When using the asterisk in combination with sum and group, the duplicates are not removed as I expect (and as it works in for example mysql):
col1 | country
-----------------
5 | sweden
20 | sweden
30 | denmark
select *, sum(col1) as s from table
group by country
the data returned is:
col1 | country | s
--------------------
5 | sweden | 25
20 | sweden | 25
30 | denmark | 30
instead of what I expected:
col1 | country | s
------------------------
5 | sweden | 25
30 | denmark | 30
If I don't use asterisk (*), the data returned is as I expect it to be.
SELECT country, sum(col1) as s from table
You are correct, SAS does not collapse WHEN you have variables in the statement that are not in the GROUP BY statement.
There will be a note to that effect in the log, about your data being merged.
If you want just the variables, you'll have to list them unfortunately, but since you have to list them in GROUP BY it's not extra work per se.
Different SQL implementations handle things differently, this is one way that SAS is different. It's handy when you do want to merge a summary stat back with the main data set though.
If you don't want this behaviour add the NOREMERGE option to your PROC SQL - but it throws an error, it still doesn't work the way you want.
See the documentation for the reference
Don't use SELECT *, ever. It's bad practice, risky, unsustainable... Read about it.
What flavor of SQL?
Your first query shouldn't work. You're basically saying...
select col1
, country
, sum(col1) as s
from table
group by country
...which will return an error:
Column 'table.col1' is invalid in the select list because it is not contained in either an aggregate function or the GROUP BY clause.
SELECT country, sum(col1) as s from table
...also should not work:
Column 'table.country' is invalid in the select list because it is not contained in either an aggregate function or the GROUP BY clause.
Given your expected output, I suspect what you are looking for is...
select min(col1) as col1
, country
, sum(col1) as s
from table
group by country
I stumbled upon a very strange behaviour while working on some T-SQL Code.
I am working on a SQL Server 2008 R2 SP2 (build nr.: 10.50.4000).
My question to you guys is if anybody has seen such a behaviour before or if anybody might be able to explain it to me.
So,
What's the situation?
We have a table, which looks like that:
product_number | id_object | position_in_product
---------------------------------------------------
1 | 101 | 1
1 | 102 | 1
1 | 103 | 1
2 | 201 | 1
2 | 202 | 1
2 | 203 | 1
Multiple object ids are allocated to one product number. The order should be defined by the position_in_product column. The funny part lies exactly in establishing that order.
Of course, after doing that the table should look like this:
product_number | id_object | position_in_product
---------------------------------------------------
1 | 101 | 1
1 | 102 | 2
1 | 103 | 3
2 | 201 | 1
2 | 202 | 2
2 | 203 | 3
What's going on?
To update the order column we create a cursor with the following statement:
DECLARE
table_runner CURSOR LOCAL FORWARD_ONLY FOR
SELECT id_object, product_number
FROM table
WHERE ident = #ident
ORDER BY product_number
By using this cursor and counting the rows with the same product_number we should be able to update the position_in_product column. (This has worked in every installation until now)
To move the cursor to the next row we use this:
FETCH next from table_runner
INTO #table_runner$id_object, #table_runner$product_number
The whole function looks like this:
OPEN table_runner
FETCH next from table_runner
INTO #table_runner$id_object, #table_runner$product_number
while ##FETCH_STATUS = 0
BEGIN
/* update_logic */
FETCH next from table_runner
INTO #table_runner$id_object, #table_runner$product_number
END
CLOSE table_runner
And that is the part, that does not work as expected.
The fetch will not give me the next row. I am getting always the same result row.
The while loop does never end, the fetch_status is always 0, but the result stays the same.
The Workaround
After searching the web for quite a while without any results i decided to try a more pragmatical way and put another FETCH statement in.
I know that the id_object variable is unique and has to change in every loop cycle,
so i remembered the last fetched id and put this under the loop fetch statement:
if #id_object_memory = #table_runner$id_object
begin
FETCH next from table_runner
INTO #table_runner$id_object, #table_runner$product_number
set #id_object_memory = #table_runner$id_object
end
else
set #id_object_memory = #table_runner$id_object
With that the loop works as expected, the column in question is updated as it should and the cursor will reach the end of the result set.
The big ?
Has anyone any explanation for that?
There are more cursor defined in the same procedure and they all work as expected.
