subs benchmark

For curiosity, a very simple performance look at doing only string substitutions -- thus for this one we can even compare against string.Template from the python standard library. We test a small template, mostly static text with just 6 variable substitutions.

For each run, time is averaged over 2000 renderings, and the best time of 4 runs is retained. All times are in ms. What you should want is automatic quoting and less time.

quotingtemplate.Stringqpy 1.7evoque 0.4mako 0.2.4genshi_text 0.5.1
automatic0.0280.0340.112
automaticR(a) 0.038
manual0.038 (b)0.1070.169
none0.0500.0250.084

(a) Restricted -- qpy 1.7 / mako 0.2.4 / genshi 0.5.1 offer no support for restricted execution or sandboxing
(b) manual quoting implies no qpy

template and data

The (automatically quoted) evoque template.

<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" 
    "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8" />
<meta http-equiv="Content-Style-Type" content="text/css; charset=UTF-8" />
<meta http-equiv="imagetoolbar" content="no" />
<style type="text/css">
.signature { color: #977; font-weight: bold; }
</style>
<title>${title}</title>
</head>
<body>
<p>Welcome back ${first}, you are logged in as </code>${username}<code> 
(last login: ${last}).</p>
<p>Your balance is: ${balance}</p>
<p>${comment}</p>
</body>
</html>

DATA = dict(title='Your balance', first="Joey", username='joe123', 
    last="2008-02-29", balance=789.19, 
    comment="Thank you <b>very</b> much!")

platform

The actual times are coming off a MacBook Pro with 2.4 GHz Intel Core 2 Duo, 2 GB of RAM, and Mac OS X 10.5, running Python 2.6.1.

caution

Please remember that performance benchmarks are only relevant when considered within an entire context, and they may vary enormously between different combinations of hardware and software, even if those difference may appear to be very slight. In addition, two different systems never quite do precisely the same thing, however simple and apparently identical the timed task may be.