How To’s

How To: Launch EC2 Instances In AWS Using The AWS CLI

 

It occurred to me recently that while I have written articles on Boto for AWS (the Python SDK) I have yet to write articles on how to use the AWS CLI, Terraform and the Go SDK. All of that will come in due time, for starters this article is going to be about the AWS CLI.

To start you will need to install the AWS CLI  following these links:
https://aws.amazon.com/cli/
https://github.com/aws/aws-cli

Note you will need to make sure you have an account with an access key and have setup the required credentials under ~/.aws/ for the CLI to work. How to do this is covered near the end of the second link above to the git repo.

After that is done you are ready to rock and roll. To test it out you can run…

aws ec2 describe-instances

Assuming your default region, and profile settings are correct it should output JSON.

Launching an EC2 instance

To launch an EC2 instance from the command line use the command below replacing the variables preceded with $ with their real values.

aws --profile $account --region $region ec2 run-instances --image-id $image_id --count $count --instance-type $instance_type --key-name $ssh_key_name --subnet-id $subnet_id

(Assuming you have setup the required dependencies like uploading your SSH key to AWS and specifying its name in the command above this should launch your VM).

It should be noted there is a lot more you can to to tweak your instance, such as changing the EBS volume size for your root disk that is launched or tagging. You will see examples of this in my shell script. The purpose of this article is to share a shell script I have written and use whenever I want to quickly launch a test VM (which is common). For more permanent things I use an infrastructure as code approach via Terraform. But the need for launching quick test VM’s never goes away, thus this shell script was born. You will notice my script auto-tags our VM’s…I do this because in our environment if you VM isn’t tagged appropriately it is deleted + it’s courtesy in an AWS environment to tag your resources, otherwise no one will ever what tree to bark up when there is a problem such as ‘are you still using this cause it looks idle?’ 🙂

My Shell Script for Launching EC2 VM’s

#!/bin/bash

# Global Settings
account="my-account"
region="us-east-1"

# Instance settings
image_id="ami-03ebd214" # ubuntu 14.04
ssh_key_name="my_ssh_key-rsa-2048"
instance_type="m4.xlarge"
subnet_id="subnet-b8214792"
root_vol_size=20
count=1

# Tags
tags_Name="my-test-instance"
tags_Owner="tuxninja"
tags_ApplicationRole="Testing"
tags_Cluster="Test Cluster"
tags_Environment="dev"
tags_OwnerEmail="tuxninja@tuxlabs.com"
tags_Project="Test"
tags_BusinessUnit="Cloud Platform Engineering"
tags_SupportEmail="tuxninja@tuxlabs.com"

echo 'creating instance...'
id=$(aws --profile $account --region $region ec2 run-instances --image-id $image_id --count $count --instance-type $instance_type --key-name $ssh_key_name --subnet-id $subnet_id --block-device-mapping "[ { \"DeviceName\": \"/dev/sda1\", \"Ebs\": { \"VolumeSize\": $root_vol_size } } ]" --query 'Instances[*].InstanceId' --output text)

echo "$id created"

# tag it

echo "tagging $id..."

aws --profile $account --region $region ec2 create-tags --resources $id --tags Key=Name,Value="$tags_Name" Key=Owner,Value="$tags_Owner"  Key=ApplicationRole,Value="$tags_ApplicationRole" Key=Cluster,Value="$tags_Cluster" Key=Environment,Value="$tags_Environment" Key=OwnerEmail,Value="$tags_OwnerEmail" Key=Project,Value="$tags_Project" Key=BusinessUnit,Value="$tags_BusinessUnit" Key=SupportEmail,Value="$tags_SupportEmail" Key=OwnerGroups,Value="$tags_OwnerGroups"

echo "storing instance details..."
# store the data
aws --profile $account --region $region ec2 describe-instances --instance-ids $id > instance-details.json

echo "create termination script"
echo "#!/bin/bash" > terminate-instance.sh
echo "aws --profile $account --region $region ec2 terminate-instances --instance-ids $id" >> terminate-instance.sh
chmod +x terminate-instance.sh

After substituting the required variables at the top with your real values you can run this script. Notice that after creating the VM I capture the instance details in a file & the ID in a variable so I can subsequently tag it, and then I create a termination script…this makes for very simple operations when you need to repeatedly start and then kill/destroy/delete a VM.

Using these scripts should come in quite handy. A copy of create-instance.sh can be found on my github here.

One other thing… I use the normal AWS CLI for automation as shown here…but for poking around interactively I use something called ‘aws-shell’ formerly ‘saw’. Check it out and you won’t be disappointed !

My next post will be on Terraform or the Go SDK…but both are coming soon!

