if i have the following select two count cases:
COUNT(CASE WHEN STATUS ='Færdig' THEN 1 END) as completed_callbacks,
COUNT(CASE WHEN SOLVED_SECONDS /60 /60 <= 2 THEN 1 END) as completed_within_2hours
and i want to devide the two results with eachother how can i achieve this?
this is my attemt however that failed:
CASE(completed_callbacks / completed_within_2hours * 100) as Percentage
i know this is a rather simple question but i havnt been able to find the answer anywhere
You have to create a derived table:
SELECT completed_callbacks / completed_within_2hours * 100
FROM (SELECT Count(CASE
WHEN status = 'Færdig' THEN 1
END) AS completed_callbacks,
Count(CASE
WHEN solved_seconds / 60 / 60 <= 2 THEN 1
END) AS completed_within_2hours
FROM yourtable
WHERE ...)
Try this:
with x as (
select 'Y' as completed, 'Y' as completed_fast from dual
union all
select 'Y' as completed, 'N' as completed_fast from dual
union all
select 'Y' as completed, 'Y' as completed_fast from dual
union all
select 'N' as completed, 'N' as completed_fast from dual
)
select
sum(case when completed='Y' then 1 else 0 end) as count_completed,
sum(case when completed='N' then 1 else 0 end) as count_not_completed,
sum(case when completed='Y' and completed_fast='Y' then 1 else 0 end) as count_completed_fast,
case when (sum(case when completed='Y' then 1 else 0 end) = 0) then 0 else
((sum(case when completed='Y' and completed_fast='Y' then 1 else 0 end) / sum(case when completed='Y' then 1 else 0 end))*100)
end pct_completed_fast
from x;
Results:
"COUNT_COMPLETED" "COUNT_NOT_COMPLETED" "COUNT_COMPLETED_FAST" "PCT_COMPLETED_FAST"
3 1 2 66.66666666666666666666666666666666666667
The trick is to use SUM rather than COUNT, along with a decode or CASE.
select
COUNT(CASE WHEN STATUS ='Færdig' THEN 1 END)
/
COUNT(CASE WHEN SOLVED_SECONDS /60 /60 <= 2 THEN 1 END)
* 100
as
Percentage
Related
Is there a way to count a number of columns which has a particular value for each rows in Hive.
I have data which looks like in input and I want to count how many columns have value 'a' and how many column have value 'b' and get the output like in 'Output'.
Is there a way to accomplish this with Hive query?
One method in Hive is:
select ( (case when cl_1 = 'a' then 1 else 0 end) +
(case when cl_2 = 'a' then 1 else 0 end) +
(case when cl_3 = 'a' then 1 else 0 end) +
(case when cl_4 = 'a' then 1 else 0 end) +
(case when cl_5 = 'a' then 1 else 0 end)
) as count_a,
( (case when cl_1 = 'b' then 1 else 0 end) +
(case when cl_2 = 'b' then 1 else 0 end) +
(case when cl_3 = 'b' then 1 else 0 end) +
(case when cl_4 = 'b' then 1 else 0 end) +
(case when cl_5 = 'b' then 1 else 0 end)
) as count_b
from t;
To get the total count, I would suggest using a subquery and adding count_a and count_b.
Use lateral view with explode on the data and do the aggregations on it.
select id
,sum(cast(col='a' as int)) as cnt_a
,sum(cast(col='b' as int)) as cnt_b
,sum(cast(col in ('a','b') as int)) as cnt_total
from tbl
lateral view explode(array(ci_1,ci_2,ci_3,ci_4,ci_5)) tbl as col
group by id
SELECT
userid,
CASE
WHEN (COUNT(CASE
WHEN onlinesportsgamewagers != 0
THEN 1
ELSE null
END)
+ COUNT(CASE
WHEN depositmade_amt != 0
THEN 1
ELSE null
END)) >= 10
THEN "VIP"
ELSE "NON-VIP"
END as VIPcheck
FROM
player_activity
WHERE
userid = 2023410
GROUP BY
year(txndate), month(txndate)
This query determines the user's VIP status for each month.
