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Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
https://pandas.pydata.org
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to_sql 2x slower in pandas 0.20.3 then 0.19.2 (reproducable) #17177

Closed gregsifr closed 6 years ago

gregsifr commented 7 years ago

Code Sample, a copy-pastable example if possible

import numpy as np
import pandas as pd

index = pd.date_range('1970-01-01',periods=100000, freq='H')
values = np.random.randn(100000,10)
df = pd.DataFrame(values, index = index)
df.to_sql('table_name','connection_string', if_exists='append')

Problem description

Since upgrading to 0.20+ I am finding that to_sql takes twice as long. I used two environments to test the code on the same machine, below are the results:

pandas 0.20.3

CPU times: user 15.9 s, sys: 3.67 s, total: 19.6 s
Wall time: 1min 13s

pandas 0.19.2

CPU times: user 5.2 s, sys: 728 ms, total: 5.93 s
Wall time: 36.8 s

Please note that each test was performed on an empty table.

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 3.5.2.final.0 python-bits: 64 OS: Linux OS-release: 4.4.0-87-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 pandas: 0.19.2 nose: None pip: 9.0.1 setuptools: 27.2.0 Cython: 0.25.2 numpy: 1.11.2 scipy: 0.18.1 statsmodels: 0.6.1 xarray: None IPython: 5.1.0 sphinx: 1.5.1 patsy: 0.4.1 dateutil: 2.6.0 pytz: 2016.10 blosc: None bottleneck: 1.2.0 tables: None numexpr: None matplotlib: 1.5.3 openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: 0.9999999 httplib2: None apiclient: None sqlalchemy: 1.1.6 pymysql: None psycopg2: 2.6.2 (dt dec pq3 ext lo64) jinja2: 2.9.4 boto: None pandas_datareader: 0.2.1 INSTALLED VERSIONS ------------------ commit: None python: 3.6.1.final.0 python-bits: 64 OS: Linux OS-release: 4.4.0-87-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 pandas: 0.20.3 pytest: 3.0.7 pip: 9.0.1 setuptools: 27.2.0 Cython: 0.25.2 numpy: 1.13.1 scipy: 0.19.0 xarray: None IPython: 5.3.0 sphinx: 1.5.6 patsy: 0.4.1 dateutil: 2.6.1 pytz: 2017.2 blosc: None bottleneck: 1.2.1 tables: 3.3.0 numexpr: 2.6.2 feather: None matplotlib: 2.0.2 openpyxl: 2.4.7 xlrd: 1.0.0 xlwt: 1.2.0 xlsxwriter: 0.9.6 lxml: 3.7.3 bs4: 4.6.0 html5lib: 0.999 sqlalchemy: 1.1.9 pymysql: None psycopg2: 2.7.3 (dt dec pq3 ext lo64) jinja2: 2.9.6 s3fs: None pandas_gbq: None pandas_datareader: None
jorisvandenbossche commented 7 years ago

@gregsifr Thanks for the report. I don't directly a reason in pandas why this got so much slower (there were no major changes in the sql code at least).

But, to check if pandas is to blame, could you run the above but with the exact same versions of sqlalchemy and psycopg2 (assuming you are using postgres, otherwise the driver for your database) ?

What might also help is to post a profile result of both (if you are using ipython/jupyter, you can do that with the %prun magic)

gregsifr commented 7 years ago

@jorisvandenbossche After making sure that both environments had sqlalchemy: 1.1.9 and psycopg2: 2.7.3 (dt dec pq3 ext lo64) installed I repeated the test. The pandas 0.19.2 environment completed the task in 39s which is about the same as before, whilst pandas 0.20.3 took 54s.

