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Building predictive apps with & -services architecture #Alan Ho @karlunho

Director of Product

Context is King

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Technology Choices Today

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Datawarehouse RDBMS J2EE AppServer

Lambda Architecture

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What is Context ?

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Historical #Events#

Recent#Events#

Now#

Lambda Architecture

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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 / ResProcessing

HistoricalEvents

RecentEvents

Netflix

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Offline = Batch Layer

Nearline = Speed Layer

Online = Serving Layer

Batch Layer (Offline) Singular Value

Decomposition

Batch like operations

High algorithm complexity

Results Flow into Cassandra

Speed Layer (Near Line)

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Medium request/response

Medium data

Medium algorithm complexity

Intermediate caching, incremental learning algos

Serving layer (Online)

Fast request/response, performance is key high availability/SLA

Fallback mechanism to Nearline/Offline operations

Low algorithm complexity

Final Thoughts:

Very complete system

Very custom and high performance

Lots of specialized systems for specialized tasks

Not practical for most enterprises

Microservices & LambdaArchitecture

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App Explosion

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How can I embed context-awareness in all customer experiences?

Micro-services

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Micro-services Best Practice

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Isolation REST API

HTTP:///

Isolation at all tiers

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Batch Storage

Near RT Storage

Batch Processing

(MR)

Near RealTime

Processing

Query DB

APIs

Direct Mail

Email

Web

Mobile

Outreach

Req / ResProcessing

HistoricalEvents

RecentEvents

Batch Processing

(MR)

Near RealTime

Processing

Req / ResProcessing

Batch Processing

(MR)

Near RealTime

Processing

Req / ResProcessing

Batch Processing

(MR)

Near RealTime

Processing

Req / ResProcessing

Batch Storage

Near RT Storage

Query DB

Batch Storage

Near RT Storage

Query DB

YARN Batch Layer# Serving Layer#

API at all tiers

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Batch Layer# Serving Layer#

Speed Layer#

Direct Mail

Email

Web

Mobile

Outreach

/Scores#

/Counts#

/Recommendations#

HistoricalEvents

RecentEvents

Real-time Scoring for Recommendations

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Recommendations Example

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Real-time Scoring Architecture

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Hadoop

In-Memory

Insights(GRASP)

Node.js

Node.jsAPI BaaS

(APIs)

Direct Mail

Email

Web

Mobile

Outreach

(Batch Scores)

(Activities / Counters)

(Query)

HistoricalEvents

RealtimeEvents

(Context)

Backend

(Mashup)

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2 6

Node.js

Batch Layer

Uses R for building predictive models

Algorithms based on Event Sequences

Lots and lots of tooling

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HadoopInsights(GRASP)

API BaaS

(Batch Scores)1

Apigee UI

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R Code#

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Scored Export to Serving Layer#

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.

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Node.js

API BaaS

Apigee UI

BaaS Node.js Counter Example

var event = new Usergrid.Counter(event_options, function (error, result) { });

// Then we POST the event to increment the countervar counter_options = { name: productName + .add_to_cart', value: 1 }event.increment (counter_options, function (error, result)

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curl -X GET https://api.usergrid.com/my-org/my-app/counters?counter=appleIpad3.add_to_cart

Input code:

Output code:#

Microservices Layer

API Layer (Swagger)

Context Injection

Service Orchestration

Fallback logic

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Node.js Node.jsAPI BaaS

Direct Mail

Email

Web

Mobile

Outreach

(Context)

Backend

Swagger Based API Design

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Node.js Code

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APIs are Everywhere

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Hadoop

In-Memory

Insights

Node.js Node.jsAPI BaaS

Direct Mail

Email

Web

Mobile

Outreach

HistoricalEvents

RealtimeEvents

Backend

Hive

RHadoop

Node.js

Storm

Spark

Conclusion

Lambda architecture is a great way to combine realtime & batch processing

Microservices complements Lambda architecture for loosely-coupled systems

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