Re: [Pytables-users] Chunk selection for optimized data access
Hi list, Il 05/06/2013 00:38, Anthony Scopatz ha scritto: On Tue, Jun 4, 2013 at 12:30 PM, Seref Arikan serefari...@gmail.com wrote: 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. 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 ;) Be Well Anthony 1. http://pytables.github.io/usersguide/parameter_files.html#hdf5-driver-management thare is also a small example of usage in the cookbook [1] [1] http://pytables.github.io/cookbook/inmemory_hdf5_files.html ciao -- Antonio Valentino -- 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] 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
On 05.06.2013 10:31, Andreas Hilboll wrote:
On 05.06.2013 03:29, Tim Burgess 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
Thanks Tim,
I adapted your example for my use case (I'm using the EArray class,
because I need to continuously update my database), and it works well.
However, when I use this with my own data (but also creating the arrays
like you did), I'm running into errors like Could not wait on barrier.
It seems like the HDF library is spawing several threads.
Any idea what's going wrong? Can I somehow avoid HDF5 multithreading at
runtime?
Update:
When setting max_blosc_threads=2 and max_numexpr_threads=2, everything
seems to work as expected (but a bit on the slow side ...). With
max_blosc_threads=4, the error pops up.
Cheers, Andreas.
--
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Re: [Pytables-users] Chunk selection for optimized data access
On 6/5/13 11:45 AM, Andreas Hilboll wrote:
On 05.06.2013 10:31, Andreas Hilboll wrote:
On 05.06.2013 03:29, Tim Burgess 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
Thanks Tim,
I adapted your example for my use case (I'm using the EArray class,
because I need to continuously update my database), and it works well.
However, when I use this with my own data (but also creating the arrays
like you did), I'm running into errors like Could not wait on barrier.
It seems like the HDF library is spawing several threads.
Any idea what's going wrong? Can I somehow avoid HDF5 multithreading at
runtime?
Update:
When setting max_blosc_threads=2 and max_numexpr_threads=2, everything
seems to work as expected (but a bit on the slow side ...). With
max_blosc_threads=4, the error pops up.
Hmm, this seems like a bad interaction among threads in numexpr and
blosc. I'm not sure why this is triggering because the libraries should
execute at different times. Hmm is your app multi-threaded?
Although Blosc has implemented a lock for preventing this situation in
the latest releases, numexpr still lacks this protection. As the
multithreading engine is the same for both packages, it should be
relatively easy to implement the lock support to numexpr too. Volunteers?
--
Francesc Alted
--
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Re: [Pytables-users] Chunk selection for optimized data access
On 6/5/13 11:45 AM, Andreas Hilboll wrote:
On 05.06.2013 10:31, Andreas Hilboll wrote:
On 05.06.2013 03:29, Tim Burgess 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
Thanks Tim,
I adapted your example for my use case (I'm using the EArray class,
because I need to continuously update my database), and it works well.
However, when I use this with my own data (but also creating the arrays
like you did), I'm running into errors like Could not wait on barrier.
It seems like the HDF library is spawing several threads.
Any idea what's going wrong? Can I somehow avoid HDF5 multithreading at
runtime?
Update:
When setting max_blosc_threads=2 and max_numexpr_threads=2, everything
seems to work as expected (but a bit on the slow side ...).
BTW, can you really notice the difference between using 1, 2 or 4
threads? Can you show some figures? Just curious.
--
Francesc Alted
--
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
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Re: [Pytables-users] Chunk selection for optimized data access
Thanks Antonio and Tim!
These are great. I think that one of these should definitely make it into
the examples/ dir.
Be Well
Anthony
On Wed, Jun 5, 2013 at 8:10 AM, Francesc Alted fal...@gmail.com wrote:
On 6/5/13 11:45 AM, Andreas Hilboll wrote:
On 05.06.2013 10:31, Andreas Hilboll wrote:
On 05.06.2013 03:29, Tim Burgess 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
Thanks Tim,
I adapted your example for my use case (I'm using the EArray class,
because I need to continuously update my database), and it works well.
However, when I use this with my own data (but also creating the arrays
like you did), I'm running into errors like Could not wait on barrier.
It seems like the HDF library is spawing several threads.
Any idea what's going wrong? Can I somehow avoid HDF5 multithreading at
runtime?
Update:
When setting max_blosc_threads=2 and max_numexpr_threads=2, everything
seems to work as expected (but a bit on the slow side ...).
BTW, can you really notice the difference between using 1, 2 or 4
threads? Can you show some figures? Just curious.
