We Keep Coding

f.read I/O differences

I am looking to optimize the size of a read operation for a file that is too big to open entirely in memory. Here is a code sample:
def read_chars(fp, BSIZE=1024):
with open(fp) as source:
size=f.seek(0,2); f.seek(0)
t0 = time.time()
chars = True
while chars:
chars = source.read(BSIZE)
for char in chars:
pass # placeholder
print ('Read %s in %.2fs' % (size, time.time() - t0))
The performance is directly related to the size of the buffer size I use, at least for relatively small buffer sizes. For example:
>>> read2('/Users/david/Desktop/Sales1M.csv', 1024)
Read 46468331 in 0.75s
>>> read2('/Users/david/Desktop/Sales1M.csv', 10)
Read 46468331 in 1.86s
>>> read2('/Users/david/Desktop/Sales1M.csv', 2)
Read 46468331 in 5.93s
What accounts for the huge difference in these numbers? At first I thought that the times would be almost the same, but perhaps the difference is related to the quantity of system file operations(?), but would like someone to give a more definitive answer to why the behavior is as it is.

Why is node.js faster than python in file reading?

I'm profiling node.js vs python in file (48KB) reading synchronously.
Node.js code
var fs = require('fs');
var stime = new Date().getTime() / 1000;
for (var i=0; i<1000; i++){
var content = fs.readFileSync('npm-debug.log');
}
console.log("Total time took is: " + ((new Date().getTime() / 1000) - stime));
Python Code
import time
stime = time.time()
for i in range(1000):
with open('npm-debug.log', mode='r') as infile:
ax = infile.read();
print("Total time is: " + str(time.time() - stime));
Timings are as follows:
$ python test.py
Total time is: 0.5195660591125488
$ node test.js
Total time took is: 0.25799989700317383
Where is the difference?
In File IO or
Python list ds allocation
Or Am I not comparing apples to apples?
EDIT:
Updated python's readlines() to read() for a good comparison
Changed the iterations to 1000 from 500
PURPOSE:
To understand the truth in node.js is slower than python is slower than C kind of things and if so slow at which place in this context.

readlines returns a list of lines in the file, so it has to read the data char by char, constantly comparing the current character to any of the newline characters, and keep composing a list of lines.
This is more complicated than simple file.read(), which would be the equivalent of what Node.js does.
Also, the length calculated by your Python script is the number of lines, while Node.js gets the number of characters.
If you want even more speed, use os.open instead of open:
import os, time
def Test_os(n):
for x in range(n):
f = os.open('Speed test.py', os.O_RDONLY)
data = ""
t = os.read(f, 1048576).decode('utf8')
while t:
data += t
t = os.read(f, 1048576).decode('utf8')
os.close(f)
def Test_open(n):
for x in range(n):
with open('Speed test.py') as f:
data = f.read()
s = time.monotonic()
Test_os(500000)
print(time.monotonic() - s)
s = time.monotonic()
Test_open(500000)
print(time.monotonic() - s)
On my machine os.open is several seconds faster than open. The output is as follows:
53.68909174999999
58.12600833400029
As you can see, open is 4.4 seconds slower than os.open, although as the number of runs decreases, so does this difference.
Also, you should try tweaking the buffer size of the os.read function as different values may give very different timings:
Here 'operation' means a single call to Test_os.
If you get rid of bytes' decoding and use io.BytesIO instead of mere bytes objects, you'll get a considerable speedup:
def Test_os(n, buf):
for x in range(n):
f = os.open('test.txt', os.O_RDONLY)
data = io.BytesIO()
while data.write(os.read(f, buf)):
...
os.close(f)
Thus, the best result is now 0.038 seconds per call instead of 0.052 (~37% speedup).

Fastest way to process a large file?

I have multiple 3 GB tab delimited files. There are 20 million rows in each file. All the rows have to be independently processed, no relation between any two rows. My question is, what will be faster?
Reading line-by-line?
with open() as infile:
for line in infile:
Reading the file into memory in chunks and processing it, say 250 MB at a time?
The processing is not very complicated, I am just grabbing value in column1 to List1, column2 to List2 etc. Might need to add some column values together.
I am using python 2.7 on a linux box that has 30GB of memory. ASCII Text.
Any way to speed things up in parallel? Right now I am using the former method and the process is very slow. Is using any CSVReader module going to help?
I don't have to do it in python, any other language or database use ideas are welcome.

