Scaling Pinterest - QConSP · Pinterest Engineering April 2012 Growth Scaling Pinterest Mar 2010 · Amazon EC2 + S3 + Edge Cast · 135 Web Engines + 75 API Engines · 10 Service Instances

Pinterest Engineering

Scaling Pinterest

Yash NelapatiAscii Artist

Saturday, August 31, 13


An online pinboard to organize and share what inspires you.

Pinterest is...



Scaling Pinterest



Scaling Pinterest



Scaling Pinterest



March 2010Growth Scaling Pinterest

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Mar 2010 Jan 2011 Jan 2012 May 2012




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Mar 2010 Jan 2011 Jan 2012 May 2012




· RackSpace

· 1 small Web Engine

· 1 small MySQL DB

· 1 Engineer + 2 Founders

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Mar 2010 Jan 2011 Jan 2012 May 2012



March 2010Growth



March 2010Growth



January 2011GrowthScaling Pinterest

Mar 2010 Jan 2011 Jan 2012

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Mar 2010 Jan 2011 Jan 2012

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· Amazon EC2 + S3 + CloudFront

· 1 NGinX, 4 Web Engines

· 1 MySQL DB + 1 Read Slave

· 1 Task Queue + 2 Task Processors

· 1 MongoDB

· 2 Engineers + 2 Founders

Mar 2010 Jan 2011 Jan 2012

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September 2011GrowthScaling Pinterest

Mar 2010 Jan 2011 Jan 2012 May 2012

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Mar 2010 Jan 2011 Jan 2012 May 2012

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· Amazon EC2 + S3 + CloudFront· 2 NGinX, 16 Web Engines + 2 API

Engines· 5 Functionally Sharded MySQL DB +

9 read slaves· 4 Cassandra Nodes· 15 Membase Nodes (3 separate

clusters)· 8 Memcache Nodes· 10 Redis Nodes· 3 Task Routers + 4 Task Processors· 4 Elastic Search Nodes· 3 Mongo Clusters· 3 Engineers (8 Total)

Mar 2010 Jan 2011 Jan 2012 May 2012

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It will fail. Keep it simple.



April 2012GrowthScaling Pinterest

Mar 2010

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Mar 2010 Jan 2011 Jan 2012 May 2012




Mar 2010

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Mar 2010 Jan 2011 Jan 2012 May 2012




Mar 2010

· Amazon EC2 + S3 + Edge Cast

· 135 Web Engines + 75 API Engines

· 10 Service Instances

· 80 MySQL DBs (m1.xlarge) + 1 slave

each

· 110 Redis Instances

· 60 Memcache Instances

· 2 Redis Task Manager + 60 Task

Processors

· 3rd party sharded Solr

· 15 Engineers (25 Total)

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Mar 2010 Jan 2011 Jan 2012 May 2012



Scaling Pinterest

January 2012Growth



Scaling Pinterest



August 2013GrowthScaling Pinterest

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April 2012 August 2013




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· Amazon EC2 + S3 + Edge Cast· 400+ Web Engines + 400+ API

Engines· 70+ MySQL DBs (hi.4xlarge on SSDs)

+ 1 slave each· 100+ Redis Instances· 230+ Memcache Instances· 10 Redis Task Manager + 500 Task

Processors· 70+ Engineers (130+ Total)

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· Amazon EC2 + S3 + Edge Cast· 400+ Web Engines + 400+ API

Engines· 70+ MySQL DBs (hi.4xlarge on SSDs)

+ 1 slave each· 100+ Redis Instances· 230+ Memcache Instances· 10 Redis Task Manager + 500 Task

Processors· 70+ Engineers (130+ Total)

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April 2012 August 2013· 6 services (80 instances)· Sharded Solr· 20 HBase· 12 Kafka + Azkabhan· 8 Zookeeper Instances· 12 Varnish



Scaling Pinterest

ELB

Routing & Filtering(Varnish)

All connection pairings managed by ZooKeeper

Puppet

StatD

Monit

GangliaAPI App(Python)

Web App(Python)

Task Processing(Python/Pyres)

MySQL Service(Java/Finagle)

Memcache Mux(Nutcracker)

Feed Service(Python/Thrift)

Follower Service(Python/Thrift)

Images(S3 + CDN) MySQL Memcache Redis HBase

Task Queue(Redis)





Tripwire

EMR

S3

Scaling Pinterest

Web App(Python)

Task Processing(Python/Pyres)

Kafka

S3 Copier

Hive Azkaban

API(Python)





Technologies



Scaling Pinterest

ChoosingYourTech

Questions to ask • Does it meet your needs?