I've included the results of %prun for you below:

Pandas 0.19.2

``` 2806282 function calls (2806162 primitive calls) in 41.962 seconds Ordered by: internal time ncalls tottime percall cumtime percall filename:lineno(function) 1 40.162 40.162 40.456 40.456 {method 'executemany' of 'psycopg2.extensions.cursor' objects} 100001 0.442 0.000 0.442 0.000 compiler.py:507(construct_params) 2 0.338 0.169 0.979 0.489 default.py:554(_init_compiled) 1 0.303 0.303 0.409 0.409 sql.py:744() 100017 0.167 0.000 0.167 0.000 {built-in method _codecs.utf_8_decode} 1200002 0.167 0.000 0.167 0.000 default.py:643() 100017 0.128 0.000 0.295 0.000 utf_8.py:15(decode) 1200000 0.106 0.000 0.106 0.000 sql.py:744() 1 0.026 0.026 0.468 0.468 default.py:593() 1 0.026 0.026 41.871 41.871 sql.py:743(_execute_insert) 2 0.016 0.008 0.016 0.008 {pandas.lib.isnullobj} 17 0.015 0.001 0.015 0.001 {built-in method numpy.core.multiarray.array} 1 0.013 0.013 41.934 41.934 sql.py:747(insert) 1 0.009 0.009 0.009 0.009 {built-in method psycopg2._psycopg._connect} 1 0.008 0.008 41.962 41.962 sql.py:1207(to_sql) 3 0.006 0.002 0.006 0.002 {method 'astype' of 'numpy.ndarray' objects} 2 0.005 0.003 0.005 0.003 {method 'copy' of 'numpy.ndarray' objects} 100064 0.005 0.000 0.005 0.000 {method 'append' of 'list' objects} 8 0.005 0.001 0.005 0.001 {method 'execute' of 'psycopg2.extensions.cursor' objects} 1 0.005 0.005 0.005 0.005 {method 'commit' of 'psycopg2.extensions.connection' objects} 4 0.001 0.000 0.001 0.000 {method 'rollback' of 'psycopg2.extensions.connection' objects} 2 0.001 0.000 0.018 0.009 common.py:165(_isnull_ndarraylike) 2 0.001 0.000 0.001 0.000 {method 'ravel' of 'numpy.ndarray' objects} 5 0.001 0.000 41.438 8.288 base.py:1104(_execute_context) 31 0.000 0.000 0.000 0.000 generic.py:2674(__setattr__) 1 0.000 0.000 0.046 0.046 sql.py:707(insert_data) 776 0.000 0.000 0.000 0.000 {built-in method builtins.isinstance} 148 0.000 0.000 0.000 0.000 {built-in method builtins.hasattr} 250/238 0.000 0.000 0.000 0.000 {built-in method builtins.getattr} 11 0.000 0.000 0.000 0.000 schema.py:898(__init__) 266/226 0.000 0.000 0.000 0.000 {built-in method builtins.len} 35/16 0.000 0.000 0.000 0.000 langhelpers.py:229(get_cls_kwargs) 9 0.000 0.000 0.000 0.000 {method 'cursor' of 'psycopg2.extensions.connection' objects} 11 0.000 0.000 0.000 0.000 sql.py:961(_sqlalchemy_type) 10 0.000 0.000 0.001 0.000 indexing.py:882(_getitem_lowerdim) 13 0.000 0.000 0.000 0.000 {pandas.lib.infer_dtype} 3/2 0.000 0.000 0.000 0.000 base.py:124(__new__) 38 0.000 0.000 0.001 0.000 langhelpers.py:761(__get__) 13 0.000 0.000 0.000 0.000 {method 'sub' of '_sre.SRE_Pattern' objects} 4 0.000 0.000 0.001 0.000 compiler.py:376(__init__) 1 0.000 0.000 0.000 0.000 internals.py:2674(_rebuild_blknos_and_blklocs) 10 0.000 0.000 0.000 0.000 internals.py:3312(iget) 20/10 0.000 0.000 0.001 0.000 