HBaseCon 2012 | Scaling GIS In Three Acts
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The document is a presentation titled "Scaling GIS in 3 Acts - Lightning Edition" presented by Nick Dimiduk on May 22, 2012. It discusses scaling geographic information systems (GIS) in three acts: Act I defines what GIS is and that it involves data on maps; Act II discusses what can be done with GIS such as geospatial queries and non-Euclidean geometry; Act III covers implementing GIS on HBase including spatial partitioning and indices.
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HBaseCon 2012 | Scaling GIS In Three Acts
- 1. SCALING GIS IN 3 ACTS presented by: Nick Dimiduk May 22, 2012 1
- 2. SCALING GIS IN 3 ACTS - Lightning Edition! - presented by: Nick Dimiduk May 22, 2012 2
- 3. HBase in Action Manning Press, Fall 2012 MULTI-BAR CHART TITLE, LEFT ALIGNED Thousands 10 9 8 7 6 hbaseinaction.com 5 Discount code: 12hb10 4 3 2 1 0 Series 1 Series 2 Series 3 Series 4 © 2012 The Climate Corporation. All Rights Reserved. Policies are underwritten by State 3 National Insurance Company, Inc. and administered by The Climate Insurance Agency LLC.
- 4. Act I: What is GIS?
- 5. GIS: Data (on maps!) MULTI-BAR CHART TITLE, LEFT ALIGNED MULTI-BAR CHART TITLE, LEFT ALIGNED Thousands 10 Thousands 10 9 9 8 8 7 7 6 6 5 5 4 4 3 3 2 2 1 1 0 0 Series 1 Series 2 Series 3 Series 4 Series 1 Series 2 Series 3 Series 4 © 2012 The Climate Corporation. All Rights Reserved. Policies are underwritten by State 5 National Insurance Company, Inc. and administered by The Climate Insurance Agency LLC.
- 6. HC SVNT DRAGONES MULTI-BAR CHART TITLE, LEFT ALIGNED Thousands 10 9 “Here are Dragons” 8 7 6 5 4 3 DRAGONES!!! 2 1 Image: Psalter World Map, 1265 0 http://en.wikipedia.org/wiki/Here_be_dragons Series 1 Series 2 Series 3 Series 4 © 2012 The Climate Corporation. All Rights Reserved. Policies are underwritten by State 6 National Insurance Company, Inc. and administered by The Climate Insurance Agency LLC.
- 7. Act II: What to do with GIS?
- 8. Geospatial Queries © 2012 The Climate Corporation. All Rights Reserved. Policies are underwritten by State 8 National Insurance Company, Inc. and administered by The Climate Insurance Agency LLC.
- 9. Non-Euclidean Geometry “Know thy surface” Image: Trigonometry on a Spehere http://en.wikipedia.org/wiki/Non-Euclidean_geometry © 2012 The Climate Corporation. All Rights Reserved. Policies are underwritten by State 9 National Insurance Company, Inc. and administered by The Climate Insurance Agency LLC.
- 10. Act III: GIS on HBase
- 11. The devil is in the Indices Image: Six iterations of the Hilbert curve http://en.wikipedia.org/wiki/Space-filling_curve © 2012 The Climate Corporation. All Rights Reserved. Policies are underwritten by State 12 National Insurance Company, Inc. and administered by The Climate Insurance Agency LLC.
- 12. Spatial Partitioning Image: USA night lights http://www.noaanews.noaa.gov/stories/s2015.htm © 2012 The Climate Corporation. All Rights Reserved. Policies are underwritten by State 14 National Insurance Company, Inc. and administered by The Climate Insurance Agency LLC.
- 13. The devil is in the (Spatial) Indices Image: German zipcodes, R*Tree http://en.wikipedia.org/wiki/R*_tree © 2012 The Climate Corporation. All Rights Reserved. Policies are underwritten by State 16 National Insurance Company, Inc. and administered by The Climate Insurance Agency LLC.
- 14. Thank you!
