Looks like other people have encountered similar problems with indices
being ignored by prepared sql statements:
http://www.postgresql.org/message-id/43250afa.7010...@arbash-meinel.com.
(If the diagnosis there is correct, then I'm guessing the server would use
a unique index where all the columns of the index are specified.) Also,
Thierry Florac's post
https://groups.google.com/forum/#!topic/sqlalchemy/k_9ZGI-e85E sounds
similar.
(I suspect my earlier hypothesis about int vs varchar is a red herring.)
I think it would be useful (albeit risky, if not careful) to have an option
to plug in parameters client-side. I presume not trivial to add to
SQLAlchemy? I don't see such an option for pyodbc.
On Monday, May 12, 2014 7:09:08 PM UTC-4, Seth P wrote:
>
> Yep, it's not a SQLAlchemy issue. The following code demonstrates the
> problem with direct pyodbc access.
>
> import pyodbc
> import time
>
> def print_timing(func):
> def wrapper(*arg):
> t1 = time.time()
> rows = func(*arg)
> t2 = time.time()
> print("%30s() len=%d, last=%s, runtime=%0.3fs" %
> (str(func).split(' at')[0][10:], len(rows), rows[-1], t2 - t1))
> return t2 - t1
> return wrapper
>
> if __name__ == '__main__':
> cnxn = pyodbc.connect('DRIVER={SQL
> Server};SERVER=Compustat;DATABASE=Compustat')
> cursor = cnxn.cursor()
> sql_select_statement_base = "SELECT datadate, prcod FROM sec_dprc
> WHERE gvkey = ? ORDER BY datadate"
> key = '001045'
>
> @print_timing
> def execute_explicit_query():
> sql_select_statement_explicit =
> sql_select_statement_base.replace("?", "'%s'" % key)
> rows = cursor.execute(sql_select_statement_explicit).fetchall()
> return rows
>
> @print_timing
> def execute_parameterized_query():
> rows = cursor.execute(sql_select_statement_base, key).fetchall()
> return rows
>
> num_iterations = 5
> explicit_runtime = 0.0
> parameterized_runtime = 0.0
> for i in range(num_iterations):
> explicit_runtime += execute_explicit_query()
> parameterized_runtime += execute_parameterized_query()
> print("Total runtime for %d explicit queries = %0.3fs." %
> (num_iterations, explicit_runtime))
> print("Total runtime for %d parameterized queries = %0.3fs." %
> (num_iterations, parameterized_runtime))
>
>
> On Monday, May 12, 2014 6:40:48 PM UTC-4, Michael Bayer wrote:
>>
>>
>> On May 12, 2014, at 6:33 PM, Seth P <spad...@gmail.com> wrote:
>>
>> Is it possible that the (primary key index (which is a composite index
>> that begins with gvkey, and is the only index on the table) isn't being
>> used because the the gvkey parameter is somehow passed as an integer rather
>> than as a string?
>>
>>
>> There’s nothing in SQLAlchemy that coerces strings to integers. If the
>> actual type of the column on the DB is an integer, then there might be some
>> conversion within pyodbc or the ODBC driver.
>>
>> if you’ve got it narrowed down this much the next step is to figure out a
>> raw pyodbc script that illustrates what the problem is.
>>
>>
>> The first EXEC below is pretty much instantaneous, whereas the second
>> takes about 8 seconds (and produces the same results).
>>
>> EXEC sp_executesql
>> N'SELECT sec_dprc.datadate AS sec_dprc_datadate, sec_dprc.prcod AS
>> sec_dprc_prcod
>> FROM sec_dprc WHERE sec_dprc.gvkey = @gvkey ORDER BY sec_dprc.datadate',
>> N'@gvkey VARCHAR(6)', '001045'
>>
>> EXEC sp_executesql
>> N'SELECT sec_dprc.datadate AS sec_dprc_datadate, sec_dprc.prcod AS
>> sec_dprc_prcod
>> FROM sec_dprc WHERE sec_dprc.gvkey = @gvkey ORDER BY sec_dprc.datadate',
>> N'@gvkey INT', 001045
>>
>>
>>
>> On Monday, May 12, 2014 5:00:27 PM UTC-4, Michael Bayer wrote:
>>>
>>>
>>> well there’s only one parameter being processed here so there is clearly
>>> negligible difference in time spent within Python as far as getting the
>>> statement ready to execute and then executing it.
>>>
>>> So the time is either in what SQL Server spends fetching the rows, or
>>> the number of rows being fetched (which seems to be the same). Which
>>> leaves pretty much that SQL Server is making a different choice about the
>>> query plan for this SELECT statement, this is typically due to an INDEX
>>> being used or not. You’d need to analyze the plan being used. With SQL
>>> Server, the option to get a plan within programmatic execution seems to be
>>> per this answer
>>> http://stackoverflow.com/questions/7359702/how-do-i-obtain-a-query-execution-planto
>>> execute “SET SHOWPLAN_TEXT ON” ahead of time.
