Capital One's Next Generation Decision in less than 2 ms

1. Next Gen Decision Making in <2ms

3. 3 VS.

7. 7 X (predictor) Spend amount Y (response) Likelihood of millionaire Simple Velocity Advanced

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12. 12 Hard Metrics Goal Latency < 40ms Ideally < 16ms Throughput Goal of 2000 events / second Durability No loss, every message gets exactly one response Availability 99.5% uptime (downtime of 1.83 days / year); Ideally 99.999% uptime (downtime of 5.26 minutes / year) Scalability Can add resources, still meet latency requirements Integration Transparently connected to existing systems – Hardware, Messaging, HDFS Soft Metrics Goal Open Source All components licensed as open source Extensibility Rules can be updated, model is regularly refreshed

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14. 14 Onyx

15. 15 Enterprise Readiness RoadmapPerformance Community

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26. 26 • Avg. 0.25ms, @70k records/sec, w/ 600GB RAM Thread Local on ~54M events Percentiles (in ms) Throughput Count Avg (ms) 90% 95% 99% 99.9% 4 9’s 5 9’s 6 9’s 70k/sec 54,126,122 0.19 1 1 1 2 2 5 6 Performance

27. 27 Durability • Two physically independent pipelines on the same cluster processing identical data • For the same tuple, we find the best-case time between two pipelines – 39 records out of 5.2M exceeded 16ms – 173 out of 5.2M exceeded 16ms in one pipeline but succeeded in the other • 99.99925% success rate – “Five Nines” • Average Latency of 0.0981ms

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30. 30 Appendix

31. 31 Streaming Technologies Evaluated • Spark Streaming • Samza • Storm • Feedzai • Infosphere Streams • Flink • Ignite • VoltDB • Cassandra • Apex • Of all evaluated technologies, Apache Apex is the only technology that is ready to bring the decision making solution to production based on: – Maturity – Fault-tolerance – Enterprise-readiness – Performance • Focus on open source • Drive Roadmap • Competitive Advantage for C1

32. 32 Stream Processing – Apache Storm • An open-source, distributed, real-time computation system – Logical operators (spouts and bolts) form statically parallelizable topologies – Very high throughput of messages with very low latency – Can provide <10ms latency end-end under normal operation • Basic abstractions provide an at-least-once processing guarantee Limitations • Nimbus is a single point of failure – Rectified by Hortonworks, but not yet available to the public (no timeline for release) • Upstream bolt/spout failure triggers re-compute on entire tree – Can only create parallel independent stream by having separate redundant topologies • Bolts/spouts share JVM  Hard to debug • Failed tuples cannot be replayed quicker than 1s • No dynamic topologies • Cannot add or remove applications without service interruption

33. 33 Stream Processing – Apache Flink • An open-source, distributed, real-time computation system – Logical operators are compiled into a DAG of tasks executed by Task Managers – Supports streaming, micro-batch, batch compute – Supports aggregate operations on streams (reduce, join, groupBy) – Capable of <10 ms end-end latency with streaming under normal operation • Can provide exactly-once processing guarantees Limitations • Failures trigger reset of ALL operators to last checkpoint – Depends on upstream message broker to track state • Operators share JVM – Failure in one brings down all tasks sharing that JVM – Hard to debug • No dynamic topologies • Young community, young product

34. 34 Stream Processing – Apache Apex • An open-source, distributed, real-time computation system on YARN • Apex is the core system powering DataTorrent, released under ASF • Demonstrated high throughput with low latency running a next-generation C1 model (avg. 0.25ms, max 2ms, @ 70k records/sec) w/ 600GB RAM • True YARN application developed on the principles of Hadoop and YARN at Yahoo! • Mature product – Core principles of Apex are derived from a proven solution in Yahoo Finance and Yahoo hadoop. – Operability in Apex is first class citizen with focus on Enterprise capabilities • DataTorrent (Apex) is executing on production clusters at Fortune 100 companies.

35. 35 Stream Processing – Apache Apex Maturity • Designed to process and manage global data for Yahoo! Finance – Primary focus is on stability, fault-tolerance and data management – Only OSS streaming technology considered designed explicitly for the financial world • Data or computation could never be lost or replicated • Architecture had to never go down • Goal was to make it rock-solid and enterprise-ready before worrying about performance • Data flow across countries – perfect for use-case that requires cross- cluster interaction Enterprise Readiness • Advanced support for: – Encryption, authentication, compression, administration, and monitoring – Deployment at scale in the cloud and on-prem – AWS, Google Cloud, Azure • Integrates with huge set of existing tools: – HDFS, Kafka, Cassandra, MongoDB, Redis, ElasticSearch, CouchDB, Splunk, etc.

