help with making my code more efficient

Mitya Sirenef msirenef at
Fri Dec 21 03:39:09 CET 2012

On 12/20/2012 08:46 PM, Larry.Martell at wrote:
> On Thursday, December 20, 2012 6:17:04 PM UTC-7, Dave Angel wrote:
>> On 12/20/2012 07:19 PM, Larry.Martell at wrote:
>>> I have a list of tuples that contains a tool_id, a time, and a message. I want to select from this list all the elements where the message matches some string, and all the other elements where the time is within some diff of any matching message for that tool.
>>> Here is how I am currently doing this:
>> No, it's not.  This is a fragment of code, without enough clues as to
>> what else is going.  We can guess, but that's likely to make a mess.
> Of course it's a fragment - it's part of a large program and I was just showing the relevant parts.
>> First question is whether this code works exactly correctly?
> Yes, the code works. I end up with just the rows I want.
>> Are you only concerned about speed, not fixing features?
> Don't know what you mean by 'fixing features'. The code does what I want, it just takes too long.
>> As far as I can tell, the logic that includes the time comparison is bogus.
> Not at all.
>> You don't do  anything there to worry about the value of tup[2], just whether some
>> item has a nearby time.  Of course, I could misunderstand the spec.
> The data comes from a database. tup[2] is a datetime column. tdiff comes from a datetime.timedelta()
>> Are you making a global called 'self' ?  That name is by convention only
>> used in methods to designate the instance object.  What's the attribute
>> self?
> Yes, self is my instance object. self.message contains the string of interest that I need to look for.
>> Can cdata have duplicates, and are they significant?
> No, it will not have duplicates.
>> Are you including  the time building that as part of your 2 hour measurement?
> No, the 2 hours is just the time to run the
> cdata[:] = [tup for tup in cdata if determine(tup)]
>> Is the list sorted in any way?
> Yes, the list is sorted by tool and datetime.
>> Chances are your performance bottleneck is the doubly-nested loop.  You
>> have a list comprehension at top-level code, and inside it calls a
>> function that also loops over the 600,000 items.  So the inner loop gets
>> executed 360 billion times.  You can cut this down drastically by some
>> judicious sorting, as well as by having a map of lists, where the map is
>> keyed by the tool.
> Thanks. I will try that.
>>> # record time for each message matching the specified message for each tool
>>> messageTimes = {}
>> You're building a dictionary;  are you actually using the value (1), or
>>   is only the key relevant?
> Only the keys.
>> A set is a dict without a value.
> Yes, I could use a set, but I don't think that would make it measurably faster.
>> But more mportantly, you never look up anything in this dictionary.  So why
>> isn't it a list?  For that matter, why don't you just use the
>> messageTimes list?
> Yes, it could be a list too.
>>> for row in cdata:   # tool, time, message
>>>      if self.message in row[2]:
>>>          messageTimes[row[0], row[1]] = 1
>>> # now pull out each message that is within the time diff for each matched message
>>> # as well as the matched messages themselves
>>> def determine(tup):
>>>      if self.message in tup[2]: return True      # matched message
>>>      for (tool, date_time) in messageTimes:
>>>          if tool == tup[0]:
>>>              if abs(date_time-tup[1]) <= tdiff:
>>>                 return True
>>>      return False
>>> cdata[:] = [tup for tup in cdata if determine(tup)]
>> As the code exists, there's no need to copy the list.  Just do a simple
>> bind.
> This statement is to remove the items from cdata that I don't want. I don't know what you mean by bind. I'm not familiar with that python function.
>>> This code works, but it takes way too long to run - e.g. when cdata has 600,000 elements (which is typical for my app) it takes 2 hours for this to run.
>>> Can anyone give me some suggestions on speeding this up?

This code probably is not faster but it's simpler and may be easier for 
you to work with
to experiment with speed-improving changes:

diffrng = 1

L = [
      # id, time, string
      (1, 5, "ok"),
      (1, 6, "ok"),
      (1, 7, "no"),
      (1, 8, "no"),

match_times = [t[1] for t in L if "ok" in t[2]]

def in_range(timeval):
     return bool( min([abs(timeval-v) for v in match_times]) <= diffrng )

print([t for t in L if in_range(t[1])])

But it really sounds like you could look into optimizing the db
query and db indexes, etc.

Lark's Tongue Guide to Python:

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