GeoServer on steroids
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GeoServer on steroids

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Setting up a GeoServer can sometimes be deceptively simple. However, going from proof-of-concept to production requires a number of steps to be taken in order to optimize the server in terms of ...

Setting up a GeoServer can sometimes be deceptively simple. However, going from proof-of-concept to production requires a number of steps to be taken in order to optimize the server in terms of availability, performance and scalability. The presentation will show how to get from a basic setup to a battle-ready, rock-solid installation.

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GeoServer on steroids GeoServer on steroids Presentation Transcript

  • 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 2013, Nottingham 20th September 2013
  • GeoSolutions  Founded in Italy in late 2006  Expertise • • Java, Java Enterprise, C++, Python •  Image Processing, GeoSpatial Data Fusion JPEG2000, JPIP, Advanced 2D visualization Supporting/Developing FOSS4G projects    MapStore, GeoServer GeoBatch, GeoNetwork Clients    Public Agencies Private Companies http://www.geo-solutions.it FOSS4G 2013, Nottingham 20th September 2013
  • Preparing raster inputs FOSS4G 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • Problematic Formats  Ascii Grid, GTOPO30, IDRISI and similar formats are bad    No internal tiling, no compression, no internal overviews JPEG2000 (with Kakadu)    ASCII formats are bad Extensible and rich, not (always) fast Can be difficult to tune for performance (might require specific encoding options) ECW and MrSID  Very fast on some types of data  Needs to be tuned to be performant FOSS4G 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • 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   Need to serve at all scales   Too many files / too large files Especially low resolution For single granules (< 2Gb) GeoTiff is generally a good fit FOSS4G 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • GeoTiff preparation  STEP 0: get to know your data  gdalinfo is your friend   CheckList Missing CRS    Missing georeferencing    Fix with gdal_translate Missing Overviews   Add a World File Fix with gdal_translate Bad Tiling   Add a .prj file Fix with gdal_translate Use gdaladdo Compression  FOSS4G 2013, Nottingham 20th September 2013 Use gdal_translate
  • GeoTiff preparation   STEP 1: fix and optimize with gdal_translate CRS and GeoReferencing   Inner Tiling    gdal_translate –a_srs “EPSG:4326” –a_ullr -180 0 -90 90 in.tif out.tif gdal_translate -co "TILED=YES" -co "BLOCKXSIZE=512" -co "BLOCKYSIZE=512" in.tif out.tif Check also GeoTiff driver creation options here 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 2013, Nottingham 20th September 2013
  • GeoTiff preparation STEP 1: fix and optimize with gdal_translate • CRS and GeoReferencing FOSS4G 2013, Nottingham 20th September 2013
  • GeoTiff preparation STEP 1: fix and optimize with gdal_translate • Inner Tiling FOSS4G 2013, Nottingham 20th September 2013
  • GeoTiff preparation STEP 2: add overviews with gdal_addo FOSS4G 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • GeoTiff preparation Compression: FOSS4G 2013, Nottingham 20th September 2013
  • GeoTiff preparation Test on a GeoTIFF image with(and without) the following features: • Tiling • Overview • Compression Results: • Overview increases performances by more than 6 times in respect of an image without it. • An uncompressed image increases the GeoServer performances by 70%. NOTE: All the tests in this section are performed on a 4 core PC with 16Gb RAM and GeoServer 2.4. FOSS4G 2013, Nottingham 20th September 2013
  • GeoTiff preparation Test on a GeoTIFF JPEG Compressed image with(and without) TurboJPEG acceleration: Results: • TurboJPEG gives a 20% better response in presence of the overview, and 6% for the other cases. FOSS4G 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • Time, Elevation and other dimensions  Indexing multiple dimensions with DB support (video here) datastore.properties timeregex.properties stringregex.properties indexer.properties FOSS4G 2013, Nottingham 20th September 2013
  • Time, Elevation and other dimensions FOSS4G 2013, Nottingham 20th September 2013
  • Proper Mosaic Preparation  ImageMosaic stitches single granules together with basic processing   Over/DownSampling in memory  ColorMask (optional)  Mosaic/Stitch   Overviews/Decimation on read   Filtered selection ColorMask again (optional) Optimize files as if you were serving them individually Keep a balance between number and dimensions of granules FOSS4G 2013, Nottingham 20th September 2013
