I have a dataset in bigquery which contains order_date: DATE and customer_id.
order_date | CustomerID
2019-01-01 | 111
2019-02-01 | 112
2020-01-01 | 111
2020-02-01 | 113
2021-01-01 | 115
2021-02-01 | 119
I try to count distinct customer_id between the months of the previous year and the same months of the current year. For example, from 2019-01-01 to 2020-01-01, then from 2019-02-01 to 2020-02-01, and then who not bought in the same period of next year 2020-01-01 to 2021-01-01, then 2020-02-01 to 2021-02-01.
The output I am expect
order_date| count distinct CustomerID|who not buy in the next period
2020-01-01| 5191 |250
2020-02-01| 4859 |500
2020-03-01| 3567 |349
..........| .... |......
and the next periods shouldn't include the previous.
I tried the code below but it works in another way
with customers as (
select distinct date_trunc(date(order_date),month) as dates,
CUSTOMER_WID
from t
where date(order_date) between '2018-01-01' and current_date()-1
)
select
dates,
customers_previous,
customers_next_period
from
(
select dates,
count(CUSTOMER_WID) as customers_previous,
count(case when customer_wid_next is null then 1 end) as customers_next_period,
from (
select prev.dates,
prev.CUSTOMER_WID,
next.dates as next_dates,
next.CUSTOMER_WID as customer_wid_next
from customers as prev
left join customers
as next on next.dates=date_add(prev.dates,interval 1 year)
and prev.CUSTOMER_WID=next.CUSTOMER_WID
) as t2
group by dates
)
order by 1,2
Thanks in advance.
If I understand correctly, you are trying to count values on a window of time, and for that I recommend using window functions - docs here and here a great article explaining how it works.
That said, my recommendation would be:
SELECT DISTINCT
periods,
COUNT(DISTINCT CustomerID) OVER 12mos AS count_customers_last_12_mos
FROM (
SELECT
order_date,
FORMAT_DATE('%Y%m', order_date) AS periods,
customer_id
FROM dataset
)
WINDOW 12mos AS ( # window of last 12 months without current month
PARTITION BY periods ORDER BY periods DESC
ROWS BETWEEN 12 PRECEEDING AND 1 PRECEEDING
)
I believe from this you can build some customizations to improve the aggregations you want.
You can generate the periods using unnest(generate_date_array()). Then use joins to bring in the customers from the previous 12 months and the next 12 months. Finally, aggregate and count the customers:
select period,
count(distinct c_prev.customer_wid),
count(distinct c_next.customer_wid)
from unnest(generate_date_array(date '2020-01-01', date '2021-01-01', interval '1 month')) period join
customers c_prev
on c_prev.order_date <= period and
c_prev.order_date > date_add(period, interval -12 month) left join
customers c_next
on c_next.customer_wid = c_prev.customer_wid and
c_next.order_date > period and
c_next.order_date <= date_add(period, interval 12 month)
group by period;
I have a table called table1:
id created_date
1001 2020-06-01
1001 2020-01-01
1001 2020-07-01
1002 2020-02-01
1002 2020-04-01
1003 2020-09-01
I'm trying to write a query that provides me a list of distinct IDs with the earliest created_date they have, along with the count of rows each id has:
id created_date count
1001 2020-01-01 3
1002 2020-02-01 2
1003 2020-09-01 1
I managed to write a window function to grab the earliest date, but I'm having trouble figuring out where to fit the count statement in one:
SELECT
id,
created_date
FROM ( SELECT
id,
created_date,
row_number() OVER(PARTITION BY id ORDER BY created_date) as row_num
FROM table1)
) AS a
WHERE row_num = 1
You would use aggregation:
select id, min(create_date), count(*)
from table1
group by id;
I find it amusing that you want to use window functions -- which are considered more advanced -- when lowly aggregation suffices.
Hi I am working in an Azure Databricks and I am looking for a SQL query solution.
