h5py: Correct way to slice array datasets

Question

I'm a bit confused here:

As far as I have understood, h5py's .value method reads an entire dataset and dumps it into an array, which is slow and discouraged (and should be generally replaced by [()]. The correct way is to use numpy-esque slicing.

However, I'm getting irritating results (with h5py 2.2.1):

import h5py
import numpy as np
>>> file = h5py.File("test.hdf5",'w')
# Just fill a test file with a numpy array test dataset
>>> file["test"] = np.arange(0,300000)

# This is TERRIBLY slow?!
>>> file["test"][range(0,300000)]
array([     0,      1,      2, ..., 299997, 299998, 299999])
# This is fast
>>> file["test"].value[range(0,300000)]
array([     0,      1,      2, ..., 299997, 299998, 299999])
# This is also fast
>>> file["test"].value[np.arange(0,300000)]
array([     0,      1,      2, ..., 299997, 299998, 299999])
# This crashes
>>> file["test"][np.arange(0,300000)]

I guess that my dataset is so small that .value doesn't hinder performance significantly, but how can the first option be that slow? What is the preferred version here?

Thanks!

UPDATE It seems that I wasn't clear enough, sorry. I do know that .value copies the whole dataset into memory while slicing only retrieves the appropiate subpart. What I'm wondering is why slicing in file is slower than copying the whole array and then slicing in memory. I always thought hdf5/h5py was implemented specifically so that slicing subparts would always be the fastest.

Answer 1

For fast slicing with h5py, stick to the "plain-vanilla" slice notation:

file['test'][0:300000]

or, for example, reading every other element:

file['test'][0:300000:2]

Simple slicing (slice objects and single integer indices) should be very fast, as it translates directly into HDF5 hyperslab selections.

The expression file['test'][range(300000)] invokes h5py's version of "fancy indexing", namely, indexing via an explicit list of indices. There's no native way to do this in HDF5, so h5py implements a (slower) method in Python, which unfortunately has abysmal performance when the lists are > 1000 elements. Likewise for file['test'][np.arange(300000)], which is interpreted in the same way.

See also:

[1] http://docs.h5py.org/en/latest/high/dataset.html#fancy-indexing

[2] https://github.com/h5py/h5py/issues/293