Aaron Bertrand

Performance Surprises and Assumptions : GROUP BY vs. DISTINCT

January 26, 2017 by in SQL Plan, T-SQL Queries | 9 Comments
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Last week, I presented my T-SQL : Bad Habits and Best Practices session during the GroupBy conference. A video replay and other materials are available here:

One of the items I always mention in that session is that I generally prefer GROUP BY over DISTINCT when eliminating duplicates. While DISTINCT better explains intent, and GROUP BY is only required when aggregations are present, they are interchangeable in many cases.

Let's start with something simple using Wide World Importers. These two queries produce the same result:

SELECT DISTINCT Description FROM Sales.OrderLines;
 
SELECT Description FROM Sales.OrderLines GROUP BY Description;

And in fact derive their results using the exact same execution plan:

Execution plan for DISTINCT / GROUP BY

Same operators, same number of reads, negligible differences in CPU and total duration (they take turns "winning").

So why would I recommend using the wordier and less intuitive GROUP BY syntax over DISTINCT? Well, in this simple case, it's a coin flip. However, in more complex cases, DISTINCT can end up doing more work. Essentially, DISTINCT collects all of the rows, including any expressions that need to be evaluated, and then tosses out duplicates. GROUP BY can (again, in some cases) filter out the duplicate rows before performing any of that work.

Let's talk about string aggregation, for example. While in SQL Server v.Next you will be able to use STRING_AGG (see posts here and here), the rest of us have to carry on with FOR XML PATH (and before you tell me about how amazing recursive CTEs are for this, please read this post, too). We might have a query like this, which attempts to return all of the Orders from the Sales.OrderLines table, along with item descriptions as a pipe-delimited list:

SELECT o.OrderID, OrderItems = STUFF((SELECT N'|' + Description
 FROM Sales.OrderLines 
 WHERE OrderID = o.OrderID
 FOR XML PATH(N''), TYPE).value(N'text()[1]', N'nvarchar(max)'),1,1,N'')
FROM Sales.OrderLines AS o;

This is a typical query for solving this kind of problem, with the following execution plan (the warning in all of the plans is just for the implicit conversion coming out of the XPath filter):

Execution plan for XML Path grouped concatenation

However, it has a problem that you might notice in the output number of rows. You can certainly spot it when casually scanning the output:

Grouped concatenation results

For every order, we see the pipe-delimited list, but we see a row for each item in each order. The knee-jerk reaction is to throw a DISTINCT on the column list:

SELECT DISTINCT o.OrderID, OrderItems = STUFF((SELECT N'|' + Description
 FROM Sales.OrderLines 
 WHERE OrderID = o.OrderID
 FOR XML PATH(N''), TYPE).value(N'text()[1]', N'nvarchar(max)'),1,1,N'')
FROM Sales.OrderLines AS o;

That eliminates the duplicates (and changes the ordering properties on the scans, so the results won't necessarily appear in a predictable order), and produces the following execution plan:

Execution plan for DISTINCT query

Another way to do this is to add a GROUP BY for the OrderID (since the subquery doesn't explicitly need to be referenced again in the GROUP BY):

SELECT o.OrderID, OrderItems = STUFF((SELECT N'|' + Description
 FROM Sales.OrderLines 
 WHERE OrderID = o.OrderID
 FOR XML PATH(N''), TYPE).value(N'text()[1]', N'nvarchar(max)'),1,1,N'')
FROM Sales.OrderLines AS o
GROUP BY o.OrderID;

This produces the same results (though order has returned), and a slightly different plan:

Execution plan for GROUP BY query

The performance metrics, however, are interesting to compare.

Duration, CPU, and Reads

The DISTINCT variation took 4X as long, used 4X the CPU, and almost 6X the reads when compared to the GROUP BY variation. (Remember, these queries return the exact same results.)

We can also compare the execution plans when we change the costs from CPU + I/O combined to I/O only, a feature exclusive to Plan Explorer. We also show the re-costed values (which are based on the actual costs observed during query execution, a feature also only found in Plan Explorer). Here is the DISTINCT plan:

Execution plan for DISTINCT, with re-costed I/O

And here is the GROUP BY plan:

Execution plan for GROUP BY, with re-costed I/O

You can see that, in the GROUP BY plan, almost all of the I/O cost is in the scans (here's the tooltip for the CI scan, showing an I/O cost of ~3.4 "query bucks"). Yet in the DISTINCT plan, most of the I/O cost is in the index spool (and here's that tooltip; the I/O cost here is ~41.4 "query bucks"). Note that the CPU is a lot higher with the index spool, too. We'll talk about "query bucks" another time, but the point is that the index spool is more than 10X as expensive as the scan – yet the scan is still the same 3.4 in both plans. This is one reason it always bugs me when people say they need to "fix" the operator in the plan with the highest cost. Some operator in the plan will always be the most expensive one; that doesn't mean it needs to be fixed.

While Adam Machanic is correct when he says that these queries are semantically different, the result is the same – we get the same number of rows, containing exactly the same results, and we did it with far fewer reads and CPU.

So while DISTINCT and GROUP BY are identical in a lot of scenarios, here is one case where the GROUP BY approach definitely leads to better performance (at the cost of less clear declarative intent in the query itself). I'd be interested to know if you think there are any scenarios where DISTINCT is better than GROUP BY, at least in terms of performance, which is far less subjective than style or whether a statement needs to be self-documenting.

This post fit into my "surprises and assumptions" series because many things we hold as truths based on limited observations or particular use cases can be tested when used in other scenarios. We just have to remember to take the time to do it as part of SQL query optimization…

References