All you wanted to know about how to make GeoServer faster but you never asked (or you did and no one answered!)

1. GeoServer on steroids
All you wanted to know about how to make GeoServer faster
but you never asked (or you did and no one answered)
Ing. Andrea Aime, GeoSolutions
Ing. Simone Giannecchini, GeoSolutions
FOSS4G 2011, Denver
12th-16th September 2011

2. GeoSolutions
 Founded in Italy in late 2006
 Expertise
• Image Processing, GeoSpatial Data Fusion
• Java, Java Enterprise, C++, Python
• JPEG2000, JPIP, Advanced 2D visualization
 Supporting/Developing FOSS4G projects
 GeoTools, GeoServer
 GeoBatch, GeoNetwork
 Clients
 Public Agencies
 Private Companies
 http://www.geo-solutions.it
FOSS4G 2011, Denver
12th-16th September 2011

3. Preparing raster inputs
FOSS4G 2011, Denver
12th-16th September 2011

4. Raster Data CheckList
 Objectives
 Fast extraction of a subset of the data
 Fast extraction of overviews
 Check-list
 Avoid having to open a large number of files per
request
 Avoid parsing of complex structures
 Avoid on-the-fly reprojection (if possible)
 Get to know your bottlenecks
 CPU vs Disk Access Time vs Memory
 Experiment with
 Format, compression, different color models, tile size,
overviews, configuration (in GeoServer of course)
FOSS4G 2011, Denver
12th-16th September 2011

5. Problematic Formats
 PNG/JPEG direct serving
 Bad formats (especially in Java)
 No tiling (or rarely supported)
 Chew a lot of memory and CPU for decompression
 Mitigate with external overviews
 NetCDF/grib1 and similar formats
 Complex formats (often with many subdatasets)
 Often contains un-calibrated data
 Must usually use multiple dimensions
 Use ImageMosaic
 Must usually massage the data before serving
 e.g. transpose X,Y,
FOSS4G 2011, Denver
12th-16th September 2011

6. Problematic Formats
 Ascii Grid, GTOPO30, IDRISI and similar formats are bad
 ASCII formats are bad
 No internal tiling, no compression, no internal
overviews
 JPEG2000 (with Kakadu)
 Extensible and rich, not (always) fast
 Can be difficult to tune for performance (might
require specific encoding options)
 ECW and MrSID
 Why bother it’s proprietary?
FOSS4G 2011, Denver
12th-16th September 2011

7. Choosing Formats and Layouts
 To remember: GeoTiff is a swiss knife
 But you don’t want to cut a tree with it!
 Tremendously flexible, good fir for most (not all) use
cases
 BigTiff pushes the GeoTiff limits farther
 Single File VS Mosaic VS Pyramids
 Use single GeoTiff when
 Overviews and Tiling stay within 4GB
 No additional dimensions
 Consider BigTiff for very large file (> 4 GB)
 Support for tiling
 Support for Overviews
 Can be inefficient with very large files + small tiling
FOSS4G 2011, Denver
12th-16th September 2011

8. Choosing Formats and Layouts
 Use ImageMosaic when:
 A single file gets too big (inefficient seeks, too much metadata
to read, etc..)
 Multiple Dimensions (time, elevation, others..)
 Avoid mosaics made of many very small files
 Single granules can be large
 Use Tiling + Overviews + Compression on granules
 Use ImagePyramid when:
 Tremendously large dataset
 Too many files / too large files
 Need to serve at all scales
 Especially low resolution
 For single granules (< 2Gb) GeoTiff is generally a good fit
FOSS4G 2011, Denver
12th-16th September 2011

9. Choosing Formats and Layouts
 Examples:
 Small dataset: single 2GB GeoTiff file
 Medium dataset: single 40GB BigTiff
 Large dataset: 400GB mosaic made of 10GB BigTiff
files
 Extra large: 4TB of imagery, built as pyramid of
mosaics of BigTiff/GeoTiff files to keep the file count
low
FOSS4G 2011, Denver
12th-16th September 2011

10. GeoTiff preparation
 STEP 0: get to know your data
 gdalinfo utility is your friend  CheckList
 Missing CRS
 Add a .prj file
 Fix with gdal_translate
 Missing georeferencing
 Add a World File
 Fix with gdal_translate
 Bad Tiling
 Fix with gdal_translate
 Missing Overviews
 Use gdaladdo
 Compression
 Use gdal_translate
FOSS4G 2011, Denver
12th-16th September 2011

