I have a table that contains order/shipment history. A basic dummy version is:
ORDERS
order_no | order_stat | stat_date
2 | Planned | 01-Jan-2000
2 | Picked | 15-Jan-2000
2 | Planned | 17-Jan-2000
2 | Planned | 05-Feb-2000
2 | Planned | 31-Mar-2000
2 | Picked | 05-Apr-2000
2 | Shipped | 10-Apr-2000
I need to figure out how long each order has been in each order status/phase. The only problem is when I create a partition on the order_no and order_stat, I get results that make sense but are not what I am looking for.
My sql:
select
order_no
,order_stat
,stat_date
,lag(stat_date, 1) over (partition by order_no order by stat_date) prev_stat_date
,stat_date - lag(stat_date, 1) over (partition by order_no order by stat_date) date_diff
,row_number() over(partition by order_no, order_stat order by stat_date) rnk
from
orders
Will give me the following results:
order_no | order_stat | stat_date | prev_stat_date | rnk
2 | Planned | 01-Jan-2000 | | 1
2 | Picked | 15-Jan-2000 | 01-Jan-2000 | 1
2 | Planned | 17-Jan-2000 | 15-Jan-2000 | 2
2 | Planned | 05-Feb-2000 | 17-Jan-2000 | 3
2 | Planned | 31-Mar-2000 | 05-Feb-2000 | 4
2 | Picked | 05-Apr-2000 | 31-Mar-2000 | 2
2 | Shipped | 10-Apr-2000 | 05-Apr-2000 | 1
I would like to have results that look like this (the rnk starts over when it reverts back to a previous order stat):
order_no | order_stat | stat_date | prev_stat_date | rnk
2 | Planned | 01-Jan-2000 | | 1
2 | Picked | 15-Jan-2000 | 01-Jan-2000 | 1
2 | Planned | 17-Jan-2000 | 15-Jan-2000 | 1
2 | Planned | 05-Feb-2000 | 17-Jan-2000 | 2
2 | Planned | 31-Mar-2000 | 05-Feb-2000 | 3
2 | Picked | 05-Apr-2000 | 31-Mar-2000 | 1
2 | Shipped | 10-Apr-2000 | 05-Apr-2000 | 1
I'm trying to get a running total count of how long it has been in the status (that starts over even if the status it changes to has existed previously instead of being included in the previous partition) but I have no idea how to approach this. Any and all insight would be greatly appreciated.
If I understand correctly, this is a gaps-and-islands problem.
The difference of row numbers can be used to identify the "island"s and then to enumerate the values:
select t.*,
row_number() over (partition by order_no, order_stat, seqnum - seqnum_2 order by stat_date) as your_rank
from (select o.*,
row_number() over (partition by order_no order by stat_date) as seqnum,
row_number() over (partition by order_no, order_stat order by stat_date) as seqnum_2
from orders o
) t;
I've left out the other columns (like the lag()) so you can see the logic. It can be a bit hard to follow why this works. If you stare at some rows from the subquery, you will probably see how the difference of the row numbers defines the groups you want.
Continuing #Gordon's Tabibitosan approach, once you have the groupings you can get both the order within each group and the elapsed number of days for each member of the group:
-- CTE for sample data
with orders (order_no, order_stat, stat_date) as (
select 2, 'Planned', date '2000-01-01' from dual
union all select 2, 'Picked', date '2000-01-15' from dual
union all select 2, 'Planned', date '2000-01-17' from dual
union all select 2, 'Planned', date '2000-02-05' from dual
union all select 2, 'Planned', date '2000-03-31' from dual
union all select 2, 'Picked ', date '2000-04-05' from dual
union all select 2, 'Shipped', date '2000-04-10' from dual
)
-- actual query
select order_no, order_stat, stat_date, grp,
dense_rank() over (partition by order_no, order_stat, grp order by stat_date) as rnk,
stat_date - min(stat_date) keep (dense_rank first order by stat_date)
over (partition by order_no, order_stat, grp) as stat_days
from (
select order_no, order_stat, stat_date,
row_number() over (partition by order_no order by stat_date)
- row_number() over (partition by order_no, order_stat order by stat_date) as grp
from orders
)
order by order_no, stat_date;
ORDER_NO ORDER_S STAT_DATE GRP RNK STAT_DAYS
---------- ------- ---------- ---------- ---------- ----------
2 Planned 2000-01-01 0 1 0
2 Picked 2000-01-15 1 1 0
2 Planned 2000-01-17 1 1 0
2 Planned 2000-02-05 1 2 19
2 Planned 2000-03-31 1 3 74
2 Picked 2000-04-05 5 1 0
2 Shipped 2000-04-10 6 1 0
The inline view is essentially what Gordon did, except it trivially does the subtraction at that level. The outer query then gets the rank the same way, but also uses an analytic function to get the earliest date for that group, and subtracts it from the current row's date. You don't have to include grp or rnk in your final result of course, they're shown to give more insight into what's happening.
