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DevOps for Startups
Email: {anything}@jedberg.net
Twitter: @jedberg
Web: www.jedberg.net
Facebook: facebook.com/jedberg
Linkedin: www.linkedin.com/in/jedberg
Jeremy Edberg
Founder and CEO
MinOps
https://minops.com

https://sql.bot
If it won’t scale, it'll fail.
The key to scaling is finding the
bottlenecks before your users
do
Why am I here?
Why should we
learn from other
people’s mistakes?
Jeremy Edberg (MinOps ) - How to build a solid infrastructure for a startup that is cheap and can scale
Takeaways
• Infrastructure as Code
• Microservices/Serverless
• Queueing Theory
• Chaos Engineering
• Logs
• Incident reviews
Infrastructure as Code
• Changes are routine, small,
easy, and repeatable
• Resources are easily
managed by users and
disposable
• Enables continuous
deployment and
improvement
• Solutions can be easily
tested, measured, and then
rolled back
• Losing track of servers
and resources
• Configuration drift
• Snowflakes
• Fear of a fully
automated system 

(lack of trust in oneself)
Infrastructure as Code
Challenges
Automate all the things!
http://hyperboleandahalf.blogspot.com/2010/06/this-is-why-ill-never-be-adult.html
• Application startup
• Configuration
• Code deployment
• System
deployment
Automate all the things!
Jeremy Edberg (MinOps ) - How to build a solid infrastructure for a startup that is cheap and can scale
Jeremy Edberg (MinOps ) - How to build a solid infrastructure for a startup that is cheap and can scale
Jeremy Edberg (MinOps ) - How to build a solid infrastructure for a startup that is cheap and can scale
Jeremy Edberg (MinOps ) - How to build a solid infrastructure for a startup that is cheap and can scale
Jeremy Edberg (MinOps ) - How to build a solid infrastructure for a startup that is cheap and can scale
Choosing your infrastructure
λ
Test and prod are different
Prod is in need of constant updates
Slow iteration and deployment
Polyglot unfriendly
Deploy in weeks, live for years
Physical Servers
Prod is immutable
Rapid iteration and deployment
Multi-tenancy
Polyglot friendly
Deploy in minutes, live for weeks
Virtual Machines
Test and prod are the same
Prod is immutable
Rapid(er) iteration and deployment
High multi-tenancy
Polyglot friendly
Deploy in seconds, live for hours
Containers
Smallest unit of compute
Super scalable
Rapid iteration
Extreme multi-tenancy
Very polyglot friendly
Easier to collaborate
Deploy independently, live for seconds
Serverless
λ
A whole lot of choices
Amazon’s EcosystemHodgepodge of services
A whole lot of choices
Amazon’s Serverless Ecosystem
Lambda
SNS
DynamoDB
SQS
S3
Kenisis
What is serverless anyway?
• There are still servers, you just don’t
manage them anymore
• It also means you don’t access them
anymore
• So you don’t need to (or get to)
optimize them.
Serverless computing is all about
speeding up development by allowing
rapid iteration and removing
management overhead
Choosing your unit of compute
• VMs

Machine as the unit of scale

Abstracts the hardware
• Containers

Application as the unit of scale

Abstracts the OS
• Serverless

Functions as the unit of scale

Abstracts the language runtime
EC2
ECS
Lambda
How do I choose?
• VMs

“I want to configure machines,
storage, networking, and my
OS”
• Containers

“I want to run servers,
configure applications, and
control scaling”
• Serverless

“Run my code when it’s
needed”
EC2
ECS
Lambda
I didn’t write
the software
myself
Monorepo or Services?
Advantages to a Monorepo
• No worrying about
dependencies
• Don’t have to account for
data movement
• Deployments are simple
• Coordination is easy
Multiple services
Internal Microservices Platform
Monolithic
Success follows a standard pattern
ReleaseTestBuild
Developer Deployment Pain: High
DevOps Deployment Pain: Medium
ReleaseTestBuild
ReleaseTestBuild
ReleaseTestBuild
ReleaseTestBuild
ReleaseTestBuild
Developer Deployment Pain: Medium
DevOps Deployment Pain: High
ReleaseTestBuild
ReleaseTestBuild
ReleaseTestBuild
ReleaseTestBuild
ReleaseTestBuild
λ
λ
λ }
Developer Deployment Pain: Low
DevOps Deployment Pain: 🔥
Jeremy Edberg (MinOps ) - How to build a solid infrastructure for a startup that is cheap and can scale
Advantages to a Service Oriented
Architecture
• Easier auto-scaling
• Easier capacity planning
• Identify problematic code-paths more easily
• Narrow in the effects of a change
• More efficient local caching
Highly aligned, loosely coupled
• Services are built by different
teams who work together to
figure out what each service
will provide.
• The service owner publishes
an API that anyone can use
and returns proper response
codes
Distributed Computing and a
Distributed Workforce
• The two go hand in hand
when you have a good
distributed systems culture
• Microservices and Micro
Teams
Proper 

Microservices 

Architecture
Service
and 

Resource 

Discovery
Network
and Traffic
Config
Automated 

Testing
Continuous
Deployment
Security
Monitoring 

and Alerting
Mature companies spend 25% of their
engineering resources on their internal platform
And when you’re done it is only “good
enough”
Building an internal
microservices platform is hard
So what about Serverless?
What do all the parts
of microservices have
in common?
Servers
Capacity planning
Right-sizing
Autoscaling
Load and performance
Patches
Tuning
Configuration
Utilization
Access control
Packages and AMIs
Serverless
Right-sizing
Autoscaling
Load and performance
Patches
Tuning
Configuration
Utilization
Access control
Packages and AMIs
Fully managed
Continuous Scaling
Function is the deployment unit
Capacity planning
Proper 