Ultimately, I want to have a query that determines if the user achieved VIP status for at least 3 months (including the current month). For the time being, it's only user 2023410, but eventually I want to run this for the whole database.
Therefore my ultimate output would be:
User - VIPcheck (3 different months w/ active status)
(one row per userID)
HAVING COUNT(CASE WHEN (COUNT(CASE WHEN onlinesportsgamewagers != 0
THEN 1
ELSE null
END)
+ COUNT(CASE WHEN depositmade_amt != 0
THEN 1
ELSE null
END)) >= 10
THEN 1
ELSE 0
END)
Tried the above having statement, but it didn't work. Any suggestions?
If I understand correctly, this gets the VIP status for one user by month:
SELECT userid, year(txndate), month(txndate),
(CASE WHEN SUM(CASE WHEN onlinesportsgamewagers <> 0 THEN 1 ELSE 0 END) +
SUM(CASE WHEN depositmade_amt <> 0 THEN 1 ELSE 0 END) >= 10
THEN 'VIP'
ELSE 'NON-VIP'
END) as VIPcheck
FROM player_activity
GROUP BY userid, year(txndate), month(txndate);
Another aggregation will get what you want:
SELECT userid,
(CASE WHEN SUM(VIPcheck = 'VIP') >= 3 THEN 'SUPER-VIP'
WHEN SUM(VIPcheck = 'VIP') >= 1 THEN 'VIP'
ELSE 'HOI POLLOI'
END) as status
FROM (SELECT userid, year(txndate), month(txndate),
(CASE WHEN SUM(CASE WHEN onlinesportsgamewagers <> 0 THEN 1 ELSE 0 END) +
SUM(CASE WHEN depositmade_amt <> 0 THEN 1 ELSE 0 END) >= 10
THEN 'VIP'
ELSE 'NON-VIP'
END) as VIPcheck
FROM player_activity
GROUP BY userid, year(txndate), month(txndate)
) uym
GROUP BY userid;
I need to count combinations of products within transactions differently to other products and I'm struggling with how to do this within a single select statement from SQL 2008. This would then become a data set to manipulate in Reporting Services
raw data looks like this
txn, prod, units
1, a, 2
1, c, 1
2, a, 1
2, b, 1
2, c, 1
3, a, 2
3, b, 1
4, a, 3
4, c, 2
So a+b should = one if in same trans number, however a or b should equal one if not paired. So a=1 and b=1 but a+b=1, a+b+a=2, a+b+a+b=2 given the example data here is my desired result with an explanation of why
txn 1 is 3 units -- 2a + c
txn 2 is 2 units -- (a+b) + c
txn 3 is 2 units -- (a+b) + a
txn 4 is 5 units -- 3a + 2c
My query is more complex than this and includes other aggregates so I would like to group by transaction which I can't do as I need to manipulate at a lower grain
Update Progress :
Possible solution, I've generated columns based on the products I'm measuring. This allows me to group on Txn as I am now aggregating that field. Unsure if there's a better way to do it as it does take a little while
CASE WHEN SUM(CASE WHEN Prod='a' then 1 else 0 end)-
SUM(CASE WHEN Prod='b' then 1 else 0 end)=0
THEN SUM(CASE WHEN Prod='a' then 1 else 0 end)
ELSE 0 END AS MixProd
, CASE WHEN SUM(CASE WHEN Prod='a' then 1 else 0 end)-
SUM(CASE WHEN Prod='b' then 1 else 0 end)!=0
THEN ABS(SUM(CASE WHEN Prod='a' then 1 else 0 end)-
SUM(CASE WHEN Prod='b' then 1 else 0 end))
ELSE 0 END AS NotMixProd
I will then need to sort out the current unit aggregate to remove the extras but this certainly gives me a start
Update Progress 2 :
This failed to handle 0 correctly where a or b was 0 it would still give a value for mix because a-b was not zero. I reverted to an earlier draft that I lost and expanded as per below
, CASE WHEN SUM(CASE WHEN Prod='a' then 1 else 0 end) = 0 THEN 0
WHEN SUM(CASE WHEN Prod='b' then 1 else 0 end) = 0 THEN 0
WHEN SUM(CASE WHEN Prod='a' then 1 else 0 end)-
SUM(CASE WHEN Prod='b' then 1 else 0 end)=0
THEN SUM(CASE WHEN Prod='a' then 1 else 0 end)
ELSE ABS(SUM(CASE WHEN Prod='a' then 1 else 0 end)-
SUM(CASE WHEN Prod='b' then 1 else 0 end))
END AS MixProd
, CASE WHEN SUM(CASE WHEN Prod='a' then 1 else 0 end)-
SUM(CASE WHEN Prod='b' then 1 else 0 end)!=0
THEN ABS(SUM(CASE WHEN Prod='a' then 1 else 0 end)-
SUM(CASE WHEN Prod='b' then 1 else 0 end))
ELSE 0 END AS NotMixProd
UPDATE: This should work in SQL Server 2008 (based on LAG solution from here).
