Syncsort & comScore Big Data Warehouse Meetup Sept 2013

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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 thatprovides 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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Panel Records Beacon Records

# of

reco

rds

Total records collected in August 20131,729,895,147,710

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 AmericaCanada 94%United States 91%

Latin AmericaArgentina 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 BrownCTOcomScore, Inc.

mbrown@comscore.com