It isn't clear exactly what you want, but you can expand even further to, for instance:
with cte1 (order_no, order_stat, stat_date, grp) as (
select order_no, order_stat, stat_date,
row_number() over (partition by order_no order by stat_date)
- row_number() over (partition by order_no, order_stat order by stat_date)
from orders
),
cte2 (order_no, order_stat, stat_date, grp, grp_date, rnk) as (
select order_no, order_stat, stat_date, grp,
min(stat_date) keep (dense_rank first order by stat_date)
over (partition by order_no, order_stat, grp),
dense_rank() over (partition by order_no, order_stat, grp order by stat_date)
from cte1
)
select order_no, order_stat, stat_date, grp, grp_date, rnk,
stat_date - grp_date as stat_days_so_far,
case
when order_stat != 'Shipped' then
coalesce(first_value(stat_date)
over (partition by order_no order by grp_date
range between 1 following and unbounded following), trunc(sysdate))
- min(stat_date) keep (dense_rank first order by stat_date)
over (partition by order_no, order_stat, grp)
end as stat_days_total,
stat_date - min(stat_date) over (partition by order_no) as order_days_so_far,
case
when max(order_stat) keep (dense_rank last order by stat_date)
over (partition by order_no) = 'Shipped' then
max(stat_date) over (partition by order_no)
else
trunc(sysdate)
end
- min(stat_date) over (partition by order_no) as order_days_total
from cte2
order by order_no, stat_date;
which for your sample data gives:
ORDER_NO ORDER_S STAT_DATE GRP GRP_DATE RNK STAT_DAYS_SO_FAR STAT_DAYS_TOTAL ORDER_DAYS_SO_FAR ORDER_DAYS_TOTAL
---------- ------- ---------- ---------- ---------- ---------- ---------------- --------------- ----------------- ----------------
2 Planned 2000-01-01 0 2000-01-01 1 0 14 0 100
2 Picked 2000-01-15 1 2000-01-15 1 0 2 14 100
2 Planned 2000-01-17 1 2000-01-17 1 0 79 16 100
2 Planned 2000-02-05 1 2000-01-17 2 19 79 35 100
2 Planned 2000-03-31 1 2000-01-17 3 74 79 90 100
2 Picked 2000-04-05 5 2000-04-05 1 0 5 95 100
2 Shipped 2000-04-10 6 2000-04-10 1 0 100 100
I've included some logic to assume that 'Shipped' is the final status, and if that hasn't been reached then the last status is still running - so counting up to today. That might be wrong, and you might have other end-status values (e.g. cancelled). Anyway, a few things for you to explore and play with...
You might be able to do something similar with match_recognize, but I'll leave that to someone else.