Assuming that my db has five columns:
ID
EVENT_DATE
JOB_TITLE
PAY
12345
2021-01-01
VP1
100,000
12345
2020-01-10
VP1
90,000
12345
2019-01-20
Analyst1
80,000
12346
2021-02-01
VP2
200,000
12346
2020-02-10
Analyst2
150,000
12346
2020-01-20
Analyst2
110,000
Basically I want the EVENT_DATE when JOB_TITLE changed the last time. This is my desired output:
ID
JOB_TITLE
PAY
LAST_JOB_CHANGE_DATE
12345
VP1
90,000
2021-01-10
12346
VP2
200,000
2021-02-01
For the last column LAST_JOB_CHANGE_DATE, we are pulling from the 2nd and 4th row of the table because that's the date when they changed job the last time.
Thank you!
You can just use INNER JOIN to accomplish that, ie
%sql
SELECT a.*
FROM yourTable a
INNER JOIN
(
SELECT id, MAX(event_date) event_date
FROM yourTable b
GROUP BY id
) b ON a.id = b.id
AND a.event_date = b.event_date
The ROW_NUMBER approach would also work well:
%sql
WITH cte AS
(
SELECT
ROW_NUMBER() OVER( PARTITION BY id ORDER BY event_date DESC ) AS rn,
*
FROM yourTable a
)
SELECT *
FROM cte
WHERE rn = 1
My results:
There's probably a simpler solution for this but the following should work.
I'm assuming you wanted the MOST resent job change for each employee. To illustrate this, I added an extra row for an Engineer1. The ROW_NUMBER() window function helps us with this.
ID
EVENT_DATE
JOB_TITLE
PAY
12345
2021-01-01
VP1
100,000
12345
2020-01-10
VP1
90,000
12345
2019-01-20
Analyst1
80,000
12345
2018-01-04
Engineer1
75,000
12346
2021-02-01
VP2
200,000
12346
2020-02-10
Analyst2
150,000
12346
2020-01-20
Analyst2
110,000
Here is the query:
SELECT <---- (4)
c.ID,
c.JOB_TITLE,
c.PAY,
c.last_job_change_date
FROM
(
SELECT <---- (3)
b.ID,
ROW_NUMBER() OVER (PARTITION BY b.ID ORDER BY b.last_job_change_date DESC) AS row_id,
b.JOB_TITLE,
b.PAY,
b.last_job_change_date
FROM
(
SELECT <---- (2)
a.ID,
a.JOB_TITLE,
a.PAY,
a.EVENT_DATE as last_job_change_date
FROM
(
SELECT <---- (1)
ID,
EVENT_DATE,
PAY,
JOB_TITLE,
LEAD(JOB_TITLE, 1) OVER (
PARTITION BY ID ORDER BY EVENT_DATE DESC) job_change
FROM yourtable
) a
WHERE JOB_TITLE <> job_change
) b
) c
WHERE row_id = 1
I used a 4 step process and annotated the query with each step:
Returns a table with a column for the subsequent job title (ordered by most recent title) of each employee.
Returns the table from (1) but removes rows where the employee did not change their job
Add row numbers so we can get the most recent job change of each employee
Return most recent job changes for each employee
I want to sum up the stake from tickets table, grouping it by customer_id and date_trunc('day') from bonus table.
The problem is that rows are being multiplied and I don't know how to solve it.
https://www.db-fiddle.com/f/yWCvFamMAY9uGtoZupiAQ/4
CREATE TABLE tickets (
ticket_id integer,
customer_id integer,
stake integer,
reg_date date
);
CREATE TABLE bonus (
bonus_id integer,
customer_id integer,
reg_date date
);
insert into tickets
values
(1,100, 12,'2019-01-10 11:00'),
(2,100, 10,'2019-01-10 12:00'),
(3,100, 30,'2019-01-10 13:00'),
(4,100, 10,'2019-01-11 14:00'),
(5,100, 15,'2019-01-11 15:00'),
(6,102, 25,'2019-01-10 10:00'),
(7,102, 25,'2019-01-10 11:10'),
(8,102, 13,'2019-01-11 12:40'),
(9,102, 9,'2019-01-12 15:00'),
(10,102, 7,'2019-01-13 18:00'),
(13,103, 15,'2019-01-12 19:00'),
(14,103, 11,'2019-01-12 22:00'),
(15,103, 11,'2019-01-14 02:00'),
(16,103, 11,'2019-01-14 10:00')
;
insert into bonus
values
(200,100,'2019-01-10 05:00'),
(201,100,'2019-01-10 06:00'),
(202,100,'2019-01-10 15:00'),
(203,100,'2019-01-10 15:50'),
(204,100,'2019-01-10 16:10'),
(205,100,'2019-01-10 16:15'),
(206,100,'2019-01-10 16:22'),
