Distinct vs group by performance

So, how do you decide which SQL command to use? Pick whichever syntax you prefer for your situation. If all you need is to remove duplicates then use DISTINCT. Or does it have to do with the complexity of the query? If so, an example would be appreciable.

Huge performance difference when using group by vs.

When the performance of Distinct and. While doing some performance turning on a procedure, I came across a case where not only does the performance vary between a statement using distinct vs. There is no difference in your queries for Oracle versions up to 10. Still, performance should be similar. My query above will be superior in versions 10.

Look behind the curtain and find out why and when their performance can diverge grossly! I have seen both a version that uses distinct and a version that uses group by and the group by version is a lot faster. For me, group by takes ms and distinct takes 1ms.

GROUP BY in Teradata appear to do the same after all.

If you have nested queries or Views then its a never ending story. Group by more efficient than distinct ? Both return same number of rows , but with some execute time difference between them. Execution time is always a very important factor considering performance as one of the major factors is teradata warehouse. So which is more efficient ? In performance wise distinct is good or group by is good?

Actually, I think I answered my own question already. By doing the group -and-aggregate over the whole logs table, we made our database process a lot of data unnecessarily. Count distinct builds a hash set for each group — in this case, each dashboard_id — to keep track of which values have been seen in which buckets. You can examine the execution plan for each query to see where the performance improvements come from. Grouped concatenation is a common problem in SQL Server, with no direct and intentional features to support it (like XMLAGG in Oracle, STRING_AGG or ARRAY_TO_STRING(ARRAY_AGG()) in PostgreSQL, and GROUP _CONCAT in MySQL).

Select Distinct Brand From TABLE where price between and 25. I dont do distinct Then i will see GE twice. This depends on the data. I need to know best practice to avoid these issues.

These are really trivial examples of how DISTINCT can make a difference in a query plan and thus the performance of a query. In real life, very few queries are this simple. But I hope that these examples will serve to illustrate that DISTINCT does add an addtional load on the SQL Server.

Do not use the DISTINCT phrase, unless the number of distinct values is high. Seeing ‘select distinct ’ in a query still makes me cringe. I used to use ‘select distinct ’ many years ago as a way to remove duplicate records in my result sets and time and experience taught me that this was not a good practice and what was needed was a better understanding of grouping and a more detailed knowledge of the underlying.

Grouping by date, rows and are the first group , rows and are the second group. The WITH ROLLUP modifier computes an extra row that is some kind of super- group. It aggregates all the rows returned by the query above, creating a single group , behaving like all the rows had the same value in the date column. In this tutorial, you have learned how to use the SQL Server SELECT DISTINCT clause to retrieve the distinct values in a specified list of columns. I am running PostgresSQL 9. Using Grouping instead of Distinct in Entity Framework to Optimize Performance On a number of pages on this web site I display a list of articles.

For example, the home page shows a list of all articles from all categories. To test distinct vs group by performance in SQL Query I wrote essentially same query using two different ways. Re: big distinct clause vs. However, we can use a subquery to group only the data that we need and then perform the joins over other tables. The performance of distinct count calculations is affected by many other factors, such as the number of distinct values in the column and in the result set.

Your mileage may vary a lot, so test everything in your specific data model. As always, the above is much easier to understand by example.