indexing.py:1286(__getitem__) 4 0.000 0.000 0.000 0.000 base.py:1233(__contains__) 1 0.000 0.000 0.001 0.001 strategies.py:48(create) 26/4 0.000 0.000 0.001 0.000 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0.017 sql.py:689(create) 2 0.000 0.000 0.000 0.000 :1(cast) 2 0.000 0.000 0.000 0.000 _collections.py:728(__missing__) 1 0.000 0.000 0.000 0.000 {method 'groupdict' of '_sre.SRE_Match' objects} 1 0.000 0.000 0.000 0.000 base.py:2064(__init__) 1 0.000 0.000 0.000 0.000 base.py:314(_generate) 1 0.000 0.000 0.000 0.000 psycopg2.py:657(create_connect_args) 1 0.000 0.000 0.000 0.000 base.py:614(__hash__) 10 0.000 0.000 0.001 0.000 indexing.py:95(_get_loc) 1 0.000 0.000 0.000 0.000 naming.py:23(__init__) 6 0.000 0.000 0.000 0.000 {method 'join' of 'str' objects} 2 0.000 0.000 0.000 0.000 pool.py:399(_return_conn) 1 0.000 0.000 0.000 0.000 elements.py:4082(_as_truncated) 10 0.000 0.000 0.000 0.000 common.py:1788(is_hashable) 2 0.000 0.000 0.000 0.000 elements.py:3748(_from_objects) 10 0.000 0.000 0.000 0.000 psycopg2.py:339(bind_processor) 2 0.000 0.000 0.000 0.000 queue.py:190(_full) 1 0.000 0.000 0.000 0.000 naming.py:54(_key_column_X_label) 2 0.000 0.000 0.000 0.000 numeric.py:1404(rollaxis) 1 0.000 0.000 0.000 0.000 base.py:1143(_engine) 2 0.000 0.000 0.000 0.000 {method 'fill' of 'numpy.ndarray' objects} 1 0.000 0.000 0.000 0.000 crud.py:168(_key_getters_for_crud_column) 2 0.000 0.000 0.000 0.000 {built-in method psycopg2._psycopg.new_type} 1 0.000 0.000 0.000 0.000 base.py:1146() 1 0.000 0.000 0.000 0.000 langhelpers.py:196(load) 1 0.000 0.000 0.005 0.005 base.py:1661(_do_commit) 1 0.000 0.000 0.000 0.000 schema.py:2486(_set_parent) 46 0.000 0.000 0.000 0.000 {built-in method builtins.callable} 1 0.000 0.000 0.000 0.000 base.py:2011(get_level_values) 1 0.000 0.000 0.000 0.000 base.py:1590(__init__) 2 0.000 0.000 0.000 0.000 sqltypes.py:749(__init__) 1 0.000 0.000 0.000 0.000 :1(bindparams) 2 0.000 0.000 0.000 0.000 attr.py:46(_memoized_attr_ref) 1 0.000 0.000 0.000 0.000 {method 'nonzero' of 'numpy.ndarray' objects} 1 0.000 0.000 0.017 0.017 sql.py:677(exists) 1 0.000 0.000 0.000 0.000 generic.py:381(_info_axis) 2 0.000 0.000 0.000 0.000 elements.py:4290(_interpret_as_column_or_from) 1 0.000 0.000 0.000 0.000 {built-in method pandas.tslib.maybe_get_tz} 6 0.000 0.000 0.000 0.000 internals.py:2621() 1 0.000 0.000 0.000 0.000 {built-in method numpy.core.multiarray.bincount} 1 0.000 0.000 0.000 0.000 naming.py:33(_column_X) 1 0.000 0.000 0.000 0.000 elements.py:1391(bindparams) 1 0.000 0.000 0.000 0.000 visitors.py:210(iterate) 1 0.000 0.000 0.000 0.000 range.py:150(_data) 2 0.000 0.000 0.000 0.000 _collections.py:181(__len__) 1 0.000 0.000 0.000 0.000 visitors.py:264(traverse) 4 0.000 0.000 0.000 0.000 base.py:271(_validate_dialect_kwargs) 3 0.000 0.000 0.000 0.000 _collections.py:150(__init__) 1 0.000 