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Scaling Instagram
         AirBnB Tech Talk 2012
                  Mike Krieger
                     Instagram
me

-   Co-founder, Instagram
-   Previously: UX & Front-end
    @ Meebo
-   Stanford HCI BS/MS
-   @mikeyk on everything
communicating and
sharing in the real world
30+ million users in less
    than 2 years
the story of how we
      scaled it
a brief tangent
the beginning
Text
2 product guys
no real back-end
   experience
analytics & python @
        meebo
CouchDB
CrimeDesk SF
let’s get hacking
good components in
   place early on
...but were hosted on a
     single machine
    somewhere in LA
less powerful than my
    MacBook Pro
okay, we launched.
   now what?
25k signups in the first
         day
everything is on fire!
best & worst day of our
      lives so far
load was through the
        roof
first culprit?
favicon.ico
404-ing on Django,
causing tons of errors
lesson #1: don’t forget
     your favicon
real lesson #1: most of
   your initial scaling
  problems won’t be
       glamorous
favicon
ulimit -n
memcached -t 4
prefork/postfork
friday rolls around
not slowing down
let’s move to EC2.
scaling = replacing all
components of a car
  while driving it at
       100mph
since...
“"canonical [architecture]
of an early stage startup
       in this era."
  (HighScalability.com)
Nginx &
Redis &
Postgres &
Django.
Nginx & HAProxy &
Redis & Memcached &
Postgres & Gearman &
Django.
24h Ops
our philosophy
1 simplicity
2 optimize for
minimal operational
     burden
3 instrument
 everything
walkthrough:
1 scaling the database
2 choosing technology
3 staying nimble
4 scaling for android
1 scaling the db
early days
django ORM, postgresql
why pg? postgis.
moved db to its own
    machine
but photos kept growing
     and growing...
...and only 68GB of
   RAM on biggest
   machine in EC2
so what now?
vertical partitioning
django db routers make
     it pretty easy
def db_for_read(self, model):
  if app_label == 'photos':
    return 'photodb'
...once you untangle all
    your foreign key
      relationships
a few months later...
photosdb > 60GB
what now?
horizontal partitioning!
aka: sharding
“surely we’ll have hired
someone experienced
before we actually need
        to shard”
you don’t get to choose
when scaling challenges
      come up
evaluated solutions
at the time, none were
up to task of being our
      primary DB
did in Postgres itself
what’s painful about
    sharding?
1 data retrieval
hard to know what your
primary access patterns
will be w/out any usage
in most cases, user ID
2 what happens if
one of your shards
  gets too big?
in range-based schemes
 (like MongoDB), you split
A-H: shard0
I-Z: shard1
A-D:   shard0
E-H:   shard2
I-P:   shard1
Q-Z:   shard2
downsides (especially on
    EC2): disk IO
instead, we pre-split
many many many
(thousands) of logical
       shards
that map to fewer
  physical ones
// 8 logical shards on 2 machines

user_id % 8 = logical shard

logical shards -> physical shard map

{
    0:   A,   1:   A,
    2:   A,   3:   A,
    4:   B,   5:   B,
    6:   B,   7:   B
}
// 8 logical shards on 2 4 machines

user_id % 8 = logical shard

logical shards -> physical shard map

{
    0:   A,   1:   A,
    2:   C,   3:   C,
    4:   B,   5:   B,
    6:   D,   7:   D
}
little known but awesome
    PG feature: schemas
not “columns” schema
- database:
  - schema:
    - table:
      - columns
machineA:
  shard0
    photos_by_user
  shard1
    photos_by_user
  shard2
    photos_by_user
  shard3
    photos_by_user
machineA:            machineA’:
  shard0               shard0
    photos_by_user       photos_by_user
  shard1               shard1
    photos_by_user       photos_by_user
  shard2               shard2
    photos_by_user       photos_by_user
  shard3               shard3
    photos_by_user       photos_by_user
machineA:            machineC:
  shard0               shard0
    photos_by_user       photos_by_user
  shard1               shard1
    photos_by_user       photos_by_user
  shard2               shard2
    photos_by_user       photos_by_user
  shard3               shard3
    photos_by_user       photos_by_user
can do this as long as
you have more logical
 shards than physical
        ones
lesson: take tech/tools
you know and try first to
adapt them into a simple
         solution
2 which tools where?
where to cache /
otherwise denormalize
        data
we <3 redis
what happens when a
 user posts a photo?
1 user uploads photo
with (optional) caption
     and location
2 synchronous write to
the media database for
      that user
3 queues!
3a if geotagged, async
 worker POSTs to Solr
3b follower delivery
can’t have every user
 who loads her timeline
look up all their followers
  and then their photos
instead, everyone gets
 their own list in Redis
media ID is pushed onto
 a list for every person
who’s following this user
Redis is awesome for
this; rapid insert, rapid
        subsets
when time to render a
feed, we take small # of
  IDs, go look up info in
       memcached
Redis is great for...
data structures that are
  relatively bounded
(don’t tie yourself to a
 solution where your in-
memory DB is your main
        data store)
caching complex objects
where you want to more
       than GET
ex: counting, sub-
 ranges, testing
   membership
especially when Taylor
Swift posts live from the
        CMAs
follow graph
v1: simple DB table
(source_id, target_id,
        status)
who do I follow?
 who follows me?
  do I follow X?
does X follow me?
DB was busy, so we
started storing parallel
   version in Redis
follow_all(300 item list)
inconsistency
extra logic
so much extra logic
exposing your support
 team to the idea of
  cache invalidation
redesign took a page
  from twitter’s book
PG can handle tens of
thousands of requests,
 very light memcached
         caching
two takeaways
1 have a versatile
complement to your core
 data storage (like Redis)
2 try not to have two
tools trying to do the
      same job
3 staying nimble
2010: 2 engineers
2011: 3 engineers
2012: 5 engineers
scarcity -> focus
engineer solutions that
 you’re not constantly
 returning to because
       they broke
1 extensive unit-tests
 and functional tests
2 keep it DRY
3 loose coupling using
 notifications / signals
4 do most of our work in
Python, drop to C when
      necessary
5 frequent code reviews,
  pull requests to keep
   things in the ‘shared
           brain’
6 extensive monitoring
munin
statsd
“how is the system right
         now?”
“how does this compare
  to historical trends?”
scaling for android
1 million new users in 12
           hours
great tools that enable
 easy read scalability
redis: slaveof <host> <port>
our Redis framework
assumes 0+ readslaves
tight iteration loops
statsd & pgfouine
know where you can
shed load if needed
(e.g. shorter feeds)
if you’re tempted to
reinvent the wheel...
don’t.
“our app servers
sometimes kernel panic
     under load”
...
“what if we write a
monitoring daemon...”
wait! this is exactly what
 HAProxy is great at
surround yourself with
 awesome advisors
culture of openness
around engineering
give back; e.g.
   node2dm
focus on making what
   you have better
“fast, beautiful photo
       sharing”
“can we make all of our
requests 50% the time?”
staying nimble = remind
   yourself of what’s
        important
your users around the
world don’t care that you
  wrote your own DB
wrapping up
unprecedented times
2 backend engineers
can scale a system to
  30+ million users
key word = simplicity
cleanest solution with the
 fewest moving parts as
        possible
don’t over-optimize or
expect to know ahead of
 time how site will scale
don’t think “someone
else will join & take care
           of this”
will happen sooner than
   you think; surround
    yourself with great
         advisors
when adding software to
stack: only if you have to,
optimizing for operational
         simplicity
few, if any, unsolvable
scaling challenges for a
      social startup
have fun

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