I have absolute no clue how to explain this.
So, thanks for reading ;)
I can't help with the cursor issue, I've never seen this before, but should point out you don't need a cursor at all to do this update. You can simply use:
WITH CTE AS
( SELECT Product_Number,
ID_Object,
Position_in_Product,
RowNumber = ROW_NUMBER() OVER(PARTITION BY Product_Number
ORDER BY id_object)
FROM T
WHERE ident = #ident
)
UPDATE CTE
SET Position_in_Product = RowNumber;
Example on SQL Fiddle
You possibly don't even need to store this column, and can just use ROW_NUMBER in a query where the position_in_product is required.
Cursors are so 2000 ;-)
Seriously though; avoid cursors at all costs. Set-based operations > looping.
Just create a view with the following:
CREATE VIEW your_view
AS
SELECT product_number
, id_object
, Row_Number() OVER (PARTITION BY product_number ORDER BY id_object) As position_in_product
FROM your_table
;
No need to ever perform the update; the row numbers will "automatically" recalculate.
let's assume i have a self referencing hierarchical table build the classical way like this one:
CREATE TABLE test
(name text,id serial primary key,parent_id integer
references test);
insert into test (name,id,parent_id) values
('root1',1,NULL),('root2',2,NULL),('root1sub1',3,1),('root1sub2',4,1),('root
2sub1',5,2),('root2sub2',6,2);
testdb=# select * from test;
name | id | parent_id
-----------+----+-----------
root1 | 1 |
root2 | 2 |
root1sub1 | 3 | 1
root1sub2 | 4 | 1
root2sub1 | 5 | 2
root2sub2 | 6 | 2
What i need now is a function (preferrably in plain sql) that would take the id of a test record and
clone all attached records (including the given one). The cloned records need to have new ids of course. The desired result
would like this for example:
Select * from cloningfunction(2);
name | id | parent_id
-----------+----+-----------
root2 | 7 |
root2sub1 | 8 | 7
root2sub2 | 9 | 7
Any pointers? Im using PostgreSQL 8.3.
Pulling this result in recursively is tricky (although possible). However, it's typically not very efficient and there is a much better way to solve this problem.
Basically, you augment the table with an extra column which traces the tree to the top - I'll call it the "Upchain". It's just a long string that looks something like this:
name | id | parent_id | upchain
root1 | 1 | NULL | 1:
root2 | 2 | NULL | 2:
root1sub1 | 3 | 1 | 1:3:
root1sub2 | 4 | 1 | 1:4:
root2sub1 | 5 | 2 | 2:5:
root2sub2 | 6 | 2 | 2:6:
root1sub1sub1 | 7 | 3 | 1:3:7:
It's very easy to keep this field updated by using a trigger on the table. (Apologies for terminology but I have always done this with SQL Server). Every time you add or delete a record, or update the parent_id field, you just need to update the upchain field on that part of the tree. That's a trivial job because you just take the upchain of the parent record and append the id of the current record. All child records are easily identified using LIKE to check for records with the starting string in their upchain.
What you're doing effectively is trading a bit of extra write activity for a big saving when you come to read the data.
When you want to select a complete branch in the tree it's trivial. Suppose you want the branch under node 1. Node 1 has an upchain '1:' so you know that any node in the branch of the tree under that node must have an upchain starting '1:...'. So you just do this:
SELECT *
FROM table
WHERE upchain LIKE '1:%'
This is extremely fast (index the upchain field of course). As a bonus it also makes a lot of activities extremely simple, such as finding partial trees, level within the tree, etc.
I've used this in applications that track large employee reporting hierarchies but you can use it for pretty much any tree structure (parts breakdown, etc.)
Notes (for anyone who's interested):
I haven't given a step-by-step of the SQL code but once you get the principle, it's pretty simple to implement. I'm not a great programmer so I'm speaking from experience.
If you already have data in the table you need to do a one time update to get the upchains synchronised initially. Again, this isn't difficult as the code is very similar to the UPDATE code in the triggers.
This technique is also a good way to identify circular references which can otherwise be tricky to spot.
