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CONTENT IDENTIFICATION
USING HBASE
Daniel Nelson
3-10-2014
2
Copyright©2013TheNielsenCompany.Confidentialandproprietary.
TV MEASUREMENT HISTORY
Traditional TV Video On
Demand
Time Shifted
Viewing
Internet-Based
Content
What are people watching?
3
Copyright©2013TheNielsenCompany.Confidentialandproprietary.
WHY IDENTIFY CONTENT?
Broadcasters:
What are people watching?
Advertisers:
How can I focus my spend?
Movies
Commercials
Programs Streaming
4
Copyright©2013TheNielsenCompany.Confidentialandproprietary.
WHAT ARE AUDIO FINGERPRINTS?
Program Audio
Audio
Fingerprints
5
Copyright©2013TheNielsenCompany.Confidentialandproprietary.
BUILDING A LIBRARY OF CONTENT
Nielsen Remote Media
Monitoring Sites
Audio
Movies
Commercials
Programs Streaming
Fingerprint
Generator Fingerprints
HBase Region
Servers
Copyright©2013TheNielsenCompany.Confidentialandproprietary.
6
NEED FOR HBASE
• Rapidly Growing Content
• Monolithic Limitations
• Storage Scalability
• Distributed Computations
7
Copyright©2013TheNielsenCompany.Confidentialandproprietary.
COLLECTING VIEWING DATA
Copyright©2013TheNielsenCompany.Confidentialandproprietary.
8
CONTENT IDENTIFICATION
Matching Process identifies Content by comparing
Unknown Fingerprints (Left) against Reference Fingerprints (Right).
Match
Unknown Fingerprints
Reference Fingerprints
ESPN
QVC
SyFy
BNN
Copyright©2013TheNielsenCompany.Confidentialandproprietary.
9
FINGERPRINTS AND HBASE
• Think of HBase as One Big Hash Table
• Fingerprints – Fit Nicely into HBase as the Key
• Keys Are Not 100% Unique – Collisions Without Loss?
• Near Constant Time Lookups
• Hash Table Load Factor will impact this ( n/k )
10
Copyright©2013TheNielsenCompany.Confidentialandproprietary.
REFERENCE HOUSE KEEPING
• Maintaining a Moving Window of Relevant Data
• Broadcast Reference Fingerprints Expire after 8 Days.
• Managing of 20+ Billion Hash Keys is no Small Task
• HBase TTL (Time To Live)
• Places an Expiration Date on All Table Data
• Hides Expired Data From Queries
• Purged on Next Compaction Cycle
11
Copyright©2013TheNielsenCompany.Confidentialandproprietary.
OPTIMIZING USE OF HBASE
• Network
• Fastest Network
• Bonded 1Gig Ethernet
• Reduce Data Volume in Network Transfers
• Protobufs – Google Protocol Buffers
• HBase Co-Processors
• Your Code Running on Region Servers
• Computations, Advanced Filtering, Transformations…
12
Copyright©2013TheNielsenCompany.Confidentialandproprietary.
HBase Region Servers
HBASE ENDPOINT CO-PROCESSORS
• Push your Business Logic into the Coprocessor.
• Keeping Co-Processor Code Simple
• Loading your Co-Processor
HBase Client
Application
Co-Processor
Co-Processor
Co-Processor
Co-Processor
Query/Response
13
Copyright©2013TheNielsenCompany.Confidentialandproprietary.
QUERYING HBASE
• Table.scan (…)
• Table.get (…)
• Table.get(List<Get>keyList)
• Query using a Co Processor
• coprocessorExec( yourProtocolClass, null, null, … )
• coprocessorExec( yourProtocolClass, startKey, endKey, … )
14
Copyright©2013TheNielsenCompany.Confidentialandproprietary.
STORING MILLIONS OF FILES
• Have a lot of Files to Store, use SAN/NAS/HDFS Right?
• SAN/NAS
• Costly
• More Hardware to Buy/Maintain
• HDFS
• Limited File Count
• Sequence Files
• Immutable
• Inefficient to Retrieve, Delete, Modify or Add Files...
• There is another way….
15
Copyright©2013TheNielsenCompany.Confidentialandproprietary.
LEVERAGING WHAT ALREADY WORKS
Example File To Store: /foo/bar/myFile.bin
• HBase Key = File Path: /foo/bar/
• Qualifier = File Name: myFile.bin
• Value = Your File Data (Serialized Object, Text, etc…)
• “List” Files - Scan using built in KeyOnlyFilter
• Wrap this with an API and your Application can use “HBase FS”
• File Retrieval, Delete, Modification, Updates, Versions
• Apply TTL to Purge Files
THANK YOU

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