geoserver on steroids

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

• Image Processing, GeoSpatial Data Fusion

• Java, Java Enterprise, C++, Python

• JPEG2000, JPIP, Advanced 2D visualization

Supporting/Developing FOSS4G projects

MapStore, GeoServer

GeoBatch, GeoNetwork

Clients

Public Agencies

Private Companies

http://www.geo-solutions.it


http://www.geo-solutions.it/



Preparing raster inputs


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)


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,


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

Very fast on some types of data

Needs to be tuned to be performant


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



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



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


GeoTiff preparation

STEP 0: get to know your data

gdalinfo 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


http://www.gdal.org/gdal_translate.html

http://geo-solutions.blogspot.com/2010/11/worldfile-explained-or-at-least-we.html



http://www.gdal.org/gdaladdo.html


GeoTiff preparation

STEP 1: fix and optimize with gdal_translate

CRS and GeoReferencing gdal_translate –a_srs “EPSG:4326” –a_ullr -180 0 -90 90 in.tif out.tif

Inner Tiling 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


http://www.gdal.org/frmt_gtiff.html


GeoTiff preparation



• CRS and GeoReferencing


GeoTiff preparation



• Inner Tiling


GeoTiff preparation


STEP 2: add overviews with gdal_addo


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



http://www.gdal.org/frmt_gtiff.html

GeoTiff preparation

Compression:


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.

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.

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!



Indexing multiple dimensions with DB support (video here)

datastore.properties

stringregex.properties

timeregex.properties

indexer.properties


http://www.youtube.com/watch?v=Xj-ftS7Di_g&feature=channel_video_title

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


20th September 2013

Proper Mosaic Configuration

STEP 0: Configure Coverage Access (see slide 34)

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


20th September 2013


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



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.

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


20th September 2013

http://www.gdal.org/gdal_retile.html

Proper Pyramid Configuration

STEP 0: Configure Coverage Access (see slide 34)

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 be larger

If memory is scarce: USE_JAI_IMAGREAD to true USE_MULTITHREADING to false*

Otherwise USE_JAI_IMAGREAD to false ALLOW_MULTITHREADING to true


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


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 be larger

If memory is scarce: USE_JAI_IMAGREAD to true

USE_MULTITHREADING to true*

Otherwise USE_JAI_IMAGREAD to false

USE_MULTITHREADING is ignored


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.

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


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


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)


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


Preparing vector inputs


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


Choosing a format

Slow formats

WFS

GML

DXF

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)


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



Table Clustering

Use views to remove unused attributes


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):


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!


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)


http://wiki.postgresql.org/wiki/Performance_Optimization







Optimize styling


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


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


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


Use translucency sparingly

Translucent display is expensive, use it sparingly

e.g. translucent fill <CssParameter name="fill-opacity">0.5</CssParameter>


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


Example


Hide as you zoom in

Add a MinScaleDenominator to the rule

This will make the layer disappear at 1:75000 (towards 1:1)


Alternative rendering

Simple rendering at low scale (up to 1:2000)

More complex rendering when zoomed in (1:1999 and above)


Alternative rendering


Point symbols

• 600 loc for 6 different points types

• Painful…


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';


Dynamic symbolizers


Output tuning


WMS output formats

JPEG PNG 8bit PNG 24bit

23.8KB 169.4KB 66KB

64KB 27KB 27KB

Compression artifacts Color reduction 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


http://geo-solutions.blogspot.co.uk/2013/03/libjpeg-turbo.html

LibJPEG-Turbo WMS Output Format


Available Color Quantizer

Paletted Images are lighter to move around!

Options: Precompute VS Compute on-the-fly

Precomputed palettes are fast but ugly

ON/OFF Transparency, no antinalising

On-the-fly palette computation options

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


http://docs.geoserver.org/latest/en/user/tutorials/palettedimage/palettedimage.html

http://docs.geoserver.org/latest/en/user/services/wms/vendor.html#format-options

WFS output formats

05

101520253035

Dimension MB

HTTP GZip compression is transparent in GeoServer, make sure proxies keep it (or pay 10x price)


Tiling & Caching


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)


Embedded GWC advantage

No double encoding when using meta-tiling, faster seeding


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


http://tinyurl.com/3apkpss

http://tinyurl.com/3apkpss

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>


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

http://geo-solutions.blogspot.co.uk/2012/05/tips-tricks-geowebcache-tweaks.html

Resource control


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


WFS request limits

Max feature returned, configured as a global limit

Return feature bbox: reduce amount of generated GML

Per layer max feature count


WCS request limits


Control flow

Control how many requests are executed in parallel, queue others:

Increase throughput

Control memory usage

Enforce fairness

More info here


http://docs.geoserver.org/latest/en/user/community/controlflow/index.html

$GEOSERVER_DATA_DIR/controlflow.properties

# don't allow more than 16 GetMap requests in parallel

ows.wms.getmap=16

Control flow

17%


Auditing

Log each and every request

Log contents driven by customizable template

Summarize and analyze requests with offline tools

More info here


http://docs.geoserver.org/stable/en/user/community/monitoring/audit.html

JVM and deploy configuration


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


http://docs.geoserver.org/latest/en/user/production/index.html

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


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)


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


Clustering advantage

66%

FOSS4G 2010 vector benchmarks (roads/buildings/isolines and so on, over the entire Spain)

GeoServer was benchmarked without local clustering


Benchmarking


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/


http://jakarta.apache.org/jmeter/



Using JMeter

Thread group: how many threads

Loop: how many requests

HTTP sampler: the request

CSV: read request params from CSV

Summary table


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


http://demo1.geo-solutions.it/share/jmeter_2011.zip





Checking results

Results table

Run the benchmarks 2-3 times, let the results stabilize

Save the results, check other optimizations, compare the results


Real world deploy


Deploy configuration


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


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


The End

Questions? [email protected]

[email protected]


geoserver on steroids

Technology

single geotiff

large use tiling

geotiff preparation

geotiff limits

small tiling foss4g

gb geotiff filemedium

bad formats

gb bigtiff files