How To: Launch EC2 Instances In AWS Using The AWS CLI Read More »

Setting up Netflix’s Edda (CMDB) in AWS on Ubuntu

If you are running any kind of environment with greater than 10 servers, than you need a CMDB (Configuration Management DataBase). CMDB’s are the brain of your fleet & it’s environment. You can store anything in a CMDB, but commonly the metadata in CMDB’s consists of any of the following physical & digital asset inventory, software licenses, software configuration data, policy information, relationships (I.E. This VM—> Compute –> Rack –> Availability Zone –> Datacenter), automation metadata, and more… they also commonly provide change history for changes in your environment.

In the world of infrastructure as code, CMDB is king.

CMDB’s enable endless automation possibilities, without them you are stuck gathering and collecting ‘current’ configuration state about your infrastructure every time you want perform an automated change or run an audit/report . In my career I have built or been a part of CMDB efforts at nearly every company I have worked for. They are simply necessary, and by their nature they tend to require the choice of ‘built by us’ vs ‘buy or run’.

However, if you have the luxury of only running in AWS, you are in luck, because Netflix (The AWS poster child)  open sourced Edda in 2012 for this purpose!

Rather than talk about the specific features of Edda refer to the blog post or documentation, I want to keep this article short and jump right into setting up Edda, which is a bit tricky, because the documentation is out of date!

Setting Up Edda (2016)

First, in AWS you need setup an EC2 VM that has at least.. 6G for OS + dependencies including Mongo, and then however much disk you need to store the metadata for your environment (keep in mind it keeps change history). Personally I just created a root partition with 100G to keep things simple. For instance type I used ‘m4.xlarge’ and the Ubuntu version is 14.04.

After booting the VM, SSH to it and create a directory wherever your storage is allocated partition wise to store Edda & it’s dependencies. I will be using /cmdb/ in my example.

Initial Install Steps

mkdir /cmdb
cd /cmdb
export JAVA_OPTS="-Xmx1g -XX:MaxPermSize=256M"
git clone https://github.com/Netflix/edda.git
sudo add-apt-repository -y ppa:webupd8team/java &> /dev/null
sudo apt-get update
sudo debconf-set-selections <<< 'oracle-java8-installer shared/accepted-oracle-license-v1-1 boolean true'
sudo apt-get install -y oracle-java8-installer
sudo apt-get install -y scala
sudo apt-get install make

cd /cmdb/edda
make build

For the record, the Edda Wiki has the build steps wrong, it appears they no long are using Gradle, but have switch to SBT… which reminds me be aware Edda is written in Scala, which isn’t as popular as Java, Python etc… in addition it’s functional programming, which I don’t personally know a lot about, but I hear it’s got quite the learning curve..so beware if you need to make custom code changes, I would not recommend it, unless you know Scala ! 🙂

After the build of Edda succeeds, install Mongo

apt-get install -y mongodb

That’s it for dependencies

Configuring Mongo

For Edda to use Mongo all we need to do is ‘use’ the database we want to use for Edda & create an associated user. (Mongo will auto-create DB’s upon insert).

mongo

> use edda
> db.addUser({user:'edda',pwd:'t00t0ri4l',Roles: { edda: ['readWrite']}, roles: []})

You can test the user is working by doing… 

$ mongo edda -u edda -p
MongoDB shell version: 2.4.9
Enter password:
connecting to: edda
Server has startup warnings:
Sat Dec 10 00:53:21.093 [initandlisten]
Sat Dec 10 00:53:21.094 [initandlisten] ** WARNING: You are running on a NUMA machine.
Sat Dec 10 00:53:21.094 [initandlisten] **          We suggest launching mongod like this to avoid performance problems:
Sat Dec 10 00:53:21.094 [initandlisten] **              numactl --interleave=all mongod [other options]
Sat Dec 10 00:53:21.094 [initandlisten]
>

Configuring Edda

Under /cmdb/edda/src/main/resources we need to modify ‘edda.properties’ with valid config values for accounts, regions & mongo access.

Relevant Mongo Values

edda.mongo.address=127.0.0.1:27017
edda.mongo.database=edda
edda.mongo.user=edda
edda.mongo.password=t00t0ri4l

Account & Region Values 

edda.accounts=dev.us-east-1
edda.dev.us-east-1.region=us-east-1
edda.dev.us-east-1.aws.accessKey=fakeaccesskey
edda.dev.us-east-1.aws.secretKey=fakesecret

The above example is using one account and only one region. The Edda configuration uses generic labels, they are very flexible, but when using them you might be confused by the name of the label as it’s intent. Don’t fall into that trap, I did, and then I found this post on Google Groups… Check it out to gain more insight on how the configuration works and can be tweaked for  your needs. There is also the standard documentation, but it’s a little light IMO.