36. 36 Apex Platform – Summary • Apex Architecture – Networks of physically independent, parallelizable operators that scale dynamically – Dynamic topology modification and deployment – Self-healing, fault tolerant, & recoverable • Durable messaging queues between operators, check-pointed in memory and on disk • Resource manager is a replicated YARN process, monitors and restarts downed operators – No single point of failure, highly modular design – Can specify locality of execution (avoids network and inter-process latency) • Guarantees at-least-once, at-most-once, or exactly-once processing Directed Acyclic Graph (DAG) Output Stream Tuple Tuple er Operator er Operator er Operator er Operator

37. 37 Apex Platform – Overview

38. 38 Apex Platform – Malhar

39. 39 Apex Platform – Cluster View Hadoop Edge Node DT RTS Management Server Hadoop Node YARN Container RTS App Master Hadoop Node YARN Container YARN Container YARN Container Thread1 Op2 Op1 Thread-N Op3 Streaming Container Hadoop Node YARN Container YARN Container YARN Container Thread1 Op2 Op1 Thread-N Op3 Streaming Container CLI REST API DT RTS Management Server REST API Part of Community Edition

40. 40 Apex Platform – Operators • Operators can be dynamically scaled • Flexible stream configuration • Parallel Redis / HDHT DAGs • Separate visualization DAG • Parallel partitioning • Durability of data • Scalability • Organization for in-memory store • Unifiers • Combine statistics from physical partitions

41. 41 Dynamic Topology Modification • Can redeploy new operators and models at run-time! • Can reconfigure settings on the fly

42. 42 Apex Platform – Failure Recovery • Physical independence of partitions is critical • Redundant STRAMs • Configurable window size and heartbeat for low-latency recovery • Downstream failures do not affect upstream components – Snapshotting only depends on previous operator, not all previous operators – Can deploy parallel DAGs with same point of origin (simpler from a hardware and deployment perspective)

43. 43 Apex Platform – Windowing • Sliding window and tumbling window • Window based on checkpoint • No artificial latency • Used for stats measurement

44. 44 • Apex – Great UI to monitor, debug, and control system performance – Fault-tolerance and recovery out of the box - no additional setup, or improvement needed • YARN is still a single point of failure, a name node failure can still impact the system – Built-in support for dynamic and automatic scaling to handle larger throughputs – Native integration with Hadoop, YARN, and Kafka – next-gen standard at C1 – Mature product • Principles derived from years at Yahoo Finance and Yahoo Hadoop • Built and planned by deep Hadoop and streaming experts – Proven performance in production at Fortune 100 companies Enterprise Readiness

45. 45 Enterprise Readiness • Storm – Widely used but abandoned by creators at Twitter for Heron in production • Storm debug-ability - topology components are bundled in one process • Resource demands – Need dedicated hardware – Can’t scale on demand or share usage • Topology creation/tear-down is expensive, topologies can’t share cluster resources – Have to manually isolate & de-commission machines – Performance in failure scenarios is insufficient for this use-case • Flink – Operational performance has not been proven • Only one company (ResearchGate) officially uses Flink in production – Architecture shares fundamental limitations of Storm with regards to dynamically scaling operators & topologies and debugability – Performance in failure scenarios is insufficient for this use-case

46. 46 Performance • Storm – Meets latency and throughput requirements only when no failures occur. – Resilience to failures only possible by running fully independent clusters – Difficult to debug and operationalize complex systems (due to shared JVM and poor resource management) • Flink – Broader toolset than Storm or Apex – ML, batch processing, and SQL-like queries – Meets latency and throughput requirements only when no failures occur. – Failures reset ALL operators back to the source – resilience only possible across clusters – Difficult to debug and operationalize complex systems (due to shared JVM) • Apex – Supports redundant parallel pipelines within the same cluster – Outstanding latency and throughput even in failure scenarios – Self-healing independent operators (simple to isolate failures) – Only framework to provide fine-grained control over data and compute locality

47. 47 Roadmap – Storm • Commercial support from from Hortonworks but limited code contributions • Twitter - Storm’s largest user - has completely abandoned Storm for Heron • Business Continuity – Enhance Storm’s enterprise readiness with high availability (HA) and failover to standby clusters – Eliminate Nimbus as a single point of failure • Operations – Apache Ambari support for Nimbus HA node setup – Elastic topologies via YARN and Apache Slider. – Incremental improvements to Storm UI to easily deploy, manage and monitor topologies. • Enterprise readiness – Declarative writing of spouts, bolts, and data-sources into topologies

48. 48 Roadmap – Flink • Fine-grained fault tolerance (avoid rollback to data source) – Q2 2015 • SQL on Flink – Q3/Q4 2015 • Integrate with distributed memory storage – No ECD • Use off-heap memory – Q1 2015 • Integration with Samoa, Tez, Mahout DSL – No ECD

