I have some data that looks like this:
CustID EventID TimeStamp
1 17 1/1/15 13:23
1 17 1/1/15 14:32
1 13 1/1/25 14:54
1 13 1/3/15 1:34
1 17 1/5/15 2:54
1 1 1/5/15 3:00
2 17 2/5/15 9:12
2 17 2/5/15 9:18
2 1 2/5/15 10:02
2 13 2/8/15 7:43
2 13 2/8/15 7:50
2 1 2/8/15 8:00
I'm trying to use the row_number function to get it to look like this:
CustID EventID TimeStamp SeqNum
1 17 1/1/15 13:23 1
1 17 1/1/15 14:32 1
1 13 1/1/25 14:54 2
1 13 1/3/15 1:34 2
1 17 1/5/15 2:54 3
1 1 1/5/15 3:00 4
2 17 2/5/15 9:12 1
2 17 2/5/15 9:18 1
2 1 2/5/15 10:02 2
2 13 2/8/15 7:43 3
2 13 2/8/15 7:50 3
2 1 2/8/15 8:00 4
I tried this:
row_number () over
(partition by custID, EventID
order by custID, TimeStamp asc) SeqNum]
but got this back:
CustID EventID TimeStamp SeqNum
1 17 1/1/15 13:23 1
1 17 1/1/15 14:32 2
1 13 1/1/25 14:54 3
1 13 1/3/15 1:34 4
1 17 1/5/15 2:54 5
1 1 1/5/15 3:00 6
2 17 2/5/15 9:12 1
2 17 2/5/15 9:18 2
2 1 2/5/15 10:02 3
2 13 2/8/15 7:43 4
2 13 2/8/15 7:50 5
2 1 2/8/15 8:00 6
how can I get it to sequence based on the change in the EventID?
This is tricky. You need a multi-step process. You need to identify the groups (a difference of row_number() works for this). Then, assign an increasing constant to each group. And then use dense_rank():
select sd.*, dense_rank() over (partition by custid order by mints) as seqnum
from (select sd.*,
min(timestamp) over (partition by custid, eventid, grp) as mints
from (select sd.*,
(row_number() over (partition by custid order by timestamp) -
row_number() over (partition by custid, eventid order by timestamp)
) as grp
from somedata sd
) sd
) sd;
Another method is to use lag() and a cumulative sum:
select sd.*,
sum(case when prev_eventid is null or prev_eventid <> eventid
then 1 else 0 end) over (partition by custid order by timestamp
) as seqnum
from (select sd.*,
lag(eventid) over (partition by custid order by timestamp) as prev_eventid
from somedata sd
) sd;
EDIT:
The last time I used Amazon Redshift it didn't have row_number(). You can do:
select sd.*, dense_rank() over (partition by custid order by mints) as seqnum
from (select sd.*,
min(timestamp) over (partition by custid, eventid, grp) as mints
from (select sd.*,
(row_number() over (partition by custid order by timestamp rows between unbounded preceding and current row) -
row_number() over (partition by custid, eventid order by timestamp rows between unbounded preceding and current row)
) as grp
from somedata sd
) sd
) sd;
Try this code block:
WITH by_day
AS (SELECT
*,
ts::date AS login_day
FROM table_name)
SELECT
*,
login_day,
FIRST_VALUE(login_day) OVER (PARTITION BY userid ORDER BY login_day , userid rows unbounded preceding) AS first_day
FROM by_day
Related
I have the following data set
Customer_ID Category FROM_DATE TO_DATE
1 5 1/1/2000 12/31/2001
1 6 1/1/2002 12/31/2003
1 5 1/1/2004 12/31/2005
2 7 1/1/2010 12/31/2011
2 7 1/1/2012 12/31/2013
2 5 1/1/2014 12/31/2015
3 7 1/1/2010 12/31/2011
3 7 1/5/2012 12/31/2013
3 5 1/1/2014 12/31/2015
The result I want to achieve is to find continuous local min/max date for Customers with the same category and identify any gap in dates:
Customer_ID FROM_Date TO_Date Category
1 1/1/2000 12/31/2001 5
1 1/1/2002 12/31/2003 6
1 1/1/2004 12/31/2005 5
2 1/1/2010 12/31/2013 7
2 1/1/2014 12/31/2015 5
3 1/1/2010 12/31/2011 7
3 1/5/2012 12/31/2013 7
3 1/1/2014 12/31/2015 5
My code works fine for customer 1 (return all 3 rows) and customer 2(return 2 rows with min and max date for each category) but for customer 3, it cannot identify the gap between 12/31/2011 and 1/5/2012 for category 7.