0.000 0.000 0.000 visitors.py:252(traverse_using) 2 0.000 0.000 0.000 0.000 url.py:250(_rfc_1738_unquote) 1 0.000 0.000 0.000 0.000 frame.py:2394(_ensure_valid_index) 1 0.000 0.000 0.000 0.000 numerictypes.py:1015() 1 0.000 0.000 0.000 0.000 compiler.py:2772(visit_unicode) 1 0.000 0.000 0.000 0.000 common.py:986(_possibly_castable) 1 0.000 0.000 0.000 0.000 attr.py:235(_memoized_attr__exec_once_mutex) 2 0.000 0.000 0.000 0.000 elements.py:706(_select_iterable) 2 0.000 0.000 0.000 0.000 compiler.py:309(type) 1 0.000 0.000 0.000 0.000 common.py:1820(is_sequence) 1 0.000 0.000 0.000 0.000 base.py:494(add) 15 0.000 0.000 0.000 0.000 {built-in method builtins.hash} 1 0.000 0.000 0.000 0.000 url.py:186(make_url) 1 0.000 0.000 0.000 0.000 common.py:1567(is_datetime64_dtype) 2 0.000 0.000 0.000 0.000 elements.py:4114(_select_iterables) 1 0.000 0.000 0.000 0.000 base.py:587(__init__) 3 0.000 0.000 0.000 0.000 events.py:279(_accept_with) 2 0.000 0.000 0.000 0.000 {built-in method _thread.allocate_lock} 2 0.000 0.000 0.000 0.000 elements.py:4119() 1 0.000 0.000 0.000 0.000 :1(text) 2 0.000 0.000 0.000 0.000 queue.py:194(_put) 1 0.000 0.000 0.000 0.000 __init__.py:1257(debug) 3 0.000 0.000 0.000 0.000 base.py:112(_listen) 1 0.000 0.000 0.000 0.000 attr.py:264(__bool__) 1 0.000 0.000 0.000 0.000 schema.py:2502(_to_schema_column_or_string) 2 0.000 0.000 0.000 0.000 _collections.py:752() 5 0.000 0.000 0.000 0.000 inspect.py:158(isfunction) 1 0.000 0.000 0.000 0.000 {pandas.tslib.is_timestamp_array} 1 0.000 0.000 0.000 0.000 _collections.py:292(update) 13 0.000 0.000 0.000 0.000 {method 'lower' of 'str' objects} 1 0.000 0.000 0.000 0.000 sqltypes.py:214(process) 1 0.000 0.000 0.000 0.000 generic.py:2641(__finalize__) 1 0.000 0.000 0.000 0.000 compiler.py:738(do_bindparam) 3 0.000 0.000 0.000 0.000 default.py:735(no_parameters) 4 0.000 0.000 0.000 0.000 result.py:237() 2 0.000 0.000 0.000 0.000 compiler.py:2929(format_label) 1 0.000 0.000 0.000 0.000 queue.py:178(_init) 1 0.000 0.000 0.000 0.000 schema.py:2557() 2 0.000 0.000 0.000 0.000 compiler.py:1709() 1 0.000 0.000 0.000 0.000 common.py:1731(is_categorical) 1 0.000 0.000 0.000 0.000 {method 'any' of 'numpy.ndarray' objects} 1 0.000 0.000 0.000 0.000 __init__.py:50() 1 0.000 0.000 0.000 0.000 elements.py:3770(_label) 12 0.000 0.000 0.000 0.000 langhelpers.py:1244(set_creation_order) 1 0.000 0.000 0.000 0.000 {pandas.lib.is_timedelta_array} 1 0.000 0.000 0.000 0.000 psycopg2.py:462(post_process_text) 1 0.000 0.000 0.000 0.000 schema.py:3610(_add_table) 8 0.000 0.000 0.000 0.000 numerictypes.py:951() 1 0.000 0.000 0.000 0.000 threading.py:72(RLock) 1 0.000 0.000 0.000 0.000 {method 'keys' of 'dict' objects} 1 0.000 0.000 0.000 0.000 range.py:158(_get_data_as_items) 9 0.000 0.000 0.000 0.000 {built-in method builtins.id} 3 0.000 0.000 0.000 0.000 __init__.py:1774(getLogger) 2 