How Orange Financial combat financial frauds over 50M transactions a day using Apache Pulsar
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You will learn how Orange Financial combat financial frauds over 50M transactions a day using Apache Pulsar. The presentation is shared at Strata Data Conference at New York, US, 2019/09.
How Orange Financial combat financial frauds over 50M transactions a day using Apache Pulsar
- 1. How Orange Financial combat financial frauds over 50M transactions a day using Apache Pulsar Vincent Xie (Bestpay), Jia Zhai (StreamNative)
- 2. About us Vincent (Weisheng) Xie ❏ Current Director @ Orange Financial ❏ Previous Tech lead of ML engineering team @ Intel Jia Zhai ❏ Co-Founder of StreamNative ❏ Apache Pulsar PMC Member ❏ Apache BookKeeper PMC Member
- 3. Agenda ❏ Background ❏ Apache Pulsar ❏ Unified Data Processing ❏ Our Practices ❏ Q & A
- 5. Orange Financial Orange Financial Services Group (Chinese: 甜橙金融), formerly known as Bestpay, is an affiliate company of China Telecom. It reached 1.13 trillion CNY ($18.37 Billion) transaction volume in 2018, with 500 million registered users and 41.9 million active users. Subsidiaries: Bestpay - a mobile wallet and payment app Jieqian - a consumer loan service Orange Wealth Orange Insurance Orange Credit Orange Financial Cloud
- 7. Source: iiMedia Research Inc.
- 8. High Industry Penetration Rate Source: China Unionpay
- 9. Source: RSA
- 10. Challenges ❏ High concurrency ❏ > 50M transactions, 1 billion events a day (peek: 35K/s) ❏ Low latency demand ❏ response < 200ms ❏ Large number of batch jobs and streaming jobs
- 11. “A merchant’s total transaction volume ($) within the past month (30days) (current transaction included)” = sum($past_29days) + sum($today_upto_current) batch streaming
- 13. Batch Layer Speed/Streaming Layer Architecture V1 - Lambda API Gateway Serving Layer
- 14. Drawbacks ❏ S/W stacks complexity ❏ Realtime / Offline / Serving stacks ❏ Multiple clusters to maintain (Kafka / Hive / Spark / Flink) ❏ Different skill sets to manipulate (Scala / Java / SQL) ❏ Segmented Logics ❏ Historical/Current ❏ Data redundancy ❏ Multiple duplications to move over
- 16. What is Apache Pulsar?
- 17. “Flexible Pub/Sub Messaging Backed by durable log storage”
- 18. Pulsar - A cloud-native architecture Stateless Serving Durable Storage
- 19. Pulsar - Segment Centric Storage ❏ Topic Partition (Managed Ledger) ❏ The storage layer for a single topic partition ❏ Segment (Ledger) ❏ Single writer, append-only ❏ Replicated to multiple bookies
- 20. Pulsar - Pub/Sub
- 21. Pulsar - Topic Partitions
- 23. Pulsar - Stream
- 24. Pulsar - Stream as a unified view on data
- 25. Pulsar - Two levels of reading API ❏ Pub/Sub (Streaming) ❏ Read data from brokers ❏ Consume / Seek / Receive ❏ Subscription Mode - Failover, Shared, Key_Shared ❏ Reprocessing data by rewinding (seeking) the cursors ❏ Segment (Batch) ❏ Read data from storage (bookkeeper or tiered storage) ❏ Fine-grained Parallelism ❏ Predicate pushdown (publish timestamp)
- 26. Unified Data Processing on Pulsar
- 28. Architecture V2 API Gateway Spark Structured Streaming Spark SQL ❏ Single Data Store (Pulsar) ❏ Single Computing Engine (Spark) ❏ Unified API
- 29. Pulsar-Spark ❏ Deeply integrated with Pulsar schema ❏ Pulsar topics as Structured Streams ❏ Pulsar Connectors for Spark Structured Streaming ❏ Pulsar Connectors for Spark SQL https://github.com/streamnative/pulsar-spark
- 30. Pulsar-Spark / Streaming Queries https://github.com/streamnative/pulsar-spark
- 31. Pulsar-Spark / Batch Queries https://github.com/streamnative/pulsar-spark
- 32. Pulsar-Spark / Write Results to Pulsar https://github.com/streamnative/pulsar-spark
- 33. PoC at Bestpay ❏ Ingest data to Pulsar ❏ Realtime Data ❏ pulsar-io-kafka: connect kafka messages (JSON) to Pulsar and store them in AVRO format with schema information ❏ Historic Data ❏ pulsar-spark: query the Hive table and insert Hive rows as Pulsar messages (AVRO) to Pulsar ❏ Data Processing ❏ Spark Structured Streaming: for stream processing ❏ Spark SQL: for batch processing and interactive queries
- 34. Benefits ❏ Complexity drop 33% (Number of clusters from 6 down to 4) ❏ Storage saving 8.7% (expect to be 28%) ❏ Time to production boosts 11x (backed with streaming SQL) ❏ Higher stability (expected)
- 35. Summary ❏ Apache Pulsar is a cloud-native messaging streaming system ❏ Multi layered architecture ❏ Segment centric storage ❏ Two levels of reading API: Pub/Sub + Segment ❏ Apache Pulsar provides a unified view of data ❏ Pulsar + Spark for a simple unified data processing
- 36. References ❏ pulsar-io-kafka: https://github.com/streamnative/pulsar-io-kafka ❏ pulsar-spark: https://github.com/streamnative/pulsar-spark ❏ Apache Pulsar as One Storage System for Both Real-time and Historical Data Analysis: https://medium.com/streamnative/apache-pulsar-as-one-storage-455222c590 17
- 37. Community ❏ Pulsar Website: https://pulsar.apache.org ❏ Twitter: @apache_pulsar / @streamnativeio ❏ Slack: https://apache-pulsar.herokuapp.com ❏ Mailing Lists [email protected], [email protected] ❏ Github https://github.com/apache/pulsar ❏ Medium https://medium.com/streamnative
- 38. Thanks!