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DOC: Update performance comparison section of io docs #28890

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merged 26 commits into from
Nov 9, 2019
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DOC: Update performance comparison section of io docs #28890

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218 changes: 110 additions & 108 deletions doc/source/user_guide/io.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -5572,7 +5572,7 @@ Performance considerations
--------------------------

This is an informal comparison of various IO methods, using pandas
0.20.3. Timings are machine dependent and small differences should be
0.24.2. Timings are machine dependent and small differences should be
ignored.

.. code-block:: ipython
Expand All @@ -5593,164 +5593,166 @@ Given the next test set:

.. code-block:: python

from numpy.random import randn
from numpy.random import randn
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can you change this example to use our formatting, meaning don import like this, rather use
np.random.randn directory, and np.random.seed

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Done

from numpy.random import seed

sz = 1000000
df = pd.DataFrame({'A': randn(sz), 'B': [1] * sz})
sz = 1000000
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seed(42)
df = pd.DataFrame({'A': randn(sz), 'B': [1] * sz})

def test_sql_write(df):
if os.path.exists('test.sql'):
os.remove('test.sql')
sql_db = sqlite3.connect('test.sql')
df.to_sql(name='test_table', con=sql_db)
sql_db.close()

def test_sql_write(df):
if os.path.exists('test.sql'):
os.remove('test.sql')
sql_db = sqlite3.connect('test.sql')
df.to_sql(name='test_table', con=sql_db)
sql_db.close()
def test_sql_read():
sql_db = sqlite3.connect('test.sql')
pd.read_sql_query("select * from test_table", sql_db)
sql_db.close()

def test_hdf_fixed_write(df):
df.to_hdf('test_fixed.hdf', 'test', mode='w')

def test_sql_read():
sql_db = sqlite3.connect('test.sql')
pd.read_sql_query("select * from test_table", sql_db)
sql_db.close()
def test_hdf_fixed_read():
pd.read_hdf('test_fixed.hdf', 'test')

def test_hdf_fixed_write_compress(df):
df.to_hdf('test_fixed_compress.hdf', 'test', mode='w', complib='blosc')

def test_hdf_fixed_write(df):
df.to_hdf('test_fixed.hdf', 'test', mode='w')
def test_hdf_fixed_read_compress():
pd.read_hdf('test_fixed_compress.hdf', 'test')

def test_hdf_table_write(df):
df.to_hdf('test_table.hdf', 'test', mode='w', format='table')

def test_hdf_fixed_read():
pd.read_hdf('test_fixed.hdf', 'test')
def test_hdf_table_read():
pd.read_hdf('test_table.hdf', 'test')

def test_hdf_table_write_compress(df):
df.to_hdf('test_table_compress.hdf', 'test', mode='w',
complib='blosc', format='table')

def test_hdf_fixed_write_compress(df):
df.to_hdf('test_fixed_compress.hdf', 'test', mode='w', complib='blosc')
def test_hdf_table_read_compress():
pd.read_hdf('test_table_compress.hdf', 'test')

def test_csv_write(df):
df.to_csv('test.csv', mode='w')

def test_hdf_fixed_read_compress():
pd.read_hdf('test_fixed_compress.hdf', 'test')
def test_csv_read():
pd.read_csv('test.csv', index_col=0)

def test_feather_write(df):
df.to_feather('test.feather')

def test_hdf_table_write(df):
df.to_hdf('test_table.hdf', 'test', mode='w', format='table')
def test_feather_read():
pd.read_feather('test.feather')

def test_pickle_write(df):
df.to_pickle('test.pkl')

def test_hdf_table_read():
pd.read_hdf('test_table.hdf', 'test')
def test_pickle_read():
pd.read_pickle('test.pkl')

def test_pickle_write_compress(df):
df.to_pickle('test.pkl.compress', compression='xz')

def test_hdf_table_write_compress(df):
df.to_hdf('test_table_compress.hdf', 'test', mode='w',
complib='blosc', format='table')
def test_pickle_read_compress():
pd.read_pickle('test.pkl.compress', compression='xz')

def test_parquet_write(df):
df.to_parquet('test.parquet')

def test_hdf_table_read_compress():
pd.read_hdf('test_table_compress.hdf', 'test')
def test_parquet_read():
pd.read_parquet('test.parquet')

When writing, the top-three functions in terms of speed are ``test_feather_write``, ``test_hdf_fixed_write`` and ``test_hdf_fixed_write_compress``.

def test_csv_write(df):
df.to_csv('test.csv', mode='w')


def test_csv_read():
pd.read_csv('test.csv', index_col=0)

.. code-block:: ipython

def test_feather_write(df):
df.to_feather('test.feather')
In [4]: %timeit test_sql_write(df)
3.29 s ± 43.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [5]: %timeit test_hdf_fixed_write(df)
19.4 ms ± 560 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)

def test_feather_read():
pd.read_feather('test.feather')
In [6]: %timeit test_hdf_fixed_write_compress(df)
19.6 ms ± 308 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [7]: %timeit test_hdf_table_write(df)
449 ms ± 5.61 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

def test_pickle_write(df):
df.to_pickle('test.pkl')
In [8]: %timeit test_hdf_table_write_compress(df)
448 ms ± 11.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [9]: %timeit test_csv_write(df)
3.66 s ± 26.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

def test_pickle_read():
pd.read_pickle('test.pkl')
In [10]: %timeit test_feather_write(df)
9.75 ms ± 117 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [11]: %timeit test_pickle_write(df)
30.1 ms ± 229 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

def test_pickle_write_compress(df):
df.to_pickle('test.pkl.compress', compression='xz')
In [12]: %timeit test_pickle_write_compress(df)
4.29 s ± 15.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [13]: %timeit test_parquet_write(df)
67.6 ms ± 706 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

def test_pickle_read_compress():
pd.read_pickle('test.pkl.compress', compression='xz')
When reading, the top three are ``test_feather_read``, ``test_pickle_read`` and
``test_hdf_fixed_read``.

