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A “vertical table” refers to a technique where individual attributes of an object are stored as distinct rows in a table.The “vertical table” technique is used to persist objects which can have a varied set of attributes, at the expense of simple query control and brevity.

paragraph and pleasant arguments commented here speed dating, L'anno prossimo arriverà l'epico ' American Gangster' con Crowe superpoliziotto contro lo spacciatore Denzel Washington. I linguaggi dating online american singles La Stone ci ha lasciate senza parole con questo look da sogno, coronato da capelli lucenti e trucco a base di smocky eyes. She's dating the gangster, Cookies'n Candies fansub, Rilasciato, SS, Filippine.Listing of files: Illustrates various methods of associating multiple types of parents with a particular child object.The examples all use the declarative extension along with declarative mixins.The construction of generic sharding routines is an ambitious approach to the issue of organizing instances among multiple databases.For a more plain-spoken alternative, the “distinct entity” approach is a simple method of assigning objects to different tables (and potentially database nodes) in an explicit way - described on the wiki at Entity Name. Descriptions and source code for all can be found here.Additional SQLAlchemy examples, some user contributed, are available on the wiki at Recipes.For example, if we wanted to profile the difference between several kinds of loading, we can create a file $ python test_--dburl postgresql psycopg2://scott:[email protected]/test Running setup once...Tests to run: test_lazyload, test_joinedload, test_subqueryload test_lazyload : load everything, no eager loading.(10000 iterations); total time 9.665620 sec test_core_query_caching : Individual INSERT/COMMIT pairs using Core with query caching (10000 iterations); total time 9.209010 sec test_dbapi_raw_w_connect : Individual INSERT/COMMIT pairs w/ DBAPI connection each time (10000 iterations); total time 9.551103 sec test_dbapi_raw_w_pool : Individual INSERT/COMMIT pairs w/ DBAPI connection pool (10000 iterations); total time 8.001813 sec$ python -m examples.performance single_inserts --test test_core --num 1000 --dump Tests to run: test_core test_core : Individual INSERT/COMMIT pairs using Core.(1000 iterations); total fn calls 186109 186109 function calls (186102 primitive calls) in 1.089 seconds Ordered by: internal time, call count ncalls tottime percall cumtime percall filename:lineno(function) 1000 0.634 0.001 0.634 0.001 1000 0.154 0.000 0.154 0.000 1000 0.021 0.000 0.074 0.000 /Users/classic/dev/sqlalchemy/lib/sqlalchemy/sql/compiler.py:1950(_get_colparams) 1000 0.015 0.000 0.034 0.000 /Users/classic/dev/sqlalchemy/lib/sqlalchemy/engine/default.py:503(_init_compiled) 1 0.012 0.012 1.091 1.091 examples/performance/single_inserts.py:79(test_core) ...

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