Think Huddle and Neustar Performance Gains with 12c

Transcription

1 Think Huddle and Neustar Performance Gains with 12c / OVERVIEW Big Data and its impact on Spatial performance Testing configurations Use cases / techniques for improving performance Performance improvement data CHALLENGES / OPPORTUNITIES With 11g, performance sometimes limited interactions with Big Data Making improvements was time-consuming 6.5x faster performance 11m 53s vs 1hr 17m 26s 5,000 4,500 4,000 3,500 3,000 2,500 2,000 1,500 1, g 12c 12c SVA SOLUTIONS Oracle Database 12c Enterprise Edition Spatial Option with GeoRaster, Network Data Model Partitioning Oracle Fusion Middleware MapViewer Oracle Business Intelligence Enterprise Edition RESULTS Achieving at least 2x performance improvement in some cases up to 300x improvement Only small configuration changes are needed Time to develop and maintain these solutions is reduced

2 May 2014 Oracle Spatial Summit

3 May 21, 2014 Walter E. Washington Convention Center Washington, DC USA

4 Steve Pierce CEO Nick Salem Distinguished Engineer

5 Performance Gains with Spatial & Graph Using Oracle Database 12c

6 Program Agenda Big Data: What s the big deal? Testing configurations Use cases and performance data on: Geometry aggregation Point in polygon Joins Q&A

7 Think Huddle Corporate Overview Our company Oracle Spatial and MapViewer Big Data and BI for Spatial Fortune 500 clients Steve Pierce Chair Oracle Spatial SIG 15 years Oracle

8 What s the Big Deal? Data growing Big Data systems for spatial processing Fundamental changes in 12c Faster vector algorithms Better caching Improved memory management 12c works for Big Data (and maintains data integrity and keeps data secure and supports relational integrity and your users can access it) BIG DATA

9 Test Environment Environment Oracle 11g Oracle 12c OS Oracle Linux 6.5 Oracle Linux 6.5 Processor E5 Xeon 2.9Ghz x 2 E5 Xeon 2.9Ghz x 2 Disk Intel SSD s Intel SSD s 1 Server 2 Oracle Databases Memory 32GB 32GB Huge Pages Yes Yes SGA/PGA 8GB/2GB 8GB/2GB Source data Census / Generated Census / Generated

10 Neustar Corporate Overview Real Intelligence. Better Decisions. Trusted company serving the internet, telco, information services, retail, media and advertising industries Publicly held - $902M+ Company 1,600+ employees worldwide HQ d in Sterling, VA Extensive data assets (4 billion global phone numbers, 2.8 billion global IP addresses, 6.4 million global domain names and 13.1 US business listings) 20 billion requests a day (DNS, text messages, phone)

11 Neustar s ElementOne Platform Geospatial Analytics in the Cloud Highly flexible and high performance mapping Rich market and trade area capability Road network / drive time analysis Heat themes GeoRaster images Aerials / terrain map overlays Scalable and high performance data retrieval Centralized secure data repository

12 Neustar s ElementOne Platform 11g vs 12c Test Benchmark Environment Oracle VM Server 3.0 (Xen version 4.1 PVM) Created 2 identical VMs with Oracle Linux 6.5 Installed Oracle 11g on one and 12c on the other Database configuration 8G of SGA / 2G of PGA 4 x 8 Core CPUs (Intel Xeon 2.70GHz) Loaded core ElementOne database on each Compare 11g to 12c w/ & w/o spatial vector acceleration

13 Use Case Geometry Aggregation Aggregation of census tracts into larger geometries based on neighbor relationships INTERACT UNION

14 Use Case Geometry Aggregation 350 Elapsed Time (s) g 12c 12c SVA Each Census tract in Virginia Aggregate Union of Neighbors SDO_ANY_INTERACT + SDO_AGGR_UNION 2,000 Aggregations Tracts have 7 neighbors = 14,000 tracts 280 tracts processed per second

15 Neustar s ElementOne on 12c SDO_AGGR_UNION CA ZIPs 4,000 3,500 3,000 2,500 2,000 1,500 1, Elapsed Time In Seconds 11g 12c 12c SVA Using SDO AGGR UNION SDO AGGR UNION + Mod/ Group Use case consisted of aggregating all ZIP Codes in California using plain SDO_AGGR_UNION and with mod/ group by functions Achieved up to 300x faster performance w\ SDO_AGGR_UNION and 40x faster with mod/group by method in12c with SVA enabled vs 11g Reduced elapsed time from 1 hour 54 seconds to just 12 seconds

