#Installation of microsoft sql server native client failed because a higher version update#
When Coalesce(CLDE.CurrentValue, 'No Update Found') = 'No Update Found' and b.ETHNICITY '' then b.ETHNICITY When Coalesce(CLDE.CurrentValue, 'No Update Found') 'No Update Found' then CLDE.CurrentValue When Coalesce(CLDP.CurrentValue, 'No Update Found') = 'No Update Found' and b.PROFESSION = '' then 'No Update Found' When Coalesce(CLDP.CurrentValue, 'No Update Found') = 'No Update Found' and b.PROFESSION '' then b.PROFESSION When Coalesce(CLDP.CurrentValue, 'No Update Found') 'No Update Found' then CLDP.CurrentValue Select DISTINCT n.ID, n.FULL_NAME, n.Company, n.Status, n.Email, Is there a way to do a "If Exists" of sorts in the where statement to check for date and if a record doesn't exist, still allow results to come back since I have coded result text in my select statement? Here is my code: Declare as date I have built my query which works really well to give me what I want BUT the problem is that when I narrow this down to a date range, if there are no records in the changelog to check against, I get nothing back. If changes aren't made, no record exists. I have a query I have built that is to pull records up if changes are found in a change log table based on certain fields in my database being changed. Looking at the error in more detail, it is trying to do this. Use theĬONVERT function to run this query. ImplicitĬonversion from data type varchar to varbinary is not allowed. ProgrammingError: (pyodbc.ProgrammingError) ('42000', ' However this produces the following error: I then want to insert this data into a new table, as shown below. When outputting the DataFrame, the data looks like this. I am reading some varbinary data from SQL Server into a Pandas Dataframe, as shown below. The difference seems to mainly sit in the non clustered Index Seek on AccessHistory table.Ĭan someone explain to me this difference and suggest some fix? Or can I just leave the temp table be? The differences in processing times are drastic: with 8% for the temp table (4% for insert and 4% for query), and 46% for the other two. Select top 3 tmp.UserId, sum(Value) as RESULTįrom. Group by tmp.UserId order by RESULT DESC
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Select top 3 tmp.UserId, sum(Value) as RESULT from tmp Select UserId, convert(date, max(UpdateDate)) as TmpDateįrom. I've got 3 queries: declare table (UserId int, TmpDate Date) I'm looking for max sum value for user, counted since update date. (additional index IX_AccessHistory_UserId_TimeStamp: UserId, TimeStamp, include Value, non clustered) UpdateHistory (100 000 rows): Id, UserId, TimeStamp, Value, many others. I'm trying to optimize aggregate query on two tables: