geoserver on steroids - geosolutions demo server
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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
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
FOSS4G 2011, Denver 12th-16th September 2011
Preparing raster inputs
FOSS4G 2011, Denver 12th-16th September 2011
FOSS4G 2011, Denver 12th-16th September 2011
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
FOSS4G 2011, Denver 12th-16th September 2011
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
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
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
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
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
GeoTiff preparation
STEP 0: get to know your data
gdalinfo utility is your friend
FOSS4G 2011, Denver 12th-16th September 2011
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
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
GeoTiff preparation
FOSS4G 2011, Denver 12th-16th September 2011
GeoTiff preparation
FOSS4G 2011, Denver 12th-16th September 2011
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
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
Time, Elevation and other dimensions
Indexing multiple dimensions with DB support (video here)
FOSS4G 2011, Denver 12th-16th September 2011
datastore.properties
stringregex.properties
timeregex.properties
indexer.properties
Time, Elevation and other dimensions
FOSS4G 2011, Denver 12th-16th September 2011
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
Proper Mosaic Configuration
STEP 0: Configure Coverage Access (see slide 22)
STEP 1: Configure Mosaic Parameters
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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
Proper Mosaic Configuration
Optional (Advanced): Configure Mosaic Parameters Directly
FOSS4G 2011, Denver 12th-16th September 2011
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
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
Proper Pyramid Configuration
FOSS4G 2011, Denver 12th-16th September 2011
STEP 0: Configure Coverage Access (see slide 22)
STEP 1: Configure Pyramid Parameters
ImagePyramid relies on ImageMosaic
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
Proper Pyramid Configuration
Optional (Advanced): Configure Mosaic Parameters Directly
FOSS4G 2011, Denver 12th-16th September 2011
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
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
FOSS4G 2011, Denver 12th-16th September 2011
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
Proper JPEG2000 Kakadu Configuration
FOSS4G 2011, Denver 12th-16th September 2011
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
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
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
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
Preparing vector inputs
FOSS4G 2011, Denver 12th-16th September 2011
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
Choosing a format
Slow formats
WFS
GML
DXF
FOSS4G 2011, Denver 12th-16th September 2011
Good formats, local and indexable
Shapefile
Directory of shapefiles
SDE
Spatial databases: PostGIS, Oracle Spatial, DB2, MySQL*, SQL server*
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
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
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
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
Optimize styling
FOSS4G 2011, Denver 12th-16th September 2011
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
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
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
Use translucency sparingly
Translucent display is expensive, use it sparingly
FOSS4G 2011, Denver 12th-16th September 2011
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
Example
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Hide as you zoom in
Add a MinScaleDenominator to the rule
This will make the layer disappear at 1:75000 (towards 1:1)
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Alternative rendering
Simple rendering at low scale (up to 1:2000)
More complex rendering when zoomed in (1:1999 and above)
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Alternative rendering
FOSS4G 2011, Denver 12th-16th September 2011
Point symbols
• 600 loc for 6 different points types
• Painful…
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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
Dynamic symbolizers
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Output tuning
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WMS output formats
JPEG PNG 8bit PNG 24bit
23.8KB 169.4KB 66KB
64KB 27KB 27KB
Compression artifacts Color reduction Large size
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WFS output formats
05
101520253035
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
Tile caching
FOSS4G 2011, Denver 12th-16th September 2011
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
Embedded GWC advantage
No double encoding when using meta-tiling, faster seeding
FOSS4G 2011, Denver 12th-16th September 2011
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
level Tile count Size (MB)
Time to seed
(hours)
Time to seed
(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
Resource control
FOSS4G 2011, Denver 12th-16th September 2011
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
WFS request limits
Max feature returned, configured as a global limit
Return feature bbox: reduce amount of generated GML
Per layer max feature count
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WCS request limits
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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
$GEOSERVER_DATA_DIR/controlflow.properties
# don't allow more than 16 GetMap requests in parallel
ows.wms.getmap=16
Control flow
17%
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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
JVM and deploy configuration
FOSS4G 2011, Denver 12th-16th September 2011
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
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
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
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
FOSS4G 2011, Denver 12th-16th September 2011
mod_proxy_balance
GeoServer
GeoServer
GeoServer
Clustering advantage
FOSS4G 2011, Denver 12th-16th September 2011
66%
FOSS4G 2010 vector benchmarks (roads/buildings/isolines and so on, over the entire Spain)
GeoServer was benchmarked without local clustering
Benchmarking
FOSS4G 2011, Denver 12th-16th September 2011
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
Using JMeter
Thread group: how many threads
Loop: how many requests
HTTP sampler: the request
CSV: read request params from CSV
Summary table
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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
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Checking results
Results table
Run the benchmarks 2-3 times, let the results stabilize
Save the results, check other optimizations, compare the results
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Real world deploy
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Deploy configuration
FOSS4G 2011, Denver 12th-16th September 2011
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
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
The End
Questions? andrea.aime@geo-solutions.it
simone.giannecchini@geo-solutions.it
FOSS4G 2011, Denver 12th-16th September 2011
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