Issue with CSV file when file is large

Your code is very inefficient in the sense that it opens and appends data for each line/row of the input file it processes—which will be a tremendous number of times if the input file is that huge (because the OS calls that are needed to do so that are relatively slow).

Plus there's at least one bug in your code I noticed—namely the line:

save_path += cik + ".csv"

which just keeps making save_path longer and longer...not what would be needed.

Anyway, here's something that should work faster, although it will likely still take a fairly long time to process a file that big. It speeds the process up by caching intermediate results. It does this by only opening the different output csv files and creating their corresponding csv.writer objects as infrequently as possible, the first time they are needed and again only if they got closed because the cache reached its maximum length.

Note that the cache may consume lot of memory iteself depending on how many unique csv output files there are and how many of them can be opened at the same time—but using a lot of memory is what makes it run faster. You'll need to play around and manually adjust the MAX_OPEN value to find the best trade-off between speed and memory-usage, all while staying below your OS's limit of how many files are allow to be opened at one time.

Also note that it might be possible to make what it does even more efficient by more intelligently choosing which existing file entry to close rather than just picking (an open) one at random. However, whether doing that would really help depends on whether there's any sort of advantageous grouping or other order to the data in the input file.

import csv
import os
import random

class CSVWriterCache(dict):
    """ Dict subclass to cache pairs of csv files and associated
        csv.writers. When a specified maximum number of them already
        exist, a random one closed, but an entry for it is retained
        and marked "closed" so it can be re-opened in append mode
        later if it's ever referenced again. This limits the number of
        files open at any given time.
    """
    _CLOSED = None  # Marker to indicate that file has seen before.

    def __init__(self, max_open, **kwargs):
        self.max_open = max_open
        self.cur_open = 0  # Number of currently opened csv files.
        self.csv_kwargs = kwargs  # keyword args for csv.writer.

    # Adding the next two non-dict special methods makes the class a
    # context manager which allows it to be used in "with" statements
    # to do automatic clean-up.
    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.close()

    def __getitem__(self, k):
        if k not in self:
            return self.__missing__(k)
        else:
            try:
                csv_writer, csv_file = self.get(k)
            except TypeError:  # Needs to be re-opened in append mode.
                csv_file = open(k, 'a', newline='')
                csv_writer = csv.writer(csv_file, **self.csv_kwargs)

            return csv_writer, csv_file

    def __missing__(self, csv_file_path):
        """ Create a csv.writer corresponding to the file path and add it
            and the file to the cache.
        """
        if self.cur_open == self.max_open:  # Limit?
            # Randomly choose a cached entry with a previously seen
            # file path that is still open (not _CLOSED). The associated
            # file is then closed, but the entry for the file path is
            # left in the dictionary so it can be recognized as having
            # been seen before and be re-opened in append mode.
            while True:
                rand_entry = random.choice(tuple(self.keys()))
                if self[rand_entry] is not self._CLOSED:
                    break
            csv_writer, csv_file = self[rand_entry]
            csv_file.close()
            self.cur_open -= 1
            self[rand_entry] = self._CLOSED  # Mark as previous seen but closed.

        csv_file = open(csv_file_path, 'w', newline='')
        csv_writer = csv.writer(csv_file, **self.csv_kwargs)
        self.cur_open += 1

        # Add pair to cache.
        super().__setitem__(csv_file_path, (csv_writer, csv_file))
        return csv_writer, csv_file

    # Added, non-standard dict method.
    def close(self):
        """ Close all the opened files in the cache and clear it out. """
        for key, entry in self.items():
            if entry is not self._CLOSED:
                entry[1].close()
                self[key] = self._CLOSED  # Not strictly necessary.
                self.cur_open -= 1  # For sanity check at end.
        self.clear()
        assert(self.cur_open == 0)  # Sanity check.

if __name__ == '__main__':
    file_path = "./master.tsv"
    save_path = "./data-sorted"
    MAX_OPEN  = 1000  # Number of opened files allowed (max is OS-dependent).
#    MAX_OPEN  = 2  # Use small value for testing.

    # Create output directory if it does not exist.
    if os.path.exists(save_path):
        if not os.path.isdir(save_path):
            raise RuntimeError("Path {!r} exists, but isn't a directory")
    else:
        print('Creating directory: {!r}'.format(save_path))
        os.makedirs(save_path)

    # Process the input file using a cache of csv.writers.
    with open(file_path, 'r') as masterfile, \
         CSVWriterCache(MAX_OPEN, quoting=csv.QUOTE_ALL) as csv_writer_cache:
        for line in masterfile:
            line_split = line.rstrip().split("|")
            cik = line_split[0].zfill(10)

            save_file_path = os.path.join(save_path, cik + ".csv")
            writer = csv_writer_cache[save_file_path][0]
            writer.writerow(line_split)

    print('{!r} file processing completed'.format(os.path.basename(file_path)))

Assuming you have enough RAM, you may be better off sorting the file in memory, e.g. into a dictionary, and writing to disk all at once. If I/O is indeed your bottleneck, you should get lots of mileage from only opening an output file once.

from collections import defaultdict
from os.path import join

file_path = ".../master.tsv"

data = collections.defaultdict(list)
with open(file_path, 'r') as masterfile:
    for line in masterfile:
        cik = line.split("|", 1)[0].zfill(10)
        data[cik].append(line)

for cik, lines in data.items():
    save_path = join(".../data-sorted", cik + ".csv")

    with open(save_path, 'w') as savefile:
        wr = csv.writer(savefile, quoting=csv.QUOTE_ALL)
        for line in lines:
            wr.writerow(line.split("|"))

It is possible that you will not have sufficient memory to load the entire file. In that case, you can dump it in chunks, which if large enough, should still end up saving you significant I/O. The chunking method below is very quick and dirty.

from collections import defaultdict
from itertools import groupby
from os.path import join

chunk_size = 10000  # units of lines

file_path = ".../master.tsv"

with open(file_path, 'r') as masterfile:
    for _, chunk in groupby(enumerate(masterfile),
                            key=lambda item: item[0] // chunk_size):
        data = defaultdict(list)
        for line in chunk:
            cik = line.split("|", 1)[0].zfill(10)
            data[cik].append(line)
        for cik, lines in data.items():
            save_path = join(".../data-sorted", cik + ".csv")

            with open(save_path, 'a') as savefile:
                wr = csv.writer(savefile, quoting=csv.QUOTE_ALL)
                for line in lines:
                    wr.writerow(line.split("|"))