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Scaling Instagram
1.
Scaling Instagram
AirBnB Tech Talk 2012 Mike Krieger Instagram
2.
me -
Co-founder, Instagram - Previously: UX & Front-end @ Meebo - Stanford HCI BS/MS - @mikeyk on everything
3.
4.
5.
6.
communicating and sharing in
the real world
7.
30+ million users
in less than 2 years
8.
the story of
how we scaled it
9.
a brief tangent
10.
the beginning
11.
Text
12.
2 product guys
13.
no real back-end
experience
14.
analytics & python
@ meebo
15.
CouchDB
16.
CrimeDesk SF
17.
18.
let’s get hacking
19.
good components in
place early on
20.
...but were hosted
on a single machine somewhere in LA
21.
22.
less powerful than
my MacBook Pro
23.
okay, we launched.
now what?
24.
25k signups in
the first day
25.
everything is on
fire!
26.
best & worst
day of our lives so far
27.
load was through
the roof
28.
first culprit?
29.
30.
favicon.ico
31.
404-ing on Django, causing
tons of errors
32.
lesson #1: don’t
forget your favicon
33.
real lesson #1:
most of your initial scaling problems won’t be glamorous
34.
favicon
35.
ulimit -n
36.
memcached -t 4
37.
prefork/postfork
38.
friday rolls around
39.
not slowing down
40.
let’s move to
EC2.
41.
42.
43.
scaling = replacing
all components of a car while driving it at 100mph
44.
since...
45.
“"canonical [architecture] of an
early stage startup in this era." (HighScalability.com)
46.
Nginx & Redis & Postgres
& Django.
47.
Nginx & HAProxy
& Redis & Memcached & Postgres & Gearman & Django.
48.
24h Ops
49.
50.
51.
our philosophy
52.
1 simplicity
53.
2 optimize for minimal
operational burden
54.
3 instrument everything
55.
walkthrough: 1 scaling the
database 2 choosing technology 3 staying nimble 4 scaling for android
56.
1 scaling the
db
57.
early days
58.
django ORM, postgresql
59.
why pg? postgis.
60.
moved db to
its own machine
61.
but photos kept
growing and growing...
62.
...and only 68GB
of RAM on biggest machine in EC2
63.
so what now?
64.
vertical partitioning
65.
django db routers
make it pretty easy
66.
def db_for_read(self, model):
if app_label == 'photos': return 'photodb'
67.
...once you untangle
all your foreign key relationships
68.
a few months
later...
69.
photosdb > 60GB
70.
what now?
71.
horizontal partitioning!
72.
aka: sharding
73.
“surely we’ll have
hired someone experienced before we actually need to shard”
74.
you don’t get
to choose when scaling challenges come up
75.
evaluated solutions
76.
at the time,
none were up to task of being our primary DB
77.
did in Postgres
itself
78.
what’s painful about
sharding?
79.
1 data retrieval
80.
hard to know
what your primary access patterns will be w/out any usage
81.
in most cases,
user ID
82.
2 what happens
if one of your shards gets too big?
83.
in range-based schemes
(like MongoDB), you split
84.
A-H: shard0 I-Z: shard1
85.
A-D:
shard0 E-H: shard2 I-P: shard1 Q-Z: shard2
86.
downsides (especially on
EC2): disk IO
87.
instead, we pre-split
88.
many many many (thousands)
of logical shards
89.
that map to
fewer physical ones
90.
// 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 }
91.
// 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 }
92.
little known but
awesome PG feature: schemas
93.
not “columns” schema
94.
- database:
- schema: - table: - columns
95.
machineA: shard0
photos_by_user shard1 photos_by_user shard2 photos_by_user shard3 photos_by_user
96.
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
97.
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
98.
can do this
as long as you have more logical shards than physical ones
99.
lesson: take tech/tools you
know and try first to adapt them into a simple solution
100.
2 which tools
where?
101.
where to cache
/ otherwise denormalize data
102.
we <3 redis
103.
what happens when
a user posts a photo?
104.
1 user uploads
photo with (optional) caption and location
105.
2 synchronous write
to the media database for that user
106.
3 queues!
107.
3a if geotagged,
async worker POSTs to Solr
108.
3b follower delivery
109.
can’t have every
user who loads her timeline look up all their followers and then their photos
110.
instead, everyone gets
their own list in Redis
111.
media ID is
pushed onto a list for every person who’s following this user
112.
Redis is awesome
for this; rapid insert, rapid subsets
113.
when time to
render a feed, we take small # of IDs, go look up info in memcached
114.
Redis is great
for...
115.
data structures that
are relatively bounded
116.
(don’t tie yourself
to a solution where your in- memory DB is your main data store)
117.
caching complex objects where
you want to more than GET
118.
ex: counting, sub-
ranges, testing membership
119.
especially when Taylor Swift
posts live from the CMAs
120.
follow graph
121.
v1: simple DB
table (source_id, target_id, status)
122.
who do I
follow? who follows me? do I follow X? does X follow me?
123.
DB was busy,
so we started storing parallel version in Redis
124.
follow_all(300 item list)
125.
inconsistency
126.
extra logic
127.
so much extra
logic
128.
exposing your support
team to the idea of cache invalidation
129.
130.
redesign took a
page from twitter’s book
131.
PG can handle
tens of thousands of requests, very light memcached caching
132.
two takeaways
133.
1 have a
versatile complement to your core data storage (like Redis)
134.
2 try not
to have two tools trying to do the same job
135.
3 staying nimble
136.
2010: 2 engineers
137.
2011: 3 engineers
138.
2012: 5 engineers
139.
scarcity -> focus
140.
engineer solutions that
you’re not constantly returning to because they broke
141.
1 extensive unit-tests
and functional tests
142.
2 keep it
DRY
143.
3 loose coupling
using notifications / signals
144.
4 do most
of our work in Python, drop to C when necessary
145.
5 frequent code
reviews, pull requests to keep things in the ‘shared brain’
146.
6 extensive monitoring
147.
munin
148.
statsd
149.
150.
“how is the
system right now?”
151.
“how does this
compare to historical trends?”
152.
scaling for android
153.
1 million new
users in 12 hours
154.
great tools that
enable easy read scalability
155.
redis: slaveof <host>
<port>
156.
our Redis framework assumes
0+ readslaves
157.
tight iteration loops
158.
statsd & pgfouine
159.
know where you
can shed load if needed
160.
(e.g. shorter feeds)
161.
if you’re tempted
to reinvent the wheel...
162.
don’t.
163.
“our app servers sometimes
kernel panic under load”
164.
...
165.
“what if we
write a monitoring daemon...”
166.
wait! this is
exactly what HAProxy is great at
167.
surround yourself with
awesome advisors
168.
culture of openness around
engineering
169.
give back; e.g.
node2dm
170.
focus on making
what you have better
171.
“fast, beautiful photo
sharing”
172.
“can we make
all of our requests 50% the time?”
173.
staying nimble =
remind yourself of what’s important
174.
your users around
the world don’t care that you wrote your own DB
175.
wrapping up
176.
unprecedented times
177.
2 backend engineers can
scale a system to 30+ million users
178.
key word =
simplicity
179.
cleanest solution with
the fewest moving parts as possible
180.
don’t over-optimize or expect
to know ahead of time how site will scale
181.
don’t think “someone else
will join & take care of this”
182.
will happen sooner
than you think; surround yourself with great advisors
183.
when adding software
to stack: only if you have to, optimizing for operational simplicity
184.
few, if any,
unsolvable scaling challenges for a social startup
185.
have fun
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