The Joe Celko's method which is similar to the njreed's answer but is more generic can be found here:
Nested-Set Model of Trees (at the middle of the article)
Nested-Set Model of Trees, part 2
Trees in SQL -- Part III
#Maximilian: You are right, we forgot your actual requirement. How about a recursive stored procedure? I am not sure if this is possible in PostgreSQL, but here is a working SQL Server version:
CREATE PROCEDURE CloneNode
#to_clone_id int, #parent_id int
AS
SET NOCOUNT ON
DECLARE #new_node_id int, #child_id int
INSERT INTO test (name, parent_id)
SELECT name, #parent_id FROM test WHERE id = #to_clone_id
SET #new_node_id = ##IDENTITY
DECLARE #children_cursor CURSOR
SET #children_cursor = CURSOR FOR
SELECT id FROM test WHERE parent_id = #to_clone_id
OPEN #children_cursor
FETCH NEXT FROM #children_cursor INTO #child_id
WHILE ##FETCH_STATUS = 0
BEGIN
EXECUTE CloneNode #child_id, #new_node_id
FETCH NEXT FROM #children_cursor INTO #child_id
END
CLOSE #children_cursor
DEALLOCATE #children_cursor
Your example is accomplished by EXECUTE CloneNode 2, null (the second parameter is the new parent node).
This sounds like an exercise from "SQL For Smarties" by Joe Celko...
I don't have my copy handy, but I think it's a book that'll help you quite a bit if this is the kind of problems you need to solve.
I learned something simple about SQL the other day:
SELECT c FROM myTbl GROUP BY C
Has the same result as:
SELECT DISTINCT C FROM myTbl
What I am curious of, is there anything different in the way an SQL engine processes the command, or are they truly the same thing?
I personally prefer the distinct syntax, but I am sure it's more out of habit than anything else.
EDIT: This is not a question about aggregates. The use of GROUP BY with aggregate functions is understood.
MusiGenesis' response is functionally the correct one with regard to your question as stated; the SQL Server is smart enough to realize that if you are using "Group By" and not using any aggregate functions, then what you actually mean is "Distinct" - and therefore it generates an execution plan as if you'd simply used "Distinct."
However, I think it's important to note Hank's response as well - cavalier treatment of "Group By" and "Distinct" could lead to some pernicious gotchas down the line if you're not careful. It's not entirely correct to say that this is "not a question about aggregates" because you're asking about the functional difference between two SQL query keywords, one of which is meant to be used with aggregates and one of which is not.
A hammer can work to drive in a screw sometimes, but if you've got a screwdriver handy, why bother?
(for the purposes of this analogy, Hammer : Screwdriver :: GroupBy : Distinct and screw => get list of unique values in a table column)
GROUP BY lets you use aggregate functions, like AVG, MAX, MIN, SUM, and COUNT.
On the other hand DISTINCT just removes duplicates.
For example, if you have a bunch of purchase records, and you want to know how much was spent by each department, you might do something like:
SELECT department, SUM(amount) FROM purchases GROUP BY department
This will give you one row per department, containing the department name and the sum of all of the amount values in all rows for that department.
What's the difference from a mere duplicate removal functionality point of view
Apart from the fact that unlike DISTINCT, GROUP BY allows for aggregating data per group (which has been mentioned by many other answers), the most important difference in my opinion is the fact that the two operations "happen" at two very different steps in the logical order of operations that are executed in a SELECT statement.
Here are the most important operations:
FROM (including JOIN, APPLY, etc.)
WHERE
GROUP BY (can remove duplicates)
Aggregations
HAVING
Window functions
SELECT
DISTINCT (can remove duplicates)
UNION, INTERSECT, EXCEPT (can remove duplicates)
ORDER BY
OFFSET
LIMIT
As you can see, the logical order of each operation influences what can be done with it and how it influences subsequent operations. In particular, the fact that the GROUP BY operation "happens before" the SELECT operation (the projection) means that:
It doesn't depend on the projection (which can be an advantage)
It cannot use any values from the projection (which can be a disadvantage)
1. It doesn't depend on the projection
An example where not depending on the projection is useful is if you want to calculate window functions on distinct values:
SELECT rating, row_number() OVER (ORDER BY rating) AS rn
FROM film
GROUP BY rating
When run against the Sakila database, this yields:
rating rn
-----------
G 1
NC-17 2
PG 3
PG-13 4
R 5
The same couldn't be achieved with DISTINCT easily:
SELECT DISTINCT rating, row_number() OVER (ORDER BY rating) AS rn
FROM film
That query is "wrong" and yields something like:
rating rn
------------
G 1
G 2
G 3
...