0.000 0.000 0.000 0.000 selectable.py:3445() 1 0.000 0.000 0.000 0.000 base.py:415(__array__) 1 0.000 0.000 0.000 0.000 default.py:238(get_pool_class) 2 0.000 0.000 0.000 0.000 _collections.py:156(union) 10 0.000 0.000 0.000 0.000 series.py:366(_values) 1 0.000 0.000 0.000 0.000 {method 'extend' of 'list' objects} 10 0.000 0.000 0.000 0.000 internals.py:129(internal_values) 2 0.000 0.000 0.000 0.000 base.py:1409(get_select_precolumns) 2 0.000 0.000 0.000 0.000 selectable.py:2789() 1 0.000 0.000 0.000 0.000 base.py:512(__setitem__) 1 0.000 0.000 0.000 0.000 langhelpers.py:284(get_func_kwargs) 2 0.000 0.000 0.000 0.000 {method 'transpose' of 'numpy.ndarray' objects} 2 0.000 0.000 0.000 0.000 {method 'values' of 'mappingproxy' objects} 1 0.000 0.000 0.000 0.000 index.py:1538(_get_freq) 1 0.000 0.000 0.000 0.000 base.py:1595(_parent) 1 0.000 0.000 0.000 0.000 {built-in method _codecs.lookup} 1 0.000 0.000 0.000 0.000 sqltypes.py:2592(_resolve_value_to_type) 2 0.000 0.000 0.000 0.000 compiler.py:718(escape_literal_column) 1 0.000 0.000 0.000 0.000 {method 'isdigit' of 'str' objects} 2 0.000 0.000 0.000 0.000 schema.py:2526() 1 0.000 0.000 0.000 0.000 dml.py:38(process_single) 1 0.000 0.000 0.000 0.000 base.py:1932(__init__) 1 0.000 0.000 0.000 0.000 numerictypes.py:1016() 2 0.000 0.000 0.000 0.000 base.py:870(_values) 1 0.000 0.000 0.000 0.000 schema.py:3374() 1 0.000 0.000 0.000 0.000 psycopg2.py:557(_psycopg2_extensions) 10 0.000 0.000 0.000 0.000 index.py:1669(is_all_dates) 1 0.000 0.000 0.000 0.000 inspect.py:259(isbuiltin) 1 0.000 0.000 0.000 0.000 base.py:139(_root) 1 0.000 0.000 0.000 0.000 {built-in method time.time} 1 0.000 0.000 0.000 0.000 series.py:2804(_try_cast) 2 0.000 0.000 0.000 0.000 internals.py:2996(is_consolidated) 1 0.000 0.000 0.000 0.000 {method 'islower' of 'str' objects} 10 0.000 0.000 0.000 0.000 {built-in method builtins.any} 4 0.000 0.000 0.000 0.000 _collections.py:271(__init__) 1 0.000 0.000 0.000 0.000 base.py:1186(__iter__) 1 0.000 0.000 0.000 0.000 index.py:600(tzinfo) 1 0.000 0.000 0.000 0.000 compiler.py:2037() 1 0.000 0.000 0.000 0.000 sqltypes.py:209(bind_processor) 2 0.000 0.000 0.000 0.000 inspect.py:2743(return_annotation) 1 0.000 0.000 0.000 0.000 log.py:216(__set__) 2 0.000 0.000 0.000 0.000 compiler.py:1409(_add_to_result_map) 7 0.000 0.000 0.000 0.000 {method 'release' of '_thread.RLock' objects} 1 0.000 0.000 0.000 0.000 range.py:213(dtype) 1 0.000 0.000 0.000 0.000 langhelpers.py:1322(only_once) 1 0.000 0.000 0.000 0.000 crud.py:640(_get_returning_modifiers) 1 0.000 0.000 0.000 0.000 range.py:139(_validate_dtype) 2 0.000 0.000 0.000 0.000 parse.py:527(unquote) 4 0.000 0.000 0.000 0.000 attr.py:71(_adjust_fn_spec) 3 0.000 0.000 0.000 0.000 registry.py:159(_key) 2 0.000 0.000 0.000 0.000 schema.py:2599(_set_parent) 1 0.000 0.000 0.000 0.000 schema.py:2586() 1 0.000 0.000 0.000 