On Apr 29, 5:21 pm, Laszlo Nagy <gand...@shopzeus.com> wrote:
> I have some ten thousand rows in a table. E.g.
>
> columns = ["color","size","weight","value"]
> rows = [
> [ "Yellow", "Big", 2, 4 ],
> [ "Blue", "Big", 3, -4 ],
> [ "Blue", "Small", 10, 55 ],
> ...
> ]
>
> Some columns are dimensions, others are measures. I want to convert this
> to an indexed version, that looks like this:
>
> dimension_names = ["color","size"] # List of dimension names
> dimension_cols = [0,1] # Column indexes for dimension values
> dimension_values = { # Dimension value occurences for each dimension
> 0: ["Yellow","Blue",....],
> 1: ["Big","Small",...],}
>
> measure_names = ["weight","value"] # List of measure names
> measure_cols = [2,3] # List of measure columns
> facts = [ # Facts, containing tuples of (dimension_value_incides,
> measure_values )
> ( (0,0) , (2,4) ),
> ( (1,0) , (3,-4) ),
> ( (1,1) , (10,55) ),
> ...
> ]
>
> This is how I try to convert to the indexed version:
>
> #1. Iterate over all rows, and collect possible dimension values.
>
> cnt = 0
> for row in iterator_factory():
> cnt += 1
> for dimension_idx,col_idx in enumerate(dimension_cols):
> dimension_values[colidx].append(row[cold_idx])
> if cnt%10000:
> dimension_values[colidx] = list(set(dimension_values[colidx]))
>
> #2. Index facts by dimension values
>
> facts = []
> for row in iterator_factory():
> dv = []
> for dimension_idx,col_idx in enumerate(dimension_cols):
> dv.append( dimension_values[col_idx].index(row[col_idx]) ) #
> THIS IS THE PROBLEMATIC LINE!
> mv = [ row[col_idx] for col_idx in measure_cols ]
> facts.append( dv,mv )
>
> (In reality, rows and facts are not stored in memory, because there can
> be 100 000+ facts. I did not want to bore you with the full source code.)
>
> And finally, here is my problem. If a dimension has many possible
> values, then the list.index() code above slows down eveything. In my
> test case, the problematic dimension had about 36 000 different values,
> and the number of rows was about 60 000. Calling index() on a list of 36
> 000 values, times 60 000, is too slow.
>
> Test performance was 262 rows/sec. If I use dv.append(0) instead of "
> dv.append( dimension_values[col_idx].index(row[col_idx]) ) " then it
> goes up to 11520 rows/sec. If I exclude the problematic dimension, and
> only leave the others (with 1000 or less values) then goes up to 3000
> rows/sec.
>
> Maybe this should be implemented in C. But I believe that the algorithm
> itself must be wrong (regardless of the language). I really think that
> I'm doing something wrong. Looks like my algorithm's processing time is
> not linear to the number of rows. Not even log(n)*n. There should be a
> more effective way to do this. But how?
>
> I had the idea of sorting the rows by a given dimension. Then it would
> be obvious to speed up the indexing part - for that dimension. PROBABLY
> sorting all rows would be faster than calling list.index() for each row.
> But... it does not seem very efficient either. What if I have 1 million
> rows and 10 dimensions? Do I sort 1 million rows on the disk 10 times?
> Some of you might have ran into the same problem before, and can tell me
> which is the most efficient way to do this.
>
> Thanks,
Have you considered using a SQL database? Personally, I would use
MS-Access and link it to Python via ODBC. That way, I could use
the Access drag-and-drop design tools and either
- copy the SQL code of working query designs to Python
or
- use the ODBC to link to said queries rather than directly
to the raw tables
Of course, Python has SQLlight now, I just don't know SQL that well.
>
> Laszlo
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