Cassandra at eBay - Cassandra Summit 2013

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    21-May-2015
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"Buy It Now! Cassandra at eBay" talk at Cassandra Summit 2013 This session will cover various use cases for Cassandra at eBay. It’ll start with overview of eBay’s heterogeneous data platform comprised of SQL & NoSQL databases, and where Cassandra fits into that. For each use case, Jay will go into detail of system design, data model & multi-datacenter deployment. To conclude, Jay will summarize the best practices that guide Cassandra utilization at eBay. http://www.datastax.com/company/news-and-events/events/cassandrasummit2013

Transcript of Cassandra at eBay - Cassandra Summit 2013

  • 1. Cassandra @ eBayJay PatelArchitect, Platform Systems@pateljay3001

2. eBay MarketplacesThousands of serversPetabytes of dataBillions of SQLs/day24x7x36599.98+% Availabilityturning over a TBevery secondMultiple DatacentersNear-Real-timeAlways online400+ million items for sale$75 billion+ per year in goods are sold on eBayBig Data112 million active usersBillions of page views/day 3. 3eBay Site Data InfrastructureDont force!One size does not fit all.Its a mixture ofmultiple SQL &NoSQL databases.We use the rightdatabase for theright problem. 4. eBay Site Data InfrastructureA heterogeneous mixtureThousands of nodes> 2K sharded logical host> 16K tables> 27K indexes> 140 billion SQLs/day> 5 PB provisionedHundreds of nodesPersistent & in-memory> 40 billion SQLs/day10+ clusters, 100+ nodes> 250 TB provisioned(local HDD + shared SSD)> 9 billion writes/day> 5 billion reads/dayHundreds of nodes> 50 TB> 2 billion ops/dayThousands of nodesThe world largestcluster with 2K+ nodesDozens of nodes 5. How do we scale RDBMS? Shard Patterns: Modulus, lookup-based, range, etc. Application sees only logical shard/database Replicate Disaster recovery, read availability & read scalability Big NOs No transactions No joins No referential integrity constraints 5 6. Why Cassandra? Multi-datacenter (active-active) Always Available - No SPOF Easy to scale up & down6 Write performance Distributed counters Hadoop supportNot replacing RDBMS, but complementing! Some use cases dont fit well in RDBMS - sparse data, big data,flexible schema, real-time analytics, Many use cases dont need top-tier set-ups. 7. Cassandra GrowthAug,2011Aug,2012ay,20131234567Billions(per day)writesasync. readssync. site readsTerabytes50100200250300350storage capacityDoesnt predictbusiness7 8. eBay Use Cases on Cassandra Time-series data, real-time insights & immediate actions Fraud detection & prevention Quality Click Pricing for affiliates Order & shipment tracking and insights Mobile notification logging & tracking Cloud CMS change history storage RedLaser server logs and analytics Server metrics collection for monitoring & alerting Taste graph based next-gen recommendation system Personalization Data Service Social Signals on eBay Product & Item pages Milos store-item availability inventory (evaluation phase) 8 9. Real-time insights & actions for9Fraud Prevention ReportingQuality Click Pricing More 10. 10System OverviewBusiness Event StreamCheckout Shipping Refund & Recoup Order placed(bin/bid)Paid Shipped RefundedRawdataSimple in-memory aggregations +/Complex Event Processing +/Cassandras distributed countersLabel printed per day per userUser segmentation for affiliate pricingOrders per hour, Multiple Cassandra clustersPaymentActinreal-timeFraud PreventionAffiliate Pricing Engine(eBay Partner Network)Order trackingReal-time reporting(Kept from several months to years) 11. A glimpse on Data Model11Historic & real-time insights per user per carrier.Sudden & drastic change might be suspicious.User bucketing based on historic& real-time buying activity. 12. A glimpse on Data Model12 13. Fraud Detection & Prevention13Shop with Confidence 14. System Overview14CassandraFraud Detection & Prevention SystemSign-ininfoBusiness events(checkout, sell,)StaaSOracleCheckout Shipping PaymentSellingReal-timeBeacons dataReal-timeInsightsOther dataMachineLearned Models 15. 15A glimpse on Data ModelCollected at sign-in& stored as key-value.Pulled periodically to StaaS fortraining machine learned models. 16. Metrics collection for monitoring & alerting16 17. System Overview17Transport (HTTP, )Scalable NIOservers basedon NettyThousands ofproductionmachinesCassandraStats for CPU, Memory, Disk, ..agent agent agent agent Server Server Server Server ServerIn-memory grid (hazelcast) for rollups 18. A glimpse on Data Model18Granular data pointsRolled up metricsfor various time intervals 19. Taste graph based recommendation system19 20. Data Model20TasteGraphTasteVector50 billion+ edges, 600 million+ writes, 3 billion+ reads, 30TB+ of data on SSD 21. System Overview21Business Event StreamRecommendation systemTaste GraphTaste Vector1. Item purchased.2a. Write purchase edge.2b. Read other edges for this user & item.4. Req. recommendations.5. Finds other items close tousers coordinates.6. Reco. shown to userMore, http://www.slideshare.net/planetcassandra/e-bay-nyc 22. Real-time Personalization Data Service22User performs search using keyword User gets personalized pages based onimplicit/explicit profile 23. System Overview23Personalization Data ServiceCacheMesh(write-back cache)Heavy writeseBay site pages (personalized)Every few minsin-memoryMySQL& XMP DBCassandraOracle(scaled out) HeavyreadsCache missuser profilesApplication SOA services (multiple)DataWarehouse 24. Data Model24 Keep column names short. Dont overload one CF with all the data:- Split hot & cold data in separate CF.- Splitting & sharding can help compaction.Static column families 25. 25Served byCassandraSocial Signals 26. Manage signals via Your Favorites26Whole page isserved byCassandraMore, http://www.slideshare.net/jaykumarpatel/cassandra-at-ebay-13920376 27. Multi-Datacenter Deployment27Topology - NTSRF - 1:1 or 2:2 or 3:3Read CL - ONE/QUORUMWrite CL - ONEData is backed up periodicallyto protect against human orsoftware errorUser request has no datacenter affinityNon-sticky load balancing 28. Multi-Datacenter DeploymentTopology - NTSRF 1:1:1 or2:2:2 29. Lessons & Best Practices One size does not fit all Use Cassandra for the right use cases. Choose proper Replication Factor and Consistency Level They alter latency, availability, durability, consistency and cost. Cassandra supports tunable consistency, but remember strong consistency is not free. Many ways to model data in Cassandra The best way depends on your use case and query patterns. De-normalize and duplicate for read performance But dont de-normalize if you dont need to.http://www.slideshare.net/jaykumarpatel/cassandra-data-modeling-best-practices29 30. Are you excited? Come Join Us!30Thank You@pateljay3001#cassandra13