0.000 {method 'pop' of 'set' objects} 1 0.000 0.000 0.000 0.000 {method 'issuperset' of 'set' objects} 2 0.000 0.000 0.000 0.000 {method 'item' of 'numpy.ndarray' objects} 8 0.000 0.000 0.000 0.000 {method 'close' of 'psycopg2.extensions.cursor' objects} 2 0.000 0.000 0.000 0.000 {method 'union' of 'set' objects} 2 0.000 0.000 0.000 0.000 elements.py:1176(__init__) 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects} 2 0.000 0.000 0.000 0.000 inspect.py:2739(parameters) 1 0.000 0.000 0.000 0.000 base.py:161(__enter__) 1 0.000 0.000 0.000 0.000 default.py:436(do_begin) 5 0.000 0.000 0.000 0.000 {method 'append' of 'collections.deque' objects} 2 0.000 0.000 0.000 0.000 {method 'isidentifier' of 'str' objects} 2 0.000 0.000 0.000 0.000 attr.py:294(for_modify) 1 0.000 0.000 0.000 0.000 internals.py:2871() 1 0.000 0.000 0.000 0.000 url.py:124() 1 0.000 0.000 0.000 0.000 {method 'upper' of 'str' objects} 1 0.000 0.000 0.000 0.000 base.py:791(nlevels) 1 0.000 0.000 0.000 0.000 schema.py:3095() 1 0.000 0.000 0.000 0.000 compiler.py:286(__init__) 1 0.000 0.000 0.000 0.000 {method 'clear' of 'dict' objects} 2 0.000 0.000 0.000 0.000 default.py:829(post_exec) 1 0.000 0.000 0.000 0.000 base.py:615() 2 0.000 0.000 0.000 0.000 type_api.py:268(result_processor) 1 0.000 0.000 0.000 0.000 type_api.py:254(bind_processor) 1 0.000 0.000 0.000 0.000 pool.py:261(_creator) 1 0.000 0.000 0.000 0.000 frame.py:199(_constructor) 1 0.000 0.000 0.000 0.000 numeric.py:484(asanyarray) 3 0.000 0.000 0.000 0.000 {method '_is_owned' of '_thread.RLock' objects} 1 0.000 0.000 0.000 0.000 base.py:553() 1 0.000 0.000 0.000 0.000 base.py:552() 1 0.000 0.000 0.000 0.000 schema.py:606(_extra_kwargs) 1 0.000 0.000 0.000 0.000 base.py:1937(__enter__) 3 0.000 0.000 0.000 0.000 inspect.py:2438(default) 4 0.000 0.000 0.000 0.000 compiler.py:244(__str__) 1 0.000 0.000 0.000 0.000 base.py:1148(_validate_index_level) 1 0.000 0.000 0.000 0.000 selectable.py:237(selectable) 2 0.000 0.000 0.000 0.000 inspect.py:2442(annotation) 2 0.000 0.000 0.000 0.000 inspect.py:2446(kind) 2 0.000 0.000 0.000 0.000 {built-in method psycopg2._psycopg.new_array_type} 1 0.000 0.000 0.000 0.000 elements.py:333(get_children) 2 0.000 0.000 0.000 0.000 default.py:826(pre_exec) 4 0.000 0.000 0.000 0.000 inspect.py:2434(name) 1 0.000 0.000 0.000 0.000 schema.py:2552() 2 0.000 0.000 0.000 0.000 langhelpers.py:786() 1 0.000 0.000 0.000 0.000 interfaces.py:771(get_dialect_cls) 2 0.000 0.000 0.000 0.000 compiler.py:566(default_from) 1 0.000 0.000 0.000 0.000 __init__.py:1524() 1 0.000 0.000 0.000 0.000 re.py:222(compile) 1 0.000 0.000 0.000 0.000 interfaces.py:787(engine_created) 10 0.000 0.000 0.000 0.000 internals.py:125(external_values) 1 0.000 0.000 0.000 0.000 schema.py:450(__init__) 1 0.000 0.000 0.000 0.000 {method 'popleft' of 'collections.deque' objects} ```

Pandas 0.20.3

