Chicago finance-big-data

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Scalability in Hadoop and Similar Systems


Big is the next big thing

Big data and Hadoop are exploding

Companies are being funded

Books are being written

Applications sprouting up everywhere

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Slow Motion Explosion

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Hadoop Explosion

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Why Now?

But Moore’s law has applied for a long time

Why is Hadoop exploding now?

Why not 10 years ago?

Why not 20?

59/18/12


Size Matters, but …

If it were just availability of data then existing big companies would adopt big data technology first

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Size Matters, but …

If it were just availability of data then existing big companies would adopt big data technology first

They didn’t

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Or Maybe Cost

If it were just a net positive value then finance companies should adopt first because they have higher opportunity value / byte

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Or Maybe Cost

If it were just a net positive value then finance companies should adopt first because they have higher opportunity value / byte

They didn’t

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Backwards adoption

Under almost any threshold argument startups would not adopt big data technology first

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Backwards adoption

Under almost any threshold argument startups would not adopt big data technology first

They did

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Everywhere at Once?

Something very strange is happening– Big data is being applied at many different scales– At many value scales– By large companies and small

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Everywhere at Once?

Something very strange is happening– Big data is being applied at many different scales– At many value scales– By large companies and small

Why?

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More data is being produced more quicklyData sizes are bigger than even a very large computer can holdCost to create and store continues to decrease

The Conventional Answer

BUSTED!


Analytics Scaling Laws

Analytics scaling is all about the 80-20 rule – Big gains for little initial effort– Rapidly diminishing returns

The key to net value is how costs scale– Old school – exponential scaling– Big data – linear scaling, low constant

Cost/performance has changed radically– IF you can use many commodity boxes


We knew that

We should have known that

We didn’t know that!

You’re kidding, people do that?


Anybody with eyes

Intern with a spreadsheet

In-house analytics

Industry-wide data consortium

NSA, non-proliferation


Net value optimum has a sharp peak well before maximum effort


But scaling laws are changing both slope and shape


More than just a little


They are changing a LOT!






Initially, linear cost scaling actually makes things worse

A tipping point is reached and things change radically …


Pre-requisites for Tipping

To reach the tipping point, Algorithms must scale out horizontally– On commodity hardware– That can and will fail

Data practice must change– Denormalized is the new black– Flexible data dictionaries are the rule– Structured data becomes rare


Yeah… but wait


The Standard Sort of Model

People talk about the law of large numbers as if it were …

Well, as if it were a law

It’s not …

It is a context and assumption dependent theorem


What if …

These assumptions are:

Changes have a – stationary, – independent, – finite variance distribution

What happens if these assumptions are wrong?

And which of them is really wrong?


For Example


End point has nice tractable distribution


What if the Assumptions are Wrong?

Take the finite variance as a simple example

This leads to Levy stable distributions

Like the Cauchy distribution


Is it Really Different?



What About Real Life?



But is it Really Infinite Variance?

Or are there other kinds of phenomena that show this?

What about the independence assumption?

What if the supposedly independent components of the system communicate?

Like we do. Everyday. All the time.


Why the Difference?

Law of large numbers

Infinitevariance

Interactingagents

Apologies and credit to Simon DaDeo, SFI

The space of all things that change

The space of interacting things


What Happens with Interactions

Social phenomena defeat the law of large numbers Distributions are well modeled by “rich get richer” processes– Pittman-Yar process, Indian Buffet

Limiting dstributions are heavy tailed, power law We see these distributions everywhere– price of cotton in the 19th century– word frequencies– popularity of Github projects– equity pricing and volumes– sizes of cities– popularity of web-sites


What are the Implications?



In a Nutshell

Scalability is much more important than we thought

Mashups are more important than we thought

Network effects are more important than we thought

Exploration is more important than we thought

Hadoop style linear scaling must be mixed with ad hoc analysis


Thank You


whoami?

Ted Dunning– @ted_dunning– [email protected] (MapR distribution for Hadoop)– [email protected] (Mahout, Hadoop, Lucene, Zookeeper, Drill)– [email protected] (me)

More info:

http://www.mapr.com/company/events/hadoop-in-finance-2012

mailto:[email protected]



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