Real Time Analytics for Twitter with a Scalable Architecture

Real Time Analytics for Big Data
A Twitter Case Study

Uri Cohen
Head of Product
@uri1803

Big Data Predictions

“Over the next few years we'll see the adoption of scalable
frameworks and platforms for handling
streaming, or near real-time, analysis and processing. In the
same way that Hadoop has been borne out of large-scale web
applications, these platforms will be driven by the needs of large-
scale location-aware mobile, social and sensor use.”
Edd Dumbill, O’REILLY

® Copyright 2011 Gigaspaces Ltd. All Rights Reserved
2

We’re Living in a Real Time World…
Facebook Real Time Real Time User Google RT Web Analytics
Social Analytics Engagement

Twitter paid tweet analytics New Real Time Google Real Time Search
Analytics Startups

3 ® Copyright 2011 Gigaspaces Ltd. All Rights Reserved

3 Flavors to Analytics

Counting Correlating Research


Analytics @ Twitter – Counting

 How many
signups, tweets, retweet
s for a topic?
 What’s the average
latency?
 Demographics
 Countries and cities
 Gender
 Age groups
 Device types
 …


Analytics @ Twitter – Correlating

 What devices fail at the
same time?
 What features get user
hooked?
 What places on the
globe are “happening”?


Analytics @ Twitter – Research

 Sentiment analysis
 “Obama is popular”
 Trends
 “People like to tweet
after watching
American Idol”
 Spam patterns
 How can you tell when
a user spams?


It’s All about Timing

“Real time” Reasonably Quick Batch
(< few Seconds) (seconds - minutes) (hours/days)


It’s All about Timing

• Event driven / stream processing
• High resolution – every tweet gets counted

• Ad-hoc querying This is what
• Medium resolution (aggregations) here
we’re
to discuss 

• Long running batch jobs (ETL, map/reduce)
• Low resolution (trends & patterns)


Twitter in Numbers (March 2011)

It takes a week for users to
send 1 billion Tweets.
Source: http://blog.twitter.com/2011/03/numbers.html



On average,
140 million
tweets get sent every day.



The highest
throughput to date is
6,939 tweets/sec.



460,000 new
accounts
are created daily.


Twitter in Numbers

5% of the users generate
75% of the content.
Source: http://www.sysomos.com/insidetwitter/


Challenge 1 – Computing Reach


Challenge 1 – Computing Reach
Tweets Followers Distinct
Followers

Count
Reach


Challenge 2 – Word Count
Tweets

Count
Word:Count

• Hottest topics
• URL mentions
• etc.

URL Mentions – Here’s One Use Case


Analyze the Problem

 (Tens of) thousands of tweets per second to
process
 Assumption: Need to process in near real time
 Aggregate counters for each word
 A few 10s of thousands of words (or hundreds of
thousands if we include URLs)
 System needs to linearly scale


It’s Really Just an Online Map/Reduce


Event Driven Flow

Store
Research
Raw

Tokenize Filter Count

Store
Counters


It’s Not That Simple…
 (Tens) of thousands of tweets to tokenize every
second, even more words to filter 
CPU bottleneck
 Tens/hundreds of thousands of counters to
update  Counters contention
 Tens/hundreds of thousands of counters to
persist  Database bottleneck
 (Tens) of thousands of tweets to store every
second in the database  Database bottleneck


Implementation Challenges

 Twitter is growing by the day,
how can this scale?
 Fault tolerance.
What happens if a server fails?
 Consistency.
Counters should be accurate!


Solutions, Solutions


Dealing with the Massive Tweet Stream
 We need a system that could scale linearly
 Event partitioning to the rescue!
 What to partition by?
Tokenizer1 Filterer 1

Tokenizer2 Filterer 2

Tokenizer
Filterer 3
3

Tokenizer
Filterer n
n


Counters Persistence & Contention
 Why not keep things in memory?
 Treat it as a SoR
IMDG
Counter
Tokenizer1 Filterer 1 Updater 1

Counter

Tokenizer Counter
Filterer 3
3 Updater 3

Tokenizer Counter
Filterer n
n Updater n


 Why not keep things in memory (data grid)?
 Question: How to partition counters?
IMDG
Counter

Counter

Tokenizer Counter
Filterer 3
3 Updater 3

Tokenizer Counter
Filterer n
n Updater n


 Why not keep things in memory?
 Question: How to partition counters? in
Facebook keeps 80% of its data
Memory (Stanford research) IMDG
Counter
RAM is 100-1000x faster than Disk
Counter
(Random seek)
• Disk: 5 -10ms
Tokenizer Counter
Filterer 3
• RAM: ~0.001msec
3 Updater 3

Tokenizer Counter
Filterer n
n Updater n


Database Bottleneck – Storing Raw Tweets
 RDBMS won’t cut it (unless you have $$$$$)
 Hadoop / NoSQL to the rescue
 Use the data grid for batching and as a reliable
transaction log
Persister
1

Persister
2

Persister
n

Counter Consistency & Resiliency
 Primary/hot backup setup, sync replication
 Transactional, atomic updates
IMDG
 “On Demand” backups
P B

P B

P B

P B


Counter Consistency & Resiliency
 Primary/hot backup setup, sync replication
 Transactional, atomic updates
IMDG
 “On Demand” backups
P B
P

P B

P B

P B


Implementing Event Flows
 Use the data grid as event bus
 Stateful objects as transactional events

Raw Tokenizer Tokenized Filterer Filtered Counter

P B


Putting It All Together
IMDG

P B

P B

B
P

B
P


Putting It All Together

• Use an IMDG IMDG
• Partition events, counters
• Collocate eventP handlers with data for low
B

latency and simple scaling
• Use atomic updates to update counters in
P B

memory
P B
• Persist asynchronously to a big data store
P B


Reducing Counter Contention - Optimization
 Store processed word1
tweets locally in word2
each partition
word3
 Use periodic batch
writes to update 1 sec
counters
 Process batches as
a whole

35 35
® Copyright 2011 Gigaspaces Ltd. All Rights Reserved

Keep Your Eyes Open…


References
 Learn and fork the code on github:
https://github.com/Gigaspaces/rt-analytics
 Detailed blog post
http://bit.ly/gs-bigdata-analytics
 Twitter in numbers:
http://blog.twitter.com/2011/03/numbers.html
 Twitter Storm:
http://bit.ly/twitter-storm
 Apache S4
http://incubator.apache.org/s4/


Real Time Analytics for Twitter with a Scalable Architecture

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