I have one table where multiple records inserted for each group of product. Now, I want to extract (SELECT) only the last entries. For more, see the screenshot. The yellow highlighted records should be return with select query.
The HAVING MAX and HAVING MIN clause for the ANY_VALUE function is now in preview
HAVING MAX and HAVING MIN were just introduced for some aggregate functions - https://cloud.google.com/bigquery/docs/release-notes#February_06_2023
with them query can be very simple - consider below approach
select any_value(t having max datetime).*
from your_table t
group by t.id, t.product
if applied to sample data in your question - output is
You might consider below as well
SELECT *
FROM sample_table
QUALIFY DateTime = MAX(DateTime) OVER (PARTITION BY ID, Product);
If you're more familiar with an aggregate function than a window function, below might be an another option.
SELECT ARRAY_AGG(t ORDER BY DateTime DESC LIMIT 1)[SAFE_OFFSET(0)].*
FROM sample_table t
GROUP BY t.ID, t.Product
Query results
You can use window function to do partition based on key and selecting required based on defining order by field.
For Example:
select * from (
select *,
rank() over (partition by product, order by DateTime Desc) as rank
from `project.dataset.table`)
where rank = 1
You can use this query to select last record of each group:
Select Top(1) * from Tablename group by ID order by DateTime Desc
I have a table like as shown below
What I would like to do is get the minimum of each subject. Though I am able to do this with row_number function, I would like to do this with groupby and min() approach. But it doesn't work.
row_number approach - works fine
SELECT * FROM (select subject_id,value,id,min_time,max_time,time_1,
row_number() OVER (PARTITION BY subject_id ORDER BY value) AS rank
from table A) WHERE RANK = 1
min() approach - doesn't work
select subject_id,id,min_time,max_time,time_1,min(value) from table A
GROUP BY SUBJECT_ID,id
As you can see just the two columns (subject_id and id) is enough to group the items together. They will help differentiate the group. But why am I not able to use the other columns in select clause. If I use the other columns, I may not get the expected output because time_1 has different values.
I expect my output to be like as shown below
In BigQuery you can use aggregation for this:
SELECT ARRAY_AGG(a ORDER BY value LIMIT 1)[SAFE_OFFSET(1)].*
FROM table A
GROUP BY SUBJECT_ID;
This uses ARRAY_AGG() to aggregate each record (the a in the argument list). ARRAY_AGG() allows you to order the result (by value) and to limit the size of the array. The latter is important for performance.
After you concatenate the arrays, you want the first element. The .* transforms the record referred to by a to the component columns.
I'm not sure why you don't want to use ROW_NUMBER(). If the problem is the lingering rank column, you an easily remove it:
SELECT a.* EXCEPT (rank)
FROM (SELECT a.*,
ROW_NUMBER() OVER (PARTITION BY subject_id ORDER BY value) AS rank
FROM A
) a
WHERE RANK = 1;
Are you looking for something like below-
SELECT
A.subject_id,
A.id,
A.min_time,
A.max_time,
A.time_1,
A.value
FROM table A
INNER JOIN(
SELECT subject_id, MIN(value) Value
FROM table
GROUP BY subject_id
) B ON A.subject_id = B.subject_id
AND A.Value = B.Value
If you do not required to select Time_1 column's value, this following query will work (As I can see values in column min_time and max_time is same for the same group)-
SELECT
A.subject_id,A.id,A.min_time,A.max_time,
--A.time_1,
MIN(A.value)
FROM table A
GROUP BY
A.subject_id,A.id,A.min_time,A.max_time
Finally, the best approach is if you can apply something like CAST(Time_1 AS DATE) on your time column. This will consider only the date part regardless of the time part. The query will be
SELECT
A.subject_id,A.id,A.min_time,A.max_time,
CAST(A.time_1 AS DATE) Time_1,
MIN(A.value)
FROM table A
GROUP BY
A.subject_id,A.id,A.min_time,A.max_time,
CAST(A.time_1 AS DATE)
-- Make sure the syntax of CAST AS DATE
-- in BigQuery is as I written here or bit different.
