I want to use rank() window function on a Redshift database to rank over specific, multiple columns. The code shall check those multiple columns per each row and assign same rank to rows that have identical values in ALL those columns.
Example image found in link below:
https://ibb.co/GJv1xQL
There are 18 distinct rows, however the rank shows 3 distinct rows, according to the ranking I wish to apply.
I tried :
select tbl.*
, dense_rank() over (partition by secondary_id order by created_on, type1, type2, money, amount nulls last ) as rank
from table tbl
where secondary_id='92d30f87-b2da-45c0-bdf7-c5ca96fe5ea6'
But the ranks assigned were wrong, and then I tried:
select tbl.*
, dense_rank() over (partition by secondary_id,created_on, type1, type2, money, amount ) as rank
from table tbl
where secondary_id='92d30f87-b2da-45c0-bdf7-c5ca96fe5ea6'
But this assigned rank=1 everywhere, in every row.
I found how to solve this.
The reason that the order by all the columns of interest was failing, is because the timestamp column contained different values in miliseconds, which was not obvious by viewing the data . So I only took into account the timestamp up until seconds and it worked! So I converted created_on column to date_trunc('s',cd.created_on) .
select tbl.* , dense_rank() over (partition by secondary_id order by date_trunc('s',created_on), type1, type2, money, amount nulls last ) as rank from table tbl where secondary_id='92d30f87-b2da-45c0-bdf7-c5ca96fe5ea6'
Related
I have a table and want to calculate the percentage of total by store_id which each (category_id, store_id) subtotal represents. My code is below:
WITH
example_table (name, store_id)
AS
(
select name, store_id
from category
join film_category using (category_id)
join film using (film_id)
join inventory using (film_id)
join rental using (inventory_id)
)
SELECT name, store_id, cast(count(*) as numeric)/(SELECT count(*) FROM example_table)
FROM example_table
GROUP BY name, store_id
ORDER BY name, store_id
This code actually works, as in, it doesn't throw an error, only they're not the results I'm looking for. Here each of the subtotals is divided by the total across both stores and all 16 names. Instead, I want the subtotals divided by their respective store totals or divided by their respective name totals.
I'm wondering how to perform calculations on those subtotals in general.
Thanks in advance,
I believe you need to explore the possibilities of using aggregate functions combined with an OVER(PARTITION BY ...) e.g.
SELECT DISTINCT
name, store_id, store_id_count, name_count
FROM (
select name, store_id
, count(*) over(partition by store_id) as store_id_count
, count(*) over(partition by name) as name_count
from category
join film_category using (category_id)
join film using (film_id)
join inventory using (film_id)
join rental using (inventory_id)
) AS example_table
When using aggregate function with the over clause you get the wanted counts on each row of the result, and it seems that in this case you need this. Note that select distinct has been used simply to reduce the final number of rows returned, you might still need to use a group by but I am not sure if you do.
Once you have the needed values within the derived table (aliases as example_table) then it should be a simple matter of some arithmetic in the overall select clause.
I need to select random values from above table where when there are multiple values (exampl:- of 3333,4444,6666) . Currently I am using below code which is biased in the final result.
insert into com_n3
select distinct number,min(district)
from com_n2
result will give more numbers with value "A" as the district. I need a unbiased random way to select from multiple entries.
you can get some random records using following query.
select number, district
from
(
select *, row_number() over (partition by number order rand()) as rank
from
temp.com_n2
) a
where a.rank=1
I have the following query:
SELECT *
FROM
(
SELECT *,
ROW_NUMBER() OVER(PARTITION BY Code ORDER BY Price ASC) as RowNum
from Offers) r
where RowNum = 1
Offers table contains about 10 million records. But there are only ~4000 distinct codes there. So I need to get the row with the lowest price for each code and there will be only 4000 rows in the result.
I have an Index on (Code, Price) columns with all other columns in INCLUDE statement.
The query runs for 2 minutes. And if I look at the execution plan, I see Index scan with 10M actual rows. So, I guess it scans the whole index to get needed values.
Why MSSQL do the whole index scan? Is it because subquery needs the whole data? How to avoid this scan? Is there a SQL hint to process only the first row in partition?
Is there another way to optimize such query?