(207,100,'2019-01-11 10:10'),
(208,100,'2019-01-11 16:10'),
(209,102,'2019-01-10 10:00'),
(210,102,'2019-01-10 11:00'),
(211,102,'2019-01-10 12:00'),
(212,102,'2019-01-10 13:00'),
(213,103,'2019-01-11 11:00'),
(214,103,'2019-01-11 18:00'),
(215,103,'2019-01-12 15:00'),
(216,103,'2019-01-12 16:00'),
(217,103,'2019-01-14 02:00')
select
customer_id,
date_trunc('day', b.reg_date),
sum(t.stake)
from tickets t
join bonus b using (customer_id)
where date_trunc('day', b.reg_date) = date_trunc('day', t.reg_date)
group by 1,2
order by 1
Output for customer 102 should be:
102,2019-01-10, 50
OK, I think you want to get the summary data of column stake in tickets table and the records's customer_id, reg_date pairs have appeared in the second table bonus, and all business has nothing to do with the bonus_id, am I right? The customer_id, reg_date pairs in bonus is duplicated, so you need a distinct on it, and then join the sum data from tickets.The complete SQL and result as below:
with stake_sum as (
select
customer_id,
reg_date,
sum(stake)
from
tickets
group by
customer_id,
reg_date
)
,bonus_date_distinct as (
select
distinct customer_id,
reg_date
from
bonus
)
select
a.*
from
stake_sum a
join
bonus_date_distinct b on a.customer_id = b.customer_id and a.reg_date = b.reg_date order by customer_id, reg_date;
customer_id | reg_date | sum
-------------+------------+-----
100 | 2019-01-10 | 52
100 | 2019-01-11 | 25
102 | 2019-01-10 | 50
103 | 2019-01-12 | 26
103 | 2019-01-14 | 22
(5 rows)
I have the following table, I am using SQL Server 2008
BayNo FixDateTime FixType
1 04/05/2015 16:15:00 tyre change
1 12/05/2015 00:15:00 oil change
1 12/05/2015 08:15:00 engine tuning
1 04/05/2016 08:11:00 car tuning
2 13/05/2015 19:30:00 puncture
2 14/05/2015 08:00:00 light repair
2 15/05/2015 10:30:00 super op
2 20/05/2015 12:30:00 wiper change
2 12/05/2016 09:30:00 denting
2 12/05/2016 10:30:00 wiper repair
2 12/06/2016 10:30:00 exhaust repair
4 12/05/2016 05:30:00 stereo unlock
4 17/05/2016 15:05:00 door handle repair
on any given day need do find the highest number of fixes made on a given bay number, and if that calculated number is repeated then it should also appear in the resultset
so would like to see the result set as follows
BayNo FixDateTime noOfFixes
1 12/05/2015 00:15:00 2
2 12/05/2016 09:30:00 2
4 12/05/2016 05:30:00 1
4 17/05/2016 15:05:00 1
I manage to get the counts of each but struggling to get the max and keep the highest calculated repeated value. can someone help please
Use window functions.
Get the count for each day by bayno and also find the min fixdatetime for each day per bayno.
Then use dense_rank to compute the highest ranked row for each bayno based on the number of fixes.
Finally get the highest ranked rows.
select distinct bayno,minfixdatetime,no_of_fixes
from (
select bayno,minfixdatetime,no_of_fixes
,dense_rank() over(partition by bayno order by no_of_fixes desc) rnk
from (
select t.*,
count(*) over(partition by bayno,cast(fixdatetime as date)) no_of_fixes,
min(fixdatetime) over(partition by bayno,cast(fixdatetime as date)) minfixdatetime
from tablename t
) x
) y
where rnk = 1
Sample Demo
You are looking for rank() or dense_rank(). I would right the query like this:
select bayno, thedate, numFixes
from (select bayno, cast(fixdatetime) as date) as thedate,
count(*) as numFixes,
rank() over (partition by cast(fixdatetime as date) order by count(*) desc) as seqnum
from t
group by bayno, cast(fixdatetime as date)
) b
where seqnum = 1;
Note that this returns the date in question. The date does not have a time component.