Functions can be created in the language of your choice like SQL, PL/pgSQL, C, Python, etc.

N.B. PL/Python is only available as an "untrusted" language, meaning it does not offer any way of restricting what users can do in it and is therefore named plpythonu.

The most common language for creating function is PL/pgSQL

Here is the basic syntax of a function:

Here is a very simple example:

Although function names do not need to be schema qualified, it is recommended that they are placed in a schema. If no schema is specified they will be created in the public schema.

To run the above function you would simply call the function via a select clause. e.g.

For more details on function see https://www.postgresql.org/docs/9.4/static/sql-createfunction.html

For more details on PL/pgSQL see https://www.postgresql.org/docs/9.4/static/plpgsql.html

Python Functions

You can write functions in python (and many other languages), first create the plpython3u extension using CREATE EXTENSION IF NOT EXISTS plpython3u. Then you can create a function using code like:

The lines above the first $$ define the function and it's input and output parameters, then the code between the two $$ marks is a string containing the python code.

First we import a module (xlrd) that handles reading .xls files from Excel. Then we open the workbook contained in the file who's name was passed in, and find the first sheet in the workbook. Finally, we loop through the rows (after skipping row 0 - the header) and return (or yield) the value of the first three cells in the current row. Using yield rather than return means our function can keep track of which row it's on.

The PL/Python extension also provides a python module plpy that provides access to the database. This allows you to query the catalog tables and run {{UPDATE}}s on tables based on the answers.

Triggers

A trigger is a set of actions that are run automatically when a specified change operation (SQL INSERT, UPDATE, DELETE or TRUNCATE statement) is performed on a specified table. Triggers are useful for tasks such as enforcing business rules, validating input data, and keeping an audit trail.

Create trigger

A trigger is a named database object that is associated with a table, and it activates when a particular event (e.g. an insert, update or delete) occurs for the table/views. The statement CREATE TRIGGER creates a new trigger in PostgreSQL. Here is the syntax :

Syntax

Parameters

Triggers that are specified to fire INSTEAD OF the trigger event must be marked FOR EACH ROW, and can only be defined on views. BEFORE and AFTER triggers on a view must be marked as FOR EACH STATEMENT. In addition, triggers may be defined to fire for TRUNCATE, though only FOR EACH STATEMENT. The following table summarizes which types of triggers may be used on tables and views:

Here is a simple example of trigger function.:

Now we can create the trigger which will fire at the time of execution; the event as specified in the trigger for the associated tables.

In the above trigger function there is new keyword 'NEW' which is a PostgreSQL extension to triggers. There are two PostgreSQL extensions to trigger 'OLD' and 'NEW'. OLD and NEW are not case sensitive.

• Within the trigger body, the OLD and NEW keywords enable you to access columns in the rows affected by a trigger

• In an INSERT trigger, only NEW.col_name can be used.

• In a UPDATE trigger, you can use OLD.col_name to refer to the columns of a row before it is updated and NEW.col_name to refer to the columns of the row after it is updated.

• In a DELETE trigger, only OLD.col_name can be used; there is no new row.

A column named with OLD is read only. You can refer to it (if you have the SELECT privilege), but not modify it. You can refer to a column named with NEW if you have the SELECT privilege for it. In a BEFORE trigger, you can also change its value with SET NEW.col_name = value if you have the UPDATE privilege for it. This means you can use a trigger to modify the values to be inserted into a new row or used to update a row. (Such a SET statement has no effect in an AFTER trigger because the row change will have already occurred.)

Here is another example of a trigger, which writes to an audit table.

For more information on triggers and trigger functions see https://www.postgresql.org/docs/9.4/static/plpgsql-trigger.html

Python

To use PostGIS from a Python application you need the Psycopg adapter so you can access PostgreSQL from Python.

Psycopg

Psycopg is the most popular PostgreSQL database adapter for the Python programming language. Its main features are the complete implementation of the Python DB API 2.0 specification and the thread safety (several threads can share the same connection). It was designed for heavily multi-threaded applications that create and destroy lots of cursors and make a large number of concurrent INSERTs or UPDATEs.

Psycopg 2 is mostly implemented in C as a libpq wrapper, resulting in being both efficient and secure. It features client-side and server-side cursors, asynchronous communication and notifications, COPY TO/COPY FROM support. Many Python types are supported out-of-the-box and adapted to matching PostgreSQL data types; adaptation can be extended and customized thanks to a flexible objects adaptation system.

Psycopg 2 is both Unicode and Python 3 friendly.

On Windows machines use the following to install psycopg2.

Very simple examples of using Psycopg2

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What about geometry?

For a simply point you can easily use the appropriate PostGIS functions e.g.

For more complicated geometries, such as LineString and Polygon geometries, you can handle them with a number of tools including

• Python GDAL/OGR for reading and writing geo-spatial data (https://pcjericks.github.io/py-gdalogr-cookbook/ )

• Shapley for analysing and manipulating geo-spatial data (https://pypi.python.org/pypi/Shapely )

• GeoDjango (https://docs.djangoproject.com/en/1.10/ref/contrib/gis/tutorial/ )

• GeoAlchemy 2 (http://geoalchemy-2.readthedocs.io/en/latest/ )

Useful references:

Python Geospatial Development, Erik Westra	Geoprocessing with Python, Chris Garrard

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Simple example using Shapely

Shapely does manipulating and analyzing data. It’s based on GEOS, the libraries used by PostGIS. With Shapely, you can do things like buffers, unions, intersections, centroids, convex hulls.

Shapely, then passes them through psycopg2 as hex-encoded WKB. Note that Shapely 1.3 or later is required to handle the export of 3D geometries with the wkb_hex property.

Of course this can be accomplished by sending the geometry's WKT, however since it is converted to text, it is lossy and may reduce angstroms of precision. Transferring geometries as hex-encoded WKB is lossless, and preserves the exact precision of each coordinate.