I Love APIs 2015 Machine learning, big data, and API technologies have drastically reduced the complexity of building predictive apps. But all these advances also mean that these apps require a new approach to system architecture. This talk discusses the lamda architecture and microservices, and best practices on decomposing your app into batch, near-realtime, an real-time services. Learn how Apigee uses both new architectures to implement predictive apps using Hadoop, Node.js, Cassandra, and ElasticSearch.

1. 1
Building predictive apps with λ & μ-services architecture#
Alan Ho @karlunho
Director of Product

2. Context is King
2

3. Technology Choices Today
3
Datawarehouse
RDBMS
J2EE AppServer

4. Lambda Architecture
4

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

13. Microservices & Lambda
Architecture
13

14. App Explosion
14
How can I embed context-awareness in
all customer experiences?

15. Micro-services
15

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

19. Real-time Scoring for
Recommendations
19

20. Recommendations Example
20

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
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