I have a large database I use for plotting and data examination. For simplicity, say it looks something like this:
| id | day | obs |
+----------+-----------+-----------+
| 1 | 500 | 4.5 |
| 2 | 500 | 4.4 |
| 3 | 500 | 4.7 |
| 4 | 500 | 4.8 |
| 5 | 600 | 5.1 |
| 6 | 600 | 5.2 |
...
This could be stock market data, where we have many points per day that are measured.
What I want to do is look at much longer trends, where the multiple points per day are unnecessarily resolved, and clog my plotting application. (I want to look at 30000 days, each has about 100 observations).
Is there a way to do something like SELECT ... LIMIT 1 PER "day"
I suppose I could perform a few SELECT DISTINCT queries to find correct ID's, but I'd rather do something simple if it is built in.
It doesn't matter if its the first, last, or an average value per day. Just a single value. I just prefer what is fastest.
Also, this I'd like to do this for Postgres, MySQL, and SQLite. My application is built to use all three and I frequently switch between them.
Thanks!
Background: This is for a Ruby on Rails plotting application, so a trick with ActiveRecord will work too. https://github.com/ZachDischner/Rails-Plotter
You need to tag your question with the brand of RDBMS you're using. Frequently for Rails developers, they're using MySQL, but the answer to your question depends on this.
For all brands except for MySQL, the correct and standard solution is to use windowing functions:
SELECT * FROM (
SELECT ROW_NUMBER() OVER (PARTITION BY day) AS RN, *
FROM stockmarketdata
) AS t
WHERE t.RN = 1;
For MySQL, which doesn't support windowing functions yet, you can simulate them in a kind of clumsy way with session variables:
SELECT * FROM (SELECT #day:=0, #r:=0) AS _init,
(
SELECT IF(day=#day, #r:=#r+1, #r:=0) AS RN, #day:=day AS d, *
FROM stockmarketdata
) AS t
WHERE t.RN = 1
You left a lot of room for options with your statement:
It doesn't matter if its the first, last, or an average value per day. Just a single value. I just prefer what is fastest.
So, I'm going to leave the id out of it and first propose going with average of obs for each group as the simplest and probably the most practical, though maybe not the fastest to be running stat functions vs. limit:
MyModel.group(:day).average(:obs)
If you wanted the minimum:
MyModel.group(:day).minimum(:obs)
If you wanted the maximum:
MyModel.group(:day).maximum(:obs)
(Note: The following 2 examples are less efficient than just entering the SQL, but might be more portable.)
But you might want all three:
ActiveRecord::Base.connection.execute(MyModel.select('MIN(obs), AVG(obs), MAX(obs)').group(:day).to_sql).to_a
Or just the data without hashes:
ActiveRecord::Base.connection.exec_query(MyModel.select('MIN(obs), AVG(obs), MAX(obs)').group(:day).to_sql)
If you want median, see this question which is more DB specific, and there are other related posts about it if you search.
And for more, some DB's like postgres have variance(...), stddev(...), etc. built-in.
Finally, check out the query section in the Rails guide and ARel for more info on constructing queries. You can do a limit in an ActiveRecord relation via first or limit for example, and in ARel, take lets you do a limit. Subqueries are possible too, as shown in answers to this question, and so is group by, etc. If you are sharing this project with others, try to limit the amount of non-portable SQL you are using unless you plan on adding support for other databases on your own and maintaining that.
I have 2 tables, a purchases table and a users table. Records in the purchases table looks like this:
purchase_id | product_ids | customer_id
---------------------------------------
1 | (99)(34)(2) | 3
2 | (45)(3)(74) | 75
Users table looks like this:
user_id | email | password
----------------------------------------
3 | joeShmoe#gmail.com | password
75 | nolaHue#aol.com | password
To get the purchase history of a user I use a query like this:
mysql_query(" SELECT * FROM purchases WHERE customer_id = '$users_id' ");
The problem is, what will happen when tens of thousands of records are inserted into the purchases table. I feel like this will take a performance toll.
So I was thinking about storing the purchases in an additional field directly in the user's row:
user_id | email | password | purchases
------------------------------------------------------
1 | joeShmoe#gmail.com | password | (99)(34)(2)
2 | nolaHue#aol.com | password | (45)(3)(74)
And when I query the user's table for things like username, etc. I can just as easily grab their purchase history using that one query.
Is this a good idea, will it help better performance or will the benefit be insignificant and not worth making the database look messier?
I really want to know what the pros do in these situations, for example how does amazon query it's database for user's purchase history since they have millions of customers. How come there queries don't take hours?
EDIT
Ok, so I guess keeping them separate is the way to go. Now the question is a design one:
Should I keep using the "purchases" table I illustrated earlier. In that design I am separating the product ids of each purchase using parenthesis and using this as the delimiter to tell the ids apart when extracting them via PHP.
Instead should I be storing each product id separately in the "purchases" table so it looks like this?:
purchase_id | product_ids | customer_id
---------------------------------------
1 | 99 | 3
1 | 34 | 3
1 | 2 | 3
2 | 45 | 75
2 | 3 | 75
2 | 74 | 75
Nope, this is a very, very, very bad idea.