11. GeoTiff preparation
 STEP 1: fix and optimize with gdal_translate
 Inner Tiling
 gdal_translate -co "TILED=YES" -co "BLOCKXSIZE=512" -co
"BLOCKYSIZE=512" in.tif out.tif
 Check also GeoTiff driver creation options here
 CRS and GeoReferencing
 gdal_translate –a_srs “EPSG:32619” –a_ullr 285409.2 2014405.2
287536.8 2011947.6 in.tif out.tif
 STEP 2: add overviews with gdal_addo
 Leverages on tiff support for multipage files and reduced
resolution pages
 gdaladdo -r cubic output.tif 2 4 8 16 32 64 128
 Choose the resampling algorithm wisely
 Chose the tile size and compression wisely (use
GDAL_TIFF_OVR_BLOCKSIZE)
 Consider external overviews
FOSS4G 2011, Denver
12th-16th September 2011

12. GeoTiff preparation
FOSS4G 2011, Denver
12th-16th September 2011

13. GeoTiff preparation
 Compression
 Consider when disk speed/space is an issue
 Control it with gdal_translate and creation options
 GeoTiff tiles can be compressed
 LZW/Deflate are good for lossless compression
 JPEG is good for visually lossless compression
 From experience
 Use LZW/Deflate on geophysical data (DEM,
acquisitions)
 USE JPEG visually lossless with Photometric
Interpretation to YCbCr for RGB
FOSS4G 2011, Denver
12th-16th September 2011

14. Time, Elevation and other
dimensions
 Use Cases:
 MetOc data (support for time, elevation)
 Data with additional indipendent dimensions
 WorkFlow
 Split in multiple GeoTiff files
 Optimize the files individually
 Use ImageMosaic
 Use a DBMS for indexing granules
 Use File Name based property collectors to turn properties into
DB rows attributes
 Filter by time, elevation and other attributes via OGC and CQL
filters
 Check back up slides for more info!
FOSS4G 2011, Denver
12th-16th September 2011

15. Time, Elevation and other
dimensions
 Indexing multiple dimensions with DB support (video
here)
datastore.properties
timeregex.properties
stringregex.properties
indexer.properties
FOSS4G 2011, Denver
12th-16th September 2011

16. Time, Elevation and other
dimensions
FOSS4G 2011, Denver
12th-16th September 2011

17. Proper Mosaic Preparation
 ImageMosaic stitches single granules together with basic
processing
 Filtered selection
 Overviews/Decimation on read
 Over/DownSampling in memory
 ColorMask (optional)
 Mosaic/Stitch
 ColorMask again (optional)
 Optimize files as if you were serving them individually
 Keep a balance between number and dimensions of
granules
FOSS4G 2011, Denver
12th-16th September 2011

18. Proper Mosaic Configuration
 STEP 0: Configure Coverage Access (see slide 22)
 STEP 1: Configure Mosaic Parameters
 ALLOW_MULTITHREADING
 Load data from different granules in
parallel
 Needs USE_JAI_IMAGE_READ set to
false (Immediate Mode)
 Use a proper Tile Size
 In-memory processing, must not be too
large
 Disk tiling should larger
 If memory is scarce:
 USE_JAI_IMAGREAD to true
 USE_MULTITHREADING to false*
 Otherwise
 USE_JAI_IMAGREAD to false
 ALLOW_MULTITHREADING to true
FOSS4G 2011, Denver
12th-16th September 2011

19. Proper Mosaic Configuration
 Optional (Advanced): Configure Mosaic Parameters
Directly
 Caching
 Load the index in memory (using JTS SRTree)
 Super fast granule lookup, good for shapefiles
 Bad if you have additional dimension to filter on
 Based on Soft References, controlled via Java switch
SoftRefLRUPolicyMSPerMB
 ExpandToRGB
 Expand colormapped imagery to RGB in
memory
 Trade performance for quality
 SuggestedSPI
 Default ImageIO Decoder
class to use
 Don’t touch unless expert
FOSS4G 2011, Denver
12th-16th September 2011

