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postgresql_autodoc - Online in the Cloud

Run postgresql_autodoc in OnWorks free hosting provider over Ubuntu Online, Fedora Online, Windows online emulator or MAC OS online emulator

This is the command postgresql_autodoc that can be run in the OnWorks free hosting provider using one of our multiple free online workstations such as Ubuntu Online, Fedora Online, Windows online emulator or MAC OS online emulator

PROGRAM:

NAME


postgresql_autodoc - creates postgresql tables overview

SYNOPSIS


postgresql_autodoc [options]

DESCRIPTION


This is a utility which will run through PostgreSQL system tables and returns HTML, DOT,
and 2 styles of XML which describes the database.

The HTML is human readable (via webbrowser). The first style of XML is actually the
fileformat of Dia, a UML diagram tool. The second type of XML is similar to the HTML but
in the Docbook 4 format. It enables yous to mix in other docbook documentation via the
XREFs, generating PDFs, HTML, RTF, or other formatted documents. Between these tools and
JavaDoc with the appropriate XREFs, documentation about a project can be generated quickly
and be easily updatable yet have a very professional look with some DSSSL work.

OPTIONS


-d <dbname>
Specify database name to connect to (default: $database)

-f <file>
Specify output file prefix (default: $database)

-h <host>
Specify database server host (default: localhost)

-p <port>
Specify database server port (default: 5432)

-u <username>
Specify database username (default: $dbuser)

--password=<pw>
Specify database password (default: blank)

-l <path>
Path to the templates (default: /usr/share/postgresql-autodoc)

-t <output>
Type of output wanted (default: All in template library)

-s <schema>
Specify a specific schema to match. Technically this is a regular expression but
anything other than a specific name may have unusual results.

--statistics
In 7.4 and later, with the contrib module pgstattuple installed we can gather
statistics on the tables in the database (average size, free space, disk space
used, dead tuple counts, etc.) This is disk intensive on large databases as all
pages must be visited.

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