluster
ECS Agent

# The launch config defines what runs on each EC2 instance
resource "aws_launch_configuration" "ecs_instance" {
name_prefix = "ecs-instance-"
# This is an Amazon ECS AMI, which has an ECS Agent
# installed that lets it talk to the ECS cluster
image_id = "ami-a98cb2c3”
}
The launch config runs AWS ECS
Linux on each server in the ASG

Define an ECS Task for each
microservice
EC2 Instance
ECS Cluster
ECS Agent
ECS Task Definition
{
"name": "example",
"cpu": 1024,
"memory": 2048,
"essential": true,
}

resource "aws_ecs_task_definition" "rails_frontend" {
family = "rails-frontend"
container_definitions = <<EOF
[{
"name": "rails-frontend",
"image": "brikis98/rails-frontend:v1",
"cpu": 1024,
"memory": 768,
"essential": true,
"portMappings": [{"containerPort": 3000, "hostPort": 3000}]
}]
EOF
}
Rails frontend ECS Task

resource "aws_ecs_task_definition" "sinatra_backend" {
family = "sinatra-backend"
[{
"name": "sinatra-backend",
"image": "brikis98/sinatra-backend:v1",
"cpu": 1024,
"memory": 768,
"essential": true,
"portMappings": [{"containerPort": 4567, "hostPort": 4567}]
}]
EOF
}
Sinatra Backend ECS Task

Define an ECS Service for each
ECS Task
EC2 Instance
ECS Cluster
ECS Agent
ECS Task Definition
{
"name": "example",
"cpu": 1024,
"memory": 2048,
"essential": true,
}
{
"desiredCount": 2
}

resource "aws_ecs_service" "rails_frontend" {
cluster = "${aws_ecs_cluster.example_cluster.id}"
task_definition =
"${aws_ecs_task_definition.rails-fronted.arn}"
desired_count = 2
}
Rails Frontend ECS Service

resource "aws_ecs_service" "sinatra_backend" {
cluster = "${aws_ecs_cluster.example_cluster.id}"
task_definition =
"${aws_ecs_task_definition.sinatra_backend.arn}"
desired_count = 2
}
Sinatra Backend ECS Service

Associate an ELB with each
ECS Service
EC2 Instance
ECS Cluster
ECS Agent
ECS Tasks
User

resource "aws_elb" "rails_frontend" {
name = "rails-frontend"
listener {
lb_port = 80
instance_port = 3000
}
}
Rails Frontend ELB

resource "aws_ecs_service" "rails_frontend" {
(...)
load_balancer {
elb_name = "${aws_elb.rails_frontend.id}"
container_name = "rails-frontend"
container_port = 3000
}
}
Associate the ELB with the Rails
Frontend ECS Service

resource "aws_elb" "sinatra_backend" {
name = "sinatra-backend"
listener {
lb_port = 4567
instance_port = 4567
}
}
Sinatra Backend ELB

resource "aws_ecs_service" "sinatra_backend" {
(...)
load_balancer {
elb_name = "${aws_elb.sinatra_backend.id}"
container_name = "sinatra-backend"
container_port = 4567
}
}
Associate the ELB with the Sinatra
Backend ECS Service

Set up service discovery
between the microservices
EC2 Instance
ECS Cluster
ECS Agent
ECS Tasks

resource "aws_ecs_task_definition" "rails_frontend" {
[{
...
"environment": [{
"name": "SINATRA_BACKEND_PORT",
"value": "tcp://${aws_elb.sinatra_backend.dns_name}:4567"
}]
}]
EOF
}
Pass the Sinatra Bckend ELB URL
as env var to Rails Frontend

It’s time to deploy!
EC2 Instance
ECS Cluster
ECS Agent
ECS Task Definition
{
"name": "example",
"cpu": 1024,
"memory": 2048,
"essential": true,
}
{
"desiredCount": 2
}

> terraform apply
aws_ecs_cluster.example_cluster: Creating...
name: "" => "example-cluster"
aws_ecs_task_definition.sinatra_backend: Creating...
...
Use the apply command to deploy
the ECS Cluster & Tasks

See the cluster in the ECS console

resource "aws_ecs_task_definition" "sinatra_backend" {
[{
"name": "sinatra-backend",
"image": "brikis98/sinatra-backend:v2",
...
}
To deploy a new image, just
update the docker tag

> terraform plan
~ aws_ecs_service.sinatra_backend
task_definition: "arn...sinatra-backend:3" => "<computed>"
-/+ aws_ecs_task_definition.sinatra_backend
arn: "arn...sinatra-backend:3" => "<computed>"
container_definitions: "bb5352f" => "2ff6ae" (forces new resource)
revision: "3" => "<computed>”
Use the plan command to verify
the changes

Apply the changes and you’ll see v2.

Advantages of ECS:
1. One of the simplest Docker cluster
management tools
2. Almost no extra cost if on AWS
3. Pluggable scheduler
4. Auto-restart of instances & Tasks
5. Automatic ALB/ELB integration

Disadvantages of ECS:
1. UI is so-so
2. Minimal monitoring built-in
3. ALB is broken

Benefits of infrastructure-as-code:
1. Reuse
2. Automation
3. Version control
4. Code review
5. Testing
6. Documentation

For more
info, see
Hello,
Startup
hello-startup.net

gruntwork.io
For DevOps help, see
Gruntwork

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