Related
I am trying to find the customer count and sales by the type of customer (New and Returning) and the number of times they have purchased.
txn_date Customer_ID Transaction_Number Sales Reference(not in the SQL table) customer type (not in the sql table)
1/2/2019 1 12345 $10 Second Purchase SLS Repeat
4/3/2018 1 65890 $20 First Purchase SLS Repeat
3/22/2019 3 64453 $30 First Purchase SLS new
4/3/2019 4 88567 $20 First Purchase SLS new
5/21/2019 4 85446 $15 Second Purchase SLS new
1/23/2018 5 89464 $40 First Purchase SLS Repeat
4/3/2019 5 99674 $30 Second Purchase SLS Repeat
4/3/2019 6 32224 $20 Second Purchase SLS Repeat
1/23/2018 6 46466 $30 First Purchase SLS Repeat
1/20/2018 7 56558 $30 First Purchase SLS new
I am using the below code to get the aggregate sales and customer count for the total customers:
select seqnum, count(distinct customer_id), sum(sales) from (
select co.*,
row_number() over (partition by customer_id order by txn_date) as seqnum
from somya co)
group by seqnum
order by seqnum;
I want to get the same data by the customer type:
for example for the new customers my result should show:
New Customers Customer_Count Sum(Sales)
1st Purchase 3 $80
2nd Purchase 1 $15
Returning Customers Customer_Count Sum(Sales)
1st Purchase 3 $90
2nd Purchase 3 $60
I am trying the below query to get the data for new and repeat customers:
New Customers:
select seqnum, count(distinct customer_id), sum(sales)
from (
select co.*,
row_number() over (partition by customer_id order by trunc(txn_date)) as seqnum,
MIN (TRUNC (TXN_DATE)) OVER (PARTITION BY customer_id) as MIN_TXN_DATE
from somya co
)
where MIN_TXN_DATE between '01-JAN-19' and '31-DEC-19'
group by seqnum
order by seqnum asc;
Returning Customers:
select seqnum, count(distinct customer_id), sum(sales)
from (
select co.*,
row_number() over (partition by customer_id order by trunc(txn_date)) as seqnum,
MIN (TRUNC (TXN_DATE)) OVER (PARTITION BY customer_id) as MIN_TXN_DATE
from somya co
)
where MIN_TXN_DATE <'01-JAN-19'
group by seqnum
order by seqnum asc;
I am not able to figure out what is wrong with my query or if there is a problem with my logic.
This is just a sample data, I have transactions from all the years in my data base so I need to narrow the transaction date in the query but as soon as I narrowing down the data using the transaction date the repeat customer query doesnt give me anything and the new customer query gives me the total customer for that period.
If I understand correctly, you need to know the first time someone becomes a customer. And then use this:
select (case when first_year < 2019 then 'returning' else 'new' end) as custtype,
seqnum, count(*), sum(sales)
from (select co.*,
row_number() over (partition by customer_id, extract(year from txn_date) order by txn_date) as seqnum,
min(extract(year from txn_date)) over (partition by customer_id) as first_year
from somya co
) s
where txn_date >= date '2019-01-01' and
txn_date < date '2020-01-01'
group by (case when first_year < 2019 then 'returning' else 'new' end),
seqnum
order by custtype, seqnum;
You can categorize your sales data to assign a customer type and a purchase sequence using windowing functions, like this:
SELECT sd.txn_date,
sd.customer_id,
sd.transaction_number,
sd.sales,
case when min(txn_date) over ( partition by customer_id ) < DATE '2019-01-01'
AND max(txn_date) OVER ( partition by customer_id ) >= DATE '2019-01-01'
THEN 'Repeat'
ELSE 'New' END customer_type,
row_number() over ( partition by customer_id order by txn_date) purchase_sequence
FROM sales_data sd
+-----------+-------------+--------------------+-------+---------------+-------------------+
| TXN_DATE | CUSTOMER_ID | TRANSACTION_NUMBER | SALES | CUSTOMER_TYPE | PURCHASE_SEQUENCE |
