Application Monitoring in a Post-Server World: Why Data Context is Critical

The Decline of the Server

▪Containerization▪Docker▪Amazon ECS

▪Zero config infrastructure-less compute▪AWS Lambda

©2008-15 New Relic, Inc. All rights reserved.

Lessons learned from Docker


Docker is the app’s lightweight VM

Long lived

Short lived

VM

AmazonEC2


6

Well, that was surprising

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49ACCOUNTS USING DOCKER

IN LAST 24 HOURS

9,974CONTAINERS REPORTING IN

IN LAST 24 HOURS


7

Apparent usage


Long lived

Short lived

AmazonEC2

VM


8

Along came New Relic Synthetics…


▪Test external availability and performance▪User authored selenium scripts run in our data

center▪Each run in its own container for security

isolation▪Most run for under a minute


9

Disposable compute container


Long lived

Short lived

VM

AmazonEC2

AWSLambd

a


What the heck’s going on?


Long lived

Short lived

? ? ?

VM

AmazonEC2

AWSLambd

a


We’re Data Nerds!

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Docker container age, count vs. hours

100

10K

1M

3.7 M

83 days

333 days



100

10K

1M

3.7 M

VM’ish




100

10K

1M

3.7 M

EC2’ish




100

10K

1M

3.7 M

Lambda’ish


Container age, by hour under 24 hours


3,741,00046% under one hour


Container age, by minute under an hour


950,00011% under one minute


Container age, by minute under an hour


27% under 5 minutes (versus a VM?)


A surprising result


Long lived

Short lived

VMVM

AmazonEC2

AWSLambd

a


June versus now: 5x data, same shape

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The evolution of computation as a serviceShort startup time (orders mag.) allows very short lived computing

Containers only exist, and only for as long, as they provide value.

Full stop.

Containers are created

Do their work

Go away


Elements of monitoring computation as a service

▪A mere list of instances doesn’t scale, nor help

▪De-provisioned containers still contribute to knowledge

▪Raw metrics just table stakes

▪Context matters to identify computational intent


Monitoring servers


Monitoring computation


Low friction install

New Relic AWS EC2 Beta addresses


Managing the dynamic

nature of AWS

Managing the scale of AWS


Increased visibility

Application Monitoring

Server Monitoring

EC2 AWS Metadata


SaaS to SaaS monitoring, under one minute


Dynamic management

Provides AWS status

Maintains accurate EC2 list

Detects blind spots

Shows instance state


Context via tags and metadata as labels

AWS metadata

AWS custom

tags


EC2 instance name

AWS metadata

Custom label

Customer example


New Relic by instance typeSelected

labelInstance

countHealth status


New Relic by availability zone


New Relic by instance type in us-east-1



Future requirements for monitoring toolsThe obvious ones

Should handle scale

Should handle dynamic lifecycle

of resources

The less obvious ones

It’s a big data problem

Ops needs ways of quickly pivoting and

drilling in

Deeper understanding requires analytics (raw metrics not

enough) ©2008-15 New Relic, Inc. All rights reserved.

We all love…

… and yet

Common AWS scenarios

No large infrastructure build out

Quickly provision

Scale out to meet demand

Am I under-provisioned?

Am I over-provisioned?

How well am I responding to demand?


Prototype examples of Analytics

Application and Server metrics

Enriched with context from AWS

As New Relic Insights events


Detecting under-provisioned

Hmm, that’s bad

The smoking gun Aha!


Detecting over-provisionedHmm, that’s too

good

Aha!That’s a waste


Evaluating availability elasticity

Through scale outBut never

de-provisioned

Response time settles back

Increased load


The Ultimate AWS control panel


RESOURCES CUSTOMER EXPERIENCE COST


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Application Monitoring in a Post-Server World: Why Data Context is Critical

Data & Analytics

Transcript of Application Monitoring in a Post-Server World: Why Data Context is Critical