I am using SQL Server 2014.
I have created a view which surfaces patient history answers such as tabaco use, alcohol use, etc. These answers are time stamped but not linked to the appointment identifiers, and I need to find the most recent answer relative to the appointment date.
The data the view surfaces looks like this:
PATIENT_ID | ANSWER_DATE | TOBACCO_USE
1 | 1/1/2018 | No
1 | 1/5/2018 | Yes
1 | 1/10/2018 | Quit
2 | 1/1/2018 | No
I know I can use ROW_NUMBER() in a inline query when I join to this table to get the ranking I need, but I really want to add ROW_NUMBER()OVER(PARTITION BY PATIENT_ID ORDER BY ANSWER_DATE DESC) as 'rnkDesc' column to the view, to make it simpler for other developers to properly join to this table.
With this new column a SELECT * from the view looks like this:
PATIENT_ID | ANSWER_DATE | TOBACCO_USE | rnkDesc
1 | 1/1/2018 | No | 3
1 | 1/5/2018 | Yes | 2
1 | 1/10/2018 | Quit | 1
2 | 1/1/2018 | No | 1
That is as expected, now I join from my appointments table like so:
FROM APPOINTMENTS appt
LEFT JOIN myHistoryView his
on appt.PATIENT_ID = his.PATIENT_ID
and his.ANSWER_DATE <= APPT.APPT_DATE
and his.rnkDesc = 1
This does not work though, as it appears like the ROW_NUMBER is evaluated before the filters are applied. If I filter my view where PATIENT_ID = 1 and ANSWER_DATE = 1/5/2018 then rnkDesc still shows 2, instead of 1 like it would if I was using ROW_NUMBER in an inline query.
I am really interested in why this behaves this way. I can code around it by using an inline query.
I know that these ranking functions are nondeterministic, and would have thought the engine would filter the result set in the view before it generates the ROW_NUMBER. I tried this with RANK and DENSE_RANK as well, at it appears that these also behave the same way. (determined before the filters are applied.)
If you implemented this with CTEs or a subquery, you'd see the same results. This is necessary because sometimes you need to generate a rank on a result, and then have that rank be unchanged by outer queries. So it is as if the rank is generated first as part of the subquery, and then it is "locked in" so you can filter the results based on that row number.
Let's imagine if one of your filters in the outer query was actually rnkDesc = 2, which is a way that sometimes you can do things like get "2nd most". Imagine if the row number was not generated until after the outer query filter was generated, this would make this type of approach impossible. How do you filter the results on the value of something that hasn't been determined yet? This is the same reason that filtering on a window function usually requires first nesting a subquery or a CTE, so you can filter on the generated results, and those results don't get renumbered/ranked in the outer dynamically.
Therefore it makes sense to lock-in windowed function results based on the nesting they occur in. You have to kind of think about this kind of nesting in terms of subresults.
So that answers your question of "Why" it is this way. I understand what you're trying to achieve though and why you want to do this. You're trying to simplify the use of the window function and have it apply dynamically to the final result. I'm honestly not sure "how" you'd do this without just including the window function in the outer query. There may be a way to embed it in a UDF, but I'm not sure.
It's important to understand that there is a clear, indeed deterministic, order of evaluation in SQL.
The ROW_NUMBER on the view has already been evaluated before the left-join occurs. The ON clause does not act as a "filter" on the table to which the join refers, but as a condition that must be met for a join between the tables to occur.
You could of course create a "parameterised view" (a table-valued inline function), which allows you to pass in the filter date to a where-clause before the ROW_NUMBER is applied in the select-clause, and then outer-apply onto it. That may be appropriate if a large number of queries use the same fuctionality.
But otherwise I'd be inclined to leave the "substance use history" view unadulterated with any row-numbering (unless it is used independently by other queries to get the absolute latest answer), and write the "the latest row on or before the current appointment" logic inline.
I need to create a SQL query which calculates some data.
For instance, I have such SQL query:
SELECT SUM(AMOUNT) FROM FIRMS WHERE FIRM_ID IN(....) GROUP BY FIRM;
which produces such data:
28,740,573
30,849,923
25,665,724
43,223,313
34,334,534
35,102,286
38,556,820
19,384,871
Now, in a second column I need to show relation between one entry and sum of all entries. Like that:
28,740,573 | 0.1123
30,849,923 | 0.1206
25,665,724 | 0.1003
43,223,313 | 0.1689
34,334,534 | 0.1342
35,102,286 | 0.1372
38,556,820 | 0.1507
19,384,871 | 0.0758
For instance, sum of all entries from first column above is gonna be 255,858,044 and the value in a first entry, second cell is gonna be 28,740,573 / 255,858,044 = 0.1123. And same for each entry in a result.
How can I do that?
UPD: Thanks #a_horse_with_no_name, I forgot to DBMS. It's Oracle.
Most databases now support the ANSI standard window functions. So, you can do:
SELECT SUM(AMOUNT),
SUM(AMOUNT) / SUM(SUM(AMOUNT)) OVER () as ratio
FROM FIRMS
WHERE FIRM_ID IN (....)
GROUP BY FIRM;
Note: Some databases do integer division. So, if AMOUNT is an integer, then you need to convert to a non-integer number in these databases. One easy method is to multiple by 1.0.
Ok, so i have one really monstrous MySQL table (900k records, 180 MB total), and i want to extract from subgroups records with higher date_updated and calculate weighted average in each group. The calculation runs for ~15 hours, and i have a strong feeling i'm doing it wrong.
First, monstrous table layout:
category
element_id
date_updated
value
weight
source_prefix
source_name
Only key here is on element_id (BTREE, ~8k unique elements).
