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One Billion Rows Per Second: Analytics for the Digital Media Markets STRATA SUMMIT NYC September 21, 2011 MICHAEL DRISCOLL CO-FOUNDER & CTO @medriscoll
Taming the Inferno of the Online Ad Markets ,[object Object]
dozens of publisher, advertiser, & audience attributes,[object Object]
Goal:  Fast Dashboards  Over Big Data dashboard queries in seconds database data crunched in minutes ingestion
Solution 1:   Relational  Database dashboard queries in minutes database MPP relational DB data crunched in minutes ingestion Hadoop
Solution 2:   HBase dashboard queries in seconds database HBase data crunched in hours ingestion Hadoop
Solution 3:   Do It Ourselves:  Druid dashboard queries in seconds database Druid data crunched in minutes ingestion Hadoop
Four Principles of Druid’s Performance at Scale SUMMARIZE 100x smaller  vs raw data DISTRIBUTE 100x throughput vs a single node PARALLELIZE 100x faster vs reading disk STORE IN-MEMORY = 10^6 Druid can filter and aggregate over 1 billion rows per second on a 50-core cluster,  or 20m rows per core per second factor speed-up
Consequences of Speed:  Data Freshness photo credit:  Lars P. http://www.flickr.com/photos/lars_p/4911238308/sizes/o/in/photostream/
Consequences of Speed:  Blue Sky Exploration photo credit:  MonkeyAt Large http://www.flickr.com/photos/monkeyatlarge/16645379/sizes/l/in/photostream/
Consequences of Speed:  Interactivity photo credit tonylanciabeta http://www.flickr.com/photos/tonysphotos/3305157904/sizes/o/in/photostream/
One Billion Rows Per Second: Analytics for the Digital Media Markets QUESTIONS?  CONTACT ME AT MIKE@METAMARKETSGROUP.COM MICHAEL DRISCOLL CO-FOUNDER & CTO @medriscoll

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One Billion Rows per Second: Analytics for the Digital Media Markets

  • 1. One Billion Rows Per Second: Analytics for the Digital Media Markets STRATA SUMMIT NYC September 21, 2011 MICHAEL DRISCOLL CO-FOUNDER & CTO @medriscoll
  • 2.
  • 3.
  • 4. Goal: Fast Dashboards Over Big Data dashboard queries in seconds database data crunched in minutes ingestion
  • 5. Solution 1: Relational Database dashboard queries in minutes database MPP relational DB data crunched in minutes ingestion Hadoop
  • 6. Solution 2: HBase dashboard queries in seconds database HBase data crunched in hours ingestion Hadoop
  • 7. Solution 3: Do It Ourselves: Druid dashboard queries in seconds database Druid data crunched in minutes ingestion Hadoop
  • 8. Four Principles of Druid’s Performance at Scale SUMMARIZE 100x smaller vs raw data DISTRIBUTE 100x throughput vs a single node PARALLELIZE 100x faster vs reading disk STORE IN-MEMORY = 10^6 Druid can filter and aggregate over 1 billion rows per second on a 50-core cluster, or 20m rows per core per second factor speed-up
  • 9. Consequences of Speed: Data Freshness photo credit: Lars P. http://www.flickr.com/photos/lars_p/4911238308/sizes/o/in/photostream/
  • 10. Consequences of Speed: Blue Sky Exploration photo credit: MonkeyAt Large http://www.flickr.com/photos/monkeyatlarge/16645379/sizes/l/in/photostream/
  • 11. Consequences of Speed: Interactivity photo credit tonylanciabeta http://www.flickr.com/photos/tonysphotos/3305157904/sizes/o/in/photostream/
  • 12. One Billion Rows Per Second: Analytics for the Digital Media Markets QUESTIONS? CONTACT ME AT MIKE@METAMARKETSGROUP.COM MICHAEL DRISCOLL CO-FOUNDER & CTO @medriscoll

Hinweis der Redaktion

  1. Across traditional desktop, mobile, and now gaming platforms, there are billions of advertising events occurring ever day. Many of these are priced and bought in real-time.Willie Sutton was once asked, why do you rob banks? That’s where the money is.For me, the reason I was enticed by this vertical is similar: that’s where the data is.
  2. Strategic implications:
  3. Practically speaking, we define this as:data freshness on the order of minutesbut queries over the data, made through our dashboard, return in secondsHadoop isn’t enough.
  4. Practically speaking, we define this as:data freshness on the order of minutesbut queries over the data, made through our dashboard, return in seconds
  5. Hadoop summarizes and precomputes a ton.
  6. Hadoop summarizes and precomputes a ton.
  7. Hadoop summarizes and precomputes a ton.
  8. We all know that things taste better when they’re fresh.Data is no different.Jeff Jonas says, no value is knowing where the traffic was five minutes ago.
  9. We all know that things taste better when they’re fresh.Data is no different.Jeff Jonas says, no value is knowing where the traffic was five minutes ago.
  10. Dialogue with the data.Eliminate the chain of data bureaucrats and put the data in the hand of the decision maker.Get in the car & drive yourself.