Running Edda

Congrats you made it, time to run Edda ! Again the documentation has this wrong (listed as gradle & Jetty)…instead were using SBT + Jetty…

$ cd /cmdb/edda/
$ ./project/sbt
> jetty:start

If everything goes smoothly you will start to see logs about crawling AWS API’s spewing to your screen 🙂 After about 2 minutes you should see data. You can check by doing a curl.

curl http://127.0.0.1:8080/api/v2/view/instances

This API URL should return a JSON object with instance ID’s for the account & region specified.

Additionally, Edda is listening on whatever private IP address you have setup, you will just need to modify the default security group to allow 8080 on your machine.

I get a bit frustrated with out of date documentation..so I hope this helps ! Happy automating !

Setting up Netflix’s Edda (CMDB) in AWS on Ubuntu Read More »

Python & The Jira Rest API

Recently, while having to work a lot more in Jira than normal I got annoyed with the Jira Web GUI. So I wrote a script to do simple management of our jira issues.

Here is the basic usage

(env) ➜  jira git:(master) ✗ ./jira-ctl.py
usage: jira-ctl.py [-h] [-lp] [-li LIST_PROJECT_ISSUES] [-ua UPDATE_ASSIGNEE]
                   [-ud UPDATE_DATE] [-usts UPDATE_STATUS]
                   [-usum UPDATE_SUMMARY]

optional arguments:
  -h, --help            show this help message and exit
  -lp, --list-projects  List all projects
  -li LIST_PROJECT_ISSUES, --list-issues LIST_PROJECT_ISSUES
                        List Issues for a specific project
  -ua UPDATE_ASSIGNEE, --update-assignee UPDATE_ASSIGNEE
                        Update an issues assignee format: <issue
                        number>,<first_last>
  -ud UPDATE_DATE, --update-date UPDATE_DATE
                        Update an issues due date format: <issue number
                        >,<yyyy-mm-dd>
  -usts UPDATE_STATUS, --update-status UPDATE_STATUS
                        Update an issues status format: <issue
                        number>,'<Open|In Progress|Resolved>'
  -usum UPDATE_SUMMARY, --update-summary UPDATE_SUMMARY
                        Update an issues summary format: <issue
                        number>,'<summary>'
(env) ➜  jira git:(master) ✗

Here is the code…

from jira.client import JIRA
import os, sys
import prettytable
import argparse

parser = argparse.ArgumentParser()
parser.add_argument('-lp', '--list-projects', action="store_true", dest="list_projects", required=False, help="List all projects")
parser.add_argument('-li', '--list-issues', action="store", dest="list_project_issues", required=False, help="List Issues for a specific project")
parser.add_argument('-ua', '--update-assignee', action="store", dest="update_assignee", required=False, help="Update an issues assignee format: <issue number>,<first_last>")
parser.add_argument('-ud', '--update-date', action="store", dest="update_date", required=False, help="Update an issues due date format: <issue number>,<yyyy-mm-dd>")
parser.add_argument('-usts', '--update-status', action="store", dest="update_status", required=False, help="Update an issues status format: <issue number>,'<Open|In Progress|Resolved>'")
parser.add_argument('-usum', '--update-summary', action="store", dest="update_summary", required=False, help="Update an issues summary format: <issue number>,'<summary>'")
args = parser.parse_args()

def get_pass():
    if os.environ.get('JIRA_PASS') == None:
        print "you must first export your JIRA_PASS in the shell by running: source getpass.sh"
        print 'or "export JIRA_PASS=<jira_password>'
        sys.exit()
    jira_password = str(os.environ['JIRA_PASS'])

    return jira_password

def connect_jira(jira_server, jira_user, jira_password):
    '''
    Connect to JIRA. Return None on error
    '''
    try:
        #print "Connecting to JIRA: %s" % jira_server
        jira_options = {'server': jira_server}
        jira = JIRA(options=jira_options,
                    # Note the tuple
                    basic_auth=(jira_user,
                                jira_password))
        return jira
    except Exception,e:
        print "Failed to connect to JIRA: %s" % e
        return None

def list_projects():
    projects = jira.projects()
    header = ["Projects"]
    table = prettytable.PrettyTable(header)
    for project in projects:
        row = [str(project)]
        table.add_row(row)
    print table

def list_project_issues(project):
    query = 'project = %s and status != Resolved order by due asc' % (project)
    #query = 'project = %s and status != Resolved order by due desc' % (project)
    #query = 'project = %s order by due desc' % (project)
    issues = jira.search_issues(query, maxResults=100)

    if issues:
        header = ["Ticket", "Summary", "Assignee", "Status", "Due Date"]
        table = prettytable.PrettyTable(header)

