Plasma & the libferris DataEngine

In a previous post I mentioned that libferris can now mount Plasma DataEngines. Of course, the opposite would have to follow; you can now access the entire virtual filesystem of libferris as a Plasma DataEngine. For those who are unfamiliar with libferris, it is my little virtual filesystem project which can mount xml, isam files, relational databases, flickr, vimeo, google spreadsheets, Firefox, XWindows, and shall we say one or two other things :)

To play around, I now have a DataEngine that ships with libferris itself (which exposes libferris to plasma), and a few custom plasmoids which basically ask the libferris data engine to "cat" files and metadata.

One of the things that libferris can mount is the XWindow system. This lets you see all your windows and their size and location. This data is exposed in xwin://localhost/window and the Extended Attributes (EA) x,y show the x and y position of each window. For example, the below command will show you the name and location of the window "foo" on your local X:
fls --show-ea=name,x,y xwin://localhost/window/foo

The libferris data engine makes "sources" on demand. You ask for a source by supplying the URL you want to read, the data engine makes the source for you and the content key contains the contents of that URL. If you want to get at metadata through the EA interface, just use @attribute in XML fashion.

The two plasmoids I made are libferris_cat and libferris_graph. The former just shows you text of the URL you have configured, the latter allows up to four files to be read and graphed. Obviously the latter plasmoid is meant for numeric data.

So to see the location of a window with ferris_cat set the URL to:
xwin://localhost/window/foo@x

Which is what I've done in the below video. Notice that there are two plasmoids so I can track the X and Y ordinates of the window as I move it.

plasma-cat-window-position-encoded.avi from Ben Martin on Vimeo.

The same data is shown using ferris_graph below.

plasma-graph-window-position-encoded.avi from Ben Martin on Vimeo.

Libferris can also mount postgresql and other relational databases. For postgresql you can run SQL and execute database functions through the filesystem as well as interact with the base tables. Lets assume you have a simple database like the one shown in the below setup:

drop database testplasma;
create database testplasma;
\c testplasma

create table folks ( name varchar, salary int, id serial );
create view stats as
select min(salary) as min, max(salary) as max, avg(salary) as avg
from folks;

insert into folks values ( 'Fred', 15 );
insert into folks values ( 'Mary', 17 );
insert into folks values ( 'Henry', 21 );

select * from stats;
min | max | avg
-----+-----+---------------------
15 | 21 | 17.6666666666666667
(1 row)

To get at this with libferris you might start by probing around the pg:// or postgresql:// URLs:

fls pg://localhost/testplasma
folks stats

fls -0 pg://localhost/testplasma/stats
15 21 17.6666666666666667 17.6666666666666667-21-15 avg-max-min
Adding the --xml switch to shows something including:
avg="17.6666666666666667" max="21" min="15"
name="17.6666666666666667-21-15" primary-key="avg-max-min"

Because the view has no primary key, libferris has used the values of the whole tuple to form a unique file name. This is less than optimal for our needs when using the DataEngine because we want a stable filename. The solution is to leave out the name and just use "*" to have libferris expand it for us! So the below URL will have ferris_cat track the "min" value in the view:
pg://localhost/testplasma/stats/*@min

The below video shows ferris_cat on the left viewing the min, the ferris_graph in the center shows min, max and average, and the avg is shown in the ferris_cat on the right. I add and remove a few folks from the table to see the effect on the plasmoids.

plasma-ferris-postgresql-encoded.avi from Ben Martin on Vimeo.

Of course, for a production PostgreSQL server you would use a scratch table and triggers to update it so that aggregates are not computed over mid to large sized tables all the time. Another advantage of triggers and a scratch table is you can easily handle rolling averages and delve into more advanced statistics while keeping the overhead known.

In short, if you can ferrisls and fcat something interesting, you should be able to drop it onto your desktop and monitor it now too :) All I need now is to get plasma onto my n810 :/ I have a feeling I'll be playing with tracking facebook and online spreadsheets using plasma+ferris soon ;p