Without compression the full compiled ppd set is about ~220MB, with pyppd it gets down to ~330KB.<br><font color="#888888"><br>Daniel</font><br><br><div class="gmail_quote">2011/8/17 Till Kamppeter <span dir="ltr"><<a href="mailto:till.kamppeter@gmail.com">till.kamppeter@gmail.com</a>></span><br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;"><div><div></div><div class="h5">On 08/17/2011 11:11 AM, Tim Waugh wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
On Tue, 2011-08-16 at 20:08 +0200, Till Kamppeter wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
Thank you. Using pyppd-compressed PPD archives the listing of the PPD<br>
files take well less than a second on my computer and extracting the<br>
mentioned PPD file takes the same order of magnitude of time as SQLite.<br>
<br>
For most CUPS users the pyppd solution is the best due to the speed.<br>
</blockquote>
<br>
I'm not sure I really understand the purpose of pyppd. Is it just to<br>
save disk space? CUPS implements its own fast indexing of PPD files, so<br>
performance seems to be covered there.<br>
</blockquote>
<br></div></div>
For ready-made (static) PPDs, like the ones from the printer manufacturers for PostScript printers, it is only saving disk space. Putting them all into one archive compresses them much more than individually gzipping them.<br>
<br>
Replacing PPD generators like /usr/lib/cups/driver/foomatic by pyppd-generated PPD archives is also a vast improvement in speed. It is much faster to grab a PPD out of a compressed archive then putting it together from the Foomatic XML data.<br>
<font color="#888888">
<br>
Till<br>
</font></blockquote></div><br>