Below is for BigQuery Standard SQL and is most efficient way for such cases like in your question
#standardSQL
SELECT AS VALUE ARRAY_AGG(t ORDER BY value LIMIT 1)[OFFSET(0)]
FROM `project.dataset.table` t
GROUP BY subject_id
Using ROW_NUMBER is not efficient and in many cases lead to Resources exceeded error.
Note: self join is also very ineffective way of achieving your objective
A bit late to the party, but here is a cte-based approach which made sense to me:
with mins as (
select subject_id, id, min(value) as min_value
from table
group by subject_id, id
)
select distinct t.subject_id, t.id, t.time_1, t.min_time, t.max_time, m.min_value
from table t
join mins m on m.subject_id = t.subject_id and m.id = t.id
I want to use a query, showing the top two best Quantity. If the table is like the picture, how can the desired result be produced
You can use DENSE_RANK(). For example:
select
id, name, quantity
from (
select
id, name, quantity,
dense_rank() over(order by quantity desc) as rk
from t
) x
where rk <= 2
DENSE_RANK() computes a number for each row according to an ordering of your choosing. Identical values get the same number, and no numbers are skipped. See SQL Fiddle.
You can use the TOP/LIMIT functions in query. Which would allow you to select a specific number of rows.
using number as 5 you can get the desired result from:
SELECT columnname FROM tablename WHERE condition LIMIT number;
or
SELECT TOP (number)/(percent) columnname FROM tablename WHERE condition;
I need to scoop up a random sample of 1% of the records in a table (with the number of rows growing every second).
My idea is to
SELECT DISTINCT
random(),
name,
age,
registrationNumber
FROM everGrowingTable
ORDER BY random desc
LIMIT (
(select count(*) from everGrowingTable) * 0.01
) -- this is attempting to get 1%
The compiler complains about the * operator. It is fine when I hard code the table size however.
I've tried IBM documentation, but this talks about calculations using known values, not values that grow (such is that case in my table)
There doesn't seem to be a Aginity SQL function that does this. I've notice the MINUS function in the Aginity Workbench Intellisense, but alas, no multiplication equivalent.
You could use window functions in a subquery to assign a random number to each record and compute the total record number, and then do the filtering in the outer query :
SELECT name, age, registrationNumber
FROM (
SELECT
name,
age,
registrationNumber,
ROW_NUMBER() OVER(ORDER BY random()) rn,
COUNT(*) OVER() cnt
FROM everGrowingTable
) x
WHERE rn <= cnt / 100
ORDER BY rn
Title says it all, why can't I use a windowed function in a where clause in SQL Server?
This query makes perfect sense:
select id, sales_person_id, product_type, product_id, sale_amount
from Sales_Log
where 1 = row_number() over(partition by sales_person_id, product_type, product_id order by sale_amount desc)
But it doesn't work. Is there a better way than a CTE/Subquery?
EDIT
For what its worth this is the query with a CTE:
with Best_Sales as (
select id, sales_person_id, product_type, product_id, sale_amount, row_number() over (partition by sales_person_id, product_type, product_id order by sales_amount desc) rank
from Sales_log
)
select id, sales_person_id, product_type, product_id, sale_amount
from Best_Sales
where rank = 1
EDIT
+1 for the answers showing with a subquery, but really I'm looking for the reasoning behind not being able to use windowing functions in where clauses.
why can't I use a windowed function in a where clause in SQL Server?
One answer, though not particularly informative, is because the spec says that you can't.
See the article by Itzik Ben Gan - Logical Query Processing: What It Is And What It Means to You and in particular the image here. Window functions are evaluated at the time of the SELECT on the result set remaining after all the WHERE/JOIN/GROUP BY/HAVING clauses have been dealt with (step 5.1).
really I'm looking for the reasoning behind not being able to use
windowing functions in where clauses.