Hinweis der Redaktion
- IntrouctionsWho am INick Dimiduk, Data Platform teamwhat I do:Help growers manage risk. Sell insurance.3 bad years = lose your farm”what willthe weather be like this spring in Jasper County, IL?”“How many consecutive days above 74 degs?”“How similar is the weather Sioux Falls, SD vs. Dayton OH?”the data:*get data stats from zimmer*
- IntrouctionsWho am INick Dimiduk, Data Platform teamwhat I do:Help growers manage risk. Sell insurance.3 bad years = lose your farm”what willthe weather be like this spring in Jasper County, IL?”“How many consecutive days above 74 degs?”“How similar is the weather Sioux Falls, SD vs. Dayton OH?”the data:*get data stats from zimmer*
- On the sideCoauthor: Amandeep Khurana, ClouderaIn print this fall“Generous” discount code
- USGS (US Geological Survey) has a boring definition“Software system capable of capturing, storing, analyzing, displaying geographically referenced information”
- TN River Gorge, ChattanoogaMaps + Data actionable insight from dataThat happens to make really pretty picturesDifferent views for different peopleReally not all that different from this whole “Big Data” thingMap = dataBase layers:Terrain information: dataRiver and lake boundaries: dataCities, roads: dataReally just data + data in a picture (interactive?)Quick Poll: “how many of you make pretty pictures in your day-to-day data activities?”
- Not all fun and gamesMost GIS built by geographers… for bureaucratsNo software engineering experience or motivation“state of the art” is ArcGISthis is not a modern technical landscapeThe World isn’t flatMostly 3D informationWhich, btw, often changes over time (4D)Reduced to 2D for everyone’s convenienceStored in a 1D world (disk platters read linearly)“Here are Dragons”Hunt-Lenox Globe, 1503-07Unknown areas, stories of lions and dragons
- What does Climate do with Geo data?Historically:StoringAnalyzingNow and future:CapturingDisplaying
- Queries against spatial dataGeometry/Geography as first-class citizensIntersperse spatial queries with other attributesit’s all just data, remember?Geometric queries:Example of an “intersection” queryAlso: containment, overlap.Describe intersection between two geometries: “Dimensionally Extended 9-intersection model (DE-9IM)”Nearest neighborNo linear measurements (miles, kilometers) involved!GIS visualizing query results from early prototype.
- Measuring units becomes trickyAngular distance is not linear distance.Know that old joke about physicists? “Assume a spherical horse”Earth is an irregular sphereApproximated into idealUsing planar (2D) coordinatesCoordinate Reference Systems180 deg does not a triangle make. Validate your assumptions.
- Or “Horizontal scaling of geospatial systems.” In the cloud.What’s the data?Vector data: 1.5B features (geometry + metadata)Raster data: whole US on 10m resolutionReference into 30-100+ years’ worth of historical time-seriesAll on AWS.
- Preformant access to data requires indexingLinearization via Space-filling curveIndex 2D data in a single dimensionPreserve locality as much as possibleZ curve, Hilbert curve, etcGeohashingFar from perfect, edge-cases still hurtWorks okay for points but not for arbitrary geometries
- Horizontal scaling requires partitioningMany (2) ways to slice it (boo)Domain-partitioning: cut the world into chunksLon, lat are fixed domain. Simple to split evenly (hemispheres)Poor distribution of work.Range-partitioning: split according to the values you haveScale according to data densityEffective partitioning requires knowledge of your dataOr a specialized data-structure (foreshadowing)
- Many (2) ways to slice it (boo)Domain-partitioning: cut the world into chunksLon, lat are fixed domain. Simple to split evenly (hemispheres)Poor distribution of work.Range-partitioning: split according to the values you haveScale according to data density
- Preformant access to data requires indexingDimensionally aware indicesKD-Trees work great for point data (nearest neighbors)R-Tree variants for arbitrary geometries, but costly to constructUniform partitions => uniform trees => uniform access performanceTwo approaches to scaling:2-layer indices1st layer: coarse-grained partition2nd layer: specialized indexThis is MD-HBaseEasier to implement. Potentially miss geometries => incomplete results!!!Persisted spatial indicesImplement persisted R-TreeCustom regions via RegionSplitPolicy (0.94+)Should be more correct… “there are dragons”
- Questions?