        for issue in issues:
                summary = issue.fields.summary
                st = str(issue.fields.status)
                dd = issue.fields.duedate
                assignee = issue.fields.assignee
                row = [issue, summary, assignee, st, dd]
                table.add_row(row)
        print table

def update_issue_assignee(issue_number, assignee):
    issue = jira.issue(issue_number)
    issue.update(assignee=assignee)
    print "updated %s with new Assignee: %s" % (issue_number, assignee)

def update_issue_duedate(issue_number, dd):
    issue = jira.issue(issue_number)
    issue.update(duedate=dd)
    print "updated %s with new Due Date: %s" % (issue_number, dd)

def update_issue_summary(issue_number, summary):
    issue = jira.issue(issue_number)
    issue.update(summary=summary)
    print "updated %s with new Summary: %s" % (issue_number, summary)

def update_issue_status(issue_number, status):
    # To figure out transitions view the following URL
    # https://jira.yourcompany.com/rest/api/2/issue/XXXXXX-22/transitions?expand=transitions.fields

    transitions = {
                    'Resolved': 11,
                    'Open': 41,
                    'In Progress': 21,
                    'Testing': 71,
                    'Transition': 81,
                    'Closed': 91,
    }

    issue = jira.issue(issue_number)
    jira.transition_issue(issue, transitions[status])
    print "updated %s with new Status: %s" % (issue_number, status)

if __name__ == "__main__":
    if not args:
        parser.print_help()
    else:
        jira_user = '<your_login>'
        jira_password = get_pass()
        jira_server = 'https://jira.yourcompany.com'

        jira = connect_jira(jira_server, jira_user, jira_password)

        if args.list_projects:
            list_projects()
        elif args.list_project_issues:
            project = args.list_project_issues
            list_project_issues(project)
        elif args.update_assignee:
            (issue_number, assignee) = args.update_assignee.split(',')
            update_issue_assignee(issue_number, assignee)
        elif args.update_date:
            (issue_number, dd) = args.update_date.split(',')
            update_issue_duedate(issue_number, dd)
        elif args.update_status:
            (issue_number, status) = args.update_status.split(',')
            update_issue_status(issue_number, status)
        elif args.update_summary:
            (issue_number, summary) = args.update_summary.split(',')
            update_issue_summary(issue_number, summary)
        else:
            parser.print_help()

Later, I found out that there is a Jira command line utility, I didn’t check and see what functionality it provided, but I enjoyed writing this anyway.

Happy coding !

Python & The Jira Rest API Read More »

How To: Maximize Availability Effeciently Using AWS Availability Zones

For the TL;DR version, skip straight to the Cassandra Examples

Intro & Background

During my years at PayPal I was fortunate enough to be a part of a pioneering architecture & engineering team that designed & delivered  a new paradigm for how we deployed & operated applications using a model that included 5 Availability Zones per Region (multiple regions) & Global Traffic Management. This new deployment pattern increased our cost efficiency and capacity while providing high availability to our production application stack. The key to increasing cost efficiency while not losing availability is how you manage your capacity. Failure detection and global traffic management go the rest of the way to make use of all your Availability Zones & Regions giving you better availability. There is a little more to it, in terms of how you deploy your applications advantageously to this design…but we will get into that with our Cassandra/AWS examples later.

Prior to this model our approach was the traditional 3 datacenter model. 2 Active + 1 DR all with 100% of the capacity required to operate independently and they required manual failover in the event of an outage.

Oh the joys of operating your own private cloud & datacenters at scale 🙂

Most companies and a recent article suggests PayPal as well, are thinking about or are moving to public cloud. Public cloud gives most companies cheaper and faster access to the economy of scale as well as an immediate tap into a global infrastructure.

Amazon Web Services (AWS) is the market share leader in public cloud today. They operate tens of Regions & Availability Zones all around the world. Deploying your application(s) on these massive scale public clouds means opportunity for operating them in more efficient ways.

Recently I came across a few articles that felt like reading a history book on going from a Failover to an Always On model.  I’ll just leave these here for inquiring minds…

http://highscalability.com/blog/2016/8/23/the-always-on-architecture-moving-beyond-legacy-disaster-rec.html
https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/44686.pdf

Ok enough with the introduction, lets move into the future, starting with the Principles of High Availability.

