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Helping the Worlds Farmers Adapt to Climate Change Strata Conference Oct 2012 Siraj Khaliq, CTO, The Climate Corporation.

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Presentation on theme: "Helping the Worlds Farmers Adapt to Climate Change Strata Conference Oct 2012 Siraj Khaliq, CTO, The Climate Corporation."— Presentation transcript:

1 Helping the Worlds Farmers Adapt to Climate Change Strata Conference Oct 2012 Siraj Khaliq, CTO, The Climate Corporation

2 Fritchton, IN – late summer, 2012

3 Louisville, IL

4 Wichita, KA

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10 Click to edit Master title style Click to edit Master text styles –Second level Third level –Fourth level »Fifth level Worst US Droughts in the Last Fifty Years

11 Click to edit Master title style -16% 2012 Estimated Corn Yield (USDA)

12 Click to edit Master title style +6% World food prices month-on-month change in July 2012 (UNFAO)

13 Large capital outlays at start of season (April) Seed, equipment, pesticide, and land Revenue comes in at harvest 1-2 years of revenue shortfall could be catastrophic Futures help with price volatility, not weather Farm Economics

14 Farmer Rich Vernon talks to NPR's David Schaper (audio) A real-life example

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17 This is set to continue

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20 To help all the world's people & businesses manage and adapt to climate change Our Mission

21 Evaluating Markets

22 Click to edit Master title style $4.2 Trillion 2012 Estimated Corn Yield (USDA)

23 Total Weather Insurance (TWI)

24 TWI Demo

25 HOW?

26 OutcomeWeather DataPolicy

27 Modeled Outcomes Weather Simulations Structure

28 How does weather impact crop yield?

29 Structure Varies based on many inputs: Temperature Precipitation Soil type Topography Farming practices Crop varietal

30 Structure Agronomically deduced candidates Model at large scale Every farm in the US (20M)

31 Structure

32 Modeled Outcomes Weather Simulations Structure

33 What weather do we expect? Weather Simulations

34 1M locations (2.5mi x 2.5mi grid) 10k scenarios/location going 2 years out 2 measurements 60Tb of data per simulation set every couple of weeks

35 Weather Simulations

36 2.5 x 2.5 Square Miles

37 Weather Simulations Expensive computation Parallelizing hard due to correlations Would take 80+ years on one fast modern server-class machine We need to generate these within days

38 Soil Moisture Modeling What's the soil moisture at farm X?

39 Soil Moisture Modeling soil type, weather, topography, crop

40 Evolution of Our Technology

41 Java frontend Pricing Server (Rserve) MySQL stations All data in MySQL Pricing servers (Rserve) Java-based webapp

42 Java frontend Pricing Server (Rserve) MySQL stations Weather data now on disk Versioning hard Java-R bridge messy Disk

43 Java frontend Pricing Server (java) Simulation Service Weather data Service Sim gen (hadoop) SimpleDB / S3 MySQL ,000 locations Rserve replaced by java Simulations & S3/SimpleDB Model gen in Hadoop Moved fully to EC2

44 Rails frontend Pricing Server (java) Marty (HBase) Geo data store Sim gen (cascalog) S3 MySQL 2011 – today 1,000,000 locations Own big geo-data store Many more hadoop jobs Eliminated SimpleDB Soil moisture dataset gen (cascalog) Structures gen (cascalog) Other hadoop jobs

45 MapReduce at TCC Python (Hadoop streaming) Some native java Most are higher-level frameworks

46 Big Wins Cascalog/Clojure EC2 Spot Instances NoSQL

47 Big Win #1 - Cascalog (defn weather-map-q "Creates a Cascalog query to extract individual measurement values of ObservationSet data and produces tuples of [date JSON-encoded map], in which each JSON-encoded map is keyed by station-id" [stations interval measurement sources start end nostra] (<- [?date ?json-aggregated-values] ; from hfs-textline (stations ?station-id) (fetch-obs-for-station [interval measurement sources start end nostra] ?station-id :> ?obs) (extract-values-by-date ?obs :> ?date ?value) (aggregate-values ?value :> ?aggregated-values) (json/generate-string ?aggregated-values :> ?json-aggregated-values)))

48 Big Win #1 - Cascalog Easily composable workflows Can unit test Hadoop flows Quick iteration

49 Big Win #2 – EC2 Spot Instances Good fit to our compute approach Can be very cheap Good availability

50 MapReduce at TCC

51 Big Win #3: NoSQL Datasets must be: Repeatably Generated Versioned Indexed

52 Big Win #3 – NoSQL Why not SQL? Time-series data, not relational Large size and ad hoc structure Specific query patterns 10s of Terabytes in size

53 NoSQL at TCC - Marty Own big geo-data store Built on HBase Billions of records

54 Learning #1 – Embrace Hadoop Defines problem clearly Focus on problem more than architecture Great tools and community support

55 Learning #2 – Be Careful Fail-fast code Test, test, test Run at small scale first

56 Learning #3 – Architecture Matters Eliminate single points of failure Consider memory usage and I/O Write simple flows with checkpointing Monitoring is invaluable

57 TCC Today 150 employees Half engineering 20 PhDs Reputation for hard science problems … by standing on the shoulders of giants

58 Open Source at TCC github.com/TheClimateCorporation Lemur (EMR / Clojure) Repoman (coming soon) Marty (coming)

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