16 Neustar s ElementOne on 12c SDO_AGGR_SET_UNION CA ZIPs 14x faster performance 12s vs 2m 51s g 12c 12c SVA SDO AGGR SET UNION

17 Neustar s ElementOne on 12c SDO_TOUCH CA ZIPs touch BGs 8x faster performance 25s vs 3m 23s For all California ZIP codes, retrieve the block groups that they touch SDO_TOUCH g 12c 12c SVA

18 Neustar s ElementOne on 12c Spatial Relationships Determine spatial relationships and interactions Inside, touch, anyinteract, overlap, disjoint SDO_UTIL and SDO_GEOM for various geography creation and manipulation Shape intersection, XOR, union, difference, point at bearing, radian, distance, convex hull, polygon smoothing and more ElementOne Trade Areas (customer threshold, non-overlapping radii + component geos, donuts, square + hex grids, drive time, heat regions) Thematic overlays and extents

19 1, Neustar s ElementOne on 12c SDO_WITHIN_DISTANCE Benchmarks 6x faster 2m 17s vs 14m 32s 11g 12c 12c SVA SDO WITHIN DISTANCE For 544 super regional shopping centers, retrieve all businesses with a 10 mile radius

20 Neustar s ElementOne on 12c SDO_GEOM.RELATE CA ZIPs & BGs 3x faster performance 2m 16s vs 7m 2s 11g 12c 12c SVA SDO_GEOM RELATE (mask = determine) Determine spatial topological relationship for California ZIP codes and surrounding block groups

21 Neustar s ElementOne on 12c SDO_GEOM.DISTANCE Benchmarks 2.6x faster performance 1m 13s vs 3m 11s Calculate distances between a single site and 112,610 surrounding sites SDO_GEOM DISTANCE g 12c 12c SVA

22 Neustar s ElementOne on 12c SDO_INSIDE Businesses inside NY BGs 2.1x faster performance 1m 22s vs 2m 53s Retrieve all businesses inside New York state block groups g 12c 12c SVA SDO_INSIDE

23 Use Case Point in Polygon Point in polygon calculations for finding houses within an area of interest What s in there?

24 Use Case Point in Polygon 11g In 11g there were ways to do this SELECT lon, lat FROM (SELECT SDO_GEOMETRY(2001, 8307, SDO_POINT_TYPE(lon, lat, NULL), NULL, NULL) geom FROM large) c, states s WHERE SDO_ANYINTERACT(s.geom,c.geom) = 'TRUE' AND s.state_abrv='ca'

25 Use Case Point In Polygon 12c Elapsed Time (s) Sdo_PointInPolygon SELECT lon, lat FROM TABLE(mdsys.sdo_PointInPolygon( CURSOR(select lon, lat from large), (select geom from states where state_abrv='ca ),.05)) 50 /*+ PARALLEL(#) */ 0 11g Relate 12c SVA PiP 12c SVA PiP (8) Near linear performance boost

26 Use Case Joins Which objects are in which region? And there? What s in there? And here?

27 Use Case Joins Elapsed Time (s) SDO_JOIN For bulk joins sdo_join('large', 'GEOM', 'STATES', 'GEOM', 'mask=anyinteract' Works in 11g Faster in 12c 0 11g 12c 12c SVA

28 Neustar s ElementOne on 12c SDO_JOIN ZIP & Block Group Tables 6.5x faster performance 11m 53s vs 1hr 17m 26s Join using mask=anyinteract 5,000 4,500 4,000 3,500 3,000 2,500 2,000 1,500 1, g 12c 12c SVA SDO_JOIN

29 Neustar s ElementOne on 12c SDO_CS.TRANSFORM US block groups 10x faster performance 2m 10s vs 22m 26s 1,600 1,400 1,200 1, g 12c 12c SVA SDO CS Coordinate Transform

30 Neustar s ElementOne on 12c SDO_GEOM.VALIDATE_GEOM Benchmarks 1.9x faster performance 15s vs 29s 11g 12c 12c SVA SDO_GEOM Validate Geometry with Context Validate all US county geometries

31 Neustar s ElementOne on 12c Miscellaneous Features Reduction in redo/undo generation using global temporary tables resulting in 1.7x performance boost 2x performance gains with inline PL/SQL functions Up to 10x faster processing time with constructing large CLOBs / BLOBs Faster response times with cross session result set cache

32 Q&A