Scale as a Competitive Advantage

Scale as a Competitive Advantage
David Chou
david.chou@microsoft.com
blogs.msdn.com/dachou

The age of “big data”
2009: 600K photos served /sec
2010: ~1PB / 60 minutes
(projected)
2008: ~1B views / day
Source: Wired Magazine: Issue 16.07, 2008.06.23; illustration by Marian Bantjes
http://www.wired.com/science/discoveries/magazine/16-07/pb_intro

“More is different”
Infinite storage. Clouds of processors. Our ability to capture, warehouse, and understand massive amounts of data is changing science, medicine, business, and technology. As our collection of facts and figures grows, so will the opportunity to find answers to fundamental questions. Because in the era of big data, more isn't just more. More is different.
Source: Wired Magazine: Issue 16.07, 2008.06.23
http://www.wired.com/science/discoveries/magazine/16-07/pb_intro

“The future belongs to the companies and people that turn data into products”
Source: “What is data science?”, An O’Reilly Radar Report, 2010.06.02, Mike Loukides
http://radar.oreilly.com/2010/06/what-is-data-science.html

Working with data at scale
45M tweets pattern visualization in minutes
#justinbieber cluster
#teaparty cluster
…. “political world has more connective tissue than of-the-moment entertainment”
Source: “Data science democratize”, 2010.07.01, Mac Slocum
http://radar.oreilly.com/2010/07/data-science-democratized.html

Big data needs big processing
Facebook (2009)
+200B pageviews /month
>3.9T feed actions /day
+300M active users
>1B chat mesgs /day
100M search queries /day
>6B minutes spent /day (ranked #2 on Internet)
+20B photos, +2B/month growth
600,000 photos served /sec
25TB log data /day processed thru Scribe
120M queries /sec on memcache
Twitter (2009)
600 requests /sec
avg 200-300 connections /sec; peak at 800
MySQL handles 2,400 requests /sec
30+ processes for handling odd jobs
process a request in 200 milliseconds in Rails
average time spent in the database is 50-100 milliseconds
+16 GB of memcached
Google (2007)
+20 petabytes of data processed /day by +100K MapReduce jobs
1 petabyte sort took ~6 hours on ~4K servers replicated onto ~48K disks
+200 GFS clusters, each at 1-5K nodes, handling +5 petabytes of storage
~40 GB /sec aggregate read/write throughput across the cluster
+500 servers for each search query < 500ms
>1B views / day on Youtube (2009)
Myspace(2007)
115B pageviews /month
5M concurrent users @ peak
+3B images, mp3, videos
+10M new images/day
160 Gbit/sec peak bandwidth
Flickr (2007)
+4B queries /day
+2B photos served
~35M photos in squid cache
~2M photos in squid’s RAM
38k req/sec to memcached (12M objects)
2 PB raw storage
+400K photos added /day
Source: multiple articles, High Scalability
http://highscalability.com/

Bing Maps
Big data collection and processing
flying planes over nearly every inch of the United States
on road photos
45-degree low-altitude aerial photos
high altitude plane photos
satellite photos
10% done (August 2010)
previous “all USA” flight image gathering exercise took 10 years
5PB storage and thousands of servers in one container
Source: “Map Wars (visiting Bing’s imaging center)”, 2010.08.10, Robert Scoble
http://scobleizer.com/2010/08/10/map-wars-visiting-bings-imaging-center/

Cloud computing
Characteristics
On-demand self-service
Broad network access
Resource pooling
Rapid elasticity
Measured service
Service models
Software as a service
Platform as a service
Infrastructure as a service
Deployment models
Private cloud
Community cloud
Public cloud
Hybrid cloud
“Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model promotes availability and is composed of five essential characteristics, three service models, and four deployment models.”
Source: The NIST Definition of Cloud Computing, Version 15, 2009.10.07, Peter Mell and Tim Grance
http://csrc.nist.gov/groups/SNS/cloud-computing/cloud-def-v15.doc

