Deduplication Using Solr: Presented by Neeraj Jain, Stubhub
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The document discusses StubHub's use of SOLR for deduplication. It describes the challenges of deduplicating a large event catalog in real-time. The legacy solution involved iterating over each field and document. The new approach uses SOLR for text similarity comparisons, extends its default behavior, and provides a REST interface. Sample output showing matched venues and their scores is also shown.
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Deduplication Using Solr: Presented by Neeraj Jain, Stubhub
- 2. Deduplication using SOLR Neeraj Jain, Software Engineer, Stubhub, Inc. [email protected]
- 3. About myself RIDE ON
- 4. StubHub is about….. Worlds Largest Ticke<ng marketplace 10M active listings We enable “access” to events We want to be more!!!
- 5. Some Fun Facts about StubHub! Ø An eBay owned company Ø Over 25 million users and growing Ø We sell one ticket per second Ø ~8.5 million page views a day, on an average Ø ~ 3 million additional page views per day on Mobile devices Ø ~10 M tickets for sale in sports, concerts and others. Ø ~ 1 TB of data processed monthly by the analytics infrastructure – This number will significantly go up as we bring in data from many of the unstructured data sources Ø ~300 Million SQL executions/day
- 6. Search at Stubhub! SOLR 1.2 SOLR 3+, Geo spa<al search SOLR 4, NRT SOLR Cloud 2010 2011 2012 2013 2014
- 7. Agenda Ø Use case Ø Challenges Ø Legacy solution Ø Our approach Ø Results
- 8. Use Case : Content Ingestion Input record Pre deduplica<on Deduplica<on Post deduplica<on Normalize Filtering Classifica<on Geocode Review Insert Update Discard Feed-‐1 Feed-‐2 Feed-‐3 Feed-‐n Form Event DB
- 9. Challenges : Deduplication Ø Problem space ² Event catalog Ø Performance considerations ² Real <me processing ² Batch processing Ø Speed and data quality
- 10. Legacy Solution : Deduplication Flow Deduplica<onModule for each field Event DB for each document Client 1: getDuplicates() 2: getSubsetByLoca.on() 3: loop 4: DuplicateList 5: upsert() Normalize Filter Compute Score Feed Ingestor Batch Job UGC
- 11. Approach : Problem Model Ø Milpitas Library vs Milpitas Public Library Ø 1601 E 7th St vs 1601 E. Seventh St. Ø Pick up the right algo, edit distance, jaccard. Library, Restaurant, etc Milpitas Library 160 N. Main St; 40 N. Milpitas Blvd. Distance : ~0.5 mi e.g. venue Boost name, street number Dup detec<on -‐ name, address etc Subset -‐ Text Similarity on Categories Subset -‐ Geo spa<al distance Venue Deduplica.on
- 12. Approach : Deduplication Flow Feed Ingestor Client Deduplica<onService QueryBuilder QueryExecuter Scorer SOLR Index Batch Job UGC 1: /dedupe 3.1: /select 2: build() 3: execute() 4: compute() 7: /update 6: DedupeResponse Event DB 8: upsert() A1: poll() IndexUpdater A2: /update, /delete NameFilter AddressFilter *Filter Filter
- 13. Approach : Deduplication Service public interface DeduplicationService<T> { /** * Checks for duplicate entity and return a DeduplicationResponse containing information about duplicates found. For each possible duplicate, there is a justification as to why it's a duplicate. * @param t entity for which duplicates need to be found. * @param options use options provided by this object to find and filter the results. * @return a not null instance of DeduplicationResponse object. * @throws DeduplicationConnectivityException if there was an issue in connecting to the dedupe data store. */ public DeduplicationResponse<T> findDuplicates(T t, DedupeOptions options) throws DeduplicationConnectivityException; }
- 14. Approach : Deduplication Service @Component(value = "VenueDeduplicationService”) public class VenueDeduplicationService implements DeduplicationService<Venue> { @Override public DeduplicationResponse<Venue> findDuplicates(Venue venue, DedupeOptions options) throws Deduplica<onConnec<vityExcep<on { } } @Component(value = "EventDeduplicationService”) public class EventDeduplicationService implements DeduplicationService<Event> { @Override public DeduplicationResponse<Event> findDuplicates(Event event, DedupeOptions options) throws DeduplicationConnectivityException { } }
- 15. Approach : Optimizations Ø How to keep the score consistent? ² <similarity class=“TfSimilarity"/> Ø Auto commit settings ² <autoSomCommit><maxTime>5</maxTime></autoSomCommit> Ø Custom PostFilter ² <queryParser name="fdist" class=“DistanceQParserPlugin"/> Ø Custom update handler ² <processor class=“VenueUpdateProcessorFactory”></processor>
- 16. Results : Sample Output Input Venue Matched Venue Score Distance Jillian's Billiards Club 101 Fourth St. Jillian's 175 4th St. 1.5573 5.6352 Lush Lounge 1092 Post St. Lush Lounge 1221 Polk St. 12.9836 16.6501 Mountain Theatre 10 Panoramic Hwy. Mountain Theater Nearby E Ridgecrest Boulevard and Pantoll Road 3.2509 5.8913
- 17. Results : Sample Output Input Venue Matched Venue Score Distance The Hedley Club at Hotel DeAnza 233 W. Santa Clara St. Hedley Club 233 W. Santa Clara St. 5.0805 0.0000 Sonya Paz Fine Art Gallery 1793 LafayeYe St. Sonya Paz Gallery and Studio 1793 LafayeYe St. Suite 110 6.6764 0.0069 Pearl Avenue Library Community Room 4270 Pearl Ave. Pearl Avenue Branch Library 4270 Pearl Ave. 5.7024 0.0000 Milpitas Library 160 N. Main St. Milpitas Library 40 N. Milpitas Blvd. 16.4318 0.7284
- 18. Summary Ø Use case ² Content inges<on Ø Challenges ² Deduplica<on Ø Legacy solution Ø Our approach ² Used SOLR for text similarity ² Extended default behavior ² REST endpoint over SOLR interface Ø Next steps ² Big data ² Performer matching ² I18n Ø Results
- 19. Thank You