G 178
NC-17 179
NC-17 180
...
This is not what we wanted. The DISTINCT operation "happens after" the projection, so we can no longer remove DISTINCT ratings because the window function was already calculated and projected. In order to use DISTINCT, we'd have to nest that part of the query:
SELECT rating, row_number() OVER (ORDER BY rating) AS rn
FROM (
SELECT DISTINCT rating FROM film
) f
Side-note: In this particular case, we could also use DENSE_RANK()
SELECT DISTINCT rating, dense_rank() OVER (ORDER BY rating) AS rn
FROM film
2. It cannot use any values from the projection
One of SQL's drawbacks is its verbosity at times. For the same reason as what we've seen before (namely the logical order of operations), we cannot "easily" group by something we're projecting.
This is invalid SQL:
SELECT first_name || ' ' || last_name AS name
FROM customer
GROUP BY name
This is valid (repeating the expression)
SELECT first_name || ' ' || last_name AS name
FROM customer
GROUP BY first_name || ' ' || last_name
This is valid, too (nesting the expression)
SELECT name
FROM (
SELECT first_name || ' ' || last_name AS name
FROM customer
) c
GROUP BY name
I've written about this topic more in depth in a blog post
There is no difference (in SQL Server, at least). Both queries use the same execution plan.
http://sqlmag.com/database-performance-tuning/distinct-vs-group
Maybe there is a difference, if there are sub-queries involved:
http://blog.sqlauthority.com/2007/03/29/sql-server-difference-between-distinct-and-group-by-distinct-vs-group-by/
There is no difference (Oracle-style):
http://asktom.oracle.com/pls/asktom/f?p=100:11:0::::P11_QUESTION_ID:32961403234212
Use DISTINCT if you just want to remove duplicates. Use GROUPY BY if you want to apply aggregate operators (MAX, SUM, GROUP_CONCAT, ..., or a HAVING clause).
I expect there is the possibility for subtle differences in their execution.
I checked the execution plans for two functionally equivalent queries along these lines in Oracle 10g:
core> select sta from zip group by sta;
---------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
---------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 58 | 174 | 44 (19)| 00:00:01 |
| 1 | HASH GROUP BY | | 58 | 174 | 44 (19)| 00:00:01 |
| 2 | TABLE ACCESS FULL| ZIP | 42303 | 123K| 38 (6)| 00:00:01 |
---------------------------------------------------------------------------
core> select distinct sta from zip;
---------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
---------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 58 | 174 | 44 (19)| 00:00:01 |
| 1 | HASH UNIQUE | | 58 | 174 | 44 (19)| 00:00:01 |
| 2 | TABLE ACCESS FULL| ZIP | 42303 | 123K| 38 (6)| 00:00:01 |
---------------------------------------------------------------------------
The middle operation is slightly different: "HASH GROUP BY" vs. "HASH UNIQUE", but the estimated costs etc. are identical. I then executed these with tracing on and the actual operation counts were the same for both (except that the second one didn't have to do any physical reads due to caching).
But I think that because the operation names are different, the execution would follow somewhat different code paths and that opens the possibility of more significant differences.
I think you should prefer the DISTINCT syntax for this purpose. It's not just habit, it more clearly indicates the purpose of the query.
For the query you posted, they are identical. But for other queries that may not be true.
For example, it's not the same as:
SELECT C FROM myTbl GROUP BY C, D
I read all the above comments but didn't see anyone pointed to the main difference between Group By and Distinct apart from the aggregation bit.
Distinct returns all the rows then de-duplicates them whereas Group By de-deduplicate the rows as they're read by the algorithm one by one.
This means they can produce different results!
For example, the below codes generate different results:
SELECT distinct ROW_NUMBER() OVER (ORDER BY Name), Name FROM NamesTable
SELECT ROW_NUMBER() OVER (ORDER BY Name), Name FROM NamesTable
GROUP BY Name
If there are 10 names in the table where 1 of which is a duplicate of another then the first query returns 10 rows whereas the second query returns 9 rows.
The reason is what I said above so they can behave differently!