+-----------+-------------+--------------------+-------+---------------+-------------------+
| 03-APR-18 | 1 | 65890 | 20 | Repeat | 1 |
| 02-JAN-19 | 1 | 12345 | 10 | Repeat | 2 |
| 22-MAR-19 | 3 | 64453 | 30 | New | 1 |
| 03-APR-19 | 4 | 88567 | 20 | New | 1 |
| 21-MAY-19 | 4 | 85446 | 15 | New | 2 |
| 23-JAN-18 | 5 | 89464 | 40 | Repeat | 1 |
| 03-APR-19 | 5 | 99674 | 30 | Repeat | 2 |
| 23-JAN-18 | 6 | 46466 | 30 | Repeat | 1 |
| 03-APR-19 | 6 | 32224 | 20 | Repeat | 2 |
| 20-JAN-18 | 7 | 56558 | 30 | New | 1 |
+-----------+-------------+--------------------+-------+---------------+-------------------+
Then, you can wrap that in a common table expression (aka "WITH" clause) and summarize by the customer type and purchase sequence:
WITH categorized_sales_data AS (
SELECT sd.txn_date,
sd.customer_id,
sd.transaction_number,
sd.sales,
case when min(txn_date) over ( partition by customer_id ) < DATE '2019-01-01' AND max(txn_date) OVER ( partition by customer_id ) >= DATE '2019-01-01' THEN 'Repeat' ELSE 'New' END customer_type,
row_number() over ( partition by customer_id order by txn_date) purchase_sequence
FROM sales_data sd)
SELECT customer_type, purchase_sequence, count(*), sum(sales)
FROM categorized_sales_data
group by customer_type, purchase_sequence
order by customer_type, purchase_sequence
+---------------+-------------------+----------+------------+
| CUSTOMER_TYPE | PURCHASE_SEQUENCE | COUNT(*) | SUM(SALES) |
+---------------+-------------------+----------+------------+
| New | 1 | 3 | 80 |
| New | 2 | 1 | 15 |
| Repeat | 1 | 3 | 90 |
| Repeat | 2 | 3 | 60 |
+---------------+-------------------+----------+------------+
Here's a full SQL with test data:
with sales_data (txn_date, Customer_ID, Transaction_Number, Sales ) as (
SELECT TO_DATE('1/2/2019','MM/DD/YYYY'), 1, 12345, 10 FROM DUAL UNION ALL
SELECT TO_DATE('4/3/2018','MM/DD/YYYY'), 1, 65890, 20 FROM DUAL UNION ALL
SELECT TO_DATE('3/22/2019','MM/DD/YYYY'), 3, 64453, 30 FROM DUAL UNION ALL
SELECT TO_DATE('4/3/2019','MM/DD/YYYY'), 4, 88567, 20 FROM DUAL UNION ALL
SELECT TO_DATE('5/21/2019','MM/DD/YYYY'), 4, 85446, 15 FROM DUAL UNION ALL
SELECT TO_DATE('1/23/2018','MM/DD/YYYY'), 5, 89464, 40 FROM DUAL UNION ALL
SELECT TO_DATE('4/3/2019','MM/DD/YYYY'), 5, 99674, 30 FROM DUAL UNION ALL
SELECT TO_DATE('4/3/2019','MM/DD/YYYY'), 6, 32224, 20 FROM DUAL UNION ALL
SELECT TO_DATE('1/23/2018','MM/DD/YYYY'), 6, 46466, 30 FROM DUAL UNION ALL
SELECT TO_DATE('1/20/2018','MM/DD/YYYY'), 7, 56558, 30 FROM DUAL ),
-- Query starts here
/* WITH */ categorized_sales_data AS (
SELECT sd.txn_date,
sd.customer_id,
sd.transaction_number,
sd.sales,
case when min(txn_date) over ( partition by customer_id ) < DATE '2019-01-01' AND max(txn_date) OVER ( partition by customer_id ) >= DATE '2019-01-01' THEN 'Repeat' ELSE 'New' END customer_type,
row_number() over ( partition by customer_id order by txn_date) purchase_sequence
FROM sales_data sd)
SELECT customer_type, purchase_sequence, count(*), sum(sales)
FROM categorized_sales_data
group by customer_type, purchase_sequence
order by customer_type, purchase_sequence
Response to comment from OP
all the customers whose first purchase date is in 2019 would be a new customer. Any customer who has transacted in 2019 but their first purchase date is before 2019 would be a repeat customer
So, change
case when min(txn_date) over ( partition by customer_id ) < DATE '2019-01-01'
AND max(txn_date) OVER ( partition by customer_id ) >= DATE '2019-01-01'
THEN 'Repeat' ELSE 'New' END customer_type
to
case when min(txn_date) over ( partition by customer_id )
BETWEEN DATE '2019-01-01' AND DATE '2020-01-01' - INTERVAL '1' SECOND
THEN 'New' ELSE 'Repeat' END customer_type
i.e., if and only if a customer's first purchase was in 2019 then they are "new".
