For me it has been useful in the past to track overall database query
speed so I could optimize the query taking the most aggregate time.
(I.e., execution time * times executed.) It looks to me like this
could be hooked in to SA pretty easily, with just a minor change to
Connection._execute_raw, using statement as the key to aggregate on.
(You could even define two _execute_raws and pick one at runtime to
avoid any overhead when not in profiling mode.) This seems to work
fine:
start = time.time()
if parameters is not None and isinstance(parameters, list)
and len(parameters) > 0 and (isinstance(parameters[0], list) or
isinstance(parameters[0], dict)):
self._executemany(cursor, statement, parameters,
context=context)
else:
self._execute(cursor, statement, parameters, context=context)
end = time.time()
self._autocommit(statement)
profile_data[statement] = profile_data.get(statement, 0) +
(end - start)
Of course, this only tells you what generated SQL is slow, not what
code caused those queries to run, but it's easy enough to grab caller
info from the stack. But am I missing other code paths that would
have to be tracked?
--
Jonathan Ellis
http://spyced.blogspot.com
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