near real time search in e-commerce

1. OCTOBER 11-14, 2016 • BOSTON, MA EXPEDIA INTERNAL CONFERENCE JUNE 13th 2017

2. Near Real time Indexing Building Real Time Search Index For E-Commerce Umesh Prasad Advisory & Professional Services @Lucidworks aka Techie/Individual Contributor Yogesh Ahuja Senior Search Architect @Lucidworks

3. ● SOLR/LUCENE ○ User → Hacker → Contributor ○ [ Lucene 2.1 to 6.5 ] ● Advisory/Consulting @ Lucidworks (4 months) ● Search & Data Platform @ Flipkart ○ 4.5 years ● Payments @ Amazon ○ 1.4 years ● Vertical Search @ Verse Innovation & Naukri ( 4.8 years ) ○ LUCENE 2.1

14. Agenda • Ecommerce Search • Need for Real Time Search • SolrCloud Solution • Alternatives • First Principle approach • Q & A

15. E-commerce Search 50 main categories 500 sub categories 231 million docs - 90 million sku - 160 million listings - result collapsing drill down filters top positions at premium

16. 800K active users 160K requests per sec - 40K service - 10k solr median : 11 ms 99th perc: 1.1 sec

17. !! Flipkart [Sherlock] has BBD Deals[an Offer] ??[expired]

18. !! Steal Deals !!- Stolen .. @Search/Sherlock Team : Please investigate Please read the code and review architecture very carefully

19. CUSTOMER EXPERIENCE

20. Product /Listing: Important Attributes Seller Rating Service catalogue service Promise Service Availability Service Offer Service Pricing Service Product aka SKU Listings

21. Summary : Lucene Document • Product/SKU [Parent Document] – Listing [Child Document] • Query = Mostly SKU Attributes [Free Text] • Filters = SKU + Listing Attributes [Drill Down] • Ranking = SKU + Listing Attributes [Explicit/Relevance] • Index Time Join aka Block Join – [Best Performance]

22. Out Of Stock, but Why Show? Index has Stale Availability Data 234K Products

23. Shard Replica Shard Replica Shard Replica Shard Replica Shard Replica Shard Replica Re-open searcher Re-open searcher Re-open searcher Re-open searcher Re-open searcher Re-open searcher Ingestion pipeline Shard Leader Auto commit Soft Commit Batch of documents For each Document Versioning Update Log Forward to Replica S1.1 : SolrCloud : Principal Engineer Lens

24. S1.2 : SolrCloud : Director Lens

25. updates / sec updates /hr normal Peak text / catalogue ~10 ~100 ~100K pricing ~100 ~1K ~10 million availability ~100 ~10K ~10 million offer ~100 ~10K ~10 million seller rating ~10 ~1K ~1 million signal 6 ~10 ~100 ~1 million signal 7 ~100 ~10K ~10 million signal 8 ~100 ~10K ~10 million S1.3 : SolrCloud : Monitoring Lens [Very High Update Rates]

26. Ingestion pipeline Catalogue Pricing Availability Offers ... Document Builder Solr/Lucene Change Propagation Documents {L1,L2 … P1} Updates Stream 1 Updates Stream 2 Updates Stream 3 ● Lucene doesn’t support Partial Updates ● Update = Delete + Add S1.4 : SolrCloud : Principal Engineer Lens

27. S1.5 : SolrCloud : Internal Email • Update = Delete + Add – Block Join Index ⇒ Update Whole Block (Product + Listings) • Updated Document gets streamed to all replicas in sync – Reduces indexing throughput • Soft commit is Not Free – Soft commit ⇒ In Memory Segment – Lots of Merges – Huge document churn / deletes – All caches still need to be re-generated – Filter Cache miss specially hurts performance

28. S2xx : Alternatives 1. Updatable DocValues a. [LUCENE-5189], [SOLR-5944], [under-the-hood],[benchmarking] b. Doesn’t scale to a large number of fields/multi valued fields 2. Parallel Indexes : Basically Term Partitioned Indexes [Updates in Redis] a. ParallelReader : Warning: It is up to you to make sure all indexes are created and modified the same way. b. Prototype worked : i. Works for small indexes + lots of updates or huge index + daily build ii. Not for large index + streaming updates + lots of qps iii. Pulling Changes + Document Building killed it 3. Lucene Codecs API : a. Works for Prototype : Research/Algorithms/small index b. Not for e-commerce marketplace use case. Index corruption/2 phase commit is difficult.

