Before describing the test and its results, a few words about the test platform.
CPU: Intel Core2 Duo E6550@2.33Ghz
RAM: Samsung PC2-4200 (2x1Gb) dual channel
Motherboard: Asus P5K SE (чипсет Intel P35)
Video: NVIDIA GeForce 8500GT 512 Mb
HDD:
Hitachi DeskStar HDT725032VLA360 (320 Gb). Divided into two partitions: 10 Gb for the operating system and the rest for data
Hitachi DeskStar HDS721680PLA380 (80 Gb). Single partition for the entire disk.
OS: Windows XP Pro SP3
The test itself was carried out according to Mateusz’s instructions. When the SQL representation of the raster was created, the resulting SQL file was written to the second disc, and before loading it into the database, the file was moved to the data partition of the first disc. After each stage of testing, all disks were defragmented using OS tools, and the machine rebooted.
This Landsat scene was used as a test image. Quite detailed information about the image can be found in the file N-38-45-45.met, which is located next to it, and I give a truncated output of gdalinfo below:
Before starting the test, a snapshot of the system was taken, and all disks were defragmented using OS tools. The source data (i.e., the raster) is located on the second partition of the 320 Gb disc.
Unfortunately, both GDAL and WKTRaster do not yet fully support the MrSID format, so the raster was converted to the ERDAS IMAGINE format (*.img) using the following command:
The conversion took 1160 s (~19 min), and the resulting file occupied ~4.7 Gb of disk space. This file was used in all subsequent operations. We have the image in the supported format, and now we need to build pyramids (overviews), which are not present in our file:
Now we can prepare the image for loading into the database. The preparation consists of using the Python script gdal2wktraster.py (shipped with WKTRaster) to generate an SQL file with the image dump.
This is quite a long process, so make sure you have some tea/coffee. On my test platform, it took 2904 s (~48 min). At the end, the script generates a report of the work done, showing how many tables will be created in the database after the script is loaded and how many tiles (blocks) will be in each table:
------------------------------------------------------------
Summary of GDAL to WKT Raster processing:
------------------------------------------------------------
Number of processed raster files: 1
List of generated tables (number of tiles):
1 N_38_45_img (103152)
2 o_2_N_38_45_img (25872)
3 o_4_N_38_45_img (6468)
4 o_8_N_38_45_img (1638)
5 o_16_N_38_45_img (420)
6 o_32_N_38_45_img (110)
7 o_64_N_38_45_img (30)
8 o_128_N_38_45_img (9)
The gdal2wktraster.py script produced the file N_38_45_img_rb_128.sql, which took up — hold on tight :-) — 13,564,596,564 bytes (~12.6 GB). There you go.
Since all the necessary preparations were made at the PostGIS configuration stage, the only remaining step is to load this monstrous script into the database.:
Before moving on to the actual testing, I will describe the process of configuring PostgreSQL + PostGIS + WKTRaster. Since PostgreSQL has been built from source, all the work normally performed by the installer (creating users, initialising database clusters, etc.) has to be done manually.
The compilation process was described in the previous post. Here I will assume that you are using Windows XP Pro; PostgreSQL, GEOS, Proj, PostGIS and WKTRaster are already compiled, and everything is in the c:\postgres directory.
There is a Python extension called matplotlib. It is very convenient and functional, allowing you to easily create and display various graphs and charts. It generates high-quality images and supports adding captions, including various special characters. I used this extension when I needed to output a histogram in my plugin.
That’s why I decided to create a package for the OSGeo4W installer. I read the instructions, experimented on a virtual machine, and here is the result: matplotlib is now available via the OSGeo4W network installer.
Recently, PostGIS has received support for raster data and the ability to load images directly into the database through the WKTRaster extension. This is one of the features that previously fell short compared to ArcSDE.
As soon as raster support became available, it was natural to want to compare PostGIS and ArcSDE. When I saw a forum topic about it, I immediately volunteered to help.