20. Proper Pyramid Preparation
 Use gdal_retile for creating the pyramid
 Prepare the list of tiles to be retiled
 Create the pyramid with GDAL retile (grab a coffee!)
 Chunks should not be too small (here 2048x2048)
 Too many files is bad anyway
 Use internal Tiling for Larger chunks size
 If the input dataset is huge use the useDirForEachRow option
 Too many files in a dir is bad practice
 Make sure the number of level is consistent
 Too few  bad performance at high scale
FOSS4G 2011, Denver
12th-16th September 2011

21. Proper Pyramid Configuration
 STEP 0: Configure Coverage Access (see slide 22)
 STEP 1: Configure Pyramid Parameters
 ALLOW_MULTITHREADING
 Load data from different granules in
parallel
 Needs USE_JAI_IMAGE_READ set to
false (Immediate Mode)
 Use a proper Tile Size
 In-memory processing, must not be too
large
 Disk tiling should larger

 If memory is scarce:
 USE_JAI_IMAGREAD to true
 USE_MULTITHREADING to false*
 Otherwise
ImagePyramid relies  USE_JAI_IMAGREAD to false
on ImageMosaic  ALLOW_MULTITHREADING to true
FOSS4G 2011, Denver
12th-16th September 2011

22. Proper Pyramid Configuration
 Optional (Advanced): Configure Mosaic Parameters
Directly
 Caching
 Load the index in memory (using JTS SRTree)
 Super fast granule lookup, good for shapefiles
 Bad if you have additional dimension to filter on
 Based on Soft References, controlled via Java switch
SoftRefLRUPolicyMSPerMB
 ExpandToRGB
 Expand colormapped imagery to RGB in
memory
 Trade performance for quality
 SuggestedSPI
 Default ImageIO Decoder
class to use
 Don’t touch unless expert
FOSS4G 2011, Denver
12th-16th September 2011

23. Proper GDAL Formats Configuration
 Fix Missing/Improper CRS with PRJ or coverage config
 Fix Missing GeoReferencing with World File
 Make sure GDAL_DATA is properly configured
 Use a proper Tile Size
 In-memory processing, must not be
too large
 Fundamental for striped data! JNI
overhead
 Disk tiling should larger
 If memory is scarce:
 USE_JAI_IMAGREAD to true
 USE_MULTITHREADING to true*
 Otherwise
 USE_JAI_IMAGREAD to false
 USE_MULTITHREADING is ignored
FOSS4G 2011, Denver
12th-16th September 2011

24. Proper JPEG2000 Kakadu
Configuration
 Fix Missing/Improper CRS with PRJ or coverage config
 Fix Missing GeoReferencing with World File
 Make sure Kakadu dll/so is properly loaded
 Use a proper Tile Size
 In-memory processing
 Must not be too large
 Disk tiling should larger
 If memory is scarce:
 USE_JAI_IMAGREAD to true
 USE_MULTITHREADING to true*
 Otherwise
 USE_JAI_IMAGREAD to false
 USE_MULTITHREADING is ignored
FOSS4G 2011, Denver
12th-16th September 2011

25. Proper GeoServer
Coverage Options Configuration
 Make sure native JAI and Image is
installed
 Enable ImageIO native acceleration
 Enable JAI Mosaicking native
acceleration
 Give JAI enough memory
 Don’t raise JAI memory Threshold too
high
 Rule of thumb: use 2 X #Core Tile
Threads (check next slide)
 Enable Tile Recycling only on trunk
 Enable Tile Recycling if memory is not
a problem
FOSS4G 2011, Denver
12th-16th September 2011

26. Proper GeoServer
Coverage Options Configuration
 Multithreaded Granule Loading
 Allows to fine tuning multithreading
for ImageMosaic
 Orthogonal to JAI Tile Threads
 Rule of Thumb: use 2 X #Core Tile
Threads
 Perform testing to fine tune
depending on layer configuration as
well as on typical requests
 ImageIO Cache threshold
 decide when we switch to disk
cache (very large WCS requests)
FOSS4G 2011, Denver
12th-16th September 2011

27. Reprojection Performance
Vs Quality
 GeoServer 2.1.x reprojects raster data using a piecewise-
linear algorithm
 The area is divided in rectangular blocks, each having its
own affine transform
 The transformation between the full trigonometric
expressions and the linear ones is driven by a tolerance,
default value is 0.333
 Larger value will make reprojection faster, but lower the
quality
 -Dorg.geotools.referencing.resampleTolerance=0.5
FOSS4G 2011, Denver
12th-16th September 2011