Microservices 

Architecture
Service
and 

Resource 

Discovery
Network
and Traffic
Config
Automated 

Testing
Continuous
Deployment
Security
Monitoring 

and Alerting
Proper 

Microservices 

Architecture
Automated 

Testing
Continuous
Deployment
Security
Security
• Shorter TTL == less
chance for an attack
to take hold
Continuous scalingNo servers to
manage
Never pay for idle
– No cold servers
(only happy
accountants)
Benefits of AWS Lambda
What does Lambda do for you?
• Scales server capacity automatically
• API to trigger execution
• Ensures function is executed in parallel
and at scale
• Logging, monitoring, etc
• Easy pricing
Monitoring
• Everything is in Cloudwatch or Cloudwatch
logs
Pricing
• Choose your RAM
from 128MB to
1500MB
• CPU and Network
scaled based on RAM
Cost Comparison
Cost Comparison
There’s about 2.5M seconds in a month, so 3M requests is about 1.2 per second
The T2.Small is $18.98 a month, more than Lambda already
Jeremy Edberg (MinOps ) - How to build a solid infrastructure for a startup that is cheap and can scale
Jeremy Edberg (MinOps ) - How to build a solid infrastructure for a startup that is cheap and can scale
Jeremy Edberg (MinOps ) - How to build a solid infrastructure for a startup that is cheap and can scale
Lambda lets you manage

your code and infrastructure

in the same place
Lambda lets your developers manage

their code and your infrastructure

in the same place
All the problems you have with
microservices are multiplied 10X
with serverless
Problems with
Serverless
• efficient dependency usage
• local dev environments
• making sure everyone has the same
dependencies
• knowing when someone else is
deploying the same function
Jeremy Edberg (MinOps ) - How to build a solid infrastructure for a startup that is cheap and can scale
Jeremy Edberg (MinOps ) - How to build a solid infrastructure for a startup that is cheap and can scale
Jeremy Edberg (MinOps ) - How to build a solid infrastructure for a startup that is cheap and can scale
Testing
• You can’t test the network, but
a good application test should
obviate the need to do so.
• Not really a solved problem.
Can do local testing.
• Can also send json to the
function and compare the
results.
Tips and Tricks
• Limit your function size
(JVM startup time
especially)
• Remember execution is
async
• Don’t assume function
container reuse but
take advantage of it
Tips and Tricks
• Remember the 500MB in /tmp
• Use function aliases
• Use the included logger
Tips and Tricks
• Set up alarms on all
Lambda Cloudwatch
metrics
• Avoid throttling by using
SNS between any service,
such as S3
• Beware of infinite loops by
functions calling each other.
Avoiding Infinte Loops
• With a distributed team, this
is an easy mistake to make
• To avoid it, pass a call stack
and check for self in the
stack
8
So where does that leave
us?
Serverless or containers?
Services or monorepo?
Proper 

Microservices 

Architecture
Service
and 

Resource 

Discovery
Network
and Traffic
Config
Automated 

Testing
Continuous
Deployment
Security
Monitoring 

and Alerting
Actionable MetricsMonitoring 

and Alerting
Actionable MetricsMonitoring 

and Alerting
Monitoring 

and Alerting Actionable Metrics
Choosing a metric
Monitoring 

and Alerting
Self Serve is the Key
• Let developers choose what
metrics to submit
• What graphs they put on
their dashboards
• What to alert on
• They are closest to the app,
so they know best
Monitoring 

and Alerting
Alert on increase of
failure, not lack of success
Increase in 500s
 Decrease in 200s
Monitoring 

and Alerting
👍 👎
P50, P90, P99
Monitoring 

and Alerting
P50, P90, P99
0
15
30
45
60
1m 2m 3m 4m 5m 6m 7m 8m 9m 10m 11m 12m 13m 14m 15m
P50 P90 P99
Monitoring 

and Alerting
Immutable Data
• If you can, write your software
such that everything in the
cache is immutable.
Moving data is
the single
biggest cost your
distributed
system will incur
But you need to move data for reliability,
so it’s a tradeoff
Use queues as often
as possible
Which is greater? 

Queues or Sliced Bread?
Queuing
• Queue anything you are
writing to a data store
• Monitor your queue
lengths for great insight
and scaling!
0
2
4
6
8
10
12
14
16
18
1 3 5 7 9 11 13 15 17 19
0
2
4
6
8
10
12
14
16
18
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Items
Seconds
Queue Depth
0
10
20
30
40
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Cumulative Flow Diagram
Items
Seconds
Arrivals
Departures
Capacity utilization increases
queues exponentially
• Every time you reduce the excess capacity
by 1/2, you double the average queue size.
• This has a direct effect on the ratio of wait
time to work time for a single work unit
• Use this to balance cost vs. latency
0
2
4
6
8
10
10 20 30 40 50 60 70 80 90 100
• Variability increases
queue sizes linearly
• Operating at high
utilization increases
variability
The price of
variability
The price of
variability
The price of
variability
Fast Medium Slow
Chaos Engineering
• Simulate things
that go wrong
• Find things that
are different
Two most important
things to test
Instance Loss
Increased Latency
Logs
• What went wrong?
• How could we have detected it
sooner?
• How could we have prevented it?
• How can we prevent this class of
problem in the future?
• How can we improve our behavior
for next time?
Ask the key questions:
Incident Reviews
Takeaways
• Infrastructure as Code
• Microservices/Serverless/
Monolith
• Queuing Theory
• Chaos Engineering
• Logs Suck
• Incident reviews
Questions?
Email: {anything}@jedberg.net
Twitter: @jedberg
Web: www.jedberg.net
Facebook: facebook.com/jedberg
Linkedin: www.linkedin.com/in/jedberg
Company: minops.com

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