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

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Slide deck for Capital One's talk on using Apache Apex for their next generation decisioning platform, achieving an ultra-low latency of under 2 ms for decision making, and handling 2,000 events burst at a net rate of 70,000 events/sec.

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  • Spark streaming is missing – nonstarter due to microbatch, lack of dynamic dag reconfiguration
  • Fast
    Easy to use
    Mature
    ***********
    Failures are not independent, nimbus, no dynamic topologies, 1 sec ack, resource usage
    Community stagnating, only Horton
    Roadmap still to bring it to enterprise (Integration with YARN, elastic topologies, high availability)

  • Easy to Use
    Fast
    Support for SQL-like queries
    ----- Meeting Notes (10/27/15 10:14) -----
    Reset to upstream data source, Shared JVM, No dynamic topologies
    Young community
    Roadmap – fine grained fault tolerance, in-memory store integration, off-heap memory, full SQL
  • Veterans from Yahoo! Finance and Hadoop
    Built for Enterprise stability and durability before performance
    *****************
  • Dynamic topologies
    Downstream components do not affect upstream
  • Fine grained control of locality
    No single point of failure
    Operators are independent
  • Independence of partitions
    Auto-scaling (throughput and latency)
  • Batch, micro-batch, and true streaming
  • Capital One's Next Generation Decision in less than 2 ms

    1. 1. Next Gen Decision Making in <2ms
    2. 2. 2
    3. 3. 3 VS.
    4. 4. 4
    5. 5. 5
    6. 6. 6
    7. 7. 7 X (predictor) Spend amount Y (response) Likelihood of millionaire Simple Velocity Advanced
    8. 8. 8
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    12. 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
    13. 13. 13
    14. 14. 14 Onyx
    15. 15. 15 Enterprise Readiness RoadmapPerformance Community
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    18. 18. 18
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    26. 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. 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
    28. 28. 28
    29. 29. 29
    30. 30. 30 Appendix
    31. 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. 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. 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. 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. 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. 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. 37 Apex Platform – Overview
    38. 38. 38 Apex Platform – Malhar
    39. 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. 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. 41 Dynamic Topology Modification • Can redeploy new operators and models at run-time! • Can reconfigure settings on the fly
    42. 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. 43 Apex Platform – Windowing • Sliding window and tumbling window • Window based on checkpoint • No artificial latency • Used for stats measurement
    44. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

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