Mike Brown's CTO of comScore's presentation from the Big Data Warehouse Meetup sponsored by Syncsort Sept 2013 NYC covering how they process over 1.7 Trillion interactions using Hadoop

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Using Hadoop to Process a
Trillion+ Events
Michael Brown, CTO | September 23rd, 2013

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comScore is a leading internet technology company that
provides Analytics for a Digital World™
NASDAQ SCOR
Clients 2,100+ Worldwide
Employees 1,000+
Headquarters Reston, Virginia, USA
Global Coverage Measurement from 172 Countries; 44 Markets Reported
Local Presence 32 Locations in 23 Countries
Big Data Over 1 Trillion Digital Interactions Captured Monthly
V0113

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Broad Client Base and Deep Expertise Across Key Industries
Media Agencies Telecom/Mobile Financial Retail Travel CPG Pharma Technology
V0910

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Panel Heat Map

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CENSUS
Unified Digital Measurement™ (UDM) Establishes Platform For
Panel + Census Data Integration
PANEL
Unified Digital Measurement (UDM)
Patent-Pending Methodology
Adopted by 90% of Top 100 U.S. Media Properties
Global PERSON
Measurement
Global DEVICE
Measurement
V0411

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2009 2010 2011 2012 2013
#ofrecords
Panel Records Beacon Records
Total records collected in August 2013
1,729,895,147,710
Worldwide Tags per Day

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Worldwide UDM™ Penetration
December 2012 Penetration Data
Europe
Austria 87%
Belgium 93%
Switzerland 89%
Germany 92%
Denmark 88%
Spain 95%
Finland 93%
France 92%
Ireland 90%
Italy 90%
Netherlands 93%
Norway 91%
Portugal 92%
Sweden 90%
United Kingdom 92%
Asia Pacific
Australia 90%
Hong Kong 95%
India 92%
Japan 82%
Malaysia 93%
New Zealand 91%
Singapore 92%
North America
Canada 94%
United States 91%
Latin America
Argentina 95%
Brazil 96%
Chile 94%
Colombia 95%
Mexico 93%
Puerto Rico 92%
Middle East & Africa
Israel 92%
South Africa 78%
Percentage of Machines Included in UDM Measurement

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High Level Data Flow
Panel
Census
Custom Code +
Delivery

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Our Cluster
Production Hadoop Cluster
 224 nodes: Mix of Dell 720xd, R710 and R510 servers
 Each R720xd has (24x1.2TB drives; 64GB RAM; 24 cores)
 6300+ total CPUs
 13.3TB total memory
 4.3PB total disk space
 Our distro is MapR M5 2.1.3

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The Project:
vCE – Validated Campaign Essentials

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 vCE provides real-time, cloud-
based, on-demand monitoring and
optimization of digital advertising
campaigns
 Deep industry penetration
 22 of the Top 25 Largest Global
Advertisers, representing 89% of
global ad dollars, are vCE/CE
clients*
 Includes ALL Top 10 CPG
Advertisers*
What is vCE?
*Source: AdAge 2012 Top 25 Global Advertisers (directly or through their advertising agency)
Allstate

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comScore - vCE

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The Problem Statement
Calculate the number of events and unique cookies for each reportable
campaign element
Key take away
 Data on input will be aggregated daily
 Need to process all data for 3 months
 Need to calculate values for every day in the 92 day period spanning all
reportable campaign elements

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Structure of the Required Output
Client Campaign Population Location Cookie Ct Period
1234 160873284 840 1 863,185 1
1234 160873284 840 1 1,719,738 2
1234 160873284 840 1 2,631,624 3
1234 160873284 840 1 3,572,163 4
1234 160873284 840 1 4,445,508 5
1234 160873284 840 1 5,308,532 6
1234 160873284 840 1 6,032,073 7
1234 160873284 840 1 6,710,645 8
1234 160873284 840 1 7,421,258 9
1234 160873284 840 1 8,154,543 10

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Counting Uniques from a Time Ordered Log File
A
B
C
D
B
A
A
Major Downsides:
Need to keep all key elements in memory.
Constrained to one machine for final aggregation.

