How do you make a query more efficient?
It’s vital you optimize your queries for minimum impact on database performance.
- Define business requirements first.
- SELECT fields instead of using SELECT *
- Avoid SELECT DISTINCT.
- Create joins with INNER JOIN (not WHERE)
- Use WHERE instead of HAVING to define filters.
- Use wildcards at the end of a phrase only.
Will both queries return same result?
If the two results sets are identical the row count will remain the same as the original query. If any rows do not match the original query those rows will be included in the result set thus the total rows returned by the UNIONed queries will exceed the row count of the original.
How is same query, different servers, different performance?
On each database you run, as far as you know, the exact same query (whether a straight up ad hoc query, stored procedure, parameterized query, whatever). On your production server, let’s call it PROD, the query runs for 3 hours before it returns, but on the test server, let’s call it TEST, it returns in about 3 seconds.
How to compare two identical SQL server queries?
Example 3: Just for fun, you can check that the query in Example 2 is identical to queries the rows where foo = ‘bar’. EXCEPT is the key to compare two querys (as @jabs said). Adding count (*) for each query to make sure both have the same results. Just in case there are some repeated rows which are deleted by except.
Which is better the first query or the second query?
With no clustered index on OrderID, just a non-clustered index on CustID The second query outperformed. Especially with the order by included in each. There was twice as many reads on the first query than the second query, and the cost percentages were 67% / 33% between the queries.
How to evaluate query performance on different data sets?
Evaluating query performance on a significantly different data set generally makes very little sense. Query plans and their efficiency can vary greatly depending on the data stats. So to get any realistic estimates, you need a database as close to the “real” one as possible.