If you use DISTINCT with multiple columns, the result set won't be grouped as it will with GROUP BY, and you can't use aggregate functions with DISTINCT.
GROUP BY has a very specific meaning that is distinct (heh) from the DISTINCT function.
GROUP BY causes the query results to be grouped using the chosen expression, aggregate functions can then be applied, and these will act on each group, rather than the entire resultset.
Here's an example that might help:
Given a table that looks like this:
name
------
barry
dave
bill
dave
dave
barry
john
This query:
SELECT name, count(*) AS count FROM table GROUP BY name;
Will produce output like this:
name count
-------------
barry 2
dave 3
bill 1
john 1
Which is obviously very different from using DISTINCT. If you want to group your results, use GROUP BY, if you just want a unique list of a specific column, use DISTINCT. This will give your database a chance to optimise the query for your needs.
If you are using a GROUP BY without any aggregate function then internally it will treated as DISTINCT, so in this case there is no difference between GROUP BY and DISTINCT.
But when you are provided with DISTINCT clause better to use it for finding your unique records because the objective of GROUP BY is to achieve aggregation.
They have different semantics, even if they happen to have equivalent results on your particular data.
Please don't use GROUP BY when you mean DISTINCT, even if they happen to work the same. I'm assuming you're trying to shave off milliseconds from queries, and I have to point out that developer time is orders of magnitude more expensive than computer time.
In Teradata perspective :
From a result set point of view, it does not matter if you use DISTINCT or GROUP BY in Teradata. The answer set will be the same.
From a performance point of view, it is not the same.
To understand what impacts performance, you need to know what happens on Teradata when executing a statement with DISTINCT or GROUP BY.
In the case of DISTINCT, the rows are redistributed immediately without any preaggregation taking place, while in the case of GROUP BY, in a first step a preaggregation is done and only then are the unique values redistributed across the AMPs.
Don’t think now that GROUP BY is always better from a performance point of view. When you have many different values, the preaggregation step of GROUP BY is not very efficient. Teradata has to sort the data to remove duplicates. In this case, it may be better to the redistribution first, i.e. use the DISTINCT statement. Only if there are many duplicate values, the GROUP BY statement is probably the better choice as only once the deduplication step takes place, after redistribution.
In short, DISTINCT vs. GROUP BY in Teradata means:
GROUP BY -> for many duplicates
DISTINCT -> no or a few duplicates only .
At times, when using DISTINCT, you run out of spool space on an AMP. The reason is that redistribution takes place immediately, and skewing could cause AMPs to run out of space.
If this happens, you have probably a better chance with GROUP BY, as duplicates are already removed in a first step, and less data is moved across the AMPs.
group by is used in aggregate operations -- like when you want to get a count of Bs broken down by column C
select C, count(B) from myTbl group by C
distinct is what it sounds like -- you get unique rows.
In sql server 2005, it looks like the query optimizer is able to optimize away the difference in the simplistic examples I ran. Dunno if you can count on that in all situations, though.
In that particular query there is no difference. But, of course, if you add any aggregate columns then you'll have to use group by.
You're only noticing that because you are selecting a single column.
Try selecting two fields and see what happens.
Group By is intended to be used like this:
SELECT name, SUM(transaction) FROM myTbl GROUP BY name
Which would show the sum of all transactions for each person.
From a 'SQL the language' perspective the two constructs are equivalent and which one you choose is one of those 'lifestyle' choices we all have to make. I think there is a good case for DISTINCT being more explicit (and therefore is more considerate to the person who will inherit your code etc) but that doesn't mean the GROUP BY construct is an invalid choice.
I think this 'GROUP BY is for aggregates' is the wrong emphasis. Folk should be aware that the set function (MAX, MIN, COUNT, etc) can be omitted so that they can understand the coder's intent when it is.
The ideal optimizer will recognize equivalent SQL constructs and will always pick the ideal plan accordingly. For your real life SQL engine of choice, you must test :)
PS note the position of the DISTINCT keyword in the select clause may produce different results e.g. contrast:
SELECT COUNT(DISTINCT C) FROM myTbl;
SELECT DISTINCT COUNT(C) FROM myTbl;
I know it's an old post. But it happens that I had a query that used group by just to return distinct values when using that query in toad and oracle reports everything worked fine, I mean a good response time. When we migrated from Oracle 9i to 11g the response time in Toad was excellent but in the reporte it took about 35 minutes to finish the report when using previous version it took about 5 minutes.