• How mature is the product?

• Is it commonly used? Can you hire people who have used it?

• Is the community active?

• How robust is it to failure?

• How well does it scale? Will you be the biggest user?

• Does it have a good debugging tools? Profiler? Backup software?

• Is the cost justified?



Scaling Pinterest

Hosting Why Amazon Web Services?• Variety of servers running Linux

• Very good peripherals, such as load balancing, DNS, map reduce, basic firewalls, and more

• Good reliability (don’t throw tomatoes at me!)

• Very active dev community

• Not cheap, but...

• New instances ready in seconds



Scaling Pinterest

Code Why Python?• Extremely mature

• Well known and well liked

• Solid active community

• Very good libraries specifically targeted to web development

• Effective rapid prototyping

• Free



Scaling Pinterest

Production Data

Why MySQL and Memcache? • Extremely mature

• Well known and well liked

• Rarely catastrophic loss of data

• Response time to request rate increases linearly

• Very good software support: XtraBackup, Innotop, Maatkit

• Solid active community

• Free



Scaling Pinterest

Why Redis?• Well known and well liked

• Active community

• Consistently good performance

• Variety of convenient and efficient data structures

• 3 Flavors of Persistence: Now, Snapshot, Never

• Free

Production Data



Scaling Pinterest

Why HBASE? (Why not MySQL)• Efficient Storage

• Handle large write throughput

• Solid Hadoop interface

• Maturing quickly, used by facebook

• Built on HDFS

• Free

Production Data



Scaling Pinterest

What happened to Cassandra, Mongo, ES, and Membase?Production

Data • Does it meet your needs?

• How mature is the product?

• Is it commonly used? Can you hire people who have used it?

• Is the community active?

• How robust is it to failure?

• How well does it scale? Will you be the biggest user?

• Does it have a good debugging tools? Profiler? Backup software?

• Is the cost justified?



If you’re the biggest user of a technology, the challenges will

be greatly amplified



Scaling Pinterest

What’s happening now?



Scaling Pinterest

Challenge: One Codebase + Lots of Engineers = Deploy Hell

• Major bugs and performance issues stall deploys

• Performance issues creep in under radar

• 7+ development teams, 1 ops team

• Workload changing more rapidly and less predictably

• Want developers to not fear moving fast

EmployeeGrowth



Scaling Pinterest

Solution: Deploy Checkpoints• Aggressive unit tests (careful! don’t erase your DB!)

• Rings of deployment

• Canary, employees only, 5% of user base, etc.

• Continuous deployment

• Production integration tests

EmployeeGrowth



Scaling Pinterest

Challenge: Increase Availability, Decrease Latency

• Push for better uptime and lower latency

• Initially, most uptime and latency issues due to DB + caching

• Fewer Instances => Few, but big failures

• More Instances => More smaller failures + more complexity

• How aggressively can you retry without hurting the system?

Uptime &Latency



Scaling Pinterest

Solution: Metrics Dashboard and Alerts

• Create dashboard + alerts, and review response times weekly

• When? Soon after launch at latest

• Profile everything

• MySQL - Maatkit, InnoTop

• Memcache - Maatkit

• Frontend - New Relic

• General Ops - StatsD, Nagios / Monit, Ganglia

Uptime &Latency



Scaling Pinterest

Solution: Configuration Manager and Failover

• Provides load balancing and automatic connection reconfiguration

• When? 30+ caches / DBs

• One option: Intermediate load balancers

• Example: HAProxy, Nginx, Varnish

• Extra latency hop

• More complication

• Configuration hassle (1 LB / 7 services?)

