I have my data that looks like this:
user_id touchpoint_number days_difference
1 1 5
1 2 20
1 3 25
1 4 10
2 1 2
2 2 30
2 3 4
I would like to create one more column that would create a cumulative sum of the days_difference, partitioned by user_id, but would reset whenever the value reaches 30 and starts counting from 0. I have been trying to do it, but I couldn't figure it out how to do it in PostgreSQL, because it has to be recursive.
The outcome I would like to have would be something like:
user_id touchpoint_number days_difference cum_sum_upto30
1 1 5 5
1 2 20 25
1 3 25 0 --- new count all over again
1 4 10 10
2 1 2 2
2 2 30 0 --- new count all over again
2 3 4 4
Do you have any cool ideas how this could be done?
This should do what you want:
with cte as (
select t.a, t.b, t.c, t.c as sumc
from t
where b = 1
union all
select t.a, t.b, t.c,
(case when t.c + cte.sumc > 30 then 0 else t.c + cte.sumc end)
from t join
cte
on t.b = cte.b + 1 and t.a = cte.a
)
select *
from cte
order by a, b;
Here is a rextester.
Related
I need to delete some rows in the dataset, of which the speed equals zero and lasting over N times (let's assume N is 2).
The structure of the table demo looks like:
id
car
speed
time
1
foo
0
1
2
foo
0
2
3
foo
0
3
4
foo
1
4
5
foo
1
5
6
foo
0
6
7
bar
0
1
8
bar
0
2
9
bar
5
3
10
bar
5
4
11
bar
5
5
12
bar
5
6
Then I hope to generate a table like the one below by using window_function:
id
car
speed
time
lasting
1
foo
0
1
3
2
foo
0
2
3
3
foo
0
3
3
4
foo
1
4
2
5
foo
1
5
2
6
foo
0
6
1
7
bar
0
1
2
8
bar
0
2
2
9
bar
5
3
4
10
bar
5
4
4
11
bar
5
5
4
12
bar
5
6
4
Then I can easily exclude those rows by using WHERE NOT (speed = 0 AND lasting > 2)
Put the code I tried here, but it didn't return the value I expected and I guess those FROM (SELECT ... FROM (SELECT ... might not be the best practice to solve the problem:
SELECT g3.*, count(id) OVER (PARTITION BY car, cumsum ORDER BY id) as num
FROM (SELECT g2.*, sum(grp2) OVER (PARTITION BY car ORDER BY id) AS cumsum
FROM (SELECT g1.*, (CASE ne0 WHEN 0 THEN 0 ELSE 1 END) AS grp2
FROM (SELECT g.*, speed - lag(speed, 1, 0) OVER (PARTITION BY car) AS ne0
FROM (SELECT *, row_number() OVER (PARTITION BY car) AS grp FROM demo) g ) g1 ) g2 ) g3
ORDER BY id;
You can use window function LAG() to check for the previous speed value for each row and SUM() window function to create the groups for the continuous values.
Then with COUNT() window function you can count the number of rows in each group so that you can filter out the rows with 0 speed in the groups that have more than 2 rows:
SELECT id, car, speed, time
FROM (
SELECT *, COUNT(*) OVER (PARTITION BY car, grp) counter
FROM (
SELECT *, SUM(flag::int) OVER (PARTITION BY car ORDER BY time) grp
FROM (
SELECT *, speed <> LAG(speed, 1, speed - 1) OVER (PARTITION BY car ORDER BY time) flag
FROM demo
) t
) t
) t
WHERE speed <> 0 OR counter <= 2
ORDER BY id;
See the demo.
Suppose I have data formatted in the following way (FYI, total row count is over 30K):
customer_id order_date order_rank
A 2017-02-19 1
A 2017-02-24 2
A 2017-03-31 3
A 2017-07-03 4
A 2017-08-10 5
B 2016-04-24 1
B 2016-04-30 2
C 2016-07-18 1
C 2016-09-01 2
C 2016-09-13 3
I need a 4th column, let's call it days_since_last_order which, in the case where order_rank = 1 then 0 else calculate the number of days since the previous order (with rank n-1).