It sounds like your code is I/O bound. This means that multiprocessing isn't going to help—if you spend 90% of your time reading from disk, having an extra 7 processes waiting on the next read isn't going to help anything.
And, while using a CSV reading module (whether the stdlib's csv or something like NumPy or Pandas) may be a good idea for simplicity, it's unlikely to make much difference in performance.
Still, it's worth checking that you really are I/O bound, instead of just guessing. Run your program and see whether your CPU usage is close to 0% or close to 100% or a core. Do what Amadan suggested in a comment, and run your program with just pass for the processing and see whether that cuts off 5% of the time or 70%. You may even want to try comparing with a loop over os.open and os.read(1024*1024) or something and see if that's any faster.
Since your using Python 2.x, Python is relying on the C stdio library to guess how much to buffer at a time, so it might be worth forcing it to buffer more. The simplest way to do that is to use readlines(bufsize) for some large bufsize. (You can try different numbers and measure them to see where the peak is. In my experience, usually anything from 64K-8MB is about the same, but depending on your system that may be different—especially if you're, e.g., reading off a network filesystem with great throughput but horrible latency that swamps the throughput-vs.-latency of the actual physical drive and the caching the OS does.)
So, for example:
bufsize = 65536
with open(path) as infile:
while True:
lines = infile.readlines(bufsize)
if not lines:
break
for line in lines:
process(line)
Meanwhile, assuming you're on a 64-bit system, you may want to try using mmap instead of reading the file in the first place. This certainly isn't guaranteed to be better, but it may be better, depending on your system. For example:
with open(path) as infile:
m = mmap.mmap(infile, 0, access=mmap.ACCESS_READ)
A Python mmap is sort of a weird object—it acts like a str and like a file at the same time, so you can, e.g., manually iterate scanning for newlines, or you can call readline on it as if it were a file. Both of those will take more processing from Python than iterating the file as lines or doing batch readlines (because a loop that would be in C is now in pure Python… although maybe you can get around that with re, or with a simple Cython extension?)… but the I/O advantage of the OS knowing what you're doing with the mapping may swamp the CPU disadvantage.
Unfortunately, Python doesn't expose the madvise call that you'd use to tweak things in an attempt to optimize this in C (e.g., explicitly setting MADV_SEQUENTIAL instead of making the kernel guess, or forcing transparent huge pages)—but you can actually ctypes the function out of libc.

I know this question is old; but I wanted to do a similar thing, I created a simple framework which helps you read and process a large file in parallel. Leaving what I tried as an answer.
This is the code, I give an example in the end
def chunkify_file(fname, size=1024*1024*1000, skiplines=-1):
"""
function to divide a large text file into chunks each having size ~= size so that the chunks are line aligned
Params :
fname : path to the file to be chunked
size : size of each chink is ~> this
skiplines : number of lines in the begining to skip, -1 means don't skip any lines
Returns :
start and end position of chunks in Bytes
"""
chunks = []
fileEnd = os.path.getsize(fname)
with open(fname, "rb") as f:
if(skiplines > 0):
for i in range(skiplines):
f.readline()
chunkEnd = f.tell()
count = 0
while True:
chunkStart = chunkEnd
f.seek(f.tell() + size, os.SEEK_SET)
f.readline() # make this chunk line aligned
chunkEnd = f.tell()
chunks.append((chunkStart, chunkEnd - chunkStart, fname))
count+=1
if chunkEnd > fileEnd:
break
return chunks
def parallel_apply_line_by_line_chunk(chunk_data):
"""
function to apply a function to each line in a chunk
Params :
chunk_data : the data for this chunk
Returns :
list of the non-None results for this chunk
"""
chunk_start, chunk_size, file_path, func_apply = chunk_data[:4]
func_args = chunk_data[4:]
t1 = time.time()
chunk_res = []
with open(file_path, "rb") as f:
f.seek(chunk_start)
cont = f.read(chunk_size).decode(encoding='utf-8')
lines = cont.splitlines()
for i,line in enumerate(lines):
ret = func_apply(line, *func_args)
if(ret != None):
chunk_res.append(ret)
return chunk_res
def parallel_apply_line_by_line(input_file_path, chunk_size_factor, num_procs, skiplines, func_apply, func_args, fout=None):
"""
function to apply a supplied function line by line in parallel
Params :
input_file_path : path to input file
chunk_size_factor : size of 1 chunk in MB
num_procs : number of parallel processes to spawn, max used is num of available cores - 1
skiplines : number of top lines to skip while processing
func_apply : a function which expects a line and outputs None for lines we don't want processed
func_args : arguments to function func_apply
fout : do we want to output the processed lines to a file
Returns :
list of the non-None results obtained be processing each line
"""
num_parallel = min(num_procs, psutil.cpu_count()) - 1
jobs = chunkify_file(input_file_path, 1024 * 1024 * chunk_size_factor, skiplines)
jobs = [list(x) + [func_apply] + func_args for x in jobs]
print("Starting the parallel pool for {} jobs ".format(len(jobs)))
lines_counter = 0
pool = mp.Pool(num_parallel, maxtasksperchild=1000) # maxtaskperchild - if not supplied some weird happend and memory blows as the processes keep on lingering
outputs = []
for i in range(0, len(jobs), num_parallel):
print("Chunk start = ", i)
t1 = time.time()
chunk_outputs = pool.map(parallel_apply_line_by_line_chunk, jobs[i : i + num_parallel])
for i, subl in enumerate(chunk_outputs):
for x in subl:
if(fout != None):
print(x, file=fout)
else:
outputs.append(x)
lines_counter += 1
del(chunk_outputs)
gc.collect()
print("All Done in time ", time.time() - t1)
print("Total lines we have = {}".format(lines_counter))
pool.close()
pool.terminate()
return outputs
Say for example, I have a file in which I want to count the number of words in each line, then the processing of each line would look like
def count_words_line(line):
return len(line.strip().split())
and then call the function like:
parallel_apply_line_by_line(input_file_path, 100, 8, 0, count_words_line, [], fout=None)
Using this, I get a speed up of ~8 times as compared to vanilla line by line reading on a sample file of size ~20GB in which I do some moderately complicated processing on each line.

