Enterprise class deployment for GeoServer and GeoWebcache Optimizing performances and robustness

Enterprise class deployment
for GeoServer and GeoWebcache
Optimizing performances and robustness
Ing. Mauro Bartolomeoli, GeoSolutions
Ing. Andrea Aime, GeoSolutions
Ing. Simone Giannecchini, GeoSolutions
FOSS4G-EU 2015, Como
14th July 2015

GeoSolutions
 Italian SME
 Expertise
• Image Processing, GeoSpatial Data Fusion
• Java, Java Enterprise, C++, Python
• JPEG2000, JPIP, Advanced 2D visualization
 Supporting/Developing FOSS4G
 MapStore, GeoServer
 GeoNetwork, GeoCollect
 Clients
 Public Agencies
 Private Companies
 http://www.geo-solutions.it
14th July 2015

Outline
 Intro to GeoServer (Optional)
 Benchmarking with JMeter
 Preparing raster and vector data
 Optimizing styling
 Output tuning
 Tiling and caching
 Configuring for Robustness
 Deploy configurations
14th July 2015

GeoServer
 GeoSpatial enterprise gateway
 Java Enterprise
 Management and Dissemination of
raster and vector data
 Standards compliant
 OGC WCS 1.0, 1.1.1 (RI), 2.0.1
 OGC WFS 1.0, 1.1 (RI), 2.0
 OGC WMS 1.1.1, 1.3
 OGC WPS 1.0.0
 Google Earth/Maps support
 KML, GeoSearch, etc..
14th July 2015

FormatsandProtocols
GeoServer
WFS
1.0, 1.1,
2.0
WMS
1.1.1
1.3.0
PostGIS
Oracle
H2
DB2
SQL Server
MySql
Spatialite
GeoCouch
Shapefile
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ArcSDE
WFS
PNG, GIF
JPEG
TIFF,
GeoTIFF
SVG, PDF
KML/KMZ
Shapefile
GML2
GML3
GeoRSS
GeoJSON
CSV/XLS
Raw vector
data
Servers
Styled
maps
DBMS
Vector files
WCS
1.0,1.1.1
2.0.1
GeoTIFF
WMS
ArcGrid
GTopo30
Img+world
Mosaic
MrSID
JPEG 2000
ECW,Pyramid, Oracle GeoRaster, PostGis Raster, NetCDF
Raster files
Raw raster
data
GeoTIFF
ArcGrid
GTopo30
Img+World
GWC
(WMTS,
TMS,
WMS-C)
KML superoverlays
Google maps tiles
OGC tiles
OSGEO tiles
Google
WPS
1.0.0
14th July 2015

Intro to GeoServer
 In case you are not that familiar with it (fool!)
 GeoServer Release Schedule
 http://geoserver.geo-solutions.it/edu/en/install_run/releases.html
 Web Administration Interface
 http://geoserver.geo-solutions.it/edu/en/install_run/wai.html
 GeoServer Data Directory
 http://geoserver.geo-solutions.it/edu/en/adding_data/structure.html
 Adding a Shapefile
 http://geoserver.geo-solutions.it/edu/en/adding_data/add_shp.html
 Adding a Postgis layer
 http://geoserver.geo-solutions.it/edu/en/adding_data/postgis_lay.html
 Adding a GeoTiff
 http://geoserver.geo-solutions.it/edu/en/adding_data/add_geotiff.html
 Adding a Style
 http://geoserver.geo-solutions.it/edu/en/pretty_maps/add_style.html
14th July 2015

Benchmarking
 Benchmarking is an important phase in project
development
 It gives you various information about your
application:
 Scalability issues
 Robustness in extreme conditions
 Presence of any bottleneck inside the application
 Performances Test against another software
14th July 2015

Local Testing vs Remote Testing
 Network sometimes can compromise the results of your
tests
 In case of high network overload you may see low
throughput
 This can be associated to a bottleneck of the
connection which results in bad performances
without concerning about the request type
 For this reason, be careful to setup your remote tests
in a separate network in order to avoid network
overload
14th July 2015