Cloud levels the playing field
2007
founded by 6 people
2008
$29M funding from VC
2009
revenue - $270M
$180M funding from Digital Sky Technologies
2010
1,200+ employees
$300M funding from Google and Softbank
Active unique players
215M monthly; 10% of world internet population (updated 2010.10); 60M daily
1M daily 4 days after launch; 10M after 60 days
3B neighborhood connections
Cloud infrastructure
12,000 Amazon EC2 nodes
Adding 1,000 servers per week (updated 2010.10)
Moving 1PB data per day (updated 2010.10)
3 Gigabits/sec of traffic between FarmVille and Facebook (at peak)
caching cluster serves another 1.5 Gigabits/sec to the application
Source(s): “How FarmVille Scales to Harvest 75 Million Players a Month”, HighScalability.com, 2010.02.08, Tedd Hoff
“Zynga Moves 1 Petabyte Of Data Daily; Adds 1,000 Servers A Week”, TechCrunch.com, 2010.09.22, LeenaRao

Cloud as a platform
Utility computing
on-demand infrastructure
self-provisioning and servicing
rapid elasticity
economy of scale
operational expenditures
Infrastructure-as-a-Service
Service delivery model
… but cloud computing != cloud hosting

Cloud as a platform
Native cloud applications
horizontal scaling (scale-out)
parallelization
shared-nothing architecture
partitioned data (sharding)
multi-tenancy
failure resilient (or fail-in-place)
service-oriented
federated composition
Platform-as-a-Service
Application development model

Service delivery models
(On-Premise)
Infrastructure
(as a Service)
Platform
(as a Service)
Software
(as a Service)
You manage
Applications
Applications
Applications
Applications
You manage
Data
Data
Data
Data
Runtime
Runtime
Runtime
Runtime
Managed by vendor
Middleware
Middleware
Middleware
Middleware
You manage
Managed by vendor
O/S
O/S
O/S
O/S
Managed by vendor
Virtualization
Virtualization
Virtualization
Virtualization
Servers
Servers
Servers
Servers
Storage
Storage
Storage
Storage
Networking
Networking
Networking
Networking

Use more pieces, not bigger pieces
LEGO 7778 Midi-scale Millennium Falcon
9.3 x 6.7 x 3.2 inches (L/W/H)
356 pieces
LEGO 10179 Ultimate Collector's Millennium Falcon
33 x 22 x 8.3 inches (L/W/H)
5,195 pieces

Live Journal (from Brad Fitzpatrick, then Founder at Live Journal, 2007)
Web Frontend
Apps & Services
Partitioned Data
Distributed
Cache
Distributed Storage

Flickr (from Cal Henderson, then Director of Engineering at Yahoo, 2007)
Web Frontend
Apps & Services
Distributed Storage
Distributed
Cache
Partitioned Data

SlideShare(from John Boutelle, CTO at Slideshare, 2008)
Web
Frontend
Apps &
Services
Distributed Cache
Partitioned Data
Distributed Storage

Twitter (from John Adams, Ops Engineer at Twitter, 2010)
Web
Frontend
Apps &
Services
Partitioned
Data
Queues
Async
Processes
Distributed
Cache
Distributed
Storage

Distributed
Storage
Facebook
(from Jeff Rothschild, VP Technology at Facebook, 2009)
2010 stats (Source: http://www.facebook.com/press/info.php?statistics)
People
+500M active users
50% of active users log on in any given day
people spend +700B minutes /month
Activity on Facebook
+900M objects that people interact with
+30B pieces of content shared /month
Global Reach
+70 translations available on the site
~70% of users outside the US
+300K users helped translate the site through the translations application
Platform
+1M developers from +180 countries
+70% of users engage with applications /month
+550K active applications
+1M websites have integrated with Facebook Platform
+150M people engage with Facebook on external websites /month
Web
Frontend
Apps &
Services
Distributed
Cache
Parallel
Processes
Partitioned
Data
Async
Processes

Cloud computing as a new paradigm
Scale-out architecture + distributed computing
small logical units of work
loosely-coupled processes
stateless
event-driven design
optimistic concurrency
partitioned data
redundancy fault-tolerance
re-try-based recoverability
parallel tasks
app server
web
data store
app server
web
data store
web
app server
data store
app server
web
data store
app server
web
data store
app server
web
data store
async tasks

Strategic advantages of cloud computing
cost reduction
cost reduction
time to market
pay by use
ability to scale

What’s next?
Data
data federation
data purification
data democratization
derived intelligence
Process
Web as a platform
federated applications
adaptive agents

Thank you!
David Chou
david.chou@microsoft.com
blogs.msdn.com/dachou
© 2010 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

Scale as a Competitive Advantage

by David Chou on Oct 22, 2010

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