28. Preparing vector inputs
FOSS4G 2011, Denver
12th-16th September 2011

29. Vector data checklikst
 What do we want from vector data:
 Binary data
 No complex parsing of data structures
 Fast extraction of a geographic subset
 Fast filtering on the most commonly used attributes
FOSS4G 2011, Denver
12th-16th September 2011

30. Choosing a format
 Slow formats  Good formats, local and
indexable
 WFS
 Shapefile
 GML
 Directory of shapefiles
 DXF
 SDE
 Spatial databases: PostGIS,
Oracle Spatial, DB2,
MySQL*, SQL server*
FOSS4G 2011, Denver
12th-16th September 2011

31. Shapefiles vs DBMS
 Speed comparison vs spatial extent depicted:
 Shapefile very fast when rendering the full dataset
 Database faster when extracting a small subset of a
very large data set
 Shapefile
 no attribute indexing, avoid if filtering on attribute is
important (filtering == reading less data, not applying
symbols)
 Database
 Rich support for complex native filters
 Use connection pooling (preferably via JNDI)
 Validate connections (with proper pooling)
FOSS4G 2011, Denver
12th-16th September 2011

32. Shapefile preparation
 Remove .qix file if present, let GeoServer 2.1.x rebuild it
(more efficient)
 If there are large DBF attributes that are not in use, get rid
of them using ogr2ogr, e.g.:
ogr2ogr -select FULLNAME,MTFCC arealm.shp
tl_2010_08013_arealm.shp
 If on Linux, enable memory mapping, faster, more scalable
(but will kill Windows):
FOSS4G 2011, Denver
12th-16th September 2011

33. Shapefile filtering
 Stuck with shapefiles and have scale dependent rules like
the following?
 Show highways first
 Show all streets when zoomed in
 Use ogr2ogr to build two shapefiles, one with just the
highways, one with everything, and build two layers, e.g.:
ogr2ogr -sql "SELECT * FROM
tl_2010_08013_roads WHERE MTFCC in ('S1100',
'S1200')" primaryRoads.shp
tl_2010_08013_roads.shp
FOSS4G 2011, Denver
12th-16th September 2011

34. PostGIS specific hints
 PostgreSQL out of the box configured for very small
hardware:
http://wiki.postgresql.org/wiki/Performance_Optimization
 Make sure to run ANALYZE after data imports (updates
optimizer stats)
 As usual, avoid large joins in SQL views, consider
materialized views
 If the dataset is massive, CLUSTER on the spatial index:
 http://postgis.refractions.net/documentation/manual-
1.3/ch05.html
 Careful with prepared statements (bad performance)
FOSS4G 2011, Denver
12th-16th September 2011

35. Optimize styling
FOSS4G 2011, Denver
12th-16th September 2011

36. Use scale dependencies
 Never show too much data
 the map should be readable, not a graphic blob. Rule of thumb:
1000 features max in the display
FOSS4G 2011, Denver
12th-16th September 2011

37. Labeling
 Labeling conflict resolution is expensive, limit to the most
inner zooms
 Halo is important for readability, but adds significant
overhead
 Careful with maxDisplacement, makes for various label
location attempts
FOSS4G 2011, Denver
12th-16th September 2011

38. FeatureTypeStyle
 GeoServer uses SLD FeatureTypeStyle objects as Z layers
for painting
 Each one allocates its own rendering surface (which can
use a lot of memory), use as few as possible
FOSS4G 2011, Denver
12th-16th September 2011

39. Use translucency sparingly
 Translucent display is expensive, use it sparingly
FOSS4G 2011, Denver
12th-16th September 2011

40. Scale dependent rules
 Too often forgotten or little used, yet very important:
 Hide layers when too zoomed in (raster/vector
example)
 Progressively show details
 Add more expensive rendering when there are less
features
 Key to any high performance / good looking map
FOSS4G 2011, Denver
12th-16th September 2011

41. Example
FOSS4G 2011, Denver
12th-16th September 2011

42. Hide as you zoom in
 Add a MinScaleDenominator to the rule
 This will make the layer disappear at 1:75000
(towards 1:1)
FOSS4G 2011, Denver
12th-16th September 2011

43. Alternative rendering
 Simple rendering at low scale (up to 1:2000)
 More complex rendering when zoomed in (1:1999
and above)
FOSS4G 2011, Denver
12th-16th September 2011