Customer_ID FROM_Date TO_Date Category
3 1/1/2010 12/31/2013 7
3 1/1/2014 12/31/2015 5
Here is my code:
SELECT Customer_ID, Category, min(From_Date), max(To_Date) FROM
(
SELECT Customer_ID, Category, From_Date,To_Date
,row_number() over (order by member_id, To_Date) - row_number() over (partition by Customer_ID order by Category) as p
FROM FFS_SAMP
) X
group by Customer_ID,Category,p
order by Customer_ID,min(From_Date),Max(To_Date)
This is a type of gaps and islands problem. Probably the safest method is to use a cumulative max() to look for overlaps with previous records. Where there is no overlap, then an "island" of records starts. So:
select customer_id, min(from_date), max(to_date), category
from (select t.*,
sum(case when prev_to_date >= from_date then 0 else 1 end) over
(partition by customer_id, category
order by from_date
) as grp
from (select t.*,
max(to_date) over (partition by customer_id, category
order by from_date
rows between unbounded preceding and 1 preceding
) as prev_to_date
from t
) t
) t
group by customer_id, category, grp;
Your attempt is quite close. You just need to fix the over() clause of the window functions:
select customer_id, category, min(from_date), max(to_date)
from (
select
fs.*,
row_number() over (partition by customer_id order from_date)
- row_number() over (partition by customer_id, category order by from_date) as grp
from ffs_samp fs
) x
group by customer_id, category, grp
order by customer_id, min(from_date)
Note that this method assumes no gaps or overlalp in the periods of a given customer, as show in your sample data.
I need to count consecutive days in order to define my cohorts. I have a table that looks like:
pat_id admin_date
----------------------------
1 3/10/2019
1 3/11/2019
1 3/23/2019
1 3/24/2019
1 3/25/2019
2 12/26/2017
2 2/27/2019
2 3/16/2019
2 3/17/2019
I want such as output:
pat_id admin_date consecutive
--------------------------------------------
1 3/10/2019 1
1 3/11/2019 2
1 3/23/2019 1
1 3/24/2019 2
1 3/25/2019 3
2 12/26/2017 1
2 2/27/2019 1
2 3/16/2019 1
2 3/17/2019 2
so that I can use these consecutive days value (per pat_id) to filter for my cohort. I've seen few posts that suggested using DateDiff/DateAdd with row_number, such as:
datediff(day, -row_number() over (partition by mrn order by admin_date), admin_date)
but datediff/dateadd functions wouldn't work on Netezza...
The closest I've got so far was:
select row_number() over (partition by mrn order by administration_date) as consecutive
which doesn't recognize gap between dates and return such an output:
pat_id admin_date consecutive
--------------------------------------------
1 3/10/2019 1
1 3/11/2019 2
1 3/23/2019 3
1 3/24/2019 4
1 3/25/2019 5
2 12/26/2017 1
2 2/27/2019 2
2 3/16/2019 3
2 3/17/2019 4
Does anyone know how to tackle this?
Use lag() to see where the groups start and a cumulative sum to define the group. The rest is just row_number():
select t.*,
row_number() over (partition by pat_id, grp order by admin_date) as consecutive
from (select t.*,
sum( case when prev_ad = admin_date - interval '1 day' then 0 else 1 end) over
(partition by pat_id order by admin_date) as grp
from (select t.*,
lag(admin_date) over (partition by pat_id order by admin_date) as prev_ad
from t
) t
)t ;
Trying to get userid recent aggregate value for session_id.
(session_id 3 has two records, recent agg value is 80.00
session_id 4 has four records, recent agg value is 95.00
session_id 6 has three records, recent agg value is 72.00
Table:session_agg
id session_id userid agg date
-- ---------- ------ ----- -------
1 3 11 60.00 1573561586
4 3 11 80.00 1573561586
6 4 11 35.00 1573561749
7 4 11 50.00 1573561751
8 4 11 70.00 1573561912
10 4 11 95.00 1573561921
11 6 14 40.00 1573561945
12 6 14 67.00 1573561967
13 6 14 72.00 1573561978
select id, session_id, userid, agg, date from session_agg
WHERE date IN (select MAX(date) from session_agg GROUP BY session_id) AND
userid = 11
If you want to stick with your current approach, then you need to correlate the session_id in the subquery which checks for the max date for each session:
SELECT id, session_id, userid, add, date
FROM session_agg sa1
WHERE
date = (SELECT MAX(date) FROM session_agg sa2 WHERE sa2.session_id = sa1.session_id) AND
userid = 11;
But, if your version of SQL supports analytic functions, ROW_NUMBER is an easier way to do this:
WITH cte AS (
SELECT *, ROW_NUMBER() OVER (PARTITION BY session_id ORDER BY date DESC) rn
FROM session_agg
)
SELECT id, session_id, userid, add, date
FROM cte
WHERE rn = 1;
Assume this is my table:
ID DATE
--------------
1 2018-11-12
2 2018-11-13
3 2018-11-14
4 2018-11-15
5 2018-11-16
6 2019-03-05
7 2019-05-07
8 2019-05-08
9 2019-05-08
I need to have partitions be determined by the first date in the partition. Where, any date that is within 2 days of the first date, belongs in the same partition.