Here is the demo: http://rextester.com/GNI23706
WITH CTE AS
(
select txn, prod, units,
row_number() over (partition by txn order by prod) rn,
(row_number() over (partition by txn order by prod))/2 rndiv2,
(row_number() over (partition by txn order by prod)+1)/2 rnplus1div2,
count(*) over (partition by txn) partitioncount
from test_data
)
select
txn,
sum(case when prev_prod = 'a' and prod = 'b' and prev_units >= units then 0
when prev_prod = 'a' and prod = 'b' and prev_units < units then units - prev_units
else units
end) units
from
(
select
txn,
prod,
units,
CASE WHEN rn%2=1
THEN MAX(CASE WHEN rn%2=0 THEN prod END) OVER (PARTITION BY txn,rndiv2)
ELSE MAX(CASE WHEN rn%2=1 THEN prod END) OVER (PARTITION BY txn,rnplus1div2)
END AS prev_prod,
CASE WHEN rn%2=1
THEN MAX(CASE WHEN rn%2=0 THEN units END) OVER (PARTITION BY txn,rndiv2)
ELSE MAX(CASE WHEN rn%2=1 THEN units END) OVER (PARTITION BY txn,rnplus1div2)
END AS prev_units
from cte
) temp
group by txn
For SQL Server 2012+, use LAG:
select
txn,
sum(
case when prev_prod = 'a' and prod = 'b' and prev_units >= units then 0
when prev_prod = 'a' and prod = 'b' and prev_units < units then units - prev_units
else units
end) units
from
(
select
txn,
prod,
units,
lag(prod) over (partition by txn order by prod) prev_prod,
lag(units) over (partition by txn order by prod) prev_units
from test_data
) temp
group by txn
I decided in the end that a temp table was the best way to go, because I couldn't group on a collation. So I eventually tweaked the code above as it was failing to pick up the spare items correctly
SUM(Units) AS OldUnits
SUM(Units) -
(CASE WHEN
SUM(CASE WHEN Prod='a' THEN 1 ELSE 0 END) = 0 THEN 0 WHEN
SUM(CASE WHEN Prod='b' THEN 1 ELSE 0 END) = 0 THEN 0 WHEN
SUM(CASE WHEN Prod='a' THEN 1 ELSE 0 END) -
SUM(CASE WHEN Prod='b' THEN 1 ELSE 0 END) = 0 THEN
SUM(CASE WHEN Prod='a' THEN 1 ELSE 0 END) WHEN
(SUM(CASE WHEN Prod='a' THEN 1 ELSE 0 END) -
SUM(CASE WHEN Prod='b' THEN 1 ELSE 0 END)) < 0 THEN
SUM(CASE WHEN Prod='a' THEN 1 ELSE 0 END) ELSE
SUM(CASE WHEN Prod='b' THEN 1 ELSE 0 END) END) AS NewUnits
This was stored in a temptable that I could then collate on Trans as the next step. Works fine for my purposes and helped me overcome a mild irrational fear I have of temptables
I am trying to find a better way to write this sql server code 2008. It works and data is accurate. Reason i ask is that i will be asked to do this for several other reports going forward and want to reduce the amount of code to upkeep going forward.