Python, process a large text file in parallel

Samples records in the data file (SAM file):
M01383 0 chr4 66439384 255 31M * 0 0 AAGAGGA GFAFHGD MD:Z:31 NM:i:0
M01382 0 chr1 241995435 255 31M * 0 0 ATCCAAG AFHTTAG MD:Z:31 NM:i:0
......
The data files are line-by-line based
The size of the data files are varies from 1G - 5G.
I need to go through the record in the data file line by line, get a particular value (e.g. 4th value, 66439384) from each line, and pass this value to another function for processing. Then some results counter will be updated.
the basic workflow is like this:
# global variable, counters will be updated in search function according to the value passed.
counter_a = 0
counter_b = 0
counter_c = 0
open textfile:
for line in textfile:
value = line.split()[3]
search_function(value) # this function takes abit long time to process
def search_function (value):
some conditions checking:
update the counter_a or counter_b or counter_c
With single process code and about 1.5G data file, it took about 20 hours to run through all the records in one data file. I need much faster code because there are more than 30 of this kind data file.
I was thinking to process the data file in N chunks in parallel, and each chunk will perform above workflow and update the global variable (counter_a, counter_b, counter_c) simultaneously. But I don't know how to achieve this in code, or wether this will work.
I have access to a server machine with: 24 processors and around 40G RAM.
Anyone could help with this? Thanks very much.

The simplest approach would probably be to do all 30 files at once with your existing code -- would still take all day, but you'd have all the files done at once. (ie, "9 babies in 9 months" is easy, "1 baby in 1 month" is hard)
If you really want to get a single file done faster, it will depend on how your counters actually update. If almost all the work is just in analysing value you can offload that using the multiprocessing module:
import time
import multiprocessing
def slowfunc(value):
time.sleep(0.01)
return value**2 + 0.3*value + 1
counter_a = counter_b = counter_c = 0
def add_to_counter(res):
global counter_a, counter_b, counter_c
counter_a += res
counter_b -= (res - 10)**2
counter_c += (int(res) % 2)
pool = multiprocessing.Pool(50)
results = []
for value in range(100000):
r = pool.apply_async(slowfunc, [value])
results.append(r)
# don't let the queue grow too long
if len(results) == 1000:
results[0].wait()
while results and results[0].ready():
r = results.pop(0)
add_to_counter(r.get())
for r in results:
r.wait()
add_to_counter(r.get())
print counter_a, counter_b, counter_c
That will allow 50 slowfuncs to run in parallel, so instead of taking 1000s (=100k*0.01s), it takes 20s (100k/50)*0.01s to complete. If you can restructure your function into "slowfunc" and "add_to_counter" like the above, you should be able to get a factor of 24 speedup.