--
Francesc Alted
--
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
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Re: [Pytables-users] pytable 30 - encoding
Hi Jeff, I have made some comments in the issue. Thanks for investigating this so thoroughly. Be Well Anthony On Tue, Jun 4, 2013 at 8:16 PM, Jeff Reback jreb...@yahoo.com wrote: Anthony, I created an issue with more info I am not sure if this is a bug, or just a way both ne/pytables treat strings that need to touch an encoded value; I found workaround by specifying the condvars to readWhere. Any more thoughts on this? thanks Jeff https://github.com/PyTables/PyTables/issues/265 I can be reached on my cell (917)971-6387 *From:* Anthony Scopatz scop...@gmail.com *To:* Jeff Reback j...@reback.net *Cc:* Discussion list for PyTables pytables-users@lists.sourceforge.net *Sent:* Tuesday, June 4, 2013 6:39 PM *Subject:* Re: [Pytables-users] pytable 30 - encoding Hi Jeff, Hmmm, Could you try doing the same thing on just an in-memory numpy array using numexpr. If this succeeds it tells us that the problem is in PyTables, not numexpr. Be Well Anthony On Tue, Jun 4, 2013 at 11:35 AM, Jeff Reback jreb...@yahoo.com wrote: Anthony, I am using numexpr 2.1 (latest) this is puzzling; doesn't matter what I pass (bytes or str) , same result? (column == 'str-2') /mnt/code/arb/test/pytables-3.py(38)module() - result = handle.root.test.table.readWhere(selector) (Pdb) handle.root.test.table.readWhere(selector) *** TypeError: string argument without an encoding (Pdb) handle.root.test.table.readWhere(selector.encode(encoding)) *** TypeError: string argument without an encoding (Pdb) *From:* Anthony Scopatz scop...@gmail.com *To:* Jeff Reback j...@reback.net; Discussion list for PyTables pytables-users@lists.sourceforge.net *Sent:* Tuesday, June 4, 2013 12:25 PM *Subject:* Re: [Pytables-users] pytable 30 - encoding Hi Jeff, Have you also updated numexpr to the most recent version? The error is coming from numexpr not compiling the expression correctly. Also, you might try making selector a str, rather than bytes: selector = (column == 'str-2') rather than selector = (column == 'str-2').encode(encoding) Be Well Anthony On Tue, Jun 4, 2013 at 8:51 AM, Jeff Reback jreb...@yahoo.com wrote: anthony,where am I going wrong here? #!/usr/local/bin/python3 import tables import numpy as np import datetime, time encoding = 'UTF-8' test_file = 'test_select.h5' handle = tables.openFile(test_file, w) node = handle.createGroup(handle.root, 'test') table = handle.createTable(node, 'table', dict( index = tables.Int64Col(), column = tables.StringCol(25), values = tables.FloatCol(shape=(3)), )) # add data r = table.row for i in range(10): r['index'] = i r['column'] = (str-%d % (i % 5)).encode(encoding) r['values'] = np.arange(3) r.append() table.flush() handle.close() # read handle = tables.openFile(test_file,r) result = handle.root.test.table.read() print(table data\n) print(result) # where print(\nselector\n) selector = (column == 'str-2').encode(encoding) print(selector) result = handle.root.test.table.readWhere(selector) print(result) and the following out: [sheep-jreback-/code/arb/test] python3 pytables-3.py table data [(b'str-0', 0, [0.0, 1.0, 2.0]) (b'str-1', 1, [0.0, 1.0, 2.0]) (b'str-2', 2, [0.0, 1.0, 2.0]) (b'str-3', 3, [0.0, 1.0, 2.0]) (b'str-4', 4, [0.0, 1.0, 2.0]) (b'str-0', 5, [0.0, 1.0, 2.0]) (b'str-1', 6, [0.0, 1.0, 2.0]) (b'str-2', 7, [0.0, 1.0, 2.0]) (b'str-3', 8, [0.0, 1.0, 2.0]) (b'str-4', 9, [0.0, 1.0, 2.0])] selector b(column == 'str-2') Traceback (most recent call last): File pytables-3.py, line 37, in module result = handle.root.test.table.readWhere(selector) File /usr/local/lib/python3.3/site-packages/tables-3.0.0-py3.3-linux-x86_64.egg/tables/_past.py, line 35, in oldfunc return obj(*args, **kwargs) File /usr/local/lib/python3.3/site-packages/tables-3.0.0-py3.3-linux-x86_64.egg/tables/table.py, line 1522, in read_where self._where(condition, condvars, start, stop, step)] File /usr/local/lib/python3.3/site-packages/tables-3.0.0-py3.3-linux-x86_64.egg/tables/table.py, line 1484, in _where compiled = self._compile_condition(condition, condvars) File /usr/local/lib/python3.3/site-packages/tables-3.0.0-py3.3-linux-x86_64.egg/tables/table.py, line 1358, in _compile_condition compiled = compile_condition(condition, typemap, indexedcols) File /usr/local/lib/python3.3/site-packages/tables-3.0.0-py3.3-linux-x86_64.egg/tables/conditions.py, line 419, in compile_condition func = NumExpr(expr, signature) File /usr/local/lib/python3.3/site-packages/numexpr-2.1-py3.3-linux-x86_64.egg/numexpr/necompiler.py, line 559, in NumExpr precompile(ex, signature, context) File /usr/local/lib/python3.3/site-packages/numexpr-2.1-py3.3-linux-x86_64.egg/numexpr/necompiler.py, line 511, in precompile constants_order, constants = getConstants(ast) File /usr/local/lib/python3.3/site-packages/numexpr-2.1-py3.3-linux-x86_64.egg/numexpr/necompiler.py, line 294, in getConstants for a
Re: [Pytables-users] Chunk selection for optimized data access
On Jun 06, 2013, at 04:19 AM, Anthony Scopatz scop...@gmail.com wrote:Thanks Antonio and Tim!These are great. I think that one of these should definitely make it into the examples/ dir.Be WellAnthonyOK. I have put up a pull request with the code added.https://github.com/PyTables/PyTables/pull/266Cheers, Tim -- 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