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First Version
Java Map-Reduce application which processes pre-aggregated data from 92 days
Map reads the data and emits each cookie as the key of the key value pair
All 130B records go though the shuffle
Each Reducer will get all the data for a particular campaign sorted by cookie
Reducer aggregates the data by grouping key ( Client / Campaign / Population ) and calculates
unique cookies for period 1-92
Volume Grew rapidly to the point the daily processing took more than a day

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M/R Data Flow
CB
Mapper MapperMapperMap Map Map
Reduce ReduceReduce
BA AC
AA BB CC
A B C

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Scaling Issue
As our volume has grown we have the following stats:
 Over 500 billion events per month
 Daily Aggregate 1.5 billion
 130 billion aggregate records for 92 days
 70K Campaigns
 Over 50 countries
 We see 15 billion distinct cookies in a month
 We only need to output 25 million rows

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Basic Approach Retrospective
Processing speed is not scaling to our needs on a sample of the input data
Diagnosis
 Most aggregations could not take significant advantage of combiners.
 Large shuffles caused poor job performance. In some cases large aggregations ran slower on the
Hadoop cluster due to shuffle and skew in data for keys.
Diagnosis
 A new approach is required to reduce the shuffle

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Counting Uniques from a Key Ordered Log File
A
D
B
C
B
A
A
Major Downsides:
Need to sort data in advance.
The sort time increases as volume grows.

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Counting Uniques from a Key Ordered Log File

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Counting Uniques from Sharded Key Ordered Log Files

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Solution to reduce the shuffle
The Problem:
 Most aggregations within comScore can not take advantage of combiners, leading to large shuffles and
job performance issues
The Idea:
 Partition and sort the data by cookie on a daily basis
 Create a custom InputFormat to merge daily partitions for monthly aggregations

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Custom Input Format with Map Side Aggregation
CB
Mapper MapperMapperMap Map Map
Reduce ReduceReduce
BA AC
A B C
A B C
Combiner Combiner Combiner
A B C

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Risks for Partitioning
Data locality
 Custom InputFormat requires reading blocks of the partitioned data over the network
 This was solved using a feature of the MapR file system. We created volumes and set the chunk size to
zero which guarantees that the data written to a volume will stay on one node
Map failures might result in long run times
 Size of the map inputs is no longer set by block size
 This was solved by creating a large number (10K) of volumes to limit the size of data processed by each
mapper

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Partitioning Summary
Benefits:
 A large portion of the aggregation can be completed in the map phase
 Applications can now take advantage of combiners
 Shuffles sizes are minimal
Results:
 Took a job from 35 hours to 3 hours with no hardware changes

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DMX & comScore

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DMX use at comScore
We use DMX from Syncsort across hundreds of servers for efficient data
processing and aggregation.
We currently run over 100+ unique jobs every day.
With these jobs we process over 150 billion rows of data through DMX!
Connect
Design
Process Accelerate

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Compression w/Sorting
Compress Log Files when processing large volumes of log data
Several advantages to Sorting Data First:
 Reduces the size of the data
 Improves application performance
Examples:
 1 Hour of one source of our data (313 GB raw, 815 million rows)
 Standard compression of time ordered data is 93GB (30% of original)
 Standard compression on a 2 key sorted set is 56GB (18% of original)
 For one day it saves 800GB
When applied to all our sources we save
 4.5 TB per day
 137 TB per month
 412TB per quarter

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TCO with Large Cluster Systems
Examine the ability to sort data to reduce disk usage
Example:
Hadoop cluster that needs to support 100TB of base compressed data
Hypothetical Configurations @ 75% disk utilization:
 Replication Factor of 3 using 1.2 TB drives
R710 (6x drives, JBOD); requires 26 servers
R510 (12x drives JBOD); requires 52 servers
R720xd (24x drives JBOD); requires 13 servers

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Useful Factoids
Visit www.comscoredatamine.com or follow @datagems for the latest gems.
Colorful, bite-sized graphical representations of the best discoveries we unearth.

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Thank You!
Michael Brown
CTO
comScore, Inc.
mbrown@comscore.com