How can i take a field where i sum for the yes/no/- (dash) in each field without doing an individual sum as i have in code. Each table is a month of detail data which i sum using in a CTE. i changed the table name for each month and Union All to put data together. Is there a better way to do this. This is a small sample of code. Thanks for the help.
WITH H AS (
SELECT 'August' AS Month_Name
, SUM(CASE WHEN G.FFS = '-' THEN 1 ELSE 0 END) AS FFS_Dash
, SUM(CASE WHEN G.FFS = 'Yes' THEN 1 ELSE 0 END) AS FFS_Yes
, SUM(CASE WHEN G.FFS = 'No' THEN 1 ELSE 0 END) AS FFS_No
, SUM(CASE WHEN G.DNA = '-' THEN 1 ELSE 0 END) AS DNA_Dash
, SUM(CASE WHEN G.DNA = 'Yes' THEN 1 ELSE 0 END) AS DNA_Yes
, SUM(CASE WHEN G.DNA = 'No' THEN 1 ELSE 0 END) AS DNA_No
FROM table08 G )
, G AS (
SELECT 'July' AS Month_Name
, SUM(CASE WHEN G.FFS = '-' THEN 1 ELSE 0 END) AS FFS_Dash
, SUM(CASE WHEN G.FFS = 'Yes' THEN 1 ELSE 0 END) AS FFS_Yes
, SUM(CASE WHEN G.FFS = 'No' THEN 1 ELSE 0 END) AS FFS_No
, SUM(CASE WHEN G.DNA = '-' THEN 1 ELSE 0 END) AS DNA_Dash
, SUM(CASE WHEN G.DNA = 'Yes' THEN 1 ELSE 0 END) AS DNA_Yes
, SUM(CASE WHEN G.DNA = 'No' THEN 1 ELSE 0 END) AS DNA_No
FROM table07 G )
select * from H
UNION ALL
select * from G
How about:
SELECT Month_Name,
SUM(CASE WHEN G.FFS = '-' THEN 1 ELSE 0 END) AS FFS_Dash,
SUM(CASE WHEN G.FFS = 'Yes' THEN 1 ELSE 0 END) AS FFS_Yes,
SUM(CASE WHEN G.FFS = 'No' THEN 1 ELSE 0 END) AS FFS_No,
SUM(CASE WHEN G.DNA = '-' THEN 1 ELSE 0 END) AS DNA_Dash,
SUM(CASE WHEN G.DNA = 'Yes' THEN 1 ELSE 0 END) AS DNA_Yes,
SUM(CASE WHEN G.DNA = 'No' THEN 1 ELSE 0 END) AS DNA_No
FROM ((select 'July' as Month_Name, G.*
from table07 G
) union all
(select 'August', H.*
from table08 H
)
) gh
GROUP BY Month_Name;
However, having tables with the same structure is usually a sign of poor database design. You should have a single table with a column representing the month.
How do I convert the following query into a pivot table using crosstab?
