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ANTI 

PATTERNS
DATA
Updated!
ines @ Engine Yard.com
@Randommood
And I’m a
happy dog!
I N E S
S O M B R A
I work with
Databases
Data antipatterns NYC Devops - 2014
I can get a little
ragey
sometimes
Disclaimer #1
I’m sorry.

!
Ask me to
slow down.
Disclaimer #2
ZOMG, the
horror!
.BACKUPS
yes, we are going there
“I know you.
You know
you. And I
know you
know that I
know you”
White Goodman

Boring Definition #1
Backups
Copy and archiving
of data
Goal is to restore
the state of a DB
Many types - blah
Anti-Pattern #1
Taking too
many
backups
Not free, they
requires resources
Full backup every
hour, really? 

What about backup
retention?
Anti-Pattern #2
Taking too
few
backups
Enough to minimize
the risk of data loss
due to corrupted
backup files
yes,
 this
 totes
 happens!
The untested backup
Anti-Pattern #3
Doing backups right
Logically
test
backups
Errorless restore
is not enough.
Test logical data
too
Doing backups right
Know your
types 
tools
Take logical and
binary backups
Continuous archiving
 hot backup utilities
Doing backups right
Practice
restores
Backups alone do not
constitute DR. Have a
plan  practice it
Server extensions and
configuration matter
when restoring
“I want a
ridiculously
good
looking
Database”
Derek Zoolander

(honestly, Ben Stiller rules)
Obvious statement #1
Many DB
choices
Cargo
culting your
database
Anti-Pattern #4
Failure to
understand use
case, strengths 
weaknesses of a
new database
RDBMS for
Session
Data
Anti-Pattern #5 Often means at least
one write per request
Any DB issue/task may
cause app to hang
Tables have a
tendency to bloat
Modeling, it’s all the same
Anti-Pattern #6
Data Model
Consistency needs
Availability needs
Scaling needs
Operational story 
cost
Doing it right
Know your
needs
Doing it right
Spike it,
forealsies
Spike it with your
data and traffic. 

Best way to gain
operational
experience
Doing it right
Leverage
new
features
Relational databases
are getting quite
versatile
Evaluate clustered
MySQL options
Are we
doing
ok?
We have a cloud deployment!
Happy team on shipping day, lmfao if you don’t celebrate like this
Cloud-based
databases,
they are real
Obvious statement #2
Databases can live in
the cloud quite well
Many IaaS, PaaS, 
DBaaS options
Easy to get started 
may be economical
Where did my instance go?
Anti-Pattern #7
Anti-Pattern #8
Cloud, it’s
just like
hardware
It’s not. Cloud
resources are
virtualized
Capacity planning
and monitoring
matter. A lot
Anti-Pattern #9
Shit doesn’t
happen
You are not
immune to
infrastructure
failures. 

Plan for it
Anti-Pattern #10
Storage is
the same
Instance storage is
not persisted (unless
you use EBS)
Data locality matters
Don’t run your cloud
DBs too hot!
Doing cloud right
Know your
cloud
deployments
Replication in the
cloud is a must-have
Put DB master 
replicas in different
AZs
Doing cloud right
Learn high
availability 
disaster
recovery
Get good at replica
promotions (some
work involved)
Understand and
invest in DR/HA.
Know your options
Doing cloud right
Know your
system
Invest in monitoring
Know your data
distribution 
querying patterns
Know baseline
behavior
And there’s
more!
Boring Definition #2
Indexes

(or indices, I prefer indexes)
Improves speed of
data retrieval
Used in random 
ordered lookups
Imply additional
writes  storage
Anti-Pattern #12
Too few
Indexes
Room for query
optimization 
increased speed
Analyze, slow logs 
monitoring tools are
your friends
Anti-Pattern #13
Too many
Indexes
They are not free.
Your DB maintains
them.
Too many will
impact your write
throughput.
Doing Indexes right
Many index
types
Many types. Learn
how your DB does
them.
You want the right
amount.
Doing Indexes right
Postgres
Indexes
summarized
B-TR EE S 

D E FAULT. NU MER IC, 

TE XT, NU LL
HA SH 

EQUA L I T Y. D ON’ T USE
G IN 

A RRAY VA LUE S  F TS
G I ST 

GEOME TRIC DATA  F TS
They Can Be
Created
Concurrently!

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Data antipatterns NYC Devops - 2014