Today I spent most of the day preparing: I downloaded source archives, read installation instructions, and compiled all necessary components. There were a few pitfalls: first, PostgreSQL 8.3.7 refused to compile, saying that utf8_and_shift_jis_2004.o could not be built. After some investigation, I found that the following files are missing
More precisely, they are present, but not in the src directory where the compiler looks for them, but in a completely different location. After moving these files to the correct directory, the compilation was successfully completed. I described the compilation process in detail in the previous post.
All other components were compiled without any issues, the only trouble was that the archive with the SVN version of PostGIS turned out to be “broken”, so I had to re-download it.
The test data set has been downloaded, and all the components have been built. Now I am waiting for the test instructions.
To some extent, PostGIS can be seen as a free, open-source alternative to ArcSDE, but until recently it lacked support for raster data. Lately, a WKTRaster project has been started to fill this gap and develop an extension to support raster data in PostGIS. Perhaps in the near future, PostGIS will be able to offer its users functionality comparable to ArcSDE.
There was an idea to compare the speed of loading a large raster in ArcSDE and PostGIS. Since there are no ready-to-use binary builds of WTKRaster, it is necessary to build and configure the PostgreSQL + PostGIS + WKTRaster bundle before performing any tests.
We will build the latest stable version of PostgreSQL (8.3.7 at the time of writing), the latest SVN version of PostGIS (1.4.0SVN at the time of writing), and the latest SVN version of WKTRaster (0.1.6SVN at the time of writing). This post is based on the official build instructions and blog posts by Mateusz Loskot.
It’s hot, although even during the day the temp doesn’t rise above +25°C. The heat is particularly noticeable when dancing: our classroom is very limited when it comes to ventilation and opening windows, so everyone is sweating within 5-10 minutes of starting the warm-up. On the other hand, it is the perfect time for stretching — muscles warm up quickly and lose heat more slowly. Ira is making the most of this: she has decided to devote the last month of this year’s course to stretching alone.
Earlier I wrote about work on translating the OSGeo4W network installer into Ukrainian. It’s time to report. Translations are done, new resource files are committed to SVN, installers are built, uploaded to the server and available for download. Download links can be found on the OSGeo4W home page.
After spending some time solving various issues, I have finally managed to build the OSGeo4W network installer from source. This is necessary to create a similar installer, but with resource files translated into Ukrainian (and possibly in Russian, if I have enough motivation and time). As for many people, English is still quite a serious obstacle, and besides, it is much more pleasant to communicate with the program in your native language.
Just half an hour ago, my patch for fTools was accepted. The changes are available as of revision f13f45e (SVN r10849). Yay!
I have prepared and submitted my first patch for fTools. The patch allows to use only selected features in the following geoprocessing tools:
Convex hull(s)
Buffers
Intersect
Clip
Difference
Dissolve
I also made changes to the Basic Statistics tool. Namely, I added the ability to work with selected features and added support for text fields (previously only numeric fields were supported).
Ticket in bugtracker #11774. I already got a reply from Carson (fTools author): the patch will be applied soon.
Finally! The simultaneous release of QGIS versions 1.0.2 “Kore” (stable release) and 1.1 “Pan” (unstable release) was officially announced yesterday. The most notable changes are outlined in the release announcement (Ukrainian version is also available) and Tim’s blog post, while the list of fixed bugs can be found in Trac.
Packages for various Linux distributions and a source code archive can be found on the download page. For Windows users there are two options: the OSGeo4W network installer and a standalone all-in-one installer.
After creating my plugin for QGIS (statistics for vector layers), I moved on to improving fTools. Namely, I started adding the ability to work with selected features to the geoprocessing tools.
Last week I finished Buffer and Convex Hull, today I had some free time and updated Dissolve. There are 5 tools left, then I can move on to another group. By a strange coincidence there are also five of them in fTools.