49. 49 Roadmap – Apex • Roadmap for next 6 months • Support creation of reusable pluggable modules (topologies) • Add additional operators to connect to existing technology – Databases – Messaging – Modeling systems • Add additional SQL-like operations – Join – Filter – GroupBy – Caching • Add ability to create cycles in graph – Allows re-use of data for ML algorithms (similar to Spark’s caching)

50. 50 Road Map Comparison • Storm – Roadmap is intended to bring Storm to enterprise readiness  Storm is not enterprise ready today according to Hortonworks • Flink – Roadmap brings Flink up to par with Spark and Apex, does not create new capabilities relative to either – Spark is more mature for batch-processing and micro-batch and Apex is more mature from a streaming standpoint. • Apex – No need to improve core architecture, focus is instead on adding functionality • Better support for ML • Better support for wide variety of business use cases • Better integration with existing tools – Stated commitment to letting the community dictate direction. From incubator proposal: • “DataTorrent plans to develop new functionality in an open, community-driven way”

51. 51 Community • Vendor and community involvement drive roadmap and project growth • Storm – Limited improvements to core components of Storm in recent months – Limited focused and active committers – Actively promoted and supported in public by Hortonworks • Flink – Some adoption in Europe, growing response in U.S. – 11 active committers, 10 are from Data Artisans (company behind Flink) – Community is very young, but there is substantial interest • Apex – Wide support network around Apex due to its evolution alongside Hadoop and YARN – Young but actively growing community: http://incubator.apache.org/projects/apex.html – Opportunity for C1 to drive growth and define the direction of this product

52. 52 Streaming Solutions Comparison • Apex – Ideal for this use case, meets all performance requirements and is ready for out-of-the- box enterprise deployment – Committer status from C1 allows us to collaboratively drive roadmap and product evolution to fit our business need. • Storm – Great for many streaming use cases but not the right fit for this effort – Performance in failure scenarios does not meet our requirements – Community involvement is waning and there is a limited road map for substantial product growth • Flink – Poised to compete with Spark in the future based on community activity and roadmap – Not ready for enterprise deployment: • Technical limitations around fault-tolerance and failure recovery • Lack of broad community involvement • Roadmap only brings it up to par with existing frameworks

53. 53 New Capabilities Provided by Proposed Architecture • Millisecond Level Streaming Solution • Fault Tolerant & Highly Available • Parallel Model Scoring for Arbitrary Number of Models • Quick Model Generation & Execution • Dynamic Scalability based on Latency or Throughput • Live Model Refresh • A/B Testing of Models in Production • System is Self Healing upon failure of components (**)

54. 54 Decisioning System Architecture - Strengths • Internal – Capital One software, running on Capital One hardware, designed by Capital One • Open source – Internally maintainable code • Living Model – Can be re-trained on current data & updated in minutes, not years – Offline models can expanded and re-developed and deployed to production at will • Extensible – Modular architecture with swappable components • A/B Model Testing in Production • Dynamic Deployment / Refresh of Models

55. 55 Hardware MDC Hardware Specifications • Server Quantity – 15 • Server Model – Supermicro • CPU – Intel Xeon E5-2695v2 2.4Ghz 12Cores • Memory – 256GB • HDD – (5) 4TB Seagate SATA • Network Switch – Cisco Nexus 6001 10GB • NIC – 2port SFP+ 10GbE MDC Software Specifications • Hadoop – v2.6.0 • Yarn – v2.6.0 • Apache Apex – v3.0 • Linux OS – RHEL v6.7 • Linux OS Kernel - 2.6.32- 573.7.1.el6.x86_64

56. 56 Performance Comparison - Redis vs. Apex-HDHT Apex-HDHT - Thread Local on ~2M events Stats Percentiles (in ms) Throughput Count Avg (ms) 90% 95% 99% 99.9% 4 9’s 5 9’s 6 9’s 70k/sec 1,807,283 0.253 1 1 1 2 2 2 2 Apex-HDHT Thread Local on ~54M events Stats Percentiles (in ms) Throughput Count Avg (ms) 90% 95% 99% 99.9% 4 9’s 5 9’s 6 9’s 70k/sec 54,126,122 0.19 1 1 1 2 2 5 6 Apex-HDHT No locality on ~2M events Stats Percentiles (in ms) Throughput Count Avg (ms) 90% 95% 99% 99.9% 4 9’s 5 9’s 6 9’s 40k/sec 2,214,777 51.651 98 126 381 489 494 495 495 Redis Thread local on ~2M events Stats Percentiles (in ms) Throughput Count Avg (ms) 90% 95% 99% 99.9% 4 9’s 5 9’s 6 9’s 8.5k/sec 2,018,057 13.654 16 18 20 21 22 22 22