Uptime &Latency



Scaling Pinterest

Co-ordination

Solution: Zookeeper• Centralized configuration management

• Used for service discovery

• Notifies of service failures

• WATCH and its callback are pretty reliable

• Experiment framework



Scaling Pinterest

Co-ordination

Zookeeper

Services

app

Register








Scaling Pinterest

Co-ordination

Zookeeper

Services

app

Register

WATCH








Scaling Pinterest

MySQL Failover

Part 1: Configuration Manager and Failover

A B

App

{“master” : “A”}

readonly=True

Zookeeper



Scaling Pinterest

MySQL Failover


A B

App

{“master” : “B”}

readonly=True

Zookeeper



Scaling Pinterest

MySQL Failover


A B

App

{“master” : “B”}

Zookeeper

readonly=False



Scaling Pinterest

Challenge: Number of Connections Rising

• Initially, entire app tier connected to all Memcache, Redis, MySQL

• On Memcache...

• 20k connections * 10kB / connection = 195MB / Memcache

• 40 Memcaches means 7.6 GB used on connections

• Connection space is not allocated from slab memory!

• Can eventually cause Memcache process to leak into swap

• On MySQL

• At least 256 kB / connection

Connections



Scaling Pinterest

Solution: Connection Pooling and Multiplexing

• Data Services, Nutcracker

• When? Once any service gets close to 10k connections

• Success: Memcache

• Once was >20k connections

• Now 1.3k connections

• But, aggressive fan-out causes...

• Network contention

• Incast congestion

Connections



Scaling Pinterest

Memcache Failures

App Nutcracker



Scaling Pinterest

Memcache Failures

App Nutcracker

Ketama Ring Adjusted



Scaling Pinterest

Finagle• RPC for high concurrency

• Twitter

• Completely asynchronous

• Previous experience with Finagle

• Lots of compatible libraries

• JVM

• Lots of bells and whistles - Ostrich, Zipkin, lago

Why Java Over Python?



Scaling Pinterest

How did you shard?



Scaling Pinterest

How we Sharded

db00001db00002 ....db00512

db00513db00514 ....db01024

db03073db03074 ....db03583

db03584db03585 ....db04096



Scaling Pinterest

High Availability

db00001db00002 ....db00512

db00513db00514 ....db01024

db03073db03074 ....db03583

db03584db03585 ....db04096

Master Master Replication



Scaling Pinterest

IncreasedLoad on DB?

db00001db00002 ....db00512

db00001db00002 ....db00256

db00256db00257 ....db00512

Split your Shards



Scaling Pinterest

ID Structure 64 bits

Shard ID Local IDType

· A lookup data structure has physical server to

shard ID range (cached by each app server process)

· Shard ID denotes which shard

· Type denotes object type (e.g., pins)

· Local ID denotes position in table



Scaling Pinterest

Objects &Mappings

Object tables (e.g., pin, board, user, comment)

· Local ID MySQL blob (JSON / Serialized thrift)

Mapping tables (e.g., user has boards, pin has likes)

· Full ID Full ID (+ timestamp)

· Naming schema is noun_verb_nounQueries are PK or index lookups (no joins)

· Data DOES NOT MOVEAll tables exist on all shards

No schema changes required (index = new table)



Scaling Pinterest

Looking Forward• Continually improve Pinner experience

• Better uptime and lower latency

• Help Pinners discover more of the things they love

• Reduce spam and abuse

• Continually collaborate and build bigger, better, faster products

• 140 Pinployees and beyond

• MySQL 5.6

What’s next?


[email protected]/yashh


mailto:[email protected]


[email protected]/yashh




Scaling Pinterest - QConSP · Pinterest Engineering April 2012 Growth Scaling Pinterest Mar 2010 · Amazon EC2 + S3 + Edge Cast · 135 Web Engines + 75 API Engines · 10 Service Instances

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