Lookout for the bottleneck
 Typical bottlenecks:
 CPU: at high load all the CPUs are pegged
 Disk: CPUs are not hot, the server is reading a lot
from the disk during the benchmark
 Network (data sources): CPUs are not hot, but the
local network (maybe towards the DB) is at full
capacity
 Network (output): all local resources are midly used,
but the outbound connection is at full capacity
 Code scalability: none of the above, every resource
seems to be in light use and usage is not going up
despire higher client load
14th July 2015

Introduction to JMeter
 Open Source performance measurement tool written in
Java
 This tool will be highly used during the workshop for
testing the GeoServer performances against Benchmark
data
 More informations can be found at the Jmeter site:
http://jakarta.apache.org/jmeter/
14th July 2015

Introduction to JMeter
 Easy-to-use UI
 Allows to setup multiple thread groups, different
parallelism and request count, to ramp up the load
 Can use CSV files to generate semi-randomized requests
 Can execute parameterized tests
 Reports results in a simple table
 Uses Assertions for checking test results
 Can be executed in batch
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Using JMeter
 Thread group: how many
threads
 Loop: how many
requests
 HTTP sampler: the
request
 CSV: read request
params from CSV
 Default: do not repeat
what’s equal in all
requests
 Performance summary
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Result Summary
 Results table
 Run the benchmarks 2-3 times, let the results stabilize
 Save the results, check other optimizations, compare the
results
14th July 2015

Exercise
 Let's start with practice!!!
 Follow the instructions on the link below
http://geoserver.geo-solutions.it/educational/en/adv_gsconfig/jmeter/jmeter_simple.html
 You should be able to reproduce the test using the
GeoServer Training instance installed in your PC
 This simple exercise will show you how to use JMeter with
a basic configuration for testing GeoServer performances
14th July 2015

Randomized Multiscale Testing
 In the example before, we ran a test on a layer always
setting the same bounding box and image size
 Now we should setup a more complicated test asking for
different bounding boxes and resolutions, aka Multiscale
testing
 Multiscale testing is useful because it stresses GeoServer
requesting new data each time, mimicking real usage
conditions and providing better testing for your SLD scale
dependencies
 With JMeter we can execute the test by passing a CSV file
containing multiple bounding boxes and image
dimensions
14th July 2015

Exercise
 Multiscale test created using wms_request.py on a mosaic
layer
 Follow the instructions on the link below
http://geoserver.geo-solutions.it/educational/en/adv_gsconfig/jmeter/jmeter_multiscale.html
 This exercise will show you how to use create a CSV file
containing requests at multiple resolutions and how to run
JMeter with it
14th July 2015

Intro to GDAL Utilities
 Online Material
 http://geoserver.geo-
solutions.it/edu/en/raster_data/processing.html
 http://www.gdal.org/gdal_utilities.html
14th July 2015

Raster Data CheckList
 Objectives
 Fast extraction of a subset of the data
 Fast extraction of the desired resolution
 Check-list
 Avoid having to open a large number of files per
request
 Avoid parsing of complex structures and slow
compressions
 Get to know your bottlenecks
 CPU vs Disk Access Time vs Memory
 Experiment with
 Format, compression, different color models, tile size,
overviews, GeoServer configuration
14th July 2015

Peculiar Formats
 PNG/JPEG direct serving (as data sources)
 Bad formats (especially in Java)
 No tiling (or rarely supported)
 Chew a lot of memory and CPU for decompression
 Mitigate with external overviews
 Any input ASCII format (GML grid, ASCII grid)
 JPEG2000
 Extensible and rich, not (always) fast, can be difficult
to tune for performance (might require specific
encoding options)
 MrSID (can work, needs tuning)
 ECW (licensing issues)
14th July 2015

GeoTiff for the win
 To remember: GeoTiff is a swiss knife
 But you don’t want to cut a tree with it!
 Tremendously flexible, good for for most (not all) use
cases
 BigTiff pushes the GeoTiff limits farther
 Use 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
14th July 2015

