application monitoring in a post-server world: why data context is critical
TRANSCRIPT
2Confidential ©2008-15 New Relic, Inc. All rights reserved.
The Decline of the Server
▪Containerization▪Docker▪Amazon ECS
▪Zero config infrastructure-less compute▪AWS Lambda
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Lessons learned from Docker
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Docker is the app’s lightweight VM
Long lived
Short lived
VM
AmazonEC2
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6
Well, that was surprising
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49ACCOUNTS USING DOCKER
IN LAST 24 HOURS
9,974CONTAINERS REPORTING IN
IN LAST 24 HOURS
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7
Apparent usage
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Long lived
Short lived
AmazonEC2
VM
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8
Along came New Relic Synthetics…
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▪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
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9
Disposable compute container
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Long lived
Short lived
VM
AmazonEC2
AWSLambd
a
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What the heck’s going on?
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Long lived
Short lived
? ? ?
VM
AmazonEC2
AWSLambd
a
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We’re Data Nerds!
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Docker container age, count vs. hours
100
10K
1M
3.7 M
83 days
333 days
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Docker container age, count vs. hours
100
10K
1M
3.7 M
VM’ish
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Docker container age, count vs. hours
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100
10K
1M
3.7 M
EC2’ish
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Docker container age, count vs. hours
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100
10K
1M
3.7 M
Lambda’ish
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Container age, by hour under 24 hours
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3,741,00046% under one hour
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Container age, by minute under an hour
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950,00011% under one minute
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Container age, by minute under an hour
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27% under 5 minutes (versus a VM?)
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A surprising result
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Long lived
Short lived
VMVM
AmazonEC2
AWSLambd
a
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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
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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
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Monitoring servers
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Monitoring computation
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Low friction install
New Relic AWS EC2 Beta addresses
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Managing the dynamic
nature of AWS
Managing the scale of AWS
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Increased visibility
Application Monitoring
Server Monitoring
EC2 AWS Metadata
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SaaS to SaaS monitoring, under one minute
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Dynamic management
Provides AWS status
Maintains accurate EC2 list
Detects blind spots
Shows instance state
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Context via tags and metadata as labels
AWS metadata
AWS custom
tags
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EC2 instance name
AWS metadata
Custom label
Customer example
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New Relic by instance typeSelected
labelInstance
countHealth status
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New Relic by availability zone
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New Relic by instance type in us-east-1
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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?
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Prototype examples of Analytics
Application and Server metrics
Enriched with context from AWS
As New Relic Insights events
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Detecting under-provisioned
Hmm, that’s bad
The smoking gun Aha!
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Detecting over-provisionedHmm, that’s too
good
Aha!That’s a waste
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Evaluating availability elasticity
Through scale outBut never
de-provisioned
Response time settles back
Increased load
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The Ultimate AWS control panel
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RESOURCES CUSTOMER EXPERIENCE COST
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