Read one file at a time, use all CPUs to run search_function():
#!/usr/bin/env python
from multiprocessing import Array, Pool
def init(counters_): # called for each child process
global counters
counters = counters_
def search_function (value): # assume it is CPU-intensive task
some conditions checking:
update the counter_a or counter_b or counter_c
counter[0] += 1 # counter 'a'
counter[1] += 1 # counter 'b'
return value, result, error
if __name__ == '__main__':
counters = Array('i', [0]*3)
pool = Pool(initializer=init, initargs=[counters])
values = (line.split()[3] for line in textfile)
for value, result, error in pool.imap_unordered(search_function, values,
chunksize=1000):
if error is not None:
print('value: {value}, error: {error}'.format(**vars()))
pool.close()
pool.join()
print(list(counters))
Make sure (for example, by writing wrappers) that exceptions do not escape next(values), search_function().

This solution works on a set of files.
For each file, it divides it into a specified number of line-aligned chunks, solves each chunk in parallel, then combines the results.
It streams each chunk from disk; this is somewhat slower, but does not consume nearly so much memory. We depend on disk cache and buffered reads to prevent head thrashing.
Usage is like
python script.py -n 16 sam1.txt sam2.txt sam3.txt
and script.py is
import argparse
from io import SEEK_END
import multiprocessing as mp
#
# Worker process
#
def summarize(fname, start, stop):
"""
Process file[start:stop]
start and stop both point to first char of a line (or EOF)
"""
a = 0
b = 0
c = 0
with open(fname, newline='') as inf:
# jump to start position
pos = start
inf.seek(pos)
for line in inf:
value = int(line.split(4)[3])
# *** START EDIT HERE ***
#
# update a, b, c based on value
#
# *** END EDIT HERE ***
pos += len(line)
if pos >= stop:
break
return a, b, c
def main(num_workers, sam_files):
print("{} workers".format(num_workers))
pool = mp.Pool(processes=num_workers)
# for each input file
for fname in sam_files:
print("Dividing {}".format(fname))
# decide how to divide up the file
with open(fname) as inf:
# get file length
inf.seek(0, SEEK_END)
f_len = inf.tell()
# find break-points
starts = [0]
for n in range(1, num_workers):
# jump to approximate break-point
inf.seek(n * f_len // num_workers)
# find start of next full line
inf.readline()
# store offset
starts.append(inf.tell())
# do it!
stops = starts[1:] + [f_len]
start_stops = zip(starts, stops)
print("Solving {}".format(fname))
results = [pool.apply(summarize, args=(fname, start, stop)) for start,stop in start_stops]
# collect results
results = [sum(col) for col in zip(*results)]
print(results)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Parallel text processor')
parser.add_argument('--num_workers', '-n', default=8, type=int)
parser.add_argument('sam_files', nargs='+')
args = parser.parse_args()
main(args.num_workers, args.sam_files)
main(args.num_workers, args.sam_files)

What you don't want to do is hand files to invidual CPUs. If that's the case, the file open/reads will likely cause the heads to bounce randomly all over the disk, because the files are likely to be all over the disk.
Instead, break each file into chunks and process the chunks.
Open the file with one CPU. Read in the whole thing into an array Text. You want to do this is one massive read to prevent the heads from thrashing around the disk, under the assumption that your file(s) are placed on the disk in relatively large sequential chunks.
Divide its size in bytes by N, giving a (global) value K, the approximate number of bytes each CPU should process. Fork N threads, and hand each thread i its index i, and a copied handle for each file.
Each thread i starts a thread-local scan pointer p into Text as offset i*K. It scans the text, incrementing p and ignores the text until a newline is found. At this point, it starts processing lines (increment p as it scans the lines). Tt stops after processing a line, when its index into the Text file is greater than (i+1)*K.
If the amount of work per line is about equal, your N cores will all finish about the same time.
(If you have more than one file, you can then start the next one).
If you know that the file sizes are smaller than memory, you might arrange the file reads to be pipelined, e.g., while the current file is being processed, a file-read thread is reading the next file.