After trying multiple different solutions, I've found the fastest query with CROSS APPLY statement:
SELECT C.*
FROM (SELECT DISTINCT Code from Offers) A
CROSS APPLY (SELECT TOP 1 *
FROM Offers B
WHERE A.Code = B.Code
ORDER by Price) C
It take ~1 second to run.
Try creating an index on ( Code, Price ) without including the other columns and then (assuming that there is a unique Id column):
select L.*
from Offers as L inner join
( select Id,
Row_Number() over ( partition by Code order by Price ) as RN
from Offers ) as R on R.Id = L.Id and R.RN = 1
An index scan on a smaller index ought to help.
Second guess would be to get the Id of the row with the lowest Price for each Code explicitly: Get distinct Code values, get Id of top 1 (to avoid problems with duplicate prices) Min( Price ) row for that Code, join with Offers to get complete rows. Again, the more compact index should help.
Not sure if you'll get any significant performance gains, but you may want to try the WITH TIES clause
Example
Select Top 1 with Ties *
From Offers
Order By Row_Number() over (Partition By Code Order By Price)
I have a table with an ID column called mmsi and another column of timestamp, with multiple timestamps per mmsi.
For each mmsi I want to calculate the standard deviation of the difference between consecutive timestamps.
I'm not very experienced with SQL but have tried to construct a function as follows:
SELECT
mmsi, stddev(time_diff)
FROM
(SELECT mmsi,
EXTRACT(EPOCH FROM (timestamp - lag(timestamp) OVER (ORDER BY mmsi ASC, timestamp ASC)))
FROM ais_messages.ais_static
ORDER BY mmsi ASC, timestamp ASC) AS time_diff
WHERE time_diff IS NOT NULL
GROUP BY mmsi;
Your query looks on the right track, but it has several problems. You labelled your subquery, which looks almost right, with an alias which you then select. But this subquery returns multiple rows and columns so this doesn't make any sense. Here is a corrected version:
SELECT
t.mmsi,
STDDEV(t.time_diff) AS std
FROM
(
SELECT
mmsi,
EXTRACT(EPOCH FROM (timestamp - LAG(timestamp) OVER
(PARTITION BY mmsi ORDER BY timestamp))) AS time_diff
FROM ais_messages.ais_static
ORDER BY mmsi, timestamp
) t
WHERE t.time_diff IS NOT NULL
GROUP BY t.mmsi
This approach should be fine but there is one edge case where it might not behave as expected. If a given mmsi group have only one record, then it would not even appear in the result set of standard deviations. This is because the LAG calculation would return NULL for that single record and it would be filtered off.
What would be the most efficient way to calculating the mode across tables with joins in DB2..
I am trying to get the value with the most frequency(count) for a given column(ID - candidate key for joined table) on a given date.
The idea is to get the most common (value) from the table which has different (value)s for some accounts (for the same ID and date). We need to make it unique for use in another table.
You can use common table expressions [CTE's], indicated by WITH, to break the logic down into logical steps. First we'll build the summary rows, then we'll assign a ranking to the rows within each group, then pick out the ones that with the highest count of records.
Let's say we want to know which flavor of each item sells the most frequently on each date (perhaps assuming a record is quantity one).
WITH s as
(
SELECT itemID, saleDate, flavor, count(*) as tally
FROM sales
GROUP BY itemID, saleDate, flavor
), r as
(
SELECT itemID, saleDate, flavor, tally,
RANK() OVER (PARTITION BY itemID, saleDate ORDER BY tally desc) as pri
FROM s
)
SELECT itemID, saleDate, flavor, tally
FROM r
WHERE pri = 1
Here the names "s" and "r" refer to the result set from their respective CTE's. These names can then be used as to represent a table in another part of the statement.
The pri column will have the RANK() of tally value on the summary row from the first section "s" within the window of itemID and saleDate. Tally is descending, because we want the largest value first, which will get a RANK() of 1. Then in the main SELECT we simply pick those summary records which were first in their partition.
By using RANK() or DENSE_RANK() we could get back multiple flavors for an itemID, saleDate, if they are tied for first place. This could be eliminated by replacing RANK() with ROW_NUMBER(), but it would arbitrarily pick one of the tied flavors as a winner, and this may not be correct answer for the problem at hand.
If we had a sales quantity column in the table, we could replace COUNT(*) with SUM(salesqty) and find what had sold the most units.