When writing, the top-three functions in terms of speed are are
``test_pickle_write``, ``test_feather_write`` and ``test_hdf_fixed_write_compress``.

.. code-block:: ipython

In [14]: %timeit test_sql_write(df)
2.37 s ± 36.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [14]: %timeit test_sql_read()
1.77 s ± 17.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [15]: %timeit test_hdf_fixed_write(df)
194 ms ± 65.9 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [15]: %timeit test_hdf_fixed_read()
19.4 ms ± 436 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [26]: %timeit test_hdf_fixed_write_compress(df)
119 ms ± 2.15 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [16]: %timeit test_hdf_fixed_read_compress()
19.5 ms ± 222 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [16]: %timeit test_hdf_table_write(df)
623 ms ± 125 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [17]: %timeit test_hdf_table_read()
38.6 ms ± 857 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [27]: %timeit test_hdf_table_write_compress(df)
563 ms ± 23.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [18]: %timeit test_hdf_table_read_compress()
38.8 ms ± 1.49 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [17]: %timeit test_csv_write(df)
3.13 s ± 49.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [19]: %timeit test_csv_read()
452 ms ± 9.04 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [30]: %timeit test_feather_write(df)
103 ms ± 5.88 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [20]: %timeit test_feather_read()
12.4 ms ± 99.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [31]: %timeit test_pickle_write(df)
109 ms ± 3.72 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [21]: %timeit test_pickle_read()
18.4 ms ± 191 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [32]: %timeit test_pickle_write_compress(df)
3.33 s ± 55.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [22]: %timeit test_pickle_read_compress()
915 ms ± 7.48 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

When reading, the top three are ``test_feather_read``, ``test_pickle_read`` and
``test_hdf_fixed_read``.

.. code-block:: ipython
In [23]: %timeit test_parquet_read()
24.4 ms ± 146 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [18]: %timeit test_sql_read()
1.35 s ± 14.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [19]: %timeit test_hdf_fixed_read()
14.3 ms ± 438 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [28]: %timeit test_hdf_fixed_read_compress()
23.5 ms ± 672 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [20]: %timeit test_hdf_table_read()
35.4 ms ± 314 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [29]: %timeit test_hdf_table_read_compress()
42.6 ms ± 2.1 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [22]: %timeit test_csv_read()
516 ms ± 27.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [33]: %timeit test_feather_read()
4.06 ms ± 115 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
For this test case ``test.pkl.compress``, ``test.parquet`` and ``test.feather`` took the least space on disk.
Space on disk (in bytes)

In [34]: %timeit test_pickle_read()
6.5 ms ± 172 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
.. code-block:: none

In [35]: %timeit test_pickle_read_compress()
588 ms ± 3.57 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
29519500 Oct 10 06:45 test.csv
16000248 Oct 10 06:45 test.feather
8281983 Oct 10 06:49 test.parquet
16000857 Oct 10 06:47 test.pkl
7552144 Oct 10 06:48 test.pkl.compress
34816000 Oct 10 06:42 test.sql
24009288 Oct 10 06:43 test_fixed.hdf
24009288 Oct 10 06:43 test_fixed_compress.hdf
24458940 Oct 10 06:44 test_table.hdf
24458940 Oct 10 06:44 test_table_compress.hdf
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I just noticed this here: it seems something went wrong with the compression (as it is exactly the same size as the non-compressed one; and also the timing is not slower). Maybe it did fall back to non-compressed because you didn't have the compression lib installed?
(however, if it does that silently, that feels like a bug to me)

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@jorisvandenbossche I found out the original code had 3-space indentation, aligning my update to the previous code was the reason some checks failed. When I made the indentation 4-space. It passed.

Let me check the notebook again about the compression.

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You are right about the compression.

IMG_20191021_103019

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@jorisvandenbossche, @datapythonista

I just noticed this here: it seems something went wrong with the compression (as it is exactly the same size as the non-compressed one; and also the timing is not slower). Maybe it did fall back to non-compressed because you didn't have the compression lib installed?
(however, if it does that silently, that feels like a bug to me)

I ran the codes again, tried version '0.25.0' and it's still the same. It seems like a bug.
What can I do to fix it?

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Can you open a separate issue, referencing this issue, explaining about the lack of compression in hdf.

@jorisvandenbossche probably worth merging this as is, and fix that in a separate PR, since it's an unrelated change. And looks like a bug in the code, and I guess the fix won't be trivial.

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Ok Marc


Space on disk (in bytes)

.. code-block:: none

34816000 Aug 21 18:00 test.sql
24009240 Aug 21 18:00 test_fixed.hdf
7919610 Aug 21 18:00 test_fixed_compress.hdf
24458892 Aug 21 18:00 test_table.hdf
8657116 Aug 21 18:00 test_table_compress.hdf
28520770 Aug 21 18:00 test.csv
16000248 Aug 21 18:00 test.feather
16000848 Aug 21 18:00 test.pkl
7554108 Aug 21 18:00 test.pkl.compress