44. Alternative rendering
FOSS4G 2011, Denver
12th-16th September 2011

45. Point symbols
• 600 loc for 6
different points types
• Painful…
FOSS4G 2011, Denver
12th-16th September 2011

46. Prepare data
 alter table pointlm add column image varchar;
 update pointlm set image = 'shop_supermarket.p.16.png' where MTFCC =
'C3081' and (FULLNAME like '%Shopping%' or FULLNAME like '%Mall%');
 update pointlm set image = 'peak.png' where MTFCC = 'C3022'
 update pointlm set image = 'amenity_prison.p.20.png' where MTFCC =
'K1236';
 update pointlm set image = 'museum.p.16.png' where MTFCC = 'K2165';
 update pointlm set image = 'airport.p.16.png' where MTFCC = 'K2451';
 update pointlm set image = 'school.png' where MTFCC = 'K2543';
 update pointlm set image = 'christian3.p.14.png' where MTFCC =
'K2582';
 update pointlm set image = 'gate2.png' where MTFCC = 'K3066';
FOSS4G 2011, Denver
12th-16th September 2011

47. Dynamic symbolizers
FOSS4G 2011, Denver
12th-16th September 2011

48. Output tuning
FOSS4G 2011, Denver
12th-16th September 2011

49. WMS output formats
JPEG PNG 8bit PNG 24bit
23.8KB 66KB 169.4KB
27KB 27KB 64KB
Compression artifacts Color reduction Large size
FOSS4G 2011, Denver
12th-16th September 2011

50. WFS output formats
35
30
25
20
15
10
5
0
Dimension MB
 HTTP GZip compression is transparent in GeoServer,
make sure proxies keep it (or pay 10x price)
FOSS4G 2011, Denver
12th-16th September 2011

51. Tile caching
FOSS4G 2011, Denver
12th-16th September 2011

52. Tile caching with GeoWebCache
 Tile oriented maps, fixed zoom levels and fixed grid
 Useful for stable layers, backgrounds
 Protocols: WMTS, TMS, WMS-C, Google Maps/Earth, VE
 Speedup compared to dynamic WMS: 10 to 100 times,
assuming tiles are already cached (whole layer pre-
seeded)
 Suitable for:
 Mostly static layer
 No (or few) dynamic parameters (CQL filters, SLD
params, SQL query params, time/elevation, format
options)
FOSS4G 2011, Denver
12th-16th September 2011

53. Embedded GWC advantage
 No double encoding when using meta-tiling, faster seeding
FOSS4G 2011, Denver
12th-16th September 2011

54. Space considerations
 Seeding Colorado, assuming 8 cores, one layer, 0.1 sec
756x756 metatile, 15KB for each tile
 Do yours: http://tinyurl.com/3apkpss
 Not enough disk space? Set a disk quota
Zoom Time to seed Time to seed
Tile count Size (MB)
level (hours) (days)
13 58,377 1 0 0
14 232,870 4 0 0
15 929,475 14 0 0
16 3,713,893 57 1 0
17 14,855,572 227 6 0
18 59,396,070 906 23 1
19 237,584,280 3,625 92 4
20 950,273,037 14,500 367 15
FOSS4G 2011, Denver
12th-16th September 2011

55. Resource control
FOSS4G 2011, Denver
12th-16th September 2011

56. WMS request limits
 Max memory per request: avoid large requests, allows to
size the server memory (max concurrent request * max
memory)
 Max time per request: avoid requests taking too much time
(e.g., using a custom style provided with dynamic SLD in
the request)
 Max errors: best effort renderer, but handling errors takes
time
FOSS4G 2011, Denver
12th-16th September 2011

57. WFS request limits
 Max feature returned, configured as a global limit
 Return feature bbox: reduce amount of generated GML
 Per layer max feature count
FOSS4G 2011, Denver
12th-16th September 2011

58. WCS request limits
FOSS4G 2011, Denver
12th-16th September 2011

59. Control flow
 Control how many requests are executed in parallel, queue
others:
 Increase throughput
 Control memory usage
 Enforce fairness
 More info here
FOSS4G 2011, Denver
12th-16th September 2011

60. Control flow
17%
$GEOSERVER_DATA_DIR/controlflow.properties
# don't allow more than 16 GetMap requests in parallel
ows.wms.getmap=16
FOSS4G 2011, Denver
12th-16th September 2011

