As I alluded to yesterday, and in my post about SQLAWebtrends, I’ve recently been doing a lot of work with the Webtrends analytics service, concerned mostly with getting data out of it via the old Windows ODBC drivers.
While turn around on new data available from reports could cause Methuselah to yawn, it could still be exceedingly time consuming loading up a spreadsheet app, defining queries in an ODBC query builder, and waiting for data to populate sheets; or at best writing several Python functions to query the last data; I would still have to spend tedious amounts of time tweaking and re-tweaking queries for different reports and/or datasets.
This lead me to make WebtrendsQT, a psql/mysql-like command line query tool for Webtrends using pyODBC.
WebtrendsQT is mostly just the ODBC extra tool provided by pyODBC, with some WT-specific changes. Namely the introduction of a “\p” command, which issues the {Call wtGetProfileList()} stored procedure against the WTSystem schema (via the system_cursor property), returning a list of profiles.
Similarly do_l (the handler for “\l”) instead of listing real schemas, lists the Webtrends ODBC equivalent templates.
do_c (“\c”) will work as you’d expect, taking a “schema” (e.g. template), and changing cursor to point to it, but also takes profile GUID as an optional first option to switch both profile and template (profiles define the data source and which report templates are available).
It took me some time to figure out that PyODBC’s lovely columns() method wouldn’t work with the Webtrends driver, as some metadata isn’t provided by the driver and causes a segfault. Instead my hack is to use the DB API Cursor.description to get name and type details for columns on a table, unfortunately in order to get this information I need a cursor that specifically targets the table in question; and to get around this I make a simple query against the table that won’t return any information, but will still return a cursor:
@memoized()
def get_columns(self, name):
columns = [['Column name', 'Type', 'Size',]]
row = self.cursor.execute(
'SELECT * FROM %s LIMIT 0' % (name,)
).fetchone()
for r in row.cursor_description:
columns.append(
[r[0],
self.db_types[r[1]],
r[3],]
)
return columns
cursor_description is PyODBC’s special “always available even after query-set has been closed” reference to the cursor.description instance.
Unlike pyDBCLI.extras.odbc, WebtrendsQT takes a set of arguments rather than a single DSN string, due to the ODBC driver requiring a specific set of details to connect.
You most likely just want to install and run the tool under Windows, which if you have any experience with Python on Windows should be easy enough using easy_install or the included setup.py; if however you don’t have any Python-Windows experience and just want to get up and running with WebtrendsQT, the FAQ has a 5 step simple guide, including a pre-rolled pair of Windows scripts, that will install everything and create a batch script with all the Python paths set up to use.
When installed just type wtqt in the cmd.exe Window, provided by the batch script, and away you go.
C:\Users\test\Desktop> wtqt ERROR: Must have a profile GUID, -p Usage: wtqt.py [-u <user>] [-p <pass>] -d <system DSN> -h <host> [-P <port>] -t <template> -p <profile> Options: -d, --systemdsn: Predefined system DSN -p, --profile : Webtrends profile GUID -t, --template : Template/schema -h, --host : Webtrends web instance -P, --port : Optional server port (default: 80) -u, --username: Optional username -k, --password: Optional password
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