The reason that they are not allowed in the WHERE clause is that it would create ambiguity. Stealing Itzik Ben Gan's example from High-Performance T-SQL Using Window Functions (p.25)
Suppose your table was
CREATE TABLE T1
(
col1 CHAR(1) PRIMARY KEY
)
INSERT INTO T1 VALUES('A'),('B'),('C'),('D'),('E'),('F')
And your query
SELECT col1
FROM T1
WHERE ROW_NUMBER() OVER (ORDER BY col1) <= 3
AND col1 > 'B'
What would be the right result? Would you expect that the col1 > 'B' predicate ran before or after the row numbering?
There is no need for CTE, just use the windowing function in a subquery:
select id, sales_person_id, product_type, product_id, sale_amount
from
(
select id, sales_person_id, product_type, product_id, sale_amount,
row_number() over(partition by sales_person_id, product_type, product_id order by sale_amount desc) rn
from Sales_Log
) sl
where rn = 1
Edit, moving my comment to the answer.
Windowing functions are not performed until the data is actually selected which is after the WHERE clause. So if you try to use a row_number in a WHERE clause the value is not yet assigned.
"All-at-once operation" means that all expressions in the same
logical query process phase are evaluated logically at the same time.
And great chapter Impact on Window Functions:
Suppose you have:
CREATE TABLE #Test ( Id INT) ;
INSERT INTO #Test VALUES ( 1001 ), ( 1002 ) ;
SELECT Id
FROM #Test
WHERE Id = 1002
AND ROW_NUMBER() OVER(ORDER BY Id) = 1;
All-at-Once operations tell us these two conditions evaluated logically at the same point of time. Therefore, SQL Server can
evaluate conditions in WHERE clause in arbitrary order, based on
estimated execution plan. So the main question here is which condition
evaluates first.
Case 1:
If ( Id = 1002 ) is first, then if ( ROW_NUMBER() OVER(ORDER BY Id) = 1 )
Result: 1002
Case 2:
If ( ROW_NUMBER() OVER(ORDER BY Id) = 1 ), then check if ( Id = 1002 )
Result: empty
So we have a paradox.
This example shows why we cannot use Window Functions in WHERE clause.
You can think more about this and find why Window Functions are
allowed to be used just in SELECT and ORDER BY clauses!
Addendum
Teradata supports QUALIFY clause:
Filters results of a previously computed ordered analytical function according to user‑specified search conditions.
SELECT Id
FROM #Test
WHERE Id = 1002
QUALIFY ROW_NUMBER() OVER(ORDER BY Id) = 1;
Snowflake - Qualify
QUALIFY does with window functions what HAVING does with aggregate functions and GROUP BY clauses.
In the execution order of a query, QUALIFY is therefore evaluated after window functions are computed. Typically, a SELECT statement’s clauses are evaluated in the order shown below:
From
Where
Group by
Having
Window
QUALIFY
Distinct
Order by
Limit
Databricks - QUALIFY clasue
Filters the results of window functions. To use QUALIFY, at least one window function is required to be present in the SELECT list or the QUALIFY clause.
You don't necessarily need to use a CTE, you can query the result set after using row_number()
select row, id, sales_person_id, product_type, product_id, sale_amount
from (
select
row_number() over(partition by sales_person_id,
product_type, product_id order by sale_amount desc) AS row,
id, sales_person_id, product_type, product_id, sale_amount
from Sales_Log
) a
where row = 1
It's an old thread, but I'll try to answer specifically the question expressed in the topic.
Why no windowed functions in where clauses?
SELECT statement has following main clauses specified in keyed-in order:
SELECT DISTINCT TOP list
FROM JOIN ON / APPLY / PIVOT / UNPIVOT
WHERE
GROUP BY WITH CUBE / WITH ROLLUP
HAVING
ORDER BY
OFFSET-FETCH
Logical Query Processing Order, or Binding Order, is conceptual interpretation order, it defines the correctness of the query. This order determines when the objects defined in one step are made available to the clauses in subsequent steps.