And calculation process:
Make hash for each group and subgroup.
CREATE TEMPORARY TABLE `temp1` (INDEX ( `ds_hash` ))
SELECT `category`,
`element_id`,
`source_prefix`,
`source_name`,
`date_updated`,
`value`,
`weight`,
MD5(CONCAT(`category`, `element_id`, `source_prefix`, `source_name`)) AS `subcat_hash`,
MD5(CONCAT(`category`, `element_id`, `date_updated`)) AS `cat_hash`
FROM `bigbigtable` WHERE `date_updated` <= '2009-04-28'
I really don't understand this fuss with hashes, but it worked faster this way. Dark magic, i presume.
Find maximum date for each subgroup
CREATE TEMPORARY TABLE `temp2` (INDEX ( `subcat_hash` ))
SELECT MAX(`date_updated`) AS `maxdate` , `subcat_hash`
FROM `temp1`
GROUP BY `subcat_hash`;
Join temp1 with temp2 to find weighted average values for categories
CREATE TEMPORARY TABLE `valuebycats` (INDEX ( `category` ))
SELECT `temp1`.`element_id`,
`temp1`.`category`,
`temp1`.`source_prefix`,
`temp1`.`source_name`,
`temp1`.`date_updated`,
AVG(`temp1`.`value`) AS `avg_value`,
SUM(`temp1`.`value` * `temp1`.`weight`) / SUM(`weight`) AS `rating`
FROM `temp1` LEFT JOIN `temp2` ON `temp1`.`subcat_hash` = `temp2`.`subcat_hash`
WHERE `temp2`.`subcat_hash` = `temp1`.`subcat_hash`
AND `temp1`.`date_updated` = `temp2`.`maxdate`
GROUP BY `temp1`.`cat_hash`;
(now that i looked through it and wrote it all down, it seems to me that i should use INNER JOIN in that last query (to avoid 900k*900k temp table)).
Still, is there a normal way to do so?
UPD: some picture for reference:
removed dead ImageShack link
UPD: EXPLAIN for proposed solution:
+----+-------------+-------+------+---------------+------------+---------+--------------------------------------------------------------------------------------+--------+----------+----------------------------------------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
+----+-------------+-------+------+---------------+------------+---------+--------------------------------------------------------------------------------------+--------+----------+----------------------------------------------+
| 1 | SIMPLE | cur | ALL | NULL | NULL | NULL | NULL | 893085 | 100.00 | Using where; Using temporary; Using filesort |
| 1 | SIMPLE | next | ref | prefix | prefix | 1074 | bigbigtable.cur.source_prefix,bigbigtable.cur.source_name,bigbigtable.cur.element_id | 1 | 100.00 | Using where |
+----+-------------+-------+------+---------------+------------+---------+--------------------------------------------------------------------------------------+--------+----------+----------------------------------------------+
Using hashses is one of the ways in which a database engine can execute a join. It should be very rare that you'd have to write your own hash-based join; this certainly doesn't look like one of them, with a 900k rows table with some aggregates.
Based on your comment, this query might do what you are looking for:
SELECT cur.source_prefix,
cur.source_name,
cur.category,
cur.element_id,
MAX(cur.date_updated) AS DateUpdated,
AVG(cur.value) AS AvgValue,
SUM(cur.value * cur.weight) / SUM(cur.weight) AS Rating
FROM eev0 cur
LEFT JOIN eev0 next
ON next.date_updated < '2009-05-01'
AND next.source_prefix = cur.source_prefix
AND next.source_name = cur.source_name
AND next.element_id = cur.element_id
AND next.date_updated > cur.date_updated
WHERE cur.date_updated < '2009-05-01'
AND next.category IS NULL
GROUP BY cur.source_prefix, cur.source_name,
cur.category, cur.element_id
The GROUP BY performs the calculations per source+category+element.
The JOIN is there to filter out old entries. It looks for later entries, and then the WHERE statement filters out the rows for which a later entry exists. A join like this benefits from an index on (source_prefix, source_name, element_id, date_updated).
There are many ways of filtering out old entries, but this one tends to perform resonably well.
Ok, so 900K rows isn't a massive table, it's reasonably big but and your queries really shouldn't be taking that long.
First things first, which of the 3 statements above is taking the most time?
The first problem I see is with your first query. Your WHERE clause doesn't include an indexed column. So this means that it has to do a full table scan on the entire table.
Create an index on the "data_updated" column, then run the query again and see what that does for you.
If you don't need the hash's and are only using them to avail of the dark magic then remove them completely.
Edit: Someone with more SQL-fu than me will probably reduce your whole set of logic into one SQL statement without the use of the temporary tables.
Edit: My SQL is a little rusty, but are you joining twice in the third SQL staement? Maybe it won't make a difference but shouldn't it be :
SELECT temp1.element_id,
temp1.category,
temp1.source_prefix,
temp1.source_name,
temp1.date_updated,
AVG(temp1.value) AS avg_value,
SUM(temp1.value * temp1.weight) / SUM(weight) AS rating
FROM temp1 LEFT JOIN temp2 ON temp1.subcat_hash = temp2.subcat_hash
WHERE temp1.date_updated = temp2.maxdate
GROUP BY temp1.cat_hash;
or
SELECT temp1.element_id,
temp1.category,
temp1.source_prefix,
temp1.source_name,
temp1.date_updated,
AVG(temp1.value) AS avg_value,
SUM(temp1.value * temp1.weight) / SUM(weight) AS rating
FROM temp1 temp2
WHERE temp2.subcat_hash = temp1.subcat_hash
AND temp1.date_updated = temp2.maxdate
GROUP BY temp1.cat_hash;
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