Thursday, March 27, 2008

Returning rows from a stored procedure with pyodbc

I'll start with the executive summary for those of you short on time or who don't care about the background:

When executing multiple statements within a SQL Server stored procedure, including a SELECT statement that you expect to return rows to a rowset via pyodbc, it is necessary to SET NOCOUNT ON.


I ran into a new issue with pyodbc yesterday - an inability to parse rows returned by a SQL Server stored procedure. The proc in question was executing a number of queries, generating logging as rows to a table variable, and then returning the log via a select statement against the table variable.

However, when I executed the statement from a pyodbc connection, it didn't work.

This was a fairly complex proc that caused changes in the database when executed and I didn't want to run it repeatedly, so I mocked up "spTest" as follows:

create proc spTest as
select 'a' as msg
union all select 'b'

When executed from Python:

db = pyodbc.connect(CNXNSTRING, True)
rows = db.execute("exec spTest")
for row in rows:
print row

This returned rows. So I added a table variable:

create proc spTest2 as
declare @msgs table (msg varchar(255))

insert into @msgs
select 'a' as msg
union all select 'b'

select * from @msgs

This, being more similar to the actual proc, did not work. I changed a few things, tried a temp table instead of a table variable, etc., but didn't get any better results. Then my esteemed colleague Pijus asked if it was possible that pyodbc was reading some other channel of information from SQL that was obscuring the results. Aha!

SQL spits out rowcounts by default. I'm not sure where these turn up in ODBC world, but I know that they do get provided to the ODBC driver, and that does constitute an additional channel of information that could be confusing pyodbc. So I SET NOCOUNT ON, and voila, the final select returns results regardless of how many other operations occur within the procedure.

1 comment:

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