Principles for High Availability – Always

  • Use Multiple Regions ( Min 2, Max as many as you need) 
  • Use Multiple Availability Zones ( Min 2, Max as many as is prudent to optimize availability and capacity with cost ) 
  • Design for an Active/Active architecture
  • Deploy at least N+1 capacity per cluster when Active/Active is not easily accomplished
    • Note: N=100% of capacity required to run your workload
  • Eliminate Single Points of Failure/Ensure redundancies in all components of the distributed system(s)
  • Make services fault tolerate/Ensure they continue in the event of various failures
  • Make services resilient by implementing timeout/retry patterns (circuit breaker, exponential back off algorithm etc)
  • Make services implement graceful failure such as degrading the quality of a response while still providing a response
  • Use an ESB (Enterprise Service Bus) to make calls to your application stack asynchronous where applicable
  • Use caching layers to speed up responses
  • Use Auto-Scaling for adding dynamic capacity for bursty workloads (This can also save money)
  • Design deployments to be easily reproduced & self-healing (i.e. using Spinnaker(Netflix), Kubernetes (Google) for containers, Scalr etc)
  • Design deployments with effective monitoring visibility –  Synthetic Transactions, Predictive Analytics, event triggers, etc.

Principles for High Availability – Never’s

  • Deploy to a single availability zone
  • Deploy to a single region
  • Depend on either a single availability zone or region to always be UP.
  • Depend on capacity in a single availability zone in a region to be AVAILABLE.
  • Give up too easily on an Active/Active design/implementation
  • Rely on datacenter failover to provide HA (Instead prefer active/active/multi-region/multi-az)
  • Make synchronous calls across regions (this negatively effects your failure domain cross region)
  • Use sticky load balancing to pin requests to a specific node for a response to succeed (i.e. if you are relying on it for ‘state’ purposes this is really bad)

Summarizing

  • YOU MUST deploy your application components to a minimum of 2 Regions.
  • Because failures of an entire region in AWS happen frequently…
  • YOU MUST deploy your application components to a minimum of 2 Availability Zones within a Region. 
  • Because an AWS AZ can go down for maintenance, outage or be out of new capacity frequently…
  • YOU MUST aim to maximize the use of Availability Zones and Regions, but balance that with cost requirements and be aware of diminishing returns
  • YOU MUST use global traffic routing (Route53) and health-check monitoring/ automated markdowns to route around failure to healthy deployments
  • YOU MUST follow the rest of the Principles of High Availability Always directives to the best of your ability

Active/Active Request Routing (AWS)

Diagram Components

  • Global Traffic Management: Using AWS Route53 as a Global Traffic Manager (configuring Traffic Flows + geolocation)
  • 3 Regions = AZ’s: us-east-1 = 4, us-west-2 = 3, eu-central-1 = 2

Additional Info on Active/Active Request Routing

  • Route53 load balances requests to the application stack within each availability zone and across 3 regions.
  • This is an Active/Active design, i.e. Route53 is configured to route requests to all available zones until one fails. (Traffic Flow + Geolocation)

The Design Above Is Actually Wasteful (Cost & Capacity must be optimized for HA)

  • This example shows all application deployments (& components) in every AZ deployed with 100% capacity required to run without another AZ
  • This means you could lose ALL, BUT ONE availability zone and still service requests from your one remaining AZ

While this would achieve the highest availability, it is not the most optimal approach because it is wasteful

  • Each application team must decide the right mix of regions, availability zones & capacity to provide HA at the lowest cost for their consumers based on their application requirements
  • Depending on how much capacity you deploy for your application per AZ it is possible to deploy too much/many and have diminishing returns on availability while wasting a lot of $$$
  • You must find the right balance for your application & business contexts ( see below examples with Cassandra for more details on how this approach can be wasteful )

Understanding Availability Zones

The crux of achieving a highly available architecture lies within the proper usage & understanding of Availability Zones. If you ignore everything else, read this section !

What Do AZ’z Provide ?

In Amazon’s own words: 

“Each region contains multiple distinct locations called Availability Zones, or AZs. Each Availability Zone is engineered to be isolated from failures in other Availability Zones, and to provide inexpensive, low-latency network connectivity to other zones in the same region. By launching instances in separate Availability Zones, you can protect your applications from the failure of a single location.” Source: http://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Concepts.RegionsAndAvailabilityZones.html

How to think of an AZ

  • In the AWS World it is helpful to use the analogy of an Availability Zone being a Rack in a traditional datacenter
  • I.E. instead of striping a Cassandra cluster across Racks, you would do it across Availability Zones.

What about using an Availability Zone as a complete application failure domain ?

  • In a traditional private datacenter an Availability Zone is a separate datacenter container, with shared network (although it can be isolated as well).
  • It is built/populated with the required infrastructure & capacity to run the applications and it has all the physical characteristics mentioned above that the AWS AZ has
  • However, the way it is used in a private datacenter can be different, because YOU have full visibility & control of capacity
  • For example, you could limit service calls such that service calls are to/from the same AZ, effectively creating a distinct application failure domain, piggy-backing the already distinct infrastructure failure domain (provided by an Availability Zone)

Why not use an AWS Availability Zone as a distinct application failure domain then ?