>>>
>>> Besides that, you can confirm where the time is being spent exactly
>>> using Python profiling. A description on how to achieve that is here:
>>> http://stackoverflow.com/questions/1171166/how-can-i-profile-a-sqlalchemy-powered-application/1175677#1175677
>>>
>>>
>>>
>>> On May 12, 2014, at 3:48 PM, Seth P <spad...@gmail.com> wrote:
>>>
>>> After tracking down some extreme slowness in loading a one-to-many
>>> relationship (e.g. myobject.foobars), I seem to have isolated the issue to
>>> engine.execute() being much slower with parameterized queries than with
>>> explicit queries. The following is actual code and output for loading
>>> 10,971 rows from a database table. (The actual table has more columns than
>>> I'm including here, and is not designed by me.) Note that each explicit
>>> query (where I explicitly set the WHERE clause parameter and pass the
>>> resulting SQL statement to engine.execute()) runs in under 0.1 seconds,
>>> whereas each parameterized query (where I let SQLAlchemy bind the WHERE
>>> clause parameter) takes over 8 seconds.
>>>
>>> The difference in runtimes is smaller when the number of rows returned
>>> is smaller, which seems odd since I would have thought that the binding of
>>> the WHERE clause parameters is just done once and would be virtually
>>> instantaneous.
>>>
>>> Any thoughts?
>>>
>>> Thanks,
>>>
>>> Seth
>>>
>>>
>>> import sqlalchemy as sa
>>> from sqlalchemy.orm import sessionmaker
>>> from sqlalchemy.ext.declarative import declarative_base
>>> import time
>>>
>>> engine = sa.create_engine('mssql+pyodbc://Compustat/Compustat')
>>> session = sessionmaker(bind=engine, autoflush=False,
>>> expire_on_commit=False)()
>>>
>>> class FooBar(declarative_base()):
>>> __tablename__ = 'sec_dprc'
>>> gvkey = sa.Column(sa.String(6), primary_key=True)
>>> datadate = sa.Column(sa.DateTime, primary_key=True)
>>> value = sa.Column(sa.Float, name='prcod')
>>>
>>> def print_timing(func):
>>> def wrapper(*arg):
>>> t1 = time.time()
>>> rows = func(*arg)
>>> t2 = time.time()
>>> print("%30s() len=%d, last=%s, runtime=%0.3fs" %
>>> (str(func).split(' at')[0][10:], len(rows), rows[-1], t2 - t1))
>>> return t2 - t1
>>> return wrapper
>>>
>>> if __name__ == '__main__':
>>>
>>> key = '001045'
>>> query = session.query(FooBar.datadate,
>>> FooBar.value).filter(sa.and_(FooBar.gvkey == key)).order_by(FooBar.datadate)
>>> sql_select_statement_base = str(query)
>>> print(sql_select_statement_base)
>>>
>>> @print_timing
>>> def execute_explicit_query():
>>> sql_select_statement_explicit =
>>> sql_select_statement_base.replace(":gvkey_1", "'%s'" % key)
>>> rows =
>>> engine.execute(sa.text(sql_select_statement_explicit)).fetchall()
>>> return rows
>>>
>>> @print_timing
>>> def execute_parameterized_query():
>>> rows = engine.execute(sa.text(sql_select_statement_base),
>>> {'gvkey_1':key}).fetchall()
>>> return rows
>>>
>>> num_iterations = 5
>>> explicit_runtime = 0.0
>>> parameterized_runtime = 0.0
>>> for i in range(num_iterations):
>>> explicit_runtime += execute_explicit_query()
>>> parameterized_runtime += execute_parameterized_query()
>>> print("Total runtime for %d explicit queries = %0.3fs." %
>>> (num_iterations, explicit_runtime))
>>> print("Total runtime for %d parameterized queries = %0.3fs." %
>>> (num_iterations, parameterized_runtime))
>>>
>>>
>>> SELECT sec_dprc.datadate AS sec_dprc_datadate, sec_dprc.prcod AS
>>> sec_dprc_prcod
>>> FROM sec_dprc
>>> WHERE sec_dprc.gvkey = :gvkey_1 ORDER BY sec_dprc.datadate
>>> execute_explicit_query() len=10971,
>>> last=(datetime.datetime(2014, 5, 9, 0, 0), 37.96), runtime=0.082s
>>> execute_parameterized_query() len=10971,
>>> last=(datetime.datetime(2014, 5, 9, 0, 0), 37.96), runtime=8.852s
>>> execute_explicit_query() len=10971,
>>> last=(datetime.datetime(2014, 5, 9, 0, 0), 37.96), runtime=0.032s
>>> execute_parameterized_query() len=10971,
>>> last=(datetime.datetime(2014, 5, 9, 0, 0), 37.96), runtime=8.754s
>>> execute_explicit_query() len=10971,
>>> last=(datetime.datetime(2014, 5, 9, 0, 0), 37.96), runtime=0.039s
>>> execute_parameterized_query() len=10971,
>>> last=(datetime.datetime(2014, 5, 9, 0, 0), 37.96), runtime=9.182s
>>> execute_explicit_query() len=10971,
>>> last=(datetime.datetime(2014, 5, 9, 0, 0), 37.96), runtime=0.028s
>>> execute_parameterized_query() len=10971,
>>> last=(datetime.datetime(2014, 5, 9, 0, 0), 37.96), runtime=9.416s
>>> execute_explicit_query() len=10971,
>>> last=(datetime.datetime(2014, 5, 9, 0, 0), 37.96), runtime=0.080s
>>> execute_parameterized_query() len=10971,
>>> last=(datetime.datetime(2014, 5, 9, 0, 0), 37.96), runtime=8.425s
>>> Total runtime for 5 explicit queries = 0.260s.
>>> Total runtime for 5 parameterized queries = 44.629s.
>>>
>>>
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