Re: [Pytables-users] Chunk selection for optimized data access
You would be suprised to see how convenient HDF5 can be in small scale data
:) There are cases where one may need to use binary serialization of a few
thousand items, but still needing metadata, indexing and other nice
features provided by HDF5/pyTables.
On Wed, Jun 5, 2013 at 2:29 AM, Tim Burgess timburg...@mac.com wrote:
I was playing around with in-memory HDF5 prior to the 3.0 release. Here's
an example based on what I was doing.
I looked over the docs and it does mention that there is an option to
throw away the 'file' rather than write it to disk.
Not sure how to do that and can't actually think of a use case where I
would want to :-)
And be wary, it is H5FD_CORE.
On Jun 05, 2013, at 08:38 AM, Anthony Scopatz scop...@gmail.com wrote:
I think that you want to set parameters.DRIVER to H5DF_CORE [1]. I
haven't ever used this personally, but it would be great to have an example
script, if someone wants to write one ;)
import numpy as np
import tables
CHUNKY = 30
CHUNKX = 8640
if __name__ == '__main__':
# create dataset and add global attrs
file_path = 'demofile_chunk%sx%d.h5' % (CHUNKY, CHUNKX)
with tables.open_file(file_path, 'w', title='PyTables HDF5 In-memory
example', driver='H5FD_CORE') as h5f:
# dummy some data
lats = np.empty([4320])
lons = np.empty([8640])
# create some simple arrays
lat_node = h5f.create_array('/', 'lat', lats, title='latitude')
lon_node = h5f.create_array('/', 'lon', lons, title='longitude')
# create a 365 x 4320 x 8640 CArray of 32bit float
shape = (365, 4320, 8640)
atom = tables.Float32Atom(dflt=np.nan)
# chunk into daily slices and then further chunk days
sst_node = h5f.create_carray(h5f.root, 'sst', atom, shape,
chunkshape=(1, CHUNKY, CHUNKX))
# dummy up an ndarray
sst = np.empty([4320, 8640], dtype=np.float32)
sst.fill(30.0)
# write ndarray to a 2D plane in the HDF5
sst_node[0] = sst
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Re: [Pytables-users] Chunk selection for optimized data access
I think I've seen this in the release notes of 3.0. This is actually something that I'm looking into as well. So any experience/feedback about creating files in memory would be much appreciated. Best regards Seref On Tue, Jun 4, 2013 at 2:09 PM, Andreas Hilboll li...@hilboll.de wrote: On 04.06.2013 05:35, Tim Burgess wrote: 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 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. Thanks a lot, Anthony and Tim! I was able to get down the readout time considerably using chunkshape=(32, 32, 256) for my 5760x2880x150 array. Now, reading times are about as fast as I expected. the downside is that now, building up the database takes up a lot of time, because i get the data in chunks of 5760x2880x1. So I guess that writing the data to disk like this causes a load of IO operations ... My new question: Is there a way to create a file in-memory? If possible, I could then build up my database in-memory and then, once it's done, just copy the arrays to an on-disk file. Is that possible? If so, how? Thanks a lot for your help! -- Andreas. -- How ServiceNow helps IT people transform IT departments: 1. A cloud service to automate IT design, transition and operations 2. Dashboards that offer high-level views of enterprise services 3. A single system of record for all IT processes http://p.sf.net/sfu/servicenow-d2d-j ___ Pytables-users mailing list Pytables-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/pytables-users -- How ServiceNow helps IT people transform IT departments: 1. A cloud service to automate IT design, transition and operations 2. Dashboards that offer high-level views of enterprise services 3. A single system of record for all IT processes http://p.sf.net/sfu/servicenow-d2d-j___ Pytables-users mailing list Pytables-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/pytables-users
Re: [Pytables-users] ANN: PyTables 3.0 final
Many thanks for keeping such a great piece of work up and running. I've just seen some features in the release notes, features which I was going to need in the very near future! Great job! Best regards Seref Arikan On Sat, Jun 1, 2013 at 12:33 PM, Antonio Valentino antonio.valent...@tiscali.it wrote: === Announcing PyTables 3.0.0 === We are happy to announce PyTables 3.0.0. PyTables 3.0.0 comes after about 5 years from the last major release (2.0) and 7 months since the last stable release (2.4.0). This is new major release and an important milestone for the PyTables project since it provides the long waited support for Python 3.x, which has been around for 4 years. Almost all of the core numeric/scientific packages for Python already support Python 3 so we are very happy that now also PyTables can provide this important feature. What's new == A short summary of main new features: - Since this release, PyTables now provides full support to Python 3 - The entire code base is now more compliant with coding style guidelines described in PEP8. - Basic support for HDF5 drivers. It now is possible to open/create an HDF5 file using one of the SEC2, DIRECT, LOG, WINDOWS, STDIO or CORE drivers. - Basic support for in-memory image files. An HDF5 file can be set from or copied into a memory buffer. - Implemented methods to get/set the user block size in a HDF5 file. - All read methods now have an optional *out* argument that allows to pass a pre-allocated array to store data. - Added support for the floating point data types with extended precision (Float96, Float128, Complex192 and Complex256). - Consistent ``create_xxx()`` signatures. Now it is possible to create all data sets Array, CArray, EArray, VLArray, and Table from existing Python objects. - Complete rewrite of the `nodes.filenode` module. Now it is fully compliant with the interfaces defined in the standard `io` module. Only non-buffered binary I/O is supported currently. Please refer to the RELEASE_NOTES document for a more detailed list of changes in this release. As always, a large amount of bugs have been addressed and squashed as well. In case you want to know more in detail what has changed in this version, please refer to: http://pytables.github.io/release_notes.html You can download a source package with generated PDF and HTML docs, as well as binaries for Windows, from: http://sourceforge.net/projects/pytables/files/pytables/3.0.0 For an online version of the manual, visit: http://pytables.github.io/usersguide/index.html What it is? === PyTables is a library for managing hierarchical datasets and designed to efficiently cope with extremely large amounts of data with support for full 64-bit file addressing. PyTables runs on top of the HDF5 library and NumPy package for achieving maximum throughput and convenient use. PyTables includes OPSI, a new indexing technology, allowing to perform data lookups in tables exceeding 10 gigarows (10**10 rows) in less than a tenth of a second. Resources = About PyTables: http://www.pytables.org About the HDF5 library: http://hdfgroup.org/HDF5/ About NumPy: http://numpy.scipy.org/ Acknowledgments === Thanks to many users who provided feature improvements, patches, bug reports, support and suggestions. See the ``THANKS`` file in the distribution package for a (incomplete) list of contributors. Most specially, a lot of kudos go to the HDF5 and NumPy makers. Without them, PyTables simply would not exist. Share your experience = Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. **Enjoy data!** -- The PyTables Developers -- Get 100% visibility into Java/.NET code with AppDynamics Lite It's a free troubleshooting tool designed for production Get down to code-level detail for bottlenecks, with 2% overhead. Download for free and get started troubleshooting in minutes. http://p.sf.net/sfu/appdyn_d2d_ap2 ___ Pytables-users mailing list Pytables-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/pytables-users -- Get 100% visibility into Java/.NET code with AppDynamics Lite It's a free troubleshooting tool designed for production Get down to code-level detail for bottlenecks, with 2% overhead. Download for free and get started troubleshooting in minutes. http://p.sf.net/sfu/appdyn_d2d_ap2___ Pytables-users mailing list Pytables-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/pytables-users
