I Love APIs 2015: Building Predictive Apps with Lamda and MicroServices
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The document discusses building predictive applications using lambda and microservices architecture, highlighting the importance of context and technology choices for data processing. It outlines the roles of batch, speed, and serving layers in processing historical and real-time data to deliver high-performance customer experiences. Additionally, it emphasizes the integration of microservices for effective orchestration and context-awareness in applications.
I Love APIs 2015: Building Predictive Apps with Lamda and MicroServices
- 1. 1 Building predictive apps with λ & μ-services architecture# Alan Ho @karlunho Director of Product
- 3. Technology Choices Today 3 Datawarehouse RDBMS J2EE AppServer
- 5. What is Context ? 5 Historical # Events# Recent# Events# Now#
- 6. Lambda Architecture 6 Batch Storage Near RT Storage Batch Processing (MR) Near RealTime Processing Query DB Batch Layer# Serving Layer# Speed Layer# APIs Direct Mail Email Web Mobile Outreach Req / Res Processing Historical Events Recent Events
- 7. Netflix 7
- 8. • Offline = Batch Layer • Nearline = Speed Layer • Online = Serving Layer
- 9. Batch Layer (Offline) • Singular Value Decomposition • Batch like operations • High algorithm complexity • Results Flow into Cassandra
- 10. Speed Layer (Near Line) 10 • Medium request/response • Medium data • Medium algorithm complexity • Intermediate caching, incremental learning algos
- 11. Serving layer (Online) • Fast request/response, performance is key – high availability/SLA • Fallback mechanism to Nearline/Offline operations • Low algorithm complexity
- 12. Final Thoughts: • Very complete system • Very custom and high performance • Lots of specialized systems for specialized tasks • Not practical for most enterprises
- 14. App Explosion 14 How can I embed context-awareness in all customer experiences?
- 16. Micro-services Best Practice 16 Isolation REST API HTTP://<Resource>/
- 17. Isolation at all tiers 17 Batch Storage Near RT Storage Batch Processing (MR) Near RealTime Processing Query DB APIs Direct Mail Email Web Mobile Outreach Req / Res Processing Historical Events Recent Events Batch Processing (MR) Near RealTime Processing Req / Res Processing Batch Processing (MR) Near RealTime Processing Req / Res Processing Batch Processing (MR) Near RealTime Processing Req / Res Processing Batch Storage Near RT Storage Query DB Batch Storage Near RT Storage Query DB YARN Batch Layer# Serving Layer#
- 18. API at all tiers 18 Batch Layer# Serving Layer# Speed Layer# Direct Mail Email Web Mobile Outreach /Scores# /Counts# /Recommendations# Historical Events Recent Events
- 21. Real-time Scoring Architecture 21 Hadoop In-Memory Insights (GRASP) Node.js Node.js API BaaS (APIs) Direct Mail Email Web Mobile Outreach (Batch Scores) (Activities / Counters) (Query) Historical Events Realtime Events (Context) Backend (Mashup) 3 4 5 1 2 6 Node.js
- 22. Batch Layer • Uses R for building predictive models • Algorithms based on Event Sequences • Lots and lots of tooling 22 Hadoop Insights (GRASP) API BaaS (Batch Scores) 1 Apigee UI
- 23. 23 R Code#
- 24. 24 Scored Export to Serving Layer#
- 25. Speed Layer • Easy to program via node.js • Low algorithm complexity – Storm is overkill for most Apigee customers • Simple aggregates via C* counters good enough • Good for capturing locations, activity stream, etc. 25 Node.js API BaaS Apigee UI
- 26. BaaS Node.js Counter Example var event = new Usergrid.Counter(event_options, function (error, result) { … }); // Then we POST the event to increment the counter var counter_options = { name: productName + ’.add_to_cart', value: 1 } event.increment (counter_options, function (error, result) 26 curl -X GET https://api.usergrid.com/my-org/my-app/counters? counter=appleIpad3.add_to_cart Input code: Output code:#
- 27. Microservices Layer • API Layer (Swagger) • Context Injection • Service Orchestration • Fallback logic 27 Node.js Node.js API BaaS Direct Mail Email Web Mobile Outreach (Context) Backend
- 28. Swagger Based API Design 28
- 29. Node.js Code 29
- 30. APIs are Everywhere 30 Hadoop In-Memory Insights Node.js Node.js API BaaS Direct Mail Email Web Mobile Outreach Historical Events Realtime Events Backend Hive RHadoop Node.js Storm Spark
- 31. Conclusion • Lambda architecture is a great way to combine realtime & batch processing • Microservices complements Lambda architecture for loosely-coupled systems 31 ©2015 Apigee. All Rights Reserved.