The solution was to change the group by and use DISTINCT and now the report runs in about 30 secs.
I hope this is useful for someone with the same situation.
Sometimes they may give you the same results but they are meant to be used in different sense/case. The main difference is in syntax.
Minutely notice the example below. DISTINCT is used to filter out the duplicate set of values. (6, cs, 9.1) and (1, cs, 5.5) are two different sets. So DISTINCT is going to display both the rows while GROUP BY Branch is going to display only one set.
SELECT * FROM student;
+------+--------+------+
| Id | Branch | CGPA |
+------+--------+------+
| 3 | civil | 7.2 |
| 2 | mech | 6.3 |
| 6 | cs | 9.1 |
| 4 | eee | 8.2 |
| 1 | cs | 5.5 |
+------+--------+------+
5 rows in set (0.001 sec)
SELECT DISTINCT * FROM student;
+------+--------+------+
| Id | Branch | CGPA |
+------+--------+------+
| 3 | civil | 7.2 |
| 2 | mech | 6.3 |
| 6 | cs | 9.1 |
| 4 | eee | 8.2 |
| 1 | cs | 5.5 |
+------+--------+------+
5 rows in set (0.001 sec)
SELECT * FROM student GROUP BY Branch;
+------+--------+------+
| Id | Branch | CGPA |
+------+--------+------+
| 3 | civil | 7.2 |
| 6 | cs | 9.1 |
| 4 | eee | 8.2 |
| 2 | mech | 6.3 |
+------+--------+------+
4 rows in set (0.001 sec)
Sometimes the results that can be achieved by GROUP BY clause is not possible to achieved by DISTINCT without using some extra clause or conditions. E.g in above case.
To get the same result as DISTINCT you have to pass all the column names in GROUP BY clause like below. So see the syntactical difference. You must have knowledge about all the column names to use GROUP BY clause in that case.
SELECT * FROM student GROUP BY Id, Branch, CGPA;
+------+--------+------+
| Id | Branch | CGPA |
+------+--------+------+
| 1 | cs | 5.5 |
| 2 | mech | 6.3 |
| 3 | civil | 7.2 |
| 4 | eee | 8.2 |
| 6 | cs | 9.1 |
+------+--------+------+
Also I have noticed GROUP BY displays the results in ascending order by default which DISTINCT does not. But I am not sure about this. It may be differ vendor wise.
Source : https://dbjpanda.me/dbms/languages/sql/sql-syntax-with-examples#group-by
In terms of usage, GROUP BY is used for grouping those rows you want to calculate. DISTINCT will not do any calculation. It will show no duplicate rows.
I always used DISTINCT if I want to present data without duplicates.
If I want to do calculations like summing up the total quantity of mangoes, I will use GROUP BY
In Hive (HQL), GROUP BY can be way faster than DISTINCT, because the former does not require comparing all fields in the table.
See: https://sqlperformance.com/2017/01/t-sql-queries/surprises-assumptions-group-by-distinct.
The way I always understood it is that using distinct is the same as grouping by every field you selected in the order you selected them.
i.e:
select distinct a, b, c from table;
is the same as:
select a, b, c from table group by a, b, c
Funtional efficiency is totally different.
If you would like to select only "return value" except duplicate one, use distinct is better than group by. Because "group by" include ( sorting + removing ) , "distinct" include ( removing )
Generally we can use DISTINCT for eliminate the duplicates on Specific Column in the table.
In Case of 'GROUP BY' we can Apply the Aggregation Functions like
AVG, MAX, MIN, SUM, and COUNT on Specific column and fetch
the column name and it aggregation function result on the same column.
Example :
select specialColumn,sum(specialColumn) from yourTableName group by specialColumn;
There is no significantly difference between group by and distinct clause except the usage of aggregate functions.
Both can be used to distinguish the values but if in performance point of view group by is better.
When distinct keyword is used , internally it used sort operation which can be view in execution plan.
Try simple example
Declare #tmpresult table
(
Id tinyint
)
Insert into #tmpresult
Select 5
Union all
Select 2
Union all
Select 3
Union all
Select 4
Select distinct
Id
From #tmpresult