GeoTiff preparation
 (Optional) Use gdal_warp to transform the data in the most
used output reference system (mind, any reprojection
ruins the data a bit)
 Use gdal_translate to add inner tiling and fix eventual
issues with coordinate reference system
 Add compression options if you disks are small/slow/not
local (consider JPEG compression with YCBCR color
interpretation for photos, LZW/Deflate for scientific data)
 Use gdaladdo to add internal overviews (remember to
replicate compression here)
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Possible structures
Single GeoTiff
with internal tiling
and overviews
(GeoTiff < 2GB,
BigTiff < 20-50GB)
Mosaic of GeoTiff, each one
with internal tiling and overviews
(< 500GB, not too many files)
Image pyramid,
each level has
lower resolution
than the previous,
each level has tiles
with inner tiling but
no overviews
(also possible to mix,
less levels and use
internal overviews)
> 500GB
14th July 2015

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
14th July 2015

Proper Mosaic Preparation
 Optimize files as if you were serving them individually
 Keep a balance between number and dimensions of
granules
 If memory is scarce:
 USE_JAI_IMAGREAD to true
 USE_MULTITHREADING to false*
 Otherwise
 USE_JAI_IMAGREAD to false
 ALLOW_MULTITHREADING to true
(Load data from different granules in
parallel)
 More Info
solutions.it/edu/en/raster_data/mosai
c_pyramid.html
14th July 2015

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), if you go larger, use
also inner tiling
 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 with usage
 Too few  bad performance at high scale
 GeoServer config  same as mosaic
 More Info
solutions.it/edu/en/raster_data/mosaic_pyramid.html
14th July 2015

Proper GeoServer
Coverage Config Opts
 Make sure native JAI is installed
 Install the TurboJPEG extension
 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)
 Play with tile Recycling against your
workflows (might help, might not)
14th July 2015

Proper GeoServer
Coverage Config Opts
 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)
14th July 2015

Exercise
 Let's check the effect of optimization in GeoServer
 Follow the instructions on the links below
http://geoserver.geo-solutions.it/educational/en/adv_gsconfig/jmeter/jmeter_mosaic.html
 This test uses JMeter for testing GeoServer performances
before and after the optimization of a Mosaic Layer.
14th July 2015

Additional Material & Exercises
 Using ImageMosaic with Footprint
solutions.it/edu/en/raster_data/imagemosaic_footprint.html
 Advanced Processing With GDAL Utilities
solutions.it/edu/en/raster_data/advanced_gdal/index.html
14th July 2015

Vector data checklist
 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
14th July 2015

Choosing a format
 Slow formats
 WFS
 GML
 DXF
 Good formats, local and
index-able
 Shapefile
 Directory of shapefiles
 SDE
 Spatial databases:
PostGIS, Oracle
Spatial, DB2, MySQL*,
SQL server*
 Swiss Knife  SQL Views
14th July 2015

DBMS Checklist
 Rich support for complex native filters
 Use connection pooling
 Validate connections (with proper pooling)
 Table Clustering
 Spatial Indexing
 Alphanumeric Indexing
 Did we mention indexes?
14th July 2015

Shapefile preparation
 Remove .qix file if present
 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):
14th July 2015

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!
14th July 2015

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
14th July 2015

PostGIS specific hints
 Careful with prepared statements (bad performances)
 USE CASE: the layer’s style allows to display the whole
layer in a single shot (no scale dependencies)  prepared
statements will slow down execution
 EXPLANATION: Postgis will choose to use the spatial
index in all cases, this makes retrieving the full data set 2-4
times slower than when using a sequential scan
 COUNTERMEASURE: Not using prepared statement allows
postgis to figure out a suitable plan based on the request
bbox instead (assuming someone run "vacuum analyze"
on the database to update the index statistics, and of
course, provided there is a spatial index to start with)
14th July 2015

SQL Server specific hints
 Switch to PostgreSQL/PostGIS!
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Oracle specific hints
 Switch to PostgreSQL/PostGIS!
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SQL Views Support
 Vector Data Swiss Knife
solutions.it/edu/en/adding_data/add_sqllayers.html
14th July 2015

Exercise
 Let's compare Shapefile and PostGIS in GeoServer
http://geoserver.geo-solutions.it/educational/en/adv_gsconfig/jmeter/jmeter_vector.html
 This test uses Jmeter for comparing GeoServer
performances using a Shapefile or an optimized PostGIS
database containing the same data
14th July 2015

