Near Real Time Indexing: Presented by Umesh Prasad & Thejus V M, Flipkart
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This document summarizes a presentation given by Umesh Prasad and Thejus V M of Flipkart on building a real-time search index for e-commerce. It discusses the need for real-time indexing to support high update rates and microservices architecture at Flipkart. It evaluates using SolrCloud but finds that update-by-delete-and-add hinders performance. The presentation then describes Flipkart's approach using a near real-time Lucene store with optimized data structures and filtering to enable low-latency search across updated documents.
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Near Real Time Indexing: Presented by Umesh Prasad & Thejus V M, Flipkart
- 1. OCTOBER 11-‐14, 2016 • BOSTON, MA
- 2. Near Real 8me Indexing Building Real Time Search Index For E-‐Commerce Umesh Prasad Tech Lead @ Flipkart Thejus V M Data Architect @ Flipkart
- 3. Agenda • Search @ Flipkart • Need for Real Time Search • SolrCloud Solu;on • Our approach • Q & A
- 6. Traffic @ Flipkart • Peak Traffic – ~ 800K ac;ve users – ~ 160K requests per second • Search Traffic – ~ 40K searches per second (Service) – ~ 10K searches per second (Solr ) • Latency – Median : 11 ms – 99th percen;le : 1.1 second
- 7. Search @ Flipkart • Catalogue – ~ 50 main categories – ~ 5000 sub-‐categories – ~ 231 million documents – ~ 90 million SKUs – ~ 160 million lis;ngs • E-‐commerce Marketplace – ~ 100K Sellers – Local Sellers – Regional Availability – Logis;cs Constraints
- 8. E-‐commerce Search • Heavy usage of drill down filters • Heavy usage of face;ng • Only top results maer • Results grouped/collapsed by products • Serviceability and delivery experience MATTERS
- 9. Agenda • Search @ Flipkart • Need for Real Time Search • SolrCloud Solu;on • Our approach • Q & A
- 10. Sorry, Stock Over !!?
- 11. Damn !! Is Offer Over ??
- 12. What !! All Steal Deals Gone ??
- 13. Product /Lis;ng: Important Aributes Seller Ra;ng Service catalogue service Promise Service Availability Service Offer Service Pricing Service Product aka SKU Lis;ngs
- 14. Summary : Lucene Document • Product/SKU (Parent Document) – Lis;ng (Child Document) • Query : Mostly SKU Aributes (Free Text) • Filters : SKU + Lis;ng Aributes (Drill Down) • Ranking : SKU + Lis;ng Aributes (Explicit/ Relevance) • Index Time Join aka Block Join (Best Performance)
- 15. Out Of Stock, but Why Show? Index has Stale Availability Data 234K Products
- 16. Challenge 1 : High Update Rates 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 ra8ng ~10 ~1K ~1 million signal 6 ~10 ~100 ~1 million signal 7 ~100 ~10K ~10 million signal 8 ~100 ~10K ~10 million
- 17. Challenge 2 : Micro Services 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
- 18. Agenda • Search @ Flipkart • Need for Real Time Search • SolrCloud Solu;on • Our approach • Q & A
- 19. SolrCloud for NRT 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 Document Versioning Update Log Forward to Replica
- 20. SolrCloud Evalua;on • Update = Delete + Add – Block Join Index Update Whole Block (Product + Lis;ngs) • Updated Document gets streamed to all replicas in sync – Reduces indexing throughput • Sol commit is Not Free – Sol commit In Memory Segment – Lots of Merges – Huge document churn / deletes – All caches s;ll need to be re-‐generated – Filter Cache miss specially hurts performance
- 21. Agenda • Search @ Flipkart • Need for Real Time Index • SolrCloud Solu;on • Our approach • Q & A
- 22. 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
- 23. 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
- 24. NRT Forward Index -‐ Considera;ons ● Lookup efficiency – 50th percen;le : ~10K matches – 99th percen;le : ~1 million matches ● Data on Java heap – Memory efficiency
- 25. NRT Forward Index -‐ Naive Implementa;on 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> DocId : 3 field: price 250 ProductId Availability Price Latency : ~10 secs for ~1 Million lookups
- 26. NRT Store -‐ Forward Index Op;mized Lookup Engine Lucene Segment 0 ProductB 1 ProductA 2 ProductC 3 ProductD DocId : 3 Field : price 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
- 27. NRT Store Filter -‐ PostFilter PostFilter(Price:[100 TO 150]) Lucene Segment 0 ProductB 1 ProductA 2 ProductC 3 ProductD DocId : 3 Don’t Delegate 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
- 28. NRT Filter NRT Store -‐ Invert index NRT Forward StoreNRT 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
- 29. Solr Integra;on Points • ValueSources • Filtering – Custom Filter Implementa;on for cached DocIdSet – Custom PostFilter • Query – Wrapper over Filter • Custom FacetComponent
- 30. 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
- 31. Accomplishments • Real ;me sor;ng • Real ;me filtering : PostFilter – Higher latency • Near real ;me filtering : cached DocIdSet – No consistency between lookup and filtering • Independent of lucene commits • Query latency comparable to DocValues – Consistent 99% performance
- 32. Accomplishments @ Flipkart ● Real ;me consump;on for ~150 Signals ● Reduc;on in shown out of stock products by 2X ● Produc;on instances of ~50K updates/second real ;me
- 33. Thank you & Ques8ons