  • You can, but this fails to be robust in the public cloud context because capacity might not always be available in the AZ and you don’t know when it will run out
  • BEING CLEAR – You can run out of capacity in an Availability Zone FOREVER. The AZ will still be available to manage existing infrastructure/capacity, but new capacity cannot be added
  • This would negatively effect auto-scaling & new provisioning and ultimately implies you cannot rely on a single AZ to be treated as a datacenter container
  • To continue operating when an AZ runs out of capacity in public cloud you have to utilize a new AZ for new capacity
  • That new AZ would follow the same isolated service calls principles except now 2 AZ’s would be called as part of a failure domain
  • Effectively multiple AZ’s would become a single failure domain organically
  • Therefore, it is not advantageous in the public cloud world to try to treat a single AZ as a failure domain
  • Instead stripe applications across as many AZ’s as necessary to provide up to N+2 capacity per Region while minimizing waste.
  • The region becomes the failure domain of your application and this is a more efficient use of AZs to provide availability in the public cloud context
  • This is why N+N regions is paramount in managing your availability in public cloud

What is the latency between Availability Zones ?

  • Tests with micro instances (they have worst network profile) show that the latency between AZ’s within a region is on average 1ms
  • Most workloads will have no challenge dealing with 1ms latency and taking full advantage of availability zones to augment their availability
  • However, some applications are very chatty, making hundreds to thousands of calls in series
  • These workloads often must be re-architected to work well in a public cloud environment

What happens if I do not use multiple Availability Zones ?

Truths…

  • An Availability Zone can become unavailable at any time, for any reason
  • An Availability Zone can become unavailable due to planned maintenance or an outage
  • An Availability Zone can run out of capacity for long periods of time and indefinitely (forever)

Thus the result of not deploying to multiple availability zones is…

  • Provisioning outages for new capacity and the inability to self-heal deployments

The reality is… 

  • If you only deploy to a single AZ in a Region, you guarantee that you will experience a 100% failure in that Region, when the AZ you rely on becomes unavailable
  • You can failover or continue (if Active/Active) to service transactions in another Region, but you can achieve higher availability per Region by striping your application deployment across multiple AZ’s in a region
  • And you have less latency to deal with when failing over to another AZ locally vs. an AZ in a remote region

Finding The Right Balance Between High Availability & Cost

Often times availability decisions come with a cost. This section will describe a more practical and better way to achieve high availability infrastructure for your application while keeping the costs within reason. It is important to note, cost optimization heavily depends on application context, because you must first figure out the lowest common denominator of AZ’s / Regions you can use and still achieve your target availability. This typically depends on the number of required AZ’s to achieve quorum for stateful services. For example, if we were configuring Cassandra for eventual consistency + high availability, we would want 5 nodes in the cluster and a replication factor of 3. We would want to the best of our ability to spread those nodes across AZ’s.

100% of application capacity deployed to every Availability Zone is bad

  • The traffic routing example in the beginning shows 100% capacity being deployed for every application component in every AZ
  • As covered previously and shown in the later sections, this is a sub-optimal deployment strategy.
  • Instead you must stripe your applications capacity across AZ’s, keeping in mind a target of at least N+1 capacity or N+2 where possible

What if I deploy 100% of application capacity to only two of Availability Zones per Region?

  • Let’s assume we are talking about MySQL or another RDBMs with ACID properties.
  • You might want to deploy MySQL as a master/master where each master is in a separate AZ and can support 100% of the required capacity
  • Given this configuration you have met the N+1 requirement per Region
  • This might be acceptable to you from the availability perspective, however keep in mind you do not control when those AZ’s become unavailable or worse run out of new capacity.
  • Always make sure an application like this is able to be redeployed through automation (i.e. self-healing) in another availability zone

Stateless/Stateful Applications & Capacity Defines the number of AZ’z per Region you need

  • Stateless applications are much easier to provide high availability for. You simply maximize redundant instances across AZ’s within reason (cost being the reason/limiting factor)
  • So effectively with stateless applications you tend to deploy to as many AZ’s as you have available
  • But what should determine the number of AZ’s you utilize per region is your stateful applications requirements for quorum or availability and how you choose to manage capacity

Examples of balancing High Availability & Cost using Apache Cassandra in AWS

This Cassandra calculator is a useful reference for this section
http://www.ecyrd.com/cassandracalculator/

Cassandra N+1 ( 4 AZ’s ) – Wasteful Version

The following assumes Cassandra is configured for eventual consistency with the following parameters:

