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![NRT Store Filter - PostFilter
PostFilter(Price:[100 TO 150])
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Availability T F F T
Status 01 10 01 00](https://image.slidesharecdn.com/lucenesolrrevolution-nrt-170618222224/85/Near-RealTime-search-Flipkart-27-320.jpg)
Uploaded byUmesh Prasad
Near RealTime search @Flipkart
This document summarizes Flipkart's approach to building a real-time search index for e-commerce. It discusses the need for real-time search to address issues like showing out of stock products. It describes Flipkart's traffic volume and catalog size. It then covers challenges like high update rates and using microservices. It evaluates Apache SolrCloud and describes Flipkart's approach using a near real-time forward index with an inverted index for filtering to enable real-time sorting, filtering and querying with latency comparable to columnar data. The system processes over 150 signals and 50k updates per second in production.
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Near RealTime search @Flipkart
- 1. OCTOBER 11-14, 2016• BOSTON, MA
- 2. Near Real timeIndexing 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 Solution • Our approach • Q & A
- 6. Traffic @ Flipkart •Peak Traffic – ~ 800K active users – ~ 160K requests per second • Search Traffic – ~ 40K searches per second (Service) – ~ 10K searches per second (Solr ) • Latency – Median : 11 ms – 99th percentile : 1.1 second
- 7. 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
- 8. E-commerce Search • Heavyusage of drill down filters • Heavy usage of faceting • Only top results matter • Results grouped/collapsed by products • Serviceability and delivery experience MATTERS
- 9. Agenda • Search @Flipkart • Need for Real Time Search • SolrCloud Solution • Our approach • Q & A
- 10.
- 11. Damn !! IsOffer Over ??
- 12. What !! AllSteal Deals Gone ??
- 13. Product /Listing: ImportantAttributes Seller Rating Service catalogue service Promise Service Availability Service Offer Service Pricing Service Product aka SKU Listings
- 14. Summary : LuceneDocument • 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)
- 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 rating ~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 Solution • 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 Ingestionpipeline Shard Leader Auto commit Soft Commit Batch of documents For Document Versioning Update Log Forward to Replica
- 20. SolrCloud Evaluation • 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
- 21. Agenda • Search @Flipkart • Need for Real Time Index • SolrCloud Solution •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 SearchFlow 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- Considerations ● Lookup efficiency – 50th percentile : ~10K matches – 99th percentile : ~1 million matches ● Data on Java heap – Memory efficiency
- 25. NRT Forward Index- Naive 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> DocId : 3 field: price 250 ProductId Availability Price Latency : ~10 secs for ~1 Million lookups
- 26. NRT Store -Forward Index Optimized 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 Integration Points •ValueSources • Filtering – Custom Filter Implementation for cached DocIdSet – Custom PostFilter • Query – Wrapper over Filter • Custom FacetComponent
- 30. Near Real TimeSolr 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 timesorting • 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
- 32. 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
- 33.