``` 2880637 function calls (2878705 primitive calls) in 54.492 seconds Ordered by: internal time ncalls tottime percall cumtime percall filename:lineno(function) 1 51.241 51.241 51.879 51.879 {method 'executemany' of 'psycopg2.extensions.cursor' objects} 100001 0.653 0.000 0.653 0.000 compiler.py:507(construct_params) 1 0.475 0.475 0.663 0.663 sql.py:640() 2 0.454 0.227 1.390 0.695 default.py:554(_init_compiled) 100017 0.416 0.000 0.416 0.000 {built-in method _codecs.utf_8_decode} 1200002 0.232 0.000 0.232 0.000 default.py:643() 100017 0.221 0.000 0.637 0.000 utf_8.py:15(decode) 1200000 0.188 0.000 0.188 0.000 sql.py:640() 1 0.141 0.141 54.075 54.075 sql.py:639(_execute_insert) 1 0.066 0.066 54.262 54.262 sql.py:643(insert) 16 0.055 0.003 0.055 0.003 {built-in method numpy.core.multiarray.array} 1 0.040 0.040 0.692 0.692 default.py:593() 2 0.035 0.018 0.035 0.018 {pandas._libs.lib.isnullobj} 533/510 0.023 0.000 0.082 0.000 {built-in method builtins.__build_class__} 77 0.019 0.000 0.019 0.000 {built-in method marshal.loads} 2 0.016 0.008 0.016 0.008 {method 'copy' of 'numpy.ndarray' objects} 1 0.015 0.015 54.311 54.311 sql.py:1104(to_sql) 1 0.012 0.012 0.012 0.012 {built-in method psycopg2._psycopg._connect} 8 0.011 0.001 0.011 0.001 {method 'execute' of 'psycopg2.extensions.cursor' objects} 106051 0.011 0.000 0.011 0.000 {method 'append' of 'list' objects} 176/1 0.010 0.000 54.493 54.493 {built-in method builtins.exec} 3 0.010 0.003 0.010 0.003 {method 'astype' of 'numpy.ndarray' objects} 4 0.010 0.003 0.011 0.003 {built-in method _imp.create_dynamic} 256 0.005 0.000 0.005 0.000 annotation.py:176(_new_annotation_type) 237 0.004 0.000 0.009 0.000 inspect.py:2065(_signature_from_function) 149/37 0.004 0.000 0.011 0.000 sre_parse.py:469(_parse) 237 0.003 0.000 0.016 0.000 inspect.py:1052(getfullargspec) 282 0.003 0.000 0.003 0.000 {built-in method posix.stat} 462 0.003 0.000 0.004 0.000 inspect.py:1170(formatargspec) 29 0.002 0.000 0.003 0.000 {built-in method builtins.eval} 3326 0.002 0.000 0.002 0.000 sre_parse.py:232(__next) 249/34 0.002 0.000 0.009 0.000 sre_compile.py:64(_compile) 77 0.002 0.000 0.002 0.000 {method 'read' of '_io.FileIO' objects} 77 0.002 0.000 0.004 0.000 :830(get_data) 174 0.002 0.000 0.002 0.000 {method 'sub' of '_sre.SRE_Pattern' objects} 5184 0.002 0.000 0.002 0.000 {built-in method builtins.isinstance} 1 0.002 0.002 0.002 0.002 {method 'commit' of 'psycopg2.extensions.connection' objects} 237 0.002 0.000 0.003 0.000 inspect.py:2678(__init__) 260/237 0.002 0.000 0.011 0.000 inspect.py:2146(_signature_from_callable) 1882 0.002 0.000 0.002 0.000 enum.py:515(__new__) 834 0.002 0.000 0.005 0.000 enum.py:801(__and__) 154 0.002 0.000 0.006 0.000 langhelpers.py:334(format_argspec_plus) 1906/1894 0.002 0.000 0.002 0.000 {built-in method builtins.getattr} 793 0.002 0.000 0.002 0.000 inspect.py:2399(__init__) 88 0.002 0.000 0.008 0.000 :1233(find_spec) 1882 0.002 0.000 0.003 0.000 enum.py:265(__call__) 2958 0.001 0.000 0.004 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TomAugspurger commented 7 years ago

@gregsifr can you profile 0.20.3 as well, and compare the outputs to see where the slowdown is?

gregsifr commented 7 years ago

@TomAugspurger Updated my previous post to include both.

jorisvandenbossche commented 7 years ago

@gregsifr thanks for the updates. So it seems the slowdown is already a bit less after making sure the same versions are installed?

That said, when looking at the profiles, I see that for 0.19.2 the psycopg2's executemany is taking 40.5 s out of 41.9 s total. While for 0.20.3, executemany is taking 51.9 s out of 54.5 s total. So it seems that the biggest part of the slowdown is still due to psycopg2? (the code inside pandas increased a little bit, but is still rather negligible compared to the psycopg2 time)

gregsifr commented 7 years ago

Thank you for doing that. I will raise a ticket in the psycopg2 library for further investigation.

gfyoung commented 6 years ago

Judging from this conversation, it appears that we're in the clear for this problem. Closing for the time being, but can reopen if it turns out that pandas is to blame.