Chicago Hadoop in Finance - Ted Dunning
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Talk about what scalability really means in terms of interacting processes and statistics of growth by Ted Dunning

Talk about what scalability really means in terms of interacting processes and statistics of growth by Ted Dunning

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  • Why is big data sooo fashionable with big and small companies from different industries? What has suddenly changed?
  • Google searches are up 10x over just four years ago.
  • Hadoop use is exploding. We chose this example, which shows job trends for Hadoop. Further evidence that you should pay attention during this talk.
  • But we have seen constant growth for a long time. And simple growth would only explain some kinds of companies starting with big data (probably big ones) and then slow adoption. Databases started with big companies and took 20 years or more to reach everywhere because the need exceeded cost at different times for different companies. The internet, on the other hand, largely happened to everybody at the same time so it changed things in nearly all industries at all scales nearly simultaneously. Why is big data exploding right now and why is it exploding at all?
  • The different kinds of scaling laws have different shape and I think that shape is the key.
  • The value of analytics always increases with more data, but the rate of increase drops dramatically after an initial quick increase.
  • In classical analytics, the cost of doing analytics increases sharply.
  • The result is a net value that has a sharp optimum in the area where value is increasing rapidly and cost is not yet increasing so rapidly.
  • New techniques such as Hadoop result in linear scaling of cost. This is a change in shape and it causes a qualitative change in the way that costs trade off against value to give net value. As technology improves, the slope of this cost line is also changing rapidly over time.
  • This next sequence shows how the net value changes with different slope linear cost models.
  • Notice how the best net value has jumped up significantly
  • And as the line approaches horizontal, the highest net value occurs at dramatically larger data scale.
  • And as the line approaches horizontal, the highest net value occurs at dramatically larger data scale.

Chicago Hadoop in Finance - Ted Dunning Presentation Transcript

  • 1. 1©MapR Technologies - Confidential Scalability in Hadoop and Similar Systems
  • 2. 2©MapR Technologies - Confidential Big is the next big thing  Big data and Hadoop are exploding  Companies are being funded  Books are being written  Applications sprouting up everywhere 2
  • 3. 3©MapR Technologies - Confidential Slow Motion Explosion 3
  • 4. 4©MapR Technologies - Confidential Hadoop Explosion 4
  • 5. 5©MapR Technologies - Confidential 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? 58/13/2013
  • 6. 6©MapR Technologies - Confidential Size Matters, but …  If it were just availability of data then existing big companies would adopt big data technology first 6
  • 7. 7©MapR Technologies - Confidential Size Matters, but …  If it were just availability of data then existing big companies would adopt big data technology first They didn’t 7
  • 8. 8©MapR Technologies - Confidential Or Maybe Cost  If it were just a net positive value then finance companies should adopt first because they have higher opportunity value / byte 8
  • 9. 9©MapR Technologies - Confidential 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 9
  • 10. 10©MapR Technologies - Confidential Backwards adoption  Under almost any threshold argument startups would not adopt big data technology first 10
  • 11. 11©MapR Technologies - Confidential Backwards adoption  Under almost any threshold argument startups would not adopt big data technology first They did 11
  • 12. 12©MapR Technologies - Confidential 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 12
  • 13. 13©MapR Technologies - Confidential 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? 13
  • 14. 14©MapR Technologies - Confidential More data is being produced more quickly Data sizes are bigger than even a very large computer can hold Cost to create and store continues to decrease The Conventional Answer
  • 15. 15©MapR Technologies - Confidential 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
  • 16. 16©MapR Technologies - Confidential We knew that We should have known that We didn’t know that! You’re kidding, people do that?
  • 17. 17©MapR Technologies - Confidential 2,0000 500 1000 1500 1 0 0.25 0.5 0.75 Scale Value Anybody with eyes Intern with a spreadsheet In-house analytics Industry-wide data consortium NSA, non-proliferation
  • 18. 18©MapR Technologies - Confidential 2,0000 500 1000 1500 1 0 0.25 0.5 0.75 Scale Value 2,0000 500 1000 1500 1 0 0.25 0.5 0.75 Scale Value Net value optimum has a sharp peak well before maximum effort
  • 19. 19©MapR Technologies - Confidential But scaling laws are changing both slope and shape
  • 20. 20©MapR Technologies - Confidential 2,0000 500 1000 1500 1 0 0.25 0.5 0.75 Scale Value More than just a little
  • 21. 21©MapR Technologies - Confidential 2,0000 500 1000 1500 1 0 0.25 0.5 0.75 Scale Value They are changing a LOT!
  • 22. 22©MapR Technologies - Confidential
  • 23. 23©MapR Technologies - Confidential
  • 24. 24©MapR Technologies - Confidential 2,0000 500 1000 1500 1 0 0.25 0.5 0.75 Scale Value
  • 25. 25©MapR Technologies - Confidential 2,0000 500 1000 1500 1 0 0.25 0.5 0.75 Scale Value
  • 26. 26©MapR Technologies - Confidential 2,0000 500 1000 1500 1 0 0.25 0.5 0.75 Scale Value Initially, linear cost scaling actually makes things worse A tipping point is reached and things change radically …
  • 27. 27©MapR Technologies - Confidential 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
  • 28. 28©MapR Technologies - Confidential Yeah… but wait
  • 29. 29©MapR Technologies - Confidential 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
  • 30. 30©MapR Technologies - Confidential 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?
  • 31. 31©MapR Technologies - Confidential For Example Time Stuff
  • 32. 32©MapR Technologies - Confidential Time Stuff End point has nice tractable distribution
  • 33. 33©MapR Technologies - Confidential What if the Assumptions are Wrong?  Take the finite variance as a simple example  This leads to Levy stable distributions  Like the Cauchy distribution
  • 34. 34©MapR Technologies - Confidential Is it Really Different?
  • 35. 35©MapR Technologies - Confidential Time Stuff
  • 36. 36©MapR Technologies - Confidential What About Real Life?
  • 37. 37©MapR Technologies - Confidential
  • 38. 38©MapR Technologies - Confidential 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.
  • 39. 39©MapR Technologies - Confidential Why the Difference? Law of large numbers Infinite variance Interacting agents Apologies and credit to Simon DaDeo, SFI The space of all things that change The space of interacting things
  • 40. 40©MapR Technologies - Confidential 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
  • 41. 41©MapR Technologies - Confidential What are the Implications?
  • 42. 42©MapR Technologies - Confidential 2,0000 500 1000 1500 1 0 0.25 0.5 0.75 Scale Value
  • 43. 43©MapR Technologies - Confidential 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
  • 44. 44©MapR Technologies - Confidential Thank You
  • 45. 45©MapR Technologies - Confidential whoami?  Ted Dunning – @ted_dunning – tdunning@maprtech.com (MapR distribution for Hadoop) – tdunning@apache.com (Mahout, Hadoop, Lucene, Zookeeper, Drill) – ted.dunning@gmail.com (me)  More info: http://www.mapr.com/company/events/hadoop-in-finance-2012