This question already has an answer here:
SQL or LINQ: how do I select records where only one paramater changes?
(1 answer)
Closed 3 years ago.
I am trying to create a report and this is my input data.
Stage Name Date
1 x 12/05/2019 10:00:03
1 x 12/05/2019 10:05:01
1 y 12/06/2019 12:00:07
2 x 12/06/2019 13:12:03
2 x 12/06/2019 13:23:00
1 y 12/08/2019 16:00:07
2 x 12/09/2019 09:17:59
This is my desired output.
Stage Name DateFrom DateTo DateDiff
1 x 12/05/2019 10:00:03 12/06/2019 12:00:07 1
1 y 12/06/2019 12:00:07 12/06/2019 13:12:03 0
2 x 12/06/2019 13:12:03 12/08/2019 16:00:07 2
1 y 12/08/2019 16:00:07 12/09/2019 09:17:59 1
I cannot use group by clause over stage and name, since it will group the 3rd and 6th rows from my input. I tried joining the table to itself, but I am not getting the desired result. Is this even possible in SQL ? Any ideas would be helpful. I am using Microsoft SQL Server.
This is a variation of the gaps and island problem. You want to group together groups of adjacent rows (ie having the same stage and name); but you want to use the start date of the next group as ending date for the current group.
Here is one way to do it:
select
stage,
name,
min(date) date_from,
lead(min(date)) over(order by min(date)) date_to,
datediff(day, min(date), lead(min(date)) over(order by min(date))) date_diff
from (
select
t.*,
row_number() over(order by date) rn1,
row_number() over(partition by stage, name order by date) rn2
from mytable t
) t
group by stage, name, rn1 - rn2
order by date_from
Demo on DB Fiddle:
stage | name | date_from | date_to | datediff
----: | :--- | :------------------ | :------------------ | -------:
1 | x | 12/05/2019 10:00:03 | 12/06/2019 12:00:07 | 1
1 | y | 12/06/2019 12:00:07 | 12/06/2019 13:12:03 | 0
2 | x | 12/06/2019 13:12:03 | 12/08/2019 16:00:07 | 2
1 | y | 12/08/2019 16:00:07 | 12/09/2019 09:17:59 | 1
2 | x | 12/09/2019 09:17:59 | null | null
Note that this does not produce exactly the result that you showed: there is an additional, pending record at the end of the resultset, that represents the "on-going" series of records. If needed, you can filter it out by nesting the query:
select *
from (
select
stage,
name,
min(date) date_from,
lead(min(date)) over(order by min(date)) date_to,
datediff(day, min(date), lead(min(date)) over(order by min(date))) date_diff
from (
select
t.*,
row_number() over(order by date) rn1,
row_number() over(partition by stage, name order by date) rn2
from mytable t
) t
group by stage, name, rn1 - rn2
) t
where date_to is not null
order by date_from
This is a variation of the gaps-and-islands problem, but it has a pretty simple solution.
Just keep every row where the previous row has a different stage or name. Then use lead() to get the next date. Here is the basic idea:
select t.stage, t.name, t.date as datefrom
lead(t.date) over (order by t.date) as dateto,
datediff(day, t.date, lead(t.date) over (order by t.date)) as diff
from (select t.*,
lag(date) over (partition by stage, name order by date) as prev_sn_date,
lag(date) over (order by date) as prev_date
from t
) t
where prev_sn_date <> prev_date or prev_sn_date is null;
If you really want to filter out the last row, you need one more step; I'm not sure if that is desirable.