The table would end up looking like this if each partition was ranked
PARTITION ID DATE
------------------------
1 1 2018-11-12
1 2 2018-11-13
1 3 2018-11-14
2 4 2018-11-15
2 5 2018-11-16
3 6 2019-03-05
4 7 2019-05-07
4 8 2019-05-08
4 9 2019-05-08
I've tried using datediff with lag to compare to the previous date but that would allow a partition to be inappropriately sized based on spacing, for example all of these dates would be included in the same partition:
ID DATE
--------------
1 2018-11-12
2 2018-11-14
3 2018-11-16
4 2018-11-18
3 2018-11-20
4 2018-11-22
Previous flawed attempt:
Mark when a date is more than 2 days past the previous date:
(case when datediff(day, lag(event_time, 1) over (partition by user_id, stage order by event_time), event_time) > 2 then 1 else 0 end)
You need to use a recursive CTE for this, so the operation is expensive.
with t as (
-- add an incrementing column with no gaps
select t.*, row_number() over (order by date) as seqnum
from t
),
cte as (
select id, date, date as mindate, seqnum
from t
where seqnum = 1
union all
select t.id, t.date,
(case when t.date <= dateadd(day, 2, cte.mindate)
then cte.mindate else t.date
end) as mindate,
t.seqnum
from cte join
t
on t.seqnum = cte.seqnum + 1
)
select cte.*, dense_rank() over (partition by mindate) as partition_num
from cte;
BatteryId TimeStamp Temprature
1 2017-02-13 12:16:14.000 23
1 2016-02-13 12:13:14.000 21
1 2015-01-13 12:16:14.000 19
2 2017-02-11 12:16:14.000 22
2 2016-02-13 12:16:14.000 16
3 2017-02-13 11:16:14.000 12
3 2016-02-13 12:15:14.000 25
I have table with multiple records for each battery as above
following sql query is returning latest record for each battery
SELECT * FROM (SELECT BatteryId, Timestamp, Temperature
ROW_NUMBER() OVER(PARTITION BY BatteryId ORDER BY timestamp DESC)
AS N FROM tblBattery) AS TT WHERE N = 1
as
BatteryId TimeStamp Temprature
1 2017-02-13 12:16:14.000 23
2 2017-02-11 12:16:14.000 22
3 2017-02-13 11:16:14.000 12
How I can add Count for each BatteryId, Here is what I need
BatteryId TimeStamp Temprature Count
1 2017-02-13 12:16:14.000 23 3
2 2017-02-11 12:16:14.000 22 2
3 2017-02-13 11:16:14.000 12 2
Use the count window function.
SELECT * FROM
(SELECT BatteryId, Timestamp, Temperature,
ROW_NUMBER() OVER(PARTITION BY BatteryId ORDER BY timestamp DESC) AS N,
COUNT(*) OVER(PARTITION BY BatteryId) as Cnt
FROM tblBattery) TT
WHERE N = 1
Hoping, i understood your problem correctly.
Please check if below query can help you.
SELECT *
FROM
(SELECT BatteryId,
TIMESTAMP,
Temperature , ROW_NUMBER() OVER(PARTITION BY BatteryId ORDER BY TIMESTAMP DESC) AS N ,
COUNT(0) OVER(PARTITION BY BatteryId ) CNT
FROM tblBattery
) AS TT
WHERE N = 1;
Add a sub query before you perform the PARTITION BY
SELECT *
FROM (SELECT
BatteryId
,Timestamp
,Temperature
,Count
,ROW_NUMBER() OVER(PARTITION BY BatteryId ORDER BY timestamp DESC) AS N
FROM (SELECT *, COUNT(BatteryId) As Count FROM tblBattery GROUP BY BatteryId)) AS TT WHERE N = 1
This should solve your issue.