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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: Velocity

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 or Amazon Kinesis (Message Broker)

• Cassandra (Time-series storage)

• ElasticSearch (Content Indexer)

To infinity …

and beyond!Architecture evolution

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Gustavo Arjones, CTO

@arjones | gustavo@socialmetrix.com

Sebastian Montini, Solutions Architect

@sebamontini | sebastian@socialmetrix.com

Feedback and QandA

http://bit.ly/awsevals