Efficient file buffering & scanning methods for large files in python

The description of the problem I am having is a bit complicated, and I will err on the side of providing more complete information. For the impatient, here is the briefest way I can summarize it:
What is the fastest (least execution
time) way to split a text file in to
ALL (overlapping) substrings of size N (bound N, eg 36)
while throwing out newline characters.
I am writing a module which parses files in the FASTA ascii-based genome format. These files comprise what is known as the 'hg18' human reference genome, which you can download from the UCSC genome browser (go slugs!) if you like.
As you will notice, the genome files are composed of chr[1..22].fa and chr[XY].fa, as well as a set of other small files which are not used in this module.
Several modules already exist for parsing FASTA files, such as BioPython's SeqIO. (Sorry, I'd post a link, but I don't have the points to do so yet.) Unfortunately, every module I've been able to find doesn't do the specific operation I am trying to do.
My module needs to split the genome data ('CAGTACGTCAGACTATACGGAGCTA' could be a line, for instance) in to every single overlapping N-length substring. Let me give an example using a very small file (the actual chromosome files are between 355 and 20 million characters long) and N=8
>>>import cStringIO
>>>example_file = cStringIO.StringIO("""\
>header
CAGTcag
TFgcACF
""")
>>>for read in parse(example_file):
... print read
...
CAGTCAGTF
AGTCAGTFG
GTCAGTFGC
TCAGTFGCA
CAGTFGCAC
AGTFGCACF
The function that I found had the absolute best performance from the methods I could think of is this:
def parse(file):
size = 8 # of course in my code this is a function argument
file.readline() # skip past the header
buffer = ''
for line in file:
buffer += line.rstrip().upper()
while len(buffer) >= size:
yield buffer[:size]
buffer = buffer[1:]
This works, but unfortunately it still takes about 1.5 hours (see note below) to parse the human genome this way. Perhaps this is the very best I am going to see with this method (a complete code refactor might be in order, but I'd like to avoid it as this approach has some very specific advantages in other areas of the code), but I thought I would turn this over to the community.
Thanks!
Note, this time includes a lot of extra calculation, such as computing the opposing strand read and doing hashtable lookups on a hash of approximately 5G in size.
Post-answer conclusion: It turns out that using fileobj.read() and then manipulating the resulting string (string.replace(), etc.) took relatively little time and memory compared to the remainder of the program, and so I used that approach. Thanks everyone!

Could you mmap the file and start pecking through it with a sliding window? I wrote a stupid little program that runs pretty small:
USER PID %CPU %MEM VSZ RSS TTY STAT START TIME COMMAND
sarnold 20919 0.0 0.0 33036 4960 pts/2 R+ 22:23 0:00 /usr/bin/python ./sliding_window.py
Working through a 636229 byte fasta file (found via http://biostar.stackexchange.com/questions/1759) took .383 seconds.
#!/usr/bin/python
import mmap
import os
def parse(string, size):
stride = 8
start = string.find("\n")
while start < size - stride:
print string[start:start+stride]
start += 1
fasta = open("small.fasta", 'r')
fasta_size = os.stat("small.fasta").st_size
fasta_map = mmap.mmap(fasta.fileno(), 0, mmap.MAP_PRIVATE, mmap.PROT_READ)
parse(fasta_map, fasta_size)

Some classic IO bound changes.
Use a lower level read operation like os.read and read in to a large fixed buffer.
Use threading/multiprocessing where one reads and buffers and the other processes.
If you have multiple processors/machines use multiprocessing/mq to divy up processing across CPUs ala map-reduce.
Using a lower level read operation wouldn't be that much of a rewrite. The others would be pretty large rewrites.

