Beautiful Pyodbc Bulk Insert From Memory

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python basic pyodbc bulk insert stack overflow basic pyodbc bulk insert ask question 11 2 in a python script i need to run a query on one datasource and insert each row from that query into a table on a different datasource i d normally do this with a single insert select statement with a tsql linked server join but i don t have a linked server connection to this particular datasource inserting many items into a sql server database · issue you appear to be hitting sql server s limit of 2100 parameters per stored procedure ref here via here when pyodbc sends a parameterized query to sql server it ends up being processed as a call to the sql server stored procedure sp prepexec sqlalchemy is producing a parameterized query using a "table value constructor" that in your case adds nine 9 parameters per row python pyodbc bulk insert statement won t parameterise using a parameter placeholder is not allowed for table names and column names in t sql the second option is using string formatting in python to build the t sql statement before execution against the database server this is also known as dynamic sql and is the only way to ac plish your task using bulk insert dynamic sql and unvalidated input in general introduces sql injection a better way to load data into microsoft sql server from the mss implementation of the pyodbc execute many also creates a transaction per row so does pymssql i looked on stack overflow but they pretty much re mended using bulk insert which is still the fastest way to copy data into mss but it has some serious drawbacks for one bulk insert needs to have a way to access the created flat file python speeding up pandas dataframe sql with fast speeding up pandas dataframe sql with fast executemany of pyodbc i also believe this helps prevent the creation of intermediate objects that spike memory consumption excessively hope this is helpful share bulk insert into sql server table using pyodbc cannot find file 1 is there a more efficient way to insert information into a i ve written a script to the list and using the pyodbc library insert the necessary information into the database the problem is that there are roughly rows that i m inserting and at the moment my code is iterating through each line and executing an insert statement for each line python speed up inserts into sql server from pyodbc in python i have a process to select data from one database redshift via psycopg2 then insert that data into sql server via pyodbc i chose to do a read write rather than a read flat file load because the row count is around 100 000 per day seemed easier to simply connect and insert memory buildup leak using fast executemany · issue 299 i m using the fast executemany option for increased performance however i ve noticed there s some kind of memory leak issue with it after each bulk insert it s as if the inserted data is retained in memory and continuously builds up until the application is killed doing nothing but turning the fast executemany option back off solves the issue pandas dataframe sql method how to speed up exporting pandas dataframe sql method how to speed up exporting to microsoft sql server 6 minutes for 11 mb showing 1 11 of 11 messages pyodbc bulk insert from memory

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