  • Proper Mosaic Configuration   STEP 0: Configure Coverage Access (see slide 34) STEP 1: Configure Mosaic Parameters  ALLOW_MULTITHREADING    Use a proper Tile Size    In-memory processing, must not be too large Disk tiling should larger If memory is scarce:    Load data from different granules in parallel Needs USE_JAI_IMAGE_READ set to false (Immediate Mode) USE_JAI_IMAGREAD to true USE_MULTITHREADING to false* Otherwise   USE_JAI_IMAGREAD to false ALLOW_MULTITHREADING to true FOSS4G 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • Proper Mosaic Configuration  Test on a Mosaic Image   USE_JAI_IMAGREAD(IR) set to true and ALLOW_MULTITHREADING(MT) set to false. ALLOW_MULTITHREADING set to true and USE_JAI_IMAGREAD set to false. Results: • The use of MULTITHREADING gives a 30% better performance. FOSS4G 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • Proper Pyramid Configuration   STEP 0: Configure Coverage Access (see slide 34) STEP 1: Configure Pyramid Parameters  ALLOW_MULTITHREADING    Use a proper Tile Size      ImagePyramid relies on ImageMosaic In-memory processing, must not be too large Disk tiling should be larger If memory is scarce:   Load data from different granules in parallel Needs USE_JAI_IMAGE_READ set to false (Immediate Mode) USE_JAI_IMAGREAD to true USE_MULTITHREADING to false* Otherwise   USE_JAI_IMAGREAD to false ALLOW_MULTITHREADING to true FOSS4G 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • 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     If memory is scarce:    In-memory processing, must not be too large Fundamental for striped data! JNI overhead Disk tiling should be larger USE_JAI_IMAGREAD to true USE_MULTITHREADING to true* Otherwise   USE_JAI_IMAGREAD to false USE_MULTITHREADING is ignored FOSS4G 2013, Nottingham 20th September 2013
  • Proper GDAL Formats Configuration Test on a ECW image with and without enabling ImageRead: • ECW is a GDAL supported format. Results:  If ImageRead is not used, then the performances are increased by more than 1,5 times. FOSS4G 2013, Nottingham 20th September 2013
  • 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     If memory is scarce:    In-memory processing Must not be too large Disk tiling should larger USE_JAI_IMAGREAD to true USE_MULTITHREADING to true* Otherwise   USE_JAI_IMAGREAD to false USE_MULTITHREADING is ignored FOSS4G 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • Reprojection Performance Vs Quality      GeoServer (since 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 2013, Nottingham 20th September 2013
  • Preparing vector inputs FOSS4G 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • Choosing a format  Slow formats  WFS  Good formats, local and indexable    GML DXF Shapefile  Directory of shapefiles  SDE  Spatial databases: PostGIS, Oracle Spatial, DB2, MySQL*, SQL server* FOSS4G 2013, Nottingham 20th September 2013
  • Shapefiles vs DBMS  Speed comparison vs spatial extent depicted:    Shapefile   Shapefile very fast when rendering the full dataset Database faster when extracting a small subset of a very large data set 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 2013, Nottingham 20th September 2013
  • DBMS Checklist            Rich support for complex native filters Use connection pooling (preferably via JNDI) Validate connections (with proper pooling) Spatial Indexing Spatial Indexing Spatial Indexing Alphanumeric Indexing Alphanumeric Indexing Alphanumeric Indexing Table Clustering Use views to remove unused attributes FOSS4G 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • 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  Or hire us to develop non-spatial indexing for shapefile! FOSS4G 2013, Nottingham 20th September 2013
  • 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/manual1.3/ch05.html Careful with prepared statements (bad performance) FOSS4G 2013, Nottingham 20th September 2013
  • Optimize styling FOSS4G 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • Use translucency sparingly  Translucent display is expensive, use it sparingly  e.g. translucent fill <CssParameter name="fill-opacity">0.5</CssParameter> FOSS4G 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • Example FOSS4G 2013, Nottingham 20th September 2013
  • Hide as you zoom in   Add a MinScaleDenominator to the rule This will make the layer disappear at 1:75000 (towards 1:1) FOSS4G 2013, Nottingham 20th September 2013
  • Alternative rendering   Simple rendering at low scale (up to 1:2000) More complex rendering when zoomed in (1:1999 and above) FOSS4G 2013, Nottingham 20th September 2013
  • Alternative rendering FOSS4G 2013, Nottingham 20th September 2013
  • Point symbols • 600 loc for 6 different points types • Painful… FOSS4G 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • Dynamic symbolizers FOSS4G 2013, Nottingham 20th September 2013