61. Auditing
 Log each and every request
 Log contents driven by customizable template
 Summarize and analyze requests with offline tools
 More info here
FOSS4G 2011, Denver
12th-16th September 2011

62. JVM and deploy configuration
FOSS4G 2011, Denver
12th-16th September 2011

63. Premise
 The options discussed here are not going to help visibly if
you did not prepare the data and the styles
 They are finishing touches that can get performance up
once the major data bottlenecks have been dealt with
 Check “Running in production” instructions here
FOSS4G 2011, Denver
12th-16th September 2011

64. JVM settings
 --server: enables the server JIT compiler
 --Xms2048m -Xmx2048m: sets the JVM use two gigabytes
of memory
 --XX:+UseParallelOldGC -XX:+UserParallelGC: enables
multi-threaded garbage collections, useful if you have
more than two cores
 --XX:NewRatio=2: informs the JVM there will be a high
number of short lived objects
 --XX:+AggressiveOpt: enable experimental optimizations
that will be defaults in future versions of the JVM
FOSS4G 2011, Denver
12th-16th September 2011

65. Native JAI and JDK
 Install native JAI and use a recent Sun JDK!
 Benchmark over a small data set (the effect is not as
visible on larger ones)
FOSS4G 2011, Denver
12th-16th September 2011

66. Setup a local cluster
 Java2D locks when drawing antialiased vectors
 Limits scalability severely
 Use Apache mod_proxy_balance and setup a GeoServer
each 2/4 cores
mod_proxy_balance
GeoServer GeoServer
GeoServer
FOSS4G 2011, Denver
12th-16th September 2011

67. Clustering advantage
 FOSS4G 2010 vector benchmarks (roads/buildings/isolines
and so on, over the entire Spain)
 GeoServer was benchmarked without local clustering
66%
FOSS4G 2011, Denver
12th-16th September 2011

68. Benchmarking
FOSS4G 2011, Denver
12th-16th September 2011

69. Using JMeter
 Good benchmarking tool
 Allows to setup multiple thread groups, different
parallelelism and request count, to ramp up the load
 Can use CSV files to generate semi-randomized requests
 Reports results in a simple table
http://jakarta.apache.org/jmeter/
FOSS4G 2011, Denver
12th-16th September 2011

70. Using JMeter
 Thread group: how many
threads
 Loop: how many
requests
 HTTP sampler: the
request
 CSV: read request
params from CSV
 Summary table
FOSS4G 2011, Denver
12th-16th September 2011

71. Generating the CSV
 Simple randomized generation tool built during WMS
shootouts, wms_request.py
 Generate csv with the bbox and width/height to be used in
JMeter scripts:
./wms_request.py -count 1200
-region -180 -90 180 90
-minres 0.002 -maxres 0.1
-minsize 256 256 -maxsize 1024 1024
 Get it here along with a corresponding JMeter script:
http://demo1.geo-solutions.it/share/jmeter_2011.zip
FOSS4G 2011, Denver
12th-16th September 2011

72. Checking results
 Results table
 Run the benchmarks 2-3 times, let the results stabilize
 Save the results, check other optimizations, compare the
results
FOSS4G 2011, Denver
12th-16th September 2011

73. Real world deploy
FOSS4G 2011, Denver
12th-16th September 2011

74. Deploy configuration
FOSS4G 2011, Denver
12th-16th September 2011

75. Raster data
 Whole Italy at 50cm per pixel
 Over 4TB, updated fully every 3 years (old data still
available for historical access)
 Custom pyramid
 100 m per pixel: one image
 20m per pixel: mosaic of 20 tiles
 4m per pixel: mosaic of few hundred tiles
 0.5m per pixel: 9000 tiles
 Each tile is 10000x10000, with overviews
FOSS4G 2011, Denver
12th-16th September 2011

76. Vector data
 Cadastral data for the whole Italy, with full history
(interval of validity for each parcel)
 100 million polygons
 A query extracts a subset relative to a certain time
interval and area the user is allowed to see
 No data from this table is ever shown below 1:50000 (SLD
scale dependencies)
 Physical table level partitioning (Oracle style) of the table
based on geographic area to parallelize and cluster data
loading, plus spatial indexing and indexes on commonly
filtered upon attributes
FOSS4G 2011, Denver
12th-16th September 2011

77. The End
Questions?
andrea.aime@geo-solutions.it
simone.giannecchini@geo-solutions.it
FOSS4G 2011, Denver
12th-16th September 2011