Writing a large hdf5 dataset using h5py

Question

At the moment, I am using h5py to generate hdf5 datasets. I have something like this

import h5py
import numpy as np
my_data=np.genfromtxt("/tmp/data.csv",delimiter=",",dtype=None,names=True)

myFile="/tmp/f.hdf"    
with h5py.File(myFile,"a") as f:
  dset = f.create_dataset('%s/%s'%(vendor,dataSet),data=my_data,compression="gzip",compression_opts=9)

This works well for a relatively large ASCII file (400MB). I would like to do the same for a even larger dataset (40GB). Is there a better or more efficient way to do this with h5py? I want to avoid loading the entire data set into memory.

Some information about the data:

1. I won't know the type of the data. Ideally, I would like to use dtype=None from np.loadtxt()
2. I won't know the size (dimensions) of the file. They vary

Answer 1

You could infer the dtypes of your data by reading a smaller chunk of rows at the start of the text file. Once you have these, you can create a resizable HDF5 dataset and iteratively write chunks of rows from your text file to it.

Here's a generator that yields successive chunks of rows from a text file as numpy arrays:

import numpy as np
import warnings


def iter_genfromtxt(path, chunksize=100, **kwargs):
    """Yields consecutive chunks of rows from a text file as numpy arrays.

    Args:
      path: Path to the text file.
      chunksize: Maximum number of rows to yield at a time.
      **kwargs: Additional keyword arguments are passed to `np.genfromtxt`,
        with the exception of `skip_footer` which is unsupported.
    Yields:
      A sequence of `np.ndarray`s with a maximum row dimension of `chunksize`.
    """
    names = kwargs.pop('names', None)
    max_rows = kwargs.pop('max_rows', None)
    skip_header = kwargs.pop('skip_header', kwargs.pop('skiprows', 0))
    if kwargs.pop('skip_footer', None) is not None:
        warnings.warn('`skip_footer` will be ignored')

    with open(path, 'rb') as f:

        # The first chunk is handled separately, since we may wish to skip rows,
        # read column headers etc.
        chunk = np.genfromtxt(f, max_rows=chunksize, skip_header=skip_header,
                              names=names, **kwargs)
        # Ensure that subsequent chunks have consistent dtypes and field names
        kwargs.update({'dtype':chunk.dtype})

        while len(chunk):
            yield chunk[:max_rows]
            if max_rows is not None:
                max_rows -= len(chunk)
                if max_rows <= 0:
                     raise StopIteration
            chunk = np.genfromtxt(f, max_rows=chunksize, **kwargs)

Now suppose we have a .csv file containing:

strings,ints,floats
a,1,0.1256290043
b,2,0.0071402451
c,3,0.2551627907
d,4,0.7958570533
e,5,0.8968247722
f,6,0.7291124437
g,7,0.4196829806
h,8,0.398944394
i,9,0.8718244087
j,10,0.67605461
k,11,0.7105670336
l,12,0.6341504091
m,13,0.1324232855
n,14,0.7062503808
o,15,0.1915132527
p,16,0.4140093777
q,17,0.1458217602
r,18,0.1183596433
s,19,0.0014556247
t,20,0.1649811301

We can read this data in chunks of 5 rows at a time, and write the resulting arrays to a resizeable dataset:

import h5py

# Initialize the generator
gen = iter_genfromtxt('/tmp/test.csv', chunksize=5, delimiter=',', names=True,
                      dtype=None)

# Read the first chunk to get the column dtypes
chunk = next(gen)
dtype = chunk.dtype
row_count = chunk.shape[0]

with h5py.File('/tmp/test.h5', 'w') as f:

    # Initialize a resizable dataset to hold the output
    maxshape = (None,) + chunk.shape[1:]
    dset = f.create_dataset('data', shape=chunk.shape, maxshape=maxshape,
                            chunks=chunk.shape, dtype=chunk.dtype)

    # Write the first chunk of rows
    dset[:] = chunk

    for chunk in gen:

        # Resize the dataset to accommodate the next chunk of rows
        dset.resize(row_count + chunk.shape[0], axis=0)

        # Write the next chunk
        dset[row_count:] = chunk

        # Increment the row count
        row_count += chunk.shape[0]

Output:

with h5py.File('/tmp/test.h5', 'r') as f:
    print(repr(f['data'][:]))

# array([(b'a', 1, 0.1256290043), (b'b', 2, 0.0071402451),
#        (b'c', 3, 0.2551627907), (b'd', 4, 0.7958570533),
#        (b'e', 5, 0.8968247722), (b'f', 6, 0.7291124437),
#        (b'g', 7, 0.4196829806), (b'h', 8, 0.398944394),
#        (b'i', 9, 0.8718244087), (b'j', 10, 0.67605461),
#        (b'k', 11, 0.7105670336), (b'l', 12, 0.6341504091),
#        (b'm', 13, 0.1324232855), (b'n', 14, 0.7062503808),
#        (b'o', 15, 0.1915132527), (b'p', 16, 0.4140093777),
#        (b'q', 17, 0.1458217602), (b'r', 18, 0.1183596433),
#        (b's', 19, 0.0014556247), (b't', 20, 0.1649811301)], 
#       dtype=[('strings', 'S1'), ('ints', '<i8'), ('floats', '<f8')])

For your dataset you will probably want to use a larger chunksize.