My thoughts are:
- try it without any compression. Assuming 32 bit floats, your monthly 5760 x 2880 is only about 65MB. Uncompressed data may perform well and at the least it will give you a baseline to work from - and will help if you are investigating IO tuning.
- I have found with CArray that the auto chunksize works fairly well. Experiment with that chunksize and with some chunksizes that you think are more appropriate (maybe temporal rather than spatial in your case).
On Jun 03, 2013, at 10:45 PM, Andreas Hilboll <li...@hilboll.de> wrote:
On Jun 03, 2013, at 10:45 PM, Andreas Hilboll <li...@hilboll.de> wrote:
On 03.06.2013 14:43, Andreas Hilboll wrote:
> Hi,
>
> I'm storing large datasets (5760 x 2880 x ~150) in a compressed EArray
> (the last dimension represents time, and once per month there'll be one
> more 5760x2880 array to add to the end).
>
> Now, extracting timeseries at one index location is slow; e.g., for four
> indices, it takes several seconds:
>
> In [19]: idx = ((5000, 600, 800, 900), (1000, 2000, 500, 1))
>
> In [20]: %time AA = np.vstack([_a[i,j] for i,j in zip(*idx)])
> CPU times: user 4.31 s, sys: 0.07 s, total: 4.38 s
> Wall time: 7.17 s
>
> I have the feeling that this performance could be improved, but I'm not
> sure about how to properly use the `chunkshape` parameter in my case.
>
> Any help is greatly appreciated :)
>
> Cheers, Andreas.
PS: If I could get significant performance gains by not using an EArray
and therefore re-creating the whole database each month, then this would
also be an option.
-- Andreas.
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