I suspect the problem is that you have so much data stored in string format, which is really wasteful for your use case, that you're running out of real memory and thrashing swap. 128 GB should be enough to avoid this... :)
Since you've indicated in comments that you need to store additional information anyway, a separate class which references a parent string would be my choice. I ran a short test using chr21.fa from chromFa.zip from hg18; the file is about 48MB and just under 1M lines. I only have 1GB of memory here, so I simply discard the objects afterwards. This test thus won't show problems with fragmentation, cache, or related, but I think it should be a good starting point for measuring parsing throughput:
import mmap
import os
import time
import sys
class Subseq(object):
__slots__ = ("parent", "offset", "length")
def __init__(self, parent, offset, length):
self.parent = parent
self.offset = offset
self.length = length
# these are discussed in comments:
def __str__(self):
return self.parent[self.offset:self.offset + self.length]
def __hash__(self):
return hash(str(self))
def __getitem__(self, index):
# doesn't currently handle slicing
assert 0 <= index < self.length
return self.parent[self.offset + index]
# other methods
def parse(file, size=8):
file.readline() # skip header
whole = "".join(line.rstrip().upper() for line in file)
for offset in xrange(0, len(whole) - size + 1):
yield Subseq(whole, offset, size)
class Seq(object):
__slots__ = ("value", "offset")
def __init__(self, value, offset):
self.value = value
self.offset = offset
def parse_sep_str(file, size=8):
file.readline() # skip header
whole = "".join(line.rstrip().upper() for line in file)
for offset in xrange(0, len(whole) - size + 1):
yield Seq(whole[offset:offset + size], offset)
def parse_plain_str(file, size=8):
file.readline() # skip header
whole = "".join(line.rstrip().upper() for line in file)
for offset in xrange(0, len(whole) - size + 1):
yield whole[offset:offset+size]
def parse_tuple(file, size=8):
file.readline() # skip header
whole = "".join(line.rstrip().upper() for line in file)
for offset in xrange(0, len(whole) - size + 1):
yield (whole, offset, size)
def parse_orig(file, size=8):
file.readline() # skip header
buffer = ''
for line in file:
buffer += line.rstrip().upper()
while len(buffer) >= size:
yield buffer[:size]
buffer = buffer[1:]
def parse_os_read(file, size=8):
file.readline() # skip header
file_size = os.fstat(file.fileno()).st_size
whole = os.read(file.fileno(), file_size).replace("\n", "").upper()
for offset in xrange(0, len(whole) - size + 1):
yield whole[offset:offset+size]
def parse_mmap(file, size=8):
file.readline() # skip past the header
buffer = ""
for line in file:
buffer += line
if len(buffer) >= size:
for start in xrange(0, len(buffer) - size + 1):
yield buffer[start:start + size].upper()
buffer = buffer[-(len(buffer) - size + 1):]
for start in xrange(0, len(buffer) - size + 1):
yield buffer[start:start + size]
def length(x):
return sum(1 for _ in x)
def duration(secs):
return "%dm %ds" % divmod(secs, 60)
def main(argv):
tests = [parse, parse_sep_str, parse_tuple, parse_plain_str, parse_orig, parse_os_read]
n = 0
for fn in tests:
n += 1
with open(argv[1]) as f:
start = time.time()
length(fn(f))
end = time.time()
print "%d %-20s %s" % (n, fn.__name__, duration(end - start))
fn = parse_mmap
n += 1
with open(argv[1]) as f:
f = mmap.mmap(f.fileno(), 0, mmap.MAP_PRIVATE, mmap.PROT_READ)
start = time.time()
length(fn(f))
end = time.time()
print "%d %-20s %s" % (n, fn.__name__, duration(end - start))
if __name__ == "__main__":
sys.exit(main(sys.argv))
1 parse 1m 42s
2 parse_sep_str 1m 42s
3 parse_tuple 0m 29s
4 parse_plain_str 0m 36s
5 parse_orig 0m 45s
6 parse_os_read 0m 34s
7 parse_mmap 0m 37s
The first four are my code, while orig is yours and the last two are from other answers here.
User-defined objects are much more costly to create and collect than tuples or plain strings! This shouldn't be that surprising, but I had not realized it would make this much of a difference (compare #1 and #3, which really only differ in a user-defined class vs tuple). You said you want to store additional information, like offset, with the string anyway (as in the parse and parse_sep_str cases), so you might consider implementing that type in a C extension module. Look at Cython and related if you don't want to write C directly.
Case #1 and #2 being identical is expected: by pointing to a parent string, I was trying to save memory rather than processing time, but this test doesn't measure that.

I have a function for process a text file and use buffer in read and write and parallel computing with async pool of workets of process. I have a AMD of 2 cores, 8GB RAM, with gnu/linux and can process 300000 lines in less of 1 second, 1000000 lines in aproximately 4 seconds and aproximately 4500000 lines (more of 220MB) in aproximately 20 seconds:
# -*- coding: utf-8 -*-
import sys
from multiprocessing import Pool
def process_file(f, fo="result.txt", fi=sys.argv[1]):
fi = open(fi, "r", 4096)
fo = open(fo, "w", 4096)
b = []
x = 0
result = None
pool = None
for line in fi:
b.append(line)
x += 1
if (x % 200000) == 0:
if pool == None:
pool = Pool(processes=20)
if result == None:
result = pool.map_async(f, b)
else:
presult = result.get()
result = pool.map_async(f, b)
for l in presult:
fo.write(l)
b = []
if not result == None:
for l in result.get():
fo.write(l)
if not b == []:
for l in b:
fo.write(f(l))
fo.close()
fi.close()
First argument is function that rceive one line, process and return result for will write in file, next is file of output and last is file of input (you can not use last argument if you receive as first parameter in your script file of input).