read_pickle much slower in v0.13 (not using cPickle when compat=False)

[patricksurry]

See discussion here http://stackoverflow.com/questions/23122180/is-pandas-read-pickle-performance-crippled-in-version-0-13

My test dataset has pickled file size 1.6Gb, and contains about 13 million records.

With 0.12 the file takes 146s to load; with 0.13 is takes 982s (about 6.7x longer).

Using cProfile, you can see that v0.13 always uses native python pickle to load, even when compat=False. In 0.12 it uses cPickle to load. Seems like something is wrong with the logic in pandas/compat/pickle_compat.py

A workaround (if you know you don't need compatibility mode) is to use cPickle.load(open('foo.pickle')) instead of pandas.read_pickle('foo.pickle').

[jtratner]

Thanks for the report! I'll try and take a look. There were some weird
unicode issues to handle but we definitely don't want to sacrifice pickle
performance

[jreback]

@patricksurry I just tried this and explicity called cPickle; its actually about 30% SLOWER than the current pickle. not exactly sure why. (was using a test frame of 100k rows, 5 columns, with a datetime index).only a 48mb file, so that could be the reason (relative to what you are seeing).

try timing your file with:

import pickle
import cPickle

def f(file,m):
    with open(file) as fh:
           m.load(fh)

%timeit f('foo.pkl',pickle')
%timeit f('foo.pkl',cPickle')
%timeit pd.read_pickle('foo.pkl')

under both 0.12 and 0.13.1

[jreback]

500 MB file

In [18]: N=10000000

In [19]: index = date_range('20000101',periods=N,freq='H')

In [20]: df2 = DataFrame(dict(dict([ ("float{0}".format(i),randn(N)) for i in range(C) ])),
               index=index)

In [21]: df2.to_pickle('foo.pkl')

In [22]: !ls -ltr *.pkl
-rw-rw-r-- 1 jreback users 480000636 Apr 27  2014 foo.pkl

In [23]: %timeit f(cPickle)
1 loops, best of 3: 610 ms per loop

In [24]: %timeit f(pickle)
1 loops, best of 3: 392 ms per loop

# this is using cPickle
In [25]: %timeit pd.read_pickle('foo.pkl')
1 loops, best of 3: 609 ms per loop

[patricksurry]

I wonder if that's because you're using a big array of float? <1s to load 10M rows sounds ridiculously fast :) (You didn't specify C, but I tried C=10 and saw similar speed.)

If you put some strings in there things get more sluggish - here's a 10x1M rows x 2 cols (one string and one float), which is about 230Mb on disk, and takes almost 4x longer to load with read_pickle compared to cPickle.

import pandas
import cPickle
from random import random, randrange
from timeit import Timer
N=1000000
t = pandas.DataFrame([[random() for _ in range(N)],['%08x'%randrange(16**8) for _ in range(N)]]).T
t2 = pandas.concat([t for _ in range(10)])
t2.to_pickle('foo.pickle')
Timer("pandas.read_pickle('foo.pickle')","import pandas").timeit(5)
==> 234.10312604904175 (~ 47s per execution)
Timer("cPickle.load(open('foo.pickle'))","import cPickle").timeit(5)
==> 65.66286087036133 (~ 13s per execution)

[jreback]

ok...can you show df.info() then can have a realistic comparison of dtypes

I agree my test is a bit contrived

[patricksurry]

I'm not sure why both cols are object - given that the first is all float?

t2.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 10000000 entries, 0 to 999999
Data columns (total 2 columns):
0 object
1 object
dtypes: object(2)>>> t2[:10]
0 1
0 0.7668373 c235317a
1 0.9184971 3fffd302
2 0.5608013 ba31311d
3 0.06004251 066232b7
4 0.7065527 9c2cbf76
5 0.1694165 0e034615
6 0.7740064 b96c7494
7 0.0637828 53de9642
8 0.8438448 4e0596b6
9 0.00191028 5dc8cd8e

[10 rows x 2 columns]

[jreback]

was about to say, you should NOT be using object dtype on non-strings, very bad idea.

Its how you constructed it with the T (i'll show u my example in a sec).

[jreback]

In [10]: N=5000000

In [11]: index = date_range('20000101',periods=N,freq='H')

In [12]: df2 = DataFrame(dict([ ("float{0}".format(i),randn(N)) for i in range(C) ]),
                index=index)

In [13]: df2['object'] = ['%08x'%randrange(16**8) for _ in range(N)]

In [14]: df2.to_pickle('foo.pkl')

In [16]: def f(m):
   ....:     with open('foo.pkl') as fh:
   ....:         m.load(fh)
   ....:         

In [17]: import pickle

In [18]: import cPickle

In [19]: %timeit f(pickle)
1 loops, best of 3: 12.3 s per loop

In [20]: %timeit pd.read_pickle('foo.pkl')
1 loops, best of 3: 2.36 s per loop

FYI (never a big fan of storing big pickles)

In [22]: df2.to_hdf('foo.h5','data')

In [23]: %timeit pd.read_hdf('foo.h5','data')
1 loops, best of 3: 1.42 s per loop

In [24]: !ls -ltr foo.*
-rw-rw-r-- 1 jreback users 315010064 Apr 27 17:55 foo.pkl
-rw-rw-r-- 1 jreback users 316068376 Apr 27 17:58 foo.h5

[jreback]

ok....back to original time (this is the vbench, ratio < 1 is better)
its a slightly different example that yours...

-------------------------------------------------------------------------------
Test name                                    | head[ms] | base[ms] |  ratio   |
-------------------------------------------------------------------------------
packers_read_pickle                          | 446.7856 | 2399.6547 |   0.1862 |

[jreback]

closed via #6899

[patricksurry]

Thanks - I'll have a look at the HDF stuff.

On Sun, Apr 27, 2014 at 6:00 PM, jreback [email protected] wrote:

In [10]: N=5000000

In [11]: index = date_range('20000101',periods=N,freq='H')

In [12]: df2 = DataFrame(dict([ ("float{0}".format(i),randn(N)) for i in range(C) ]),
index=index)

In [13]: df2['object'] = ['%08x'%randrange(16**8) for _ in range(N)]

In [14]: df2.to_pickle('foo.pkl')

In [16]: def f(m):
....: with open('foo.pkl') as fh:
....: m.load(fh)
....:

In [17]: import pickle

In [18]: import cPickle

In [19]: %timeit f(pickle)
1 loops, best of 3: 12.3 s per loop

In [20]: %timeit pd.read_pickle('foo.pkl')
1 loops, best of 3: 2.36 s per loop

FYI (never a big fan of storing big pickles)

In [22]: df2.to_hdf('foo.h5','data')

In [23]: %timeit pd.read_hdf('foo.h5','data')
1 loops, best of 3: 1.42 s per loop

In [24]: !ls -ltr foo.*
-rw-rw-r-- 1 jreback users 315010064 Apr 27 17:55 foo.pkl
-rw-rw-r-- 1 jreback users 316068376 Apr 27 17:58 foo.h5

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