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Functional Programming Goodness -- Python to the Rescue

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Functional Programming Goodness -- Python to the Rescue

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Here's the situation.

A vendor sent us three separate files which need to be merged. 70,000+ records each. They're CSV files, so column position doesn't much matter. The column name (in row 1) is what matters.

I looked at three solutions. Two of which are merely OK. The third was some functional programming that was very cool.

Option 1 -- OS Sort/Merge

To get the files into a consistent order, we need to sort. The Linux sort, however, is biased toward columns that are known positionally.

So, we need to exploit the Decorate-Sort-Undecorate design pattern. So we have a shell script something like the following.
decorate.py a.csv | sort >a_sorted.csv
decorate.py b.csv | sort >b_sorted.csv
decorate.py c.csv | sort >c_sorted.csv
sort -m a_sorted.csv b_sorted.csv c_sorted.csv | undecorate.py >result.csv

This works well because decorate.py and undecorate.py are such simple programs. Here's decorate.py.
from __future__ import print_function
import csv
import sys
with open(sys.argv[1],"rb") as source:
rdr= csv.DictReader( source )
for row in rdr:
  print( row['the key'], row )

Undecorate is similar. It uses the str.partition() method to remove the decoration.

Note that the initial "decorate" steps can be run concurrently, leading to some time reduction. This scales well. It doesn't use much memory; the OS concurrency management means that it uses every core available.

I didn't benchmark this, BTW.

Option 2 -- Big In-Memory Dict

Since the files aren't insanely big, they do fit in memory. This is pretty simple, also.
import csv
from collections import defaultdict
# build the result set
result = defaultdict( dict )
for f in ( 'a.csv', 'b.csv', 'c.csv' ):
with open( f, 'rb' ) as source:
  rdr = csv.DictReader( source )
  for row in rdr:
      result[row['key']].update( row )
# find the column titles
keys = set()
for row in result:
  keys |= set( result[row].keys() )
# write the result set
with open( 'output.csv', 'wb' ) as target:
  wtr= csv.DictWriter( target, sorted(keys) )
  wtr.writerow( dict(zip(keys,keys)) )
  for row in result:
      wtr.writerow( result[row] )

This isn't too bad. For insanely big files, however, it won't scale well.

Elapsed time for the real files (which were zipped, adding processing that's not relevant to this posting) was 218 seconds on my little laptop.

Option 3 -- Functional Programming

The functional programming approach is a bit more code than option 1. But it's way cool and very extensible. It offers more flexibility without the memory limitation of the big dictionary.

Let's start with the end in mind.

We're doing a 3-file merge. The algorithm for 2-file merge is really simple. The algorithm for an n-file merge, however, is not so simple. We can easily build up an n-file merge as a composition of n-1 pair-wise merges.

Here's how it should look.
with open('temp.csv','wb') as output:
  wtr= csv.DictWriter( output, sorted(fieldNames) )
  wtr.writerow( dict( zip( fieldNames, fieldNames )))
  for row in merge( merge( s1, s2 ), s3 ):
      wtr.writerow( row )
We're doing merge( merge( s1, s2 ), s3 ) to compose a 3-file merge from 2 2-file merges. And yes, it can be just that simple.

Composable Sort

To be "composable", we must write iterator functions which read and write data of the same type. In our case, since we're using a DictReader, our various functions must work with an iterable over dicts which yields dicts.

In order to merge, the input must be sorted. Here's our composable sort.
def key_sort( source, key='key' ):
  def get_key( x ):
     return int(x[key])
  for row in sorted(source, key=get_key ):
     yield row
Yes, we need to pre-process the keys, they're not simple text; they're numbers.

Composable 2-File Merge

The composable merge has a similar outline. It's a loop over the inputs and it yields outputs of the same type.

def merge( f1, f2, key='key' ):
"""Merge two sequences of row dictionaries on a key column."""
r1, r2 = None, None
r1= f1.next()
r2= f2.next()
while True:
    if r1[key] == r2[key]:
        yield r1
        r1, r2 = None, None
        r1= f1.next()
        r2= f2.next()
    elif r1[key] < r2[key]:
        yield r1
        r1= None
        r1= f1.next()
    elif r1[key] > r2[key]:
        yield r2
        r2= None
        r2= f2.next()
        raise Exception # Yes, this is impossible
except StopIteration:
if r1 is not None:
    yield r1
    for r1 in f1:
       yield r1
elif r2 is not None:
    yield r2
    for r2 in f2:
        yield r2
    pass # Exhausted with an exact match.

This runs in 214 seconds. Not a big improvement in time. However, the improvement in flexibility is outstanding. And the elegant simplicity is delightful. Having the multi-way state managed entirely through the Generator Function/Iterator abstraction is amazing.

Also, this demonstrates that the bulk of the time is spent reading the zipped CSV files and writing the final CSV output file. The actual merge algorithm doesn't dominate the complexity.

Take a look at an Indigo.Design sample application to learn more about how apps are created with design to code software.


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