Tonight I spoke at the baypiggies event at Google. My onscreen notes are repeated below.

Standard profile results

 >>> import hotshot, pstats
 >>> p = hotshot.Profile().runcall(main)
 >>> pstats.Stats(p).sort_stats('time').print_stats()

        58666 function calls (56532 primitive calls) in 0.980 CPU seconds

  Ordered by: internal time

  ncalls  tottime  percall  cumtime  percall filename:lineno(function)
       1    0.240    0.240    0.240    0.240 :0(create)
     472    0.220    0.000    0.220    0.000 :0(call)
    1696    0.050    0.000    0.050    0.000 sre_parse.py:182(__next)
    6022    0.030    0.000    0.030    0.000 :0(append)
  181/27    0.030    0.000    0.060    0.002 sre_compile.py:27(_compile)
     216    0.020    0.000    0.030    0.000 interface.py:850(fromFunction)
      26    0.020    0.001    0.020    0.001 advice.py:69(addClassAdvisor)
      23    0.020    0.001    0.220    0.010 netconnect:125(__init__)
1381/201    0.020    0.000    0.040    0.000 ro.py:58(_flatten)
       1    0.020    0.020    0.980    0.980 netconnect:23(?)
       1    0.020    0.020    0.020    0.020 Tkinter.py:31(?)
       1    0.010    0.010    0.010    0.010 urllib.py:1086(quote)
    2590    0.010    0.000    0.010    0.000 :0(get)
       1    0.010    0.010    0.010    0.010 compat.py:115(Connection)
       1    0.010    0.010    0.010    0.010 tksupport.py:24(?)

Displaying as html

Other web graphs: /home/drewp/projects/ffg-dash/test/sparqlBench.py

% ./pstats2web /tmp/pro -o out.html

Displaying with kcachegrind

Ubuntu packages: kcachegrind-convertors, kcachegrind

% hotshot2calltree /tmp/pro > calltree

% kcachegrind calltree

see callee map of the 'parseTerm' function

Displaying with exhibit

Check out exhibit at http://simile.mit.edu/exhibit/

% ./pstats2web /tmp/pro -j out.js -m 60

View profileResults.html in a browser, which reads out.js from the same directory.

Still looking for

statistical profiler to leave running all the time
at least a faster reporter (lsprof?)
something to charge twisted deferred callbacks to their creator function instead of to the eventloop
more useful filter groups in exhibit. callees? file is involved in top 10?

http://revyu.com gathers people's reviews of anything on the web. The web is so big that you don't even think of lots of things that you could review and would be happy to review. So what happens is that when you come to http://revyu.com, you notice the list of recent reviews, realize that you are familiar with some of them, so you pile on a few more reviews of your own.

That mechanism for capturing more reviews won't scale very well if revyu.com becomes a lot more popular. Every time I visit, there could be a huge number of new reviews. I might like to look through them to see what I can offer, but I don't have time to scan the list of 80 new links.

This nearly-useless css hack can help. It measures the colors that your browser applies to visited/non-visited links to separate a known set of links into the ones you've seen and (probably) haven't seen. That's just what I want on revyu.com-- a list of the recently-reviewed URLs that I have also seen. It's fine to show all the recent reviews too, just for interest, but I'm much less likely to have a pile-on review for those.

I wrote yet another web cam tool. This one takes a "GET /" request and returns one frame grabbed from a usb camera with v4l.

Then I got distracted on my python profile result viewers, and worked with dreid on making them cooler. I presented the results of that at google the next week.

drewp for 2007 February
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drewp for 2007 February

Standard profile results

Displaying as html

Displaying with kcachegrind

Displaying with exhibit

Still looking for