  • Cluster Size=5, Replication Factor=3, Write/Read=1
  • Region=us-east-1, Available AZ’s=4

Diagram Explanation

  • In this configuration you can lose 2 nodes without data availability impact
  • If you lose a 3rd node you would lose access to 60% of your data
  • Your node availability is N+2. HOWEVER, because you have only 4 AZ’s in us-east-1 that can be used, 2 nodes are required to be in 1 AZ
  • The available AZ’s in the region reduces your overall availability as a whole to N+1 in the event of ‘us-east-1e’ becoming unavailable
  • Thus the 5th node is completely unnecessary and wasteful ! 

cassandran1wasteful

Cassandra N+1 Cost Optimized (4 AZ’s) Version

The following assumes Cassandra is configured for eventual consistency with the following parameters:

  • Set Cluster Size=4, Replication Factor=2
  • Region=us-east-1, Available AZ’s=4

Diagram Explanation

  • You can now lose 1 node or one availability zone and still service 100% of requests
  • Losing a 2nd node result in 50% impact to data accessibility

cassandran1optimized

Cassandra N+2 Cost Optimized (4 AZ’s) Version

  • Set Cluster Size=4, Replication Factor=3
  • Region=us-east-1, Available AZ’s=4

Diagram Explanation

  • In order to optimize for cost and achieve N+2 you must have at least 4 AZs available
  • Otherwise, 100% of data per node is required to achieve N+2 with only 3 AZ’s
  • A better scenario is having 4 AZ’s or more where you can play with Replication Factor (aka data availability thus capacity needed to run the service)
  • To achieve cost optimized N+2 with 4 AZ’s we will set cluster size to 4 and Replication Factor to 3 resulting in this diagram/outcome

cassandran2optimized

This achieves the following

  • You can lose any 2 nodes or AZ’s above, and continue to run. If you lose a 3rd node you would have 75% impact to your data availability and need to fail/markdown the region for another region to takeover.
  • Also if you have a 4 node cluster with a replication factor=4 you have 100% of data on each node…this is not cost optimized for availability, but allows you to lose 3/4 nodes.
  • However, N+3 is considered too many eggs in one region and thus is not the right approach.
  • Instead if you architect for N+2 in each region + have global traffic management routing to active/active deployments, you will maximize your availability.

Cassandra With Only 2 Availability Zones

  • us-east-1 has 4 AZ’s this is actually a high number of available regions for AWS, in regions like us-west-2 and us-central-1 you only have 3, and 2 AZ’s respectively.
  • To clearly show the risk of ignoring availability zones & capacity while architecting for availability let’s look at the extreme case of deploying in eu-central-1 changing nothing else
  • Again we use a Cluster Size=5,Replication Factor=3 as our initial example
  • eu-central-1 has only 2 AZ’s

cassandra2azbadness

  • As you can see if you lose eu-central-1a you lose 3 nodes at one time and 60% of your data becomes inaccessible.
  • If you lose eu-central-1b you are ok because you have > 100% of the required data available provided by your remaining nodes, but you cannot lose another node.
  • You cannot and should not ever deploy a service this way. Instead you need to tune the Cassandra cluster for the number of AZ’s.
  • Setting Cluster Size=2 and Replication Factor=2 for eu-central-1 allows an N+1 design
  • Unfortunately, cost optimization is not possible with only 2 AZ’s

cassandra2azn1

  • You can lose either node or AZ, and still have access to 100% of your data. You must lose access to the region or both AZs/nodes to have a service impact
  • You cannot achieve true N+2 with only 2 AZ’s in a region (You can achieve it via node redundancy only)
  • What I recommend in this case is have multi-region deployments (kudos if they happen to be in different public clouds), but within AWS deploy to eu-central-1 + eu-west-1 using N+1 deployments & global traffic management to achieve higher availability than a single region deployment would allow.

I hope this lengthy overview helps someone somewhere struggling with how to deploy applications to Availability Zones in AWS.
Feel free to contact me with any questions or corrections, thanks for reading !

How To: Maximize Availability Effeciently Using AWS Availability Zones Read More »

Consul for Service Discovery

Why Service Discovery ?

Service Discovery effectively replaces the process of having to manually assign or automate your own DNS entries for nodes on your network. Service Discovery aims to move even further away from treating VM’s like pets to cattle, by getting rid of the age old practice of Hostname & FQDN having contextual value. Instead when using services discovery nodes are automatically registered by an agent and automatically are configured in DNS for both nodes and services running on the machine.

Consul

Consul by Hashicorp is becoming the de-facto standard for Service Discovery. Consul’s full features & simplistic deployment model make it an optimal choice for organizations looking to quickly deploy Service Discovery capabilities in their environment.

