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Building predictive apps with λ & μ-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 / Res Processing

Historical Events

Recent Events

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://<Resource>/

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 / 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#

API at all tiers

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Batch Layer# Serving Layer#

Speed Layer#

Direct Mail

Email

Web

Mobile

Outreach

/Scores#

/Counts#

/Recommendations#

Historical Events

Recent Events

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.js API BaaS

(APIs)

Direct Mail

Email

Web

Mobile

Outreach

(Batch Scores)

(Activities / Counters)

(Query)

Historical Events

Realtime Events

(Context)

Backend

(Mashup)

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Node.js

Batch Layer

•  Uses R for building predictive models

•  Algorithms based on Event Sequences

•  Lots and lots of tooling

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Hadoop Insights (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.js API 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.js API BaaS

Direct Mail

Email

Web

Mobile

Outreach

Historical Events

Realtime Events

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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