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, have labels show up when
zoomed in
 Show details as you zoom in
 Eagerly add MinScaleDenominator to your SLD rules
 Add more expensive rendering when there are less
features
 Key to get both a good looking and fast map
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Example
14th July 2015

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
14th July 2015

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
14th July 2015

Use translucency sparingly
 Translucent display is expensive, use it sparingly
 e.g. translucent fill <CssParameter name="fill-opacity">0.5</CssParameter>
14th July 2015

Exercise
 Let's compare bad styles with properly setup ones
http://geoserver.geo-solutions.it/educational/en/adv_gsconfig/jmeter/jmeter_styles.html
 This test uses JMeter to compare a style without scale
dependencies, showing too much data and too many
labels, with a one with proper scale dependencies and
providing a better looking output at the same output
14th July 2015

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/few dynamic parameters (CQL filters, SLD params,
SQL query params, time/elevation, format options)
14th July 2015

Embedded GWC advantage
 No double encoding when using meta-tiling, faster seeding
14th July 2015

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
14th July 2015

DiskQuota
 Online Material
solutions.it/edu/en/caching_data/diskquotas.html#disk-quota-
configuration
14th July 2015

More Tweaks
 REST Interface  Automate/Script management
 Truncate
 Remove Cached tiles
 Seed
 Pre-generate cache
 Reseed
 Regenerate tiles (due to data update, for instance)
 Every seed/truncate operation can be filtered by:
 Bounding box / Zoom limits
 / Format / GridSet
 Parameters (style, time, other dimensions, cql_filter…)
 Thread number
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More Tweaks
 REST Seed example
14th July 2015
curl -u admin:password -H 'Content-type: text/xml' -H 'Accept: text/xml' -XPOST -d '
<seedRequest>
<name>topp:states</name>
<type>seed</type>
<gridSetId>EPSG:4326</gridSetId>
<bounds>
<coords>
<double>-100</double><double>-
80</double><double>100</double><double>80</double>
</coords>
</bounds>
<zoomStart>0</zoomStart><zoomStop>8</zoomStop>
<format>image/png8</format>
<threadCount>04</threadCount>
<parameters>
<entry>
<string>CQL_FILTER</string>
<string>TOTPOP BETWEEN 0 AND 10000</string>
</entry>
</parameters>
</seedRequest>
' http://localhost:8080/geoserver/gwc/rest/seed/topp:states.xml

More Tweaks
 Online Resources for GWC REST API
 Documentation
 http://geowebcache.org/docs/current/rest/seed.html
 http://docs.geoserver.org/stable/en/user/geowebcache/rest/seed
.html
 Bash script for seed/reseed/truncate/mass truncate operations
 https://github.com/geosolutions-
it/scripts/blob/master/geowebcache/gwc.sh
 Sample script for clean cache in geoserver cluster
 https://github.com/geosolutions-
it/scripts/blob/master/geowebcache/cleancache.sh
14th July 2015

More Tweaks
 Clustering Single Shared Cache
 Large, shared disk with fast R/W access
 Perfect for bkg layers
 Problematic set up in public cloud
 Clustering Multiple Indipendent Caches
 Exploit local, small, instance-based caches
 Good for short lived caches
 Duplication hit  rsynch can help
 Real-World Use Case
 Dedicated Instances with shared cache for Bkg Layers
 Indipendent Caches for other layers
14th July 2015

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>
14th July 2015

More Tweaks
 Use the right formats
 JPEG for background data (e.g. ortos)
 PNG8 + precomputed palette for background
data (e.g. ortos)
 PNG8 full for overlays with transparency
 The format impacts also the disk space needed!
(as well as the generation time)
 Check this blog post
14th July 2015

Exercise
 Let's compare GeoServer WMS with GeoWebCache
fullWMS.
http://geoserver.geo-solutions.it/educational/en/adv_gsconfig/jmeter/jmeter_gwc.html
 This test uses JMeter for comparing GeoServer WMS
performances against those of GeoWebCache fullWMS
 Mind, this is not the same as comparing WMS with pure
tile caching, GWC has to read the tiles from disk,
decompress, assemble the output image, and compress it
again in PNG (some speedup still, but not 10 times)
14th July 2015

