•  SaaS Company – since 2008

•  Social Media Analytics track and measure activity of brands and personality, providing information to market research & brand comparison

•  Multi Language Technology (English, Portuguese and Spanish)

•  Leader in Latin America, with operations in 5 countries, customers in LatAm 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 conversation my brand and my competitors are 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 & Report • Clear & 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 tunning

Architecture – 1st iteration

What we've learned: •  Multi-instance is harder to administrate, but

minimize instability impact on customers

•  Vertical scalability: poor resource management

•  MySQL schema changes translates into downtime

Architecture – 2nd iteration

What we needed: •  Separation of Responsabilities (crawling,

processing)

•  Horizontal Scalability

•  Fast Provisioning

•  Costs reduction

Architecture – 2nd iteration

What we changed: •  Migrated to AWS

•  RabbitMQ (Single Node)

•  Replace MySQL for RDS

•  Cloud Formation

•  Auto Scaling Groups

Architecture – 2nd iteration

What we've learned: •  PIOPs à

•  Tuning the auto scaling policies can be hard

•  Cloud Formation: 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

•  EMR (Hadoop/Hive)

•  CloudFormation + Chef

•  Glacier + S3 lifecycles policies

Architecture – 3rd iteration

What we've learned: •  Spot instances + Reserved instances

•  Hive = SQL à SQL scripts are hard to test

•  Bulk upserts on RDS can be expensive (PIOPS)

•  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 on AWS ecosystem

•  Datastax AMI + Opscenter integration •  MongoDB MMS: automation magic! •  Apache Spark unit testing + ec2 launch scripts

•  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. eg: LogStash (ELK)

•  Always have a Source of Truth (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

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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

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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 Q&A

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