Lazy Method for Reading Big File in Python?
Solution 1:
To write a lazy function, just use yield
:
def read_in_chunks(file_object, chunk_size=1024):
"""Lazy function (generator) to read a file piece by piece.
Default chunk size: 1k."""
while True:
data = file_object.read(chunk_size)
if not data:
break
yield data
with open('really_big_file.dat') as f:
for piece in read_in_chunks(f):
process_data(piece)
Another option would be to use iter
and a helper function:
f = open('really_big_file.dat')
def read1k():
return f.read(1024)
for piece in iter(read1k, ''):
process_data(piece)
If the file is line-based, the file object is already a lazy generator of lines:
for line in open('really_big_file.dat'):
process_data(line)
Solution 2:
If your computer, OS and python are 64-bit, then you can use the mmap module to map the contents of the file into memory and access it with indices and slices. Here an example from the documentation:
import mmap
with open("hello.txt", "r+") as f:
# memory-map the file, size 0 means whole file
map = mmap.mmap(f.fileno(), 0)
# read content via standard file methods
print map.readline() # prints "Hello Python!"
# read content via slice notation
print map[:5] # prints "Hello"
# update content using slice notation;
# note that new content must have same size
map[6:] = " world!\n"
# ... and read again using standard file methods
map.seek(0)
print map.readline() # prints "Hello world!"
# close the map
map.close()
If either your computer, OS or python are 32-bit, then mmap-ing large files can reserve large parts of your address space and starve your program of memory.
Solution 3:
file.readlines()
takes in an optional size argument which approximates the number of lines read in the lines returned.
bigfile = open('bigfilename','r')
tmp_lines = bigfile.readlines(BUF_SIZE)
while tmp_lines:
process([line for line in tmp_lines])
tmp_lines = bigfile.readlines(BUF_SIZE)