I am trying to find the average number of purchases by buyer by store without surfacing buyer because there are millions.
I'm getting an error of invalid identifier trying to group by store and am not sure what I'm missing or if there's a better way to do this. The sample data looks like this, but with millions of records.
Purchase_ID
Buyer_ID
Store
abc
1a
East
abd
1a
East
abe
1b
East
abf
1c
West
abg
1c
West
abh
1d
South
abi
1e
North
abj
1f
North
And the ideal output would look like:
t.store
average_purchases_per_store
East
1.5
West
2
South
1
North
1
Sample code:
SELECT t.store,AVG(T.distinct_purchases) as average_purchases_per_store
FROM
(SELECT COUNT(DISTINCT(purchase_id)) AS distinct_purchases
FROM table GROUP BY buyer) AS T GROUP BY t.store
Any help would be hugely appreciated.
Greg's answer is almost correct, but he lost the DISTINCT thus is a ling repeats, the value is lost:
with T1(PURCHASE_ID,BUYER_ID, STORE) as (
select * from values
('abc','1a','East'),
('abc','1a','East'),
('abd','1a','East'),
('abe','1b','East'),
('abf','1c','West'),
('abg','1c','West'),
('abh','1d','South'),
('abi','1e','North'),
('abj','1f','North')
), BUYER_PURCHASES as (
select BUYER_ID
,STORE
,count(distinct PURCHASE_ID) as PURCHASES
from T1
group by 1,2
)
select STORE
,avg(PURCHASES) as average_purchases_per_store
from BUYER_PURCHASES
group by STORE
gives:
STORE
AVERAGE_PURCHASES_PER_STORE
East
1.5
West
2
North
1
South
1
You just need to aggregate to buyers and stores first, and from that intermediate result aggregate to store:
create or replace table T1(PURCHASE_ID string, BUYER_ID string, STORE string);
insert into T1 (PURCHASE_ID,BUYER_ID, STORE) values
('abc','1a','East'),
('abd','1a','East'),
('abe','1b','East'),
('abf','1c','West'),
('abg','1c','West'),
('abh','1d','South'),
('abi','1e','North'),
('abj','1f','North');
with BUYER_PURCHASES as
(
select BUYER_ID
,STORE
,count(*) as PURCHASES
from T1
group by BUYER_ID, STORE
)
select STORE
,avg(PURCHASES) as average_purchases_per_store
from BUYER_PURCHASES
group by STORE
;
Output:
STORE
AVERAGE_PURCHASES_PER_STORE
East
1.5
West
2
South
1
North
1
Note that you don't need to use the distinct keyword unless you have to filter out duplicate rows. If you do have duplicates, that should be addressed on ETL/ELT.
Hopefully this is enough to get you started. There's literally thousands of possible approaches that depending on your datasets (you mentioned there's millions of rows) may provide you more flexibility or speed etc. High level approach would be to reduce the number of rows as quickly as possible. The first count distinct query should include as many predicates as you can to prevent any extra work. Hope this helps :-)
SELECT
STORE
,AVG(DISTINCT_STORE_PURCHASES) AVG_PURCHASES_PER_STORE
,AVG(DISTINCT_BUYER_PURCHASES) AVG_BUYER_PURCHASES_PER_STORE
FROM
(SELECT
STORE
, COUNT(DISTINCT PURCHASE_ID) OVER (PARTITION BY BUYER_ID) DISTINCT_BUYER_PURCHASES
, DIV0(COUNT(DISTINCT PURCHASE_ID) OVER (PARTITION BY STORE), COUNT(DISTINCT BUYER_ID) OVER (PARTITION BY STORE) ) DISTINCT_STORE_PURCHASES
FROM CTE)
GROUP BY
STORE ;
I have a table similar to the below:
id | name | direction |
--------------------------------------
1 Jhon Washington, DC
2 Diego Miami, Florida
3 Michael Orlando, Florida
4 Jenny Olympia, washington
5 Joe Austin, Texas
6 Barack Denver, Colorado
and I want to count how many people live in a specific state:
Washington 2
Florida 2
Texas 1
Colorado 1
How can I do this? (By the way this is just an question with an academic point of view )
Thanks in advance!
Postgres offers the function split_part(), which will break up a string by a delimiter. You want the second part (the part after the comma):
select split_part(direction, ', ', 2) as state, count(*)
from t
group by split_part(direction, ', ', 2);
Initially I would obtain the state from the direction field. Once you have that, it's quite simple:
SELECT state, count(*) as total FROM initial_table group by state.
To obtain the state, some functions depending on the dbms are useful. It depends on the language.
A possible pseudocode (given a function like substring_index of MySQL) for the query would be:
SELECT substring_index(direction,',',-1) as state, count(*) as total
FROM initial_table group by substring_index(direction,',',-1)
Edit: As it is suggested above, the query should return 1 for the Washington state.
My way do making such a queries is two-step - first, prepare fields you need, second, do you grouping or other calculation. That way you're following DRY principle and don't repeating yourself. I think CTE is the best tool for this:
with cte as (
-- we don't need other fields, only state
select
split_part(direction, ', ', 2) as state
from table1
)
select state, count(*)
from cte
group by state
sql fiddle demo
If you writing queries that way, it's easy to change grouping field in the future.
Hope that helps, and remember - readability counts! :)
...Spent several hours trying what not and researching this forum. Quite pessimistic at this point about the usefulness of Google Big Query (GBQ) for anything more than trivial queries, but here is one last desperate try, maybe someone has better ideas:
Let's say we have a COUNTRY table with average population weight(in kilograms) and height (in meters) per country as follows:
country | continent | weight | height |
============================================
US | America | 200 | 2.00 |
Canada | America | 170 | 1.90 |
France | Europe | 160 | 1.78 |
Germany | Europe | 110 | 2.00 |
Let's say you want to pick out and live in the European country with "smallest" people, where you define the measure "smallness" as the weighted sum of body weight and height with some constant weights, such as 0.6 for body weight and 0.4 for body height.
In Oracle or MS SQL server this can be done elegantly and compactly by using analytic window functions such as rank() and row_number(), for example:
select country, combined_score
from (select
country
,( 0.6*rank(weight) over() + 0.4*rank(height) over() ) combined_score
from country
where continent = 'Europe')
order by combined_score
Note that the ranking is done after the filtering for continent. The continent filter is dynamic (say input from a web form), so the ranking can not be pre-calculated and stored in the table in advance!
In GBQ there are no rank() , row_number() or over(). Even if you try some "poor man" hacks it is still not going to work because GBQ does not support correlated queries. Here are similar attempts by other people with some pretty unsatisfactory and inefficient results:
BigQuery SQL running totals
Row number in BigQuery?
Any ideas how this can be done? I can even restructure the data to use nested records, if it helps. Thank you in advance!
In your specific example, I think you can compute the result without using RANK and OVER at all:
SELECT country, score
FROM (SELECT country, 0.6 * weight + 0.4 * height AS score
FROM t WHERE continent = 'Europe')
ORDER BY score;
However, I'm assuming that this is a toy example and that your real problem involves use of RANK more in line with your example query. In that case, BigQuery does not yet support analytic functions directly, but we'll consider this to be a feature request. :-)
An equivalent for RANK in BigQuery is row_number().
For example, the top 5 contributors to Wikipedia, with row_number giving their place:
SELECT
ROW_NUMBER() OVER() row_number,
contributor_username,
count,
FROM (
SELECT contributor_username, COUNT(*) count,
FROM [publicdata:samples.wikipedia]
GROUP BY contributor_username
ORDER BY COUNT DESC
LIMIT 5)
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