Skip to main content

HDF5 is not for fast access

HDF5 is a good solution for storing large datasets on disk. Python's h5py library makes it possible to pretend that data stored on disk is just like an in memory array. It is important to keep in mind that the data is really stored on disk and is read in every time a slice or index into the data is taken.

import numpy
import h5py


def create_data(length=1e4):
  data = numpy.random.rand(length)
  with h5py.File('test.h5', 'w') as fp:
    fp.create_dataset('test', data=data)
  return data


def access_each_h5():
  y = 0
  with h5py.File('test.h5', 'r') as fp:
    for n in range(fp['test'].size):
      y += fp['test'][n]
  return y

def access_each_array(data):
  y = 0
  for n in range(data.size):
    y += data[n]
  return y


d = create_data()

>>> run test.py
>>> %timeit access_each_array(d)
100 loops, best of 3: 4.14 ms per loop
>>> %timeit access_each_h5()
1 loops, best of 3: 1.9 s per loop
That sobering difference in performance reminds us that we can't - performance wise - equate the two. When processing data from an hdf5 file, it is best to read in as large chunks as your memory will allow and do the heavy lifting in memory.
Labels:

Comments

Post a Comment

Popular posts from this blog

A note on Python's __exit__() and errors

Python's context managers are a very neat way of handling code that needs a teardown once you are done. Python objects have do have a destructor method ( __del__ ) called right before the last instance of the object is about to be destroyed. You can do a teardown there. However there is a lot of fine print to the __del__ method. A cleaner way of doing tear-downs is through Python's context manager , manifested as the with keyword. class CrushMe: def __init__(self): self.f = open('test.txt', 'w') def foo(self, a, b): self.f.write(str(a - b)) def __enter__(self): return self def __exit__(self, exc_type, exc_val, exc_tb): self.f.close() return True with CrushMe() as c: c.foo(2, 3) One thing that is important, and that got me just now, is error handling. I made the mistake of ignoring all those 'junk' arguments ( exc_type, exc_val, exc_tb ). I just skimmed the docs and what popped out is that you need to return True or...
Post a Comment
Read more

Remove field code from Word document

e.g. before submitting a MS, or hand manipulating some formatting because Word does things (like cross-references) so half-assed [from here ] Select all the text (CTRL-A) Press Ctrl+Shift+F9 Editing to remove anonymous comments that only contain thanks. I really appreciate the thanks, but it makes it harder to find comments that carry pertinent information. I'm also going to try and paste informative comments in the body of the post to make them easier to find.
24 comments
Read more

h5py and multiprocessing

The HDF5 format has been working awesome for me, but I ran into danger when I started to mix it with multiprocessing. It was the worst kind of danger: the intermittent error. Here are the dangers/issues in order of escalation (TL;DR is use a generator to feed data from your file into the child processes as they spawn. It's the easiest way. Read on for harder ways.) An h5py file handle can't be pickled and therefore can't be passed as an argument using pool.map() If you set the handle as a global and access it from the child processes you run the risk of racing which leads to corrupted reads. My personal runin was that my code sometimes ran fine but sometimes would complain that there are NaNs or Infinity in the data. This wasted some time tracking down. Other people have had this kind of problem [ 1 ]. Same problem if you pass the filename and have the different processes open individual instances of the file separately. The hard way to solve this problem is to sw...
Post a Comment
Read more