I liked the post (well, as an HDF5 user, I found it depressing...).<p>My main qualm with it was the claim about 100x worse performance than just using numpy.memmap().  To the author&#x27;s credit, he posted his benchmarking code so we could try it ourselves.  (Much appreciated.)  But as it turned out, there were problems with his benchmark.  A fair comparison shows a mixed picture -- hdf5 is faster in some cases, and numpy.memmap is faster in other cases.  (You can read my back-and-forth about the benchmarking code in the blog&#x27;s comments.)<p>One minor complaint about presentation: Once the benchmarking claims were shown to be bogus, the author should have removed that section from the post, or added an inline &quot;EDIT:&quot; comment.  Instead, he merely revised the text to remove any specific numbers, and he didn&#x27;t add any inline text indicating that the post had been edited.<p>I think the rest of the post (without performance complaints) is strong enough to stand on its own.  After all, performance isn&#x27;t everything.  In fact, I&#x27;d say it&#x27;s a minor consideration compared to the other points.<p>When it comes to performance, I think the main issue is this: When you have to &quot;roll your own&quot; solution, you become intimately aware of the performance trade-offs you&#x27;re making.  HDF5 is so configurable and yet so opaque that it&#x27;s tough to understand <i>why</i> you&#x27;re not seeing the performance you expect.