Components of Consul

  1. The Consul Agent
  2. An optional JSON config file for each service located under /etc/consul.d/<service>.json
    1. If you do not specific a JSON file, consul can still start and will provide discovery for the nodes (they will have DNS as well)

A Quick Example of Consul

How easy is it to deploy console ?

  1. Download / Decompress and install the Consul agent – https://www.consul.io/downloads.html
  2. Define services in a JSON file (if you want) – https://www.consul.io/intro/getting-started/services.html
  3. Start the agent on the nodes – https://www.consul.io/intro/getting-started/join.html
  4.  Make 1 node join 1 other node (does not matter which node) to join the cluster, which gets you access to all cluster metadata

Steps 1 and 2 Above

  1. After downloading the Consul binary to each machine and decompressing it, copy it to /usr/local/bin/ so it’s in your path.
  2. Create the directory
    sudo mkdir /etc/consul.d
  3. Optionally, run the following to create a JSON file defining a fake service running
echo '{"service": {"name": "web", "tags": ["rails"], "port": 80}}' \
    >/etc/consul.d/web.json

Step 3 Above

Run the agent on each node, changing IP accordingly.

tuxninja@consul-d415:~$ consul agent -server -bootstrap-expect 1 -data-dir /tmp/consul -node=agent-one -bind=10.73.172.110 -config-dir /etc/consul.d

Step 4 Above

tuxninja@consul-d415:~$ consul join 10.73.172.108
Successfully joined cluster by contacting 1 nodes.

Wow, simple…ok now for the examples….

Show cluster members

tuxninja@consul-dcb3:~$ consul join 10.73.172.110
Successfully joined cluster by contacting 1 nodes.

Look up DNS for a node

tuxninja@consul-dcb3:~$ dig @127.0.0.1 -p 8600 agent-one.node.consul
; <<>> DiG 9.9.5-3ubuntu0.8-Ubuntu <<>> @127.0.0.1 -p 8600 agent-one.node.consul
; (1 server found)
;; global options: +cmd
;; Got answer:
;; ->>HEADER<<- opcode: QUERY, status: NOERROR, id: 2450
;; flags: qr aa rd; QUERY: 1, ANSWER: 1, AUTHORITY: 0, ADDITIONAL: 0
;; WARNING: recursion requested but not available
;; QUESTION SECTION:
;agent-one.node.consul.		IN	A
;; ANSWER SECTION:
agent-one.node.consul.	0	IN	A	10.73.172.110
;; Query time: 1 msec
;; SERVER: 127.0.0.1#8600(127.0.0.1)
;; WHEN: Tue May 03 21:43:47 UTC 2016
;; MSG SIZE  rcvd: 76
tuxninja@consul-dcb3:~$

Lookup DNS for a service

tuxninja@consul-dcb3:~$  dig @127.0.0.1 -p 8600 web.service.consul
; <<>> DiG 9.9.5-3ubuntu0.8-Ubuntu <<>> @127.0.0.1 -p 8600 web.service.consul
; (1 server found)
;; global options: +cmd
;; Got answer:
;; ->>HEADER<<- opcode: QUERY, status: NOERROR, id: 55798
;; flags: qr aa rd; QUERY: 1, ANSWER: 1, AUTHORITY: 0, ADDITIONAL: 0
;; WARNING: recursion requested but not available
;; QUESTION SECTION:
;web.service.consul.		IN	A
;; ANSWER SECTION:
web.service.consul.	0	IN	A	10.73.172.110
;; Query time: 2 msec
;; SERVER: 127.0.0.1#8600(127.0.0.1)
;; WHEN: Tue May 03 21:46:54 UTC 2016
;; MSG SIZE  rcvd: 70
tuxninja@consul-dcb3:~$

Query the REST API for Nodes

tuxninja@consul-dcb3:~$ curl localhost:8500/v1/catalog/nodes
[{"Node":"agent-one","Address":"10.73.172.110","TaggedAddresses":{"wan":"10.73.172.110"},"CreateIndex":3,"ModifyIndex":1311},{"Node":"agent-two","Address":"10.73.172.108","TaggedAddresses":{"wan":"10.73.172.108"},"CreateIndex":1338,"ModifyIndex":1339}

Query the REST API for Services

tuxninja@consul-dcb3:~$ curl http://localhost:8500/v1/catalog/service/web
[{"Node":"agent-one","Address":"10.73.172.110","ServiceID":"web","ServiceName":"web","ServiceTags":["rails"],"ServiceAddress":"","ServicePort":80,"ServiceEnableTagOverride":false,"CreateIndex":5,"ModifyIndex":772}

Consul for Service Discovery Read More »