  • Output tuning FOSS4G 2013, Nottingham 20th September 2013
  • WMS output formats JPEG PNG 8bit PNG 24bit 23.8KB 66KB 169.4KB 27KB 27KB 64KB Compression artifacts Color reduction FOSS4G 2013, Nottingham 20th September 2013 Large size
  • LibJPEG-Turbo WMS Output Format   GeoServer Extension Leverages LibJPEG-Turbo for accelerate JPEG encoding  40% to 80% increase in throughput  Up to 40% decrease in average response times  Check our blog post here FOSS4G 2013, Nottingham 20th September 2013
  • LibJPEG-Turbo WMS Output Format FOSS4G 2013, Nottingham 20th September 2013
  • Available Color Quantizer  Paletted Images are lighter to move around!  Options: Precompute VS Compute on-the-fly  Precomputed palettes are fast but ugly   On-the-fly palette computation options    ON/OFF Transparency, no antinalising Octree  fast, supports ON/OFF Transparency. Default for opaque images Mediancut  slower, supports full Transparency. Default for translucent images Check this page and this one as well in the GeoServer doc FOSS4G 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • Tiling & Caching FOSS4G 2013, Nottingham 20th September 2013
  • 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 preseeded) Suitable for:   Mostly static layer No (or few) dynamic parameters (CQL filters, SLD params, SQL query params, time/elevation, format options) FOSS4G 2013, Nottingham 20th September 2013
  • Embedded GWC advantage  No double encoding when using meta-tiling, faster seeding FOSS4G 2013, Nottingham 20th September 2013
  • 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 13 14 15 16 17 18 19 20 Tile count Size (MB) 58,377 232,870 929,475 3,713,893 14,855,572 59,396,070 237,584,280 950,273,037 1 4 14 57 227 906 3,625 14,500 Time to seed Time to seed (hours) (days) 0 0 0 0 0 0 1 0 6 0 23 1 92 4 367 15 FOSS4G 2013, Nottingham 20th September 2013
  • More Tweaks  Client-side caching of tiles  Does not work with browsers in private mode <expireClientsList> <expirationRule minZoom="0" expiration="7200" /> <expirationRule minZoom="10" expiration="600" /> </expireClientsList> FOSS4G 2013, Nottingham 20th September 2013
  • More Tweaks  Use the right formats        JPEG for background data (e.g. ortos) PNG8 + precomputed palette for background data (e.g. ortos) PNG full for overlays with transparency PNG8 full for overlays with transparency Don’t compress things twice! The format impacts also the disk space needed! (as well as the generation time) Check this blog post FOSS4G 2013, Nottingham 20th September 2013
  • Resource control FOSS4G 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • WCS request limits FOSS4G 2013, Nottingham 20th September 2013
  • Control flow  Control how many requests are executed in parallel, queue others:   Control memory usage   Increase throughput Enforce fairness More info here FOSS4G 2013, Nottingham 20th September 2013
  • Control flow 17% $GEOSERVER_DATA_DIR/controlflow.properties # don't allow more than 16 GetMap requests in parallel ows.wms.getmap=16 FOSS4G 2013, Nottingham 20th September 2013
  • Auditing  Log each and every request  Log contents driven by customizable template  Summarize and analyze requests with offline tools  More info here FOSS4G 2013, Nottingham 20th September 2013
  • JVM and deploy configuration FOSS4G 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • Clustering advantage   FOSS4G 2010 vector benchmarks (roads/buildings/isolines and so on, over the entire Spain) GeoServer was benchmarked without local clustering 66% FOSS4G 2013, Nottingham 20th September 2013
  • Benchmarking FOSS4G 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • Using JMeter      Thread group: how many threads Loop: how many requests HTTP sampler: the request CSV: read request params from CSV Summary table FOSS4G 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • Checking results  Results table  Run the benchmarks 2-3 times, let the results stabilize  Save the results, check other optimizations, compare the results FOSS4G 2013, Nottingham 20th September 2013
  • Real world deploy FOSS4G 2013, Nottingham 20th September 2013
  • Deploy configuration FOSS4G 2013, Nottingham 20th September 2013
  • Raster data    Whole Italy at 50cm per pixel Over 4TB, updated fully every 3 years (old data still available for historical access) Custom pyramid   20m per pixel: mosaic of 20 tiles  4m per pixel: mosaic of few hundred tiles   100 m per pixel: one image 0.5m per pixel: 9000 tiles Each tile is 10000x10000, with overviews FOSS4G 2013, Nottingham 20th September 2013
  • 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 2013, Nottingham 20th September 2013
  • The End Questions? andrea.aime@geo-solutions.it simone.giannecchini@geo-solutions.it FOSS4G 2013, Nottingham 20th September 2013