Table: txn
customer_id | time_stamp
-------------------------
1 | 00:01:03
1 | 00:02:04
2 | 00:03:05
2 | 00:04:06
Looking to query the time difference between each first transaction and next transaction of customer_id
Results:
Customer ID | Time Diff
1 | 61
select customer_ID, ...
from txn
You want lead() . . . but date/time functions are notoriously database-specific. In SQL Server:
select t.*,
datediff(second,
time_stamp,
lead(time_stamp) over (partition by customer_id order by time_stamp)
) as diff_seconds
from t;
In BigQuery:
select t.*,
timestamp_diff(time_stamp,
lead(time_stamp) over (partition by customer_id order by time_stamp),
second
) as diff_seconds
from t;
I am playing around with bigquery and hit an interesting use case. I have a collection of customers and account balances. The account balances collection records any account balance change.
Customers:
+---------+--------+
| ID | Name |
+---------+--------+
| 1 | Alice |
| 2 | Bob |
+---------+--------+
Accounts balances:
+---------+---------------+---------+------------+
| ID | customer_id | value | timestamp |
+---------+---------------+---------+------------+
| 1 | 1 | -500 | 2019-02-12 |
| 2 | 1 | -200 | 2019-02-10 |
| 3 | 2 | 200 | 2019-02-10 |
| 4 | 1 | 0 | 2019-02-09 |
+---------+---------------+---------+------------+
The goal is to find out, for how long a customer has a negative account balance. The resulting collection would look like this:
+---------+--------+---------------------------------+
| ID | Name | Negative account balance since |
+---------+--------+---------------------------------+
| 1 | Alice | 2 days |
+---------+--------+---------------------------------+
Bob is not in the collection, because his last account record shows a positive value.
I think following steps are involved:
get last account balance per customer, see if it is negative
go through the account balance values until you hit a positive (or no more) value
compute datediff
Is something like this even possible in sql? Do you have any ideas on who to create such query? To get customers that currently have a negative account balance, I use this query:
SELECT customer_id FROM (
SELECT t.account_balance, ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY timestamp DESC) as seqnum FROM `account_balances` t
) t
WHERE seqnum = 1 AND account_balance<0
Below is for BigQuery Standard SQL
#standardSQL
SELECT customer_id, name,
SUM(IF(negative_positive < 0, days, 0)) negative_days,
SUM(IF(negative_positive = 0, days, 0)) zero_days,
SUM(IF(negative_positive > 0, days, 0)) positive_days
FROM (
SELECT customer_id, negative_positive, grp,
1 + DATE_DIFF(MAX(ts), MIN(ts), DAY) days
FROM (
SELECT customer_id, ts, SIGN(value) negative_positive,
COUNTIF(flag) OVER(PARTITION BY customer_id ORDER BY ts) grp
FROM (
SELECT *, SIGN(value) = IFNULL(LEAD(SIGN(value)) OVER(PARTITION BY customer_id ORDER BY ts), 0) flag
FROM `project.dataset.balances`
)
)
GROUP BY customer_id, negative_positive, grp
)
LEFT JOIN `project.dataset.customers`
ON id = customer_id
GROUP BY customer_id, name
You can test, play with above using sample data from your question as in below example
#standardSQL
WITH `project.dataset.balances` AS (
SELECT 1 customer_id, -500 value, DATE '2019-02-12' ts UNION ALL
SELECT 1, -200, '2019-02-10' UNION ALL
SELECT 2, 200, '2019-02-10' UNION ALL
SELECT 1, 0, '2019-02-09'
), `project.dataset.customers` AS (
