© 2014 Amazon.com, Inc. and its affiliates. All rights reserved. May not be copied, modified, or distributed in whole or in part without the express consent of Amazon.com, Inc.

November 13, 2014 | Las Vegas, NV

ARC202

Real-World Real-Time Analytics

Gustavo Arjones | @arjones

CTO, Socialmetrix

Sebastian Montini | @sebamontini

Solutions Architect, Socialmetrix

• SaaS Company—since 2008

• Social media analytics track and measure activity

of brands and personality, providing information to

market research and brand comparison

• Multilanguage technology (English, Portuguese,

and Spanish)

• Leader in Latin America, with operations in 5

countries, customers in Latin America and US

• 1 out of 34 Twitter Certified Program worldwide

Our customers

Ranking Brand 1 Brand 2 Brand 3

Q2 Q3 Q2 Q3 Q2 Q3

1° Flavor Breakfast Flavor Flavor Advertising Flavor

2° Healthy Flavor Packaging Brand I love Flavor Breakfast

3° Components Components Healthy Packaging Healthy Healthy

4° Advertising Healthy Components Addiction Components Advertising

5° Enquires Desire Prices Consumption Prices Components

TOTAL 1.401 8.189 463 5.519 1.081 2.445

Share of topics

Which conversations are my brand and my competitors’ brands driving?

smx.io/reinvent #reinvent

Challenges

Challenges: Variety

• Different data sources

• Different API

• SLA

• Method (pull or push)

• Rate-limit, backoff strategy

Challenges: Velocity• Updates every second

• Top users, top hashtags each

minute

• After event analysis are made

with batch over complete

dataset

• Spikes of 20,000+ tweets per

minute

Last TV

Debate

Results

Announced

Challenges: Meaning

•Disambiguation

•Data Enrichment– Demographics

– Sentiment

– Influencers

•Human analysis

PAN

Orange Telecom

Oi Telecom Hi!

Challenges: Alert and report

•Clear and

understandable UI

•Slice-dice for business

(not BI experts)

•Real-time alerts for

anomalies

Architecture evolution

Drivers for architecture evolution

• More customers, bigger customers

• Add new features

• Keep costs under control

Architecture evolution

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Architecture—1st iteration

What we needed:

• Complete data isolation

• Trying different solutions/offerings

Architecture—1st iteration

What we did:

• All-in-one approach

• Multi-instance architecture

• Simple vertical scalability

• MySQL performance tuning

Architecture—1st iteration

What we've learned:

• Multi-instance is harder to administrate, but

minimizes instability impact on customers

• Vertical scalability: poor resource management

• MySQL schema changes translate into downtime

Architecture—2nd iteration

What we needed:

• Separation of responsibilities (crawling, processing)

• Horizontal scalability

• Fast provisioning

• Cost reduction

Architecture—2nd iteration

What we changed:

• Migrated to AWS

• RabbitMQ (Single Node)

• Replace MySQL for

Amazon RDS

• AWS CloudFormation

• Auto Scaling groups

Architecture—2nd iteration

What we've learned:

• PIOPS

• Tuning the Auto Scaling policies can be hard

• AWS CloudFormation: great for migration, not

enough for daily ops

Architecture—3rd iteration

What we needed:

• Deliver new features (NRT, more complex analytics)

• Scale fast

• Be resilient against failure

• Adding and improving data sources

• Keep costs under control (always)

Architecture—3rd iteration

What we changed:

• Apache Storm

• RabbitMQ HA

• Amazon Elastic MapReduce

(Hadoop/Hive)

• AWS CloudFormation + Chef

• Amazon Glacier + Amazon S3

lifecycles policies

Architecture—3rd iteration

What we've learned:

• Spot Instances + Reserved Instances

• Hive = SQL SQL scripts are hard to test

• Bulk upserts on Amazon RDS can be expensive (PIOPS)

• Amazon DynamoDB is great, but expensive (for

our use-case)

Dashboard

Architecture—4th iteration

What we needed:

• Monitor millions of social media profiles

• Make data accessible (exploration, PoC)

• Improve UI response times

• Testing our data pipelines

• Reprocessing (faster)

Architecture—4th iteration

What we changed:

• Cassandra (DSE)

• MongoDB MMS

• Apache Spark

What we've learned:

• Leverage AWS ecosystem

• Datastax AMI + Opscenter integration

• MongoDB MMS: automation magic!

• Apache Spark unit testing + Amazon EC2

launch scripts

• Amazon EMR doesn’t have the latest stable

versions

Architecture—4th iteration

Architecture evolution

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Costs Customers

Lessons learned

Lessons learned

• Automate since Day 1 (CloudFormation + Chef)

• Monitor systems activity, understand your data

patterns, e.g. LogStash (ELK)

• Always have a Source of Truth (Amazon S3 +

Glacier)

• Make your Source of Truth searchable

Lessons Learned (II)

•Approximation is a good thing: HLL, CMS, Bloom

•Write your pipelines considering reprocessing

needs

• Avoid at all costs framework explosion

•AWS ecosystem allows rapid prototype

Socialmetrix NextGen

2015

Architecture evolution

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Architecture nextgen

• Reduce moving parts

• Apache Spark as central processing framework

– Realtime (Micro-batch)

– Batch-processing

• Kafka (Message Broker)

• Cassandra (Time-series storage)

• ElasticSearch (Content Indexer)

To infinity …

and beyond!Architecture evolution

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#1 #2 #3 #4 NextGen

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Gustavo Arjones, CTO

@arjones | gustavo@socialmetrix.com

Sebastian Montini, Solutions Architect

@sebamontini | sebastian@socialmetrix.com

Let’s talk at Venetian—Titian Hallway

Feedback and QandA

Please give us your feedback on this

presentation

© 2014 Amazon.com, Inc. and its affiliates. All rights reserved. May not be copied, modified, or distributed in whole or in part without the express consent of Amazon.com, Inc.

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ARC202: Real-World

Real-Time AnalyticsThank you!