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Read/Sort Large text file avoiding line-by-line read using mmep or hdf5
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Read/Sort Large text file avoiding line-by-line read using mmep or hdf5

I have a large data file (N,4) which I am mapping line-by-line. My files are 10 GBs, a simplistic implementation is given below. Though the following works, it takes huge amount of time.

I would like to implement this logic such that the text file is read directly and I can access the elements. Thereafter, I need to sort the whole (mapped) file based on column-2 elements.

The examples I see online assumes smaller piece of data (d) and using f[:] = d[:]but I can't do that since d is huge in my case and eats my RAM.

PS: I know how to load the file using np.loadtxt and sort them using argsort, but that logic fails (memory error) for GB file size. Would appreciate any direction.

nrows, ncols = 20000000, 4 
f = np.memmap('memmapped.dat', dtype=np.float32,
              mode='w+', shape=(nrows, ncols))

filename = "my_file.txt"

with open(filename) as file:

    for i, line in enumerate(file):
        floats = [float(x) for x in line.split(',')]
        f[i, :] = floats
del f

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