select (SUM(CASE WHEN added_customer=false
THEN 1
ELSE 0
END)) AS CUSTOMERS_NOT_ADDED, (SUM(CASE WHEN added_customer=true
THEN 1
ELSE 0
END)) AS CUSTOMERS_ADDED,
(select (SUM(CASE WHEN added_sales_order=false
THEN 1
ELSE 0
END))
FROM shipments_data
) AS SALES_ORDER_NOT_ADDED,
(select (SUM(CASE WHEN added_sales_order=true
THEN 1
ELSE 0
END))
FROM shipments_data
) AS SALES_ORDER_ADDED,
(select (SUM(CASE WHEN added_fulfillment=false
THEN 1
ELSE 0
END))
FROM shipments_data
) AS ITEM_FULFILLMENT_NOT_ADDED,
(select (SUM(CASE WHEN added_fulfillment=true
THEN 1
ELSE 0
END))
FROM shipments_data
) AS ITEM_FULFILLMENT_ADDED,
(select (SUM(CASE WHEN added_invoice=false
THEN 1
ELSE 0
END))
FROM shipments_data
) AS INVOICE_NOT_ADDED,
(select (SUM(CASE WHEN added_invoice=true
THEN 1
ELSE 0
END))
FROM shipments_data
) AS INVOICE_ADDED,
(select (SUM(CASE WHEN added_ra=false
THEN 1
ELSE 0
END))
FROM shipments_data
) AS RA_NOT_ADDED,
(select (SUM(CASE WHEN added_ra=true
THEN 1
ELSE 0
END))
FROM shipments_data
) AS RA_ADDED,
(select (SUM(CASE WHEN added_credit_memo=false
THEN 1
ELSE 0
END))
FROM shipments_data
) AS CREDIT_MEMO_NOT_ADDED,
(select (SUM(CASE WHEN added_credit_memo=true
THEN 1
ELSE 0
END))
FROM shipments_data
) AS CREDIT_MEMO_ADDED
FROM shipments_data;
This query gives me data in a standard row format however I would like to show this as a pivot table in the following format:
Added Not_Added
Customers 100 0
Sales Orders 50 50
Item Fulfillemnts 0 100
Invoices 0 100
...
I am using Heroku PostgreSQL, which is running v9.1.6
Also, I'm not sure if my above query can be optimized or if this is poor form. If it can be optimized/improved I would love to learn how.
The tablefunc module that supplies crosstab() is available for 9.1 (like for any other version this side of the millennium). Doesn't Heroku let you install additional modules? Have you tried:
CREATE EXTENSION tablefunc;
For examples how to use it, refer to the manual or this related question:
PostgreSQL Crosstab Query
OR try this search - there are a couple of good answers with examples on SO.
To get you started (like most of the way ..) use this largely simplified and re-organized query as base for the crosstab() call:
SELECT 'added'::text AS col
,SUM(CASE WHEN added_customer THEN 1 ELSE 0 END) AS customers
,SUM(CASE WHEN added_sales_order THEN 1 ELSE 0 END) AS sales_order
,SUM(CASE WHEN added_fulfillment THEN 1 ELSE 0 END) AS item_fulfillment
,SUM(CASE WHEN added_invoice THEN 1 ELSE 0 END) AS invoice
,SUM(CASE WHEN added_ra THEN 1 ELSE 0 END) AS ra
,SUM(CASE WHEN added_credit_memo THEN 1 ELSE 0 END) AS credit_memo
FROM shipments_data
UNION ALL
SELECT 'not_added' AS col
,SUM(CASE WHEN NOT added_customer THEN 1 ELSE 0 END) AS customers
,SUM(CASE WHEN NOT added_sales_order THEN 1 ELSE 0 END) AS sales_order
,SUM(CASE WHEN NOT added_fulfillment THEN 1 ELSE 0 END) AS item_fulfillment
,SUM(CASE WHEN NOT added_invoice THEN 1 ELSE 0 END) AS invoice
,SUM(CASE WHEN NOT added_ra THEN 1 ELSE 0 END) AS ra
,SUM(CASE WHEN NOT added_credit_memo THEN 1 ELSE 0 END) AS credit_memo
FROM shipments_data;
If your columns are defined NOT NULL, you can further simplify the CASE expressions.
If performance is crucial, you can get all aggregates in a single scan in a CTE and split values into two rows in the next step.
WITH x AS (
SELECT count(NULLIF(added_customer, FALSE)) AS customers
,sum(added_sales_order::int) AS sales_order
...
,count(NULLIF(added_customer, TRUE)) AS not_customers
,sum((NOT added_sales_order)::int) AS not_sales_order
...
FROM shipments_data
)
SELECT 'added'::text AS col, customers, sales_order, ... FROM x
UNION ALL
SELECT 'not_added', not_customers, not_sales_order, ... FROM x;
I also demonstrate two alternative ways to build your aggregates - both built on the assumption that all columns are boolean NOT NULL. Both alternatives are syntactically shorter, but not faster. In previous testes all three methods performed about the same.