You're breaking first normal form because you don't know how to page through a large data set.
Amazon and Yahoo! and Google bring back (potentially) millions of records - but they only display them to you in chunks of 10 or 25 or 50 at a time.
They're also smart about guessing or calculating which ones are most likely to be of interest to you - they show you those first.
Which purchases in my history am I most likely to be interested in? The most recent ones, of course.
You should consider building these into your design before you violate relational database fundamentals.
Your database already looks messy, since you are storing multiple product_ids in a single field, instead of creating an "association" table like this.
_____product_purchases____
purchase_id | product_id |
--------------------------
1 | 99 |
1 | 34 |
1 | 2 |
You can still fetch it in one query:
SELECT * FROM purchases p LEFT JOIN product_purchases pp USING (purchase_id)
WHERE purchases.customer_id = $user_id
But this also gives you more possibilities, like finding out how many product #99 were bought, getting a list of all customers that purchased product #34 etc.
And of course don't forget about indexes, that will make all of this much faster.
By doing this with your schema, you will break the entity-relationship of your database.
You might want to look into Memcached, NoSQL, and Redis.
These are all tools that will help you improve your query performances, mostly by storing data in the RAM.
For example - run the query once, store it in the Memcache, if the user refresh the page, you get the data from Memcache, not from MySQL, which avoids querying your database a second time.
Hope this helps.
First off, tens of thousands of records is nothing. Unless you're running on a teensy weensy machine with limited ram and harddrive space, a database won't even blink at 100,000 records.
As for storing purchase details in the users table... what happens if a user makes more than one purchase?
MySQL is hugely extensible, and don't let the fact that it's free convince you of otherwise. Keeping the two tables separate is probably best, not only because it keeps the db more normal, but having more indices will speed queries. A 10,000 record database is relatively small in deference to multi-hundred-million record health record databases.
As far as Amazon and Google, they hire hundreds of developers to write specialized query languages for their specific application needs... not something developers like us have the resources to fund.
Yeah, so I'm filling out a requirements document for a new client project and they're asking for growth trends and performance expectations calculated from existing data within our database.
The best source of data for something like this would be our logs table as we pretty much log every single transaction that occurs within our application.
Now, here's the issue, I don't have a whole lot of experience with MySql when it comes to collating cumulative sum and running averages. I've thrown together the following query which kind of makes sense to me, but it just keeps locking up the command console. The thing takes forever to execute and there are only 80k records within the test sample.
So, given the following basic table structure:
id | action | date_created
1 | 'merp' | 2007-06-20 17:17:00
2 | 'foo' | 2007-06-21 09:54:48
3 | 'bar' | 2007-06-21 12:47:30
... thousands of records ...
3545 | 'stab' | 2007-07-05 11:28:36
How would I go about calculating the average number of records created for each given day of the week?
day_of_week | average_records_created
1 | 234
2 | 23
3 | 5
4 | 67
5 | 234
6 | 12
7 | 36
I have the following query which makes me want to murderdeathkill myself by casting my body down an elevator shaft... and onto some bullets:
SELECT
DISTINCT(DAYOFWEEK(DATE(t1.datetime_entry))) AS t1.day_of_week,
AVG((SELECT COUNT(*) FROM VMS_LOGS t2 WHERE DAYOFWEEK(DATE(t2.date_time_entry)) = t1.day_of_week)) AS average_records_created
FROM VMS_LOGS t1
GROUP BY t1.day_of_week;
Halps? Please, don't make me cut myself again. :'(
How far back do you need to go when sampling this information? This solution works as long as it's less than a year.
Because day of week and week number are constant for a record, create a companion table that has the ID, WeekNumber, and DayOfWeek. Whenever you want to run this statistic, just generate the "missing" records from your master table.
Then, your report can be something along the lines of:
select
DayOfWeek
, count(*)/count(distinct(WeekNumber)) as Average
from
MyCompanionTable
group by
DayOfWeek
Of course if the table is too large, then you can instead pre-summarize the data on a daily basis and just use that, and add in "today's" data from your master table when running the report.
I rewrote your query as:
SELECT x.day_of_week,
AVG(x.count) 'average_records_created'
FROM (SELECT DAYOFWEEK(t.datetime_entry) 'day_of_week',
COUNT(*) 'count'
FROM VMS_LOGS t
GROUP BY DAYOFWEEK(t.datetime_entry)) x
GROUP BY x.day_of_week
The reason why your query takes so long is because of your inner select, you are essentialy running 6,400,000,000 queries. With a query like this your best solution may be to develop a timed reporting system, where the user receives an email when the query is done and the report is constructed or the user logs in and checks the report after.
Even with the optimization written by OMG Ponies (bellow) you are still looking at around the same number of queries.
SELECT x.day_of_week,
AVG(x.count) 'average_records_created'
FROM (SELECT DAYOFWEEK(t.datetime_entry) 'day_of_week',
COUNT(*) 'count'
FROM VMS_LOGS t
GROUP BY DAYOFWEEK(t.datetime_entry)) x
GROUP BY x.day_of_week
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