Advanced GeoServer Configuration

Resource Limits
 Limit the amount of resources dedicated to an individual
request
 Improve fairness between requests, by preventing
individual requests from hijacking the server and/or
running for a very long time
 EXTREMELY IMPORTANT in production environment
 WHEN TO TWEAK THEM?
 Frequent OOM Errors despite plenty of RAM
 Requests that keep running for a long time (e.g. CPU
usage peaks even if no requests are being sent)
 DB Connection being killed by the DBMS while in
usage (ok, you might also need to talk to the DBA..)
14th July 2015

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
14th July 2015

WFS request limits
 Max feature returned, configured as a global limit
 Return feature bbox: reduce amount of generated GML
 Per layer max feature count
14th July 2015

WCS request limits
14th July 2015

Exercise
 Let's show how resource limits can be used to avoid
issues with slow requests
http://geoserver.geo-solutions.it/educational/en/adv_gsconfig/jmeter/jmeter_wmslimits.html
 This test artificially limits the database resources to
quickly generate a starvation situation in which the limits
kick-in
14th July 2015

Control flow
 Control how many requests are executed in parallel, queue
others:
 Increase throughput
 Control memory usage
 Enforce fairness
 More info
 http://docs.geoserver.org/latest/en/user/community/controlflow/i
ndex.html
14th July 2015

$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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Resource Limits
 They need to be tweaked together with Control- Flow
 Limiting individual requests  Resource Limits
 Limiting amount of parallel request  Control Flow
 When time is involved make sure you keep into account all
pieces of the response chain
 E.G. Limited rendering time for WMS on data coming
from WMS, items to take into account are
 LifeTime of DB Connection (usually long)
 WaitTime for a new connection (we don’t want to
queue requests at the connection pool when they
area already eating memory!)
14th July 2015

Connection Pooling Tricks
 Connection pool size should be proportional to the number of
concurrent requests you want to serve (obvious no?)
 Activate connection validation
 Mind networking tools that might cut connections sitting idle
(yes, your server is not always busy), they might cut the
connection in “bad” ways (10 minutes timeout before the pool
realizes the TCP connection attempt gives up)
 Read more at
solutions.it/edu/en/adv_gsconfig/db_pooling.html
 https://docs.google.com/document/d/1O02PeDRYKt2xLW
G21k6BmcaRPzVRvXb4SQcrcZf4bHQ/edit?usp=sharing
14th July 2015

Exercise
 Let's show how control-flow works in GeoServer
http://geoserver.geo-
solutions.it/educational/en/adv_gsconfig/jmeter/jmeter_controlflow.html
before and after configuring the control-flow plugin.
14th July 2015

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
14th July 2015

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
14th July 2015

Setup a local cluster
 Oracle Java2D locks when drawing antialiased vectors
 Limits scalability severely
 Two options
 Use OpenJDK, it’s slower at rendering but scales up
well
 Use Apache mod_proxy_balance and setup a
GeoServer each 2/4 cores
mod_proxy_balance
GeoServer GeoServer GeoServer
14th July 2015

Clustering advantage
66%
 FOSS4G 2010 vector benchmarks (roads/buildings/isolines
and so on, over the entire Spain)
 GeoServer was benchmarked using Oracle JDK without
local clustering
14th July 2015

Marlin renderer
 The OpenJDK Java2D renderer scales up, but it’s not
super-fast when the load is small (1 request at a time)
 Marlin-renderer to the rescue:
https://github.com/bourgesl/marlin-renderer
 Complex map, 10
parallel requests,
different zoom
levels have different
details showing up
14th July 2015

Upgrade!
 Performance tends to go up version by version
 Please do use a recent GeoServer version
 FOSS4G 2010
vector
benchmark with
different
versions of
GeoServer
14th July 2015

Exercise
 Let's show how Marlin renderer can speed up map
rendering under Windows, using GeoServer 2.5.x
http://geoserver.geo-solutions.it/educational/en/adv_gsconfig/jmeter/jmeter_marlin.html
before and after setting up Marlin renderer
14th July 2015

Clustering with GeoServer
 Online Material
solutions.it/edu/en/clustering/index.html
14th July 2015

The End
Questions?
info@geo-solutions.it
14th July 2015

Enterprise class deployment for GeoServer and GeoWebcache Optimizing performances and robustness

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