----- Relational result
1. FROM
1.1. ON JOIN / APPLY / PIVOT / UNPIVOT
2. WHERE
3. GROUP BY
3.1. WITH CUBE / WITH ROLLUP
4. HAVING
---- After the HAVING step the Underlying Query Result is ready
5. SELECT
5.1. SELECT list
5.2. DISTINCT
----- Relational result
----- Non-relational result (a cursor)
6. ORDER BY
7. TOP / OFFSET-FETCH
----- Non-relational result (a cursor)
For example, if the query processor can bind to (access) the tables or views defined in the FROM clause, these objects and their columns are made available to all subsequent steps.
Conversely, all clauses preceding the SELECT clause cannot reference any column aliases or derived columns defined in SELECT clause. However, those columns can be referenced by subsequent clauses such as the ORDER BY clause.
OVER clause determines the partitioning and ordering of a row set before the associated window function is applied. That is, the OVER clause defines a window or user-specified set of rows within an Underlying Query Result set and window function computes result against that window.
Msg 4108, Level 15, State 1, …
Windowed functions can only appear in the SELECT or ORDER BY clauses.
The reason behind is because the way how Logical Query Processing works in T-SQL. Since the underlying query result is established only when logical query processing reaches the SELECT step 5.1. (that is, after processing the FROM, WHERE, GROUP BY and HAVING steps), window functions are allowed only in the SELECT and ORDER BY clauses of the query.
Note to mention, window functions are still part of relational layer even Relational Model doesn't deal with ordered data. The result after the SELECT step 5.1. with any window function is still relational.
Also, speaking strictly, the reason why window function are not allowed in the WHERE clause is not because it would create ambiguity, but because the order how Logical Query Processing processes SELECT statement in T-SQL.
Links: here, here and here
Finally, there's the old-fashioned, pre-SQL Server 2005 way, with a correlated subquery:
select *
from Sales_Log sl
where sl.id = (
Select Top 1 id
from Sales_Log sl2
where sales_person_id = sl.sales_person_id
and product_type = sl.product_type
and product_id = sl.product_id
order by sale_amount desc
)
I give you this for completeness, merely.
Basically first "WHERE" clause condition is read by sql and the same column/value id looked into the table but in table row_num=1 is not there still. Hence it will not work.
Thats the reason we will use parentheses first and after that we will write the WHERE clause.
Yes unfortunately when you do a windowed function SQL gets mad at you even if your where predicate is legitimate. You make a cte or nested select having the value in your select statement, then reference your CTE or nested select with that value later. Simple example that should be self explanatory. If you really HATE cte's for some performance issue on doing a large data set you can always drop to temp table or table variable.
declare #Person table ( PersonID int identity, PersonName varchar(8));
insert into #Person values ('Brett'),('John');
declare #Orders table ( OrderID int identity, PersonID int, OrderName varchar(8));
insert into #Orders values (1, 'Hat'),(1,'Shirt'),(1, 'Shoes'),(2,'Shirt'),(2, 'Shoes');
--Select
-- p.PersonName
--, o.OrderName
--, row_number() over(partition by o.PersonID order by o.OrderID)
--from #Person p
-- join #Orders o on p.PersonID = o.PersonID
--where row_number() over(partition by o.PersonID order by o.orderID) = 2
-- yields:
--Msg 4108, Level 15, State 1, Line 15
--Windowed functions can only appear in the SELECT or ORDER BY clauses.
;
with a as
(
Select
p.PersonName
, o.OrderName
, row_number() over(partition by o.PersonID order by o.OrderID) as rnk
from #Person p
join #Orders o on p.PersonID = o.PersonID
)
select *
from a
where rnk >= 2 -- only orders after the first one.