So, the above would return:
customer_id order_date order_rank days_since_last_order
A 2017-02-19 1 0
A 2017-02-24 2 5
A 2017-03-31 3 35
A 2017-07-03 4 94
A 2017-08-10 5 38
B 2016-04-24 1 0
B 2016-04-30 2 6
C 2016-07-18 1 79
C 2016-09-01 2 45
C 2016-09-13 3 12
Is there an easier way to calculate the above with a window function (or similar) rather than join the entire dataset against itself (eg. on A.order_rank = B.order_rank - 1) and doing the calc?
Thanks!
use the lag window function
SELECT
customer_id
, order_date
, order_rank
, COALESCE(
DATE(order_date)
- DATE(LAG(order_date) OVER (PARTITION BY customer_id ORDER BY order_date))
, 0)
FROM <table_name>
I am trying to get the top n users by post using hive. The table looks like this.
Score User
10 1
20 2
50 1
20 2
0 3
3 1
40 2
...
I want to generate output which shows like
Rows Users
3 1
3 2
1 3
here is my query
SELECT * FROM (SELECT COUNT(score) as Score, UserID AS COUNT FROM A WHERE UserID IS NOT NULL GROUP BY UserID,score LIMIT 10) A;
The output I get is something like this
0 0
0 1
0 2
0 3
0 4
0 5
0 6
0 7
0 8
0 9
Can someone guide me where I am going wrong.
SELECT COUNT(score) as Score, UserID FROM A WHERE UserID IS NOT NULL GROUP BY UserID LIMIT 10
Suppose I have data in table like:
id level flag
1 1 0
1 2 0
1 3 1
1 4 0
1 5 1
1 6 0
1 7 0
1 8 1
1 9 1
1 10 0
2 1 0
2 2 0
2 3 0
2 4 0
2 5 1
2 6 1
2 7 1
......
I want to update flag to 0 after first 1 value for flag. For example, with above sample data,
for id = 1, the first flag value =1 is level=3, then all flag values for level>3 should be updated to 0.
For id = 2, should update flag = 0 for all level>5
How to implement it with sql even one sql statement?
You should be able to do this with a WHERE EXISTS on the same table:
UPDATE t1
SET flag = 0
FROM TheTable t1
WHERE EXISTS (
SELECT 1
FROM TheTable t2
WHERE t2.id = t1.id
AND t2.level < t1.level
AND t2.flag = 1
)
SQL Fiddle demo
You can do this with an exists statement:
update table t
set flag = 0
where exists (select 1
from table t2
where t2.id = t.id and
t2.level < t.level and
t2.flag = 1
);
Recently i needed to implement a way to allow for Table Records to be Ranked.
Initially i deployed an Update statement to seed the ranks:
;with cte as (
select
t.id,
Rank() Over (
Partition by t.field2
Order by t.id
) as [Rank],
t.index,
t.field2,
t.field3 ,
t.field4
from dbo.Table t
where t.field2 = #fldValue
) Update cte
set index = [Rank]
But now i need to be able to have the end-user re-order the ranks. Any suggestions on how to allow an end-user to take Rank value 92 to Rank value 15 and have everything be re-ranked appropriately.
I had thought about doing this via cursor but am trying to do this via Set based operation.
My first goto was to do a Procedural based operation but need to get more inline with Set based operation.
Table Schema
Table:
id bigint
field2 int
field3 int ---> This field will be the key pivoting column for ranking
field4 int
Data:
id field2 field3 field4
1 0 1 1
2 0 1 1
3 0 1 1
4 0 1 2
5 0 1 2
6 0 1 1
7 0 1 1
8 0 1 1
9 0 1 1
10 0 1 2
11 0 1 2
12 0 1 1
13 0 1 1
14 0 1 1
15 0 1 2
16 0 1 1
17 0 1 2
18 0 1 2
19 0 1 1