SELECT 1 id, 'Alice' name UNION ALL
SELECT 2, 'Bob'
)
SELECT customer_id, name,
SUM(IF(negative_positive < 0, days, 0)) negative_days,
SUM(IF(negative_positive = 0, days, 0)) zero_days,
SUM(IF(negative_positive > 0, days, 0)) positive_days
FROM (
SELECT customer_id, negative_positive, grp,
1 + DATE_DIFF(MAX(ts), MIN(ts), DAY) days
FROM (
SELECT customer_id, ts, SIGN(value) negative_positive,
COUNTIF(flag) OVER(PARTITION BY customer_id ORDER BY ts) grp
FROM (
SELECT *, SIGN(value) = IFNULL(LEAD(SIGN(value)) OVER(PARTITION BY customer_id ORDER BY ts), 0) flag
FROM `project.dataset.balances`
)
)
GROUP BY customer_id, negative_positive, grp
)
LEFT JOIN `project.dataset.customers`
ON id = customer_id
GROUP BY customer_id, name
-- ORDER BY customer_id
with result
Row customer_id name negative_days zero_days positive_days
1 1 Alice 3 1 0
2 2 Bob 0 0 1
I would like to identify the returning customers from an Oracle(11g) table like this:
CustID | Date
-------|----------
XC321 | 2016-04-28
AV626 | 2016-05-18
DX970 | 2016-06-23
XC321 | 2016-05-28
XC321 | 2016-06-02
So I can see which customers returned within various windows, for example within 10, 20, 30, 40 or 50 days. For example:
CustID | 10_day | 20_day | 30_day | 40_day | 50_day
-------|--------|--------|--------|--------|--------
XC321 | | | 1 | |
XC321 | | | | 1 |
I would even accept a result like this:
CustID | Date | days_from_last_visit
-------|------------|---------------------
XC321 | 2016-05-28 | 30
XC321 | 2016-06-02 | 5
I guess it would use a partition by windowing clause with unbounded following and preceding clauses... but I cannot find any suitable examples.
Any ideas...?
Thanks
No need for window functions here, you can simply do it with conditional aggregation using CASE EXPRESSION :
SELECT t.custID,
COUNT(CASE WHEN (last_visit- t.date) <= 10 THEN 1 END) as 10_day,
COUNT(CASE WHEN (last_visit- t.date) between 11 and 20 THEN 1 END) as 20_day,
COUNT(CASE WHEN (last_visit- t.date) between 21 and 30 THEN 1 END) as 30_day,
.....
FROM (SELECT s.custID,
LEAD(s.date) OVER(PARTITION BY s.custID ORDER BY s.date DESC) as last_visit
FROM YourTable s) t
GROUP BY t.custID
Oracle Setup:
CREATE TABLE customers ( CustID, Activity_Date ) AS
SELECT 'XC321', DATE '2016-04-28' FROM DUAL UNION ALL
SELECT 'AV626', DATE '2016-05-18' FROM DUAL UNION ALL
SELECT 'DX970', DATE '2016-06-23' FROM DUAL UNION ALL
SELECT 'XC321', DATE '2016-05-28' FROM DUAL UNION ALL
SELECT 'XC321', DATE '2016-06-02' FROM DUAL;
Query:
SELECT *
FROM (
SELECT CustID,
Activity_Date AS First_Date,
COUNT(1) OVER ( PARTITION BY CustID
ORDER BY Activity_Date
RANGE BETWEEN CURRENT ROW AND INTERVAL '10' DAY FOLLOWING )
- 1 AS "10_Day",
COUNT(1) OVER ( PARTITION BY CustID
ORDER BY Activity_Date
RANGE BETWEEN CURRENT ROW AND INTERVAL '20' DAY FOLLOWING )
- 1 AS "20_Day",
COUNT(1) OVER ( PARTITION BY CustID
ORDER BY Activity_Date
RANGE BETWEEN CURRENT ROW AND INTERVAL '30' DAY FOLLOWING )
- 1 AS "30_Day",
COUNT(1) OVER ( PARTITION BY CustID
ORDER BY Activity_Date
RANGE BETWEEN CURRENT ROW AND INTERVAL '40' DAY FOLLOWING )
- 1 AS "40_Day",
COUNT(1) OVER ( PARTITION BY CustID
ORDER BY Activity_Date
RANGE BETWEEN CURRENT ROW AND INTERVAL '50' DAY FOLLOWING )
- 1 AS "50_Day",
ROW_NUMBER() OVER ( PARTITION BY CustID ORDER BY Activity_Date ) AS rn
FROM Customers
)
WHERE rn = 1;
Output
USTID FIRST_DATE 10_Day 20_Day 30_Day