Logistics: Getting Connected and Executing Queries
Building and Saving SQL code snippets
A simple text editor is the best environment for working on (and saving!) your SQL queries. Linux users are encouraged to use either 'Kate' or 'Kwrite'. Windows users should look into Notetab Light or SciTE. Mac users should check out TextWrangler.
Connecting to the database
In general, the simplest way to interact with our composite soil survey database is by connecting to the best with an SSH client. Mac/Linux users have this functionality built-in. Windows users will need to use something like Putty, or Xming. Once an ssh connection with the beast has been setup, you can connect to the database with the following:
psql -U soil ssurgo_combined
where psql is the postgresql client program, -U soil means connect as the user called "soil", and ssurgo_combinedis the name of the actual database.
You can quit from the database shell using \q (followed by enter). Typing \? (followed by enter) will give a list of commands available in the psql shell.
Query structure and SQL
Numerous resources exisit for learning about SQL. See the attached PDF presentation at the bottom of this page for some SSURGO-related examples. I would recommend looking at the first couple chapters from the PostgreSQL book by Douglas and Douglas (the big purple book on the shelf). For an interactive learning approach SQL Zoo seems like a good start. In general most queries will have the format:
SELECT column_x, column_y, column_z FROM table_x WHERE column_x = 'something' -- optional GROUP BY column_x ORDER BY column_x ; -- semi-colon denotes end of SQL statement
A simple query: column selection and filtering
Once connected it is possible to issue queries to the database. The results of a query are normally returned to the screen. For example, asking for the horizon boundaries and horizon names from the component identified with the given cokey (467038:646635) might look like this:
-- the query SELECT cokey, hzname, hzdept_r, hzdepb_r FROM chorizon WHERE cokey = '467038:646635' ; -- the results cokey | hzname | hzdept_r | hzdepb_r ---------------+--------+----------+---------- 467038:646635 | Ap | 0 | 18 467038:646635 | Bw | 18 | 41 467038:646635 | Bk1 | 41 | 69 467038:646635 | Bk2 | 69 | 109 467038:646635 | Bk3 | 109 | 145 467038:646635 | Bk4 | 145 | 183
A more complicated query: column calculation and aggregation
Compute the total water holding capacity for a given component, identified by (467038:646635). Note that several operations are being performed on the data:
- compute the available water holding capacity (in centemeters) for each horizon [green text]
- compute the profile depth (in centemeters) for each horizon [green text]
- aggregate the profile data by summing AHC and depth of each horizon [red text]
-- the query SELECT cokey, sum( (hzdepb_r - hzdept_r) * awc_r) AS component_whc, sum( (hzdepb_r - hzdept_r) ) AS depth FROM chorizon WHERE cokey = '467038:646635' GROUP BY cokey ; -- the results cokey | component_whc | depth ---------------+---------------+------- 467038:646635 | 27.91 | 183
Note that it is also possible to save the results of a query into a new table. This is the usual strategy when performing any analysis that returns geometry (soil polygons, etc.) as geometry data cannot be visualized in a text display. Tables created in this manner can be exported from the database for map creation or further analysis. Spatial tables can be viewed directly by applications like QGIS or Mapserver. A simple method of accessing PostGIS data is not yet possible with ArcGIS.