29. S3xx : Drawing Board

30. ProductA brand : Apple availability : T price : 45000 ProductB brand : Samsung availability : T price : 23000 ProductC brand : Apple availability : F price : 5000 Document ID Mappings Posting List (Inverted Index) DocValues (columunar data) Lucene Segment Lucene Index 0 ProductA 1 ProductB 2 ProductC 45000 23000 5000Price availability : T brand : Samsung brand : Apple 0 , 2 1 0 , 1 Terms Sparse Bitsets

31. A Typical Search Flow Query Rewrite Results Query Matching Ranking Faceting Stats Posting List Doc Values Other Components Lucene Segment Inverted Index Forward Index NRT Store samsung mobiles Offer : exchange offer price desc category : mobiles brand : samsung Offer : exchange offer

32. NRT Forward Index - Considerations ● Lookup efficiency – 50th percentile : ~10K matches – 99th percentile : ~1 million matches ● Data on Java heap – Memory efficiency

33. HashMap based Implementation NRT Forward IndexLucene Segment Lookup Engine 0 ProductB 1 ProductA 2 ProductC 3 ProductD ProductD ProductA ProductB ProductC ProductD True False False True 100 150 200 250 ProductId(3) <ProductD,price> 250 ProductId Availability Price Latency : ~10 secs for ~1 Million lookups DocId : 3 field : price

34. Foreign Key + Array Based Implementation Lookup Engine Lucene Segment 0 ProductB 1 ProductA 2 ProductC 3 ProductD 250 DocId - NrtId 0 1 2 3 3 0 1 2 NrtId(3) 2 Price(2) NRT Forward Index (Segment Independent) 100 200 250 150Price 0 ProductA 1 ProductC 2 ProductD 3 ProductB Availability T F F T Status 01 10 01 00 Latency : ~100 ms for ~1 Million lookups DocId : 3 field : price

35. NRT Store Filter - PostFilter PostFilter(Price:[100 TO 150]) Lucene Segment 0 ProductB 1 ProductA 2 ProductC 3 ProductD Don’t Delegate DocId - NrtId 0 1 2 3 3 0 1 2 DocId : 3 NrtId(3) 2 Price(2) NRT Forward Index (Segment Independent) 100 200 250 150Price 0 ProductA 1 ProductC 2 ProductD 3 ProductB Availability T F F T Status 01 10 01 00 for d in [matched-docs] collect d

36. NRT Filter NRT Store - Invert index NRT Forward Store NRT Inverter Lucene Segment 0 ProductB 1 ProductA 2 ProductC 3 ProductD NRT DocIdSet Cache Availability : T 0 3 Offer : O1 2 3 Offer:O1 DocIdSet

37. Solr Integration Points • ValueSources • Filtering – Custom Filter Implementation for cached DocIdSet – Custom PostFilter • Query – Wrapper over Filter • Custom FacetComponent

38. Near Real Time Solr Architecture Solr Kafka Ingestion pipeline NRT Forward Index Ranking Matching Faceting Redis Bootstrap NRT Inverted store Solr Master NRT Updates Lucene Updates Catalogue Pricing Availability Offers Seller Quality Commit + Replicate + Reopen Lucene Others

39. Accomplishments • Real time sorting • Real time filtering : PostFilter – Higher latency • Near real time filtering : cached DocIdSet – No consistency between lookup and filtering • Independent of lucene commits • Query latency comparable to DocValues – Consistent 99% performance

40. Accomplishments @ Flipkart ● Real time consumption for ~150 Signals ● Reduction in shown out of stock products by 2X ● Production instances of ~50K updates/second real time

41. Thank you & Questions

42. Search @ Flipkart • Catalogue – ~ 50 main categories – ~ 5000 sub-categories – ~ 231 million documents – ~ 90 million SKUs – ~ 160 million listings • E-commerce Marketplace – ~ 100K Sellers – Local Sellers – Regional Availability – Logistics Constraints

43. Professional LinkedIn through Logos

44. Personal Journey through Pictures

45. F U T U R E D I R E C T I O N

near real time search in e-commerce

near real time search in e-commerce

Recommended

More Related Content

What's hot

What's hot(20)

Similar to near real time search in e-commerce

Similar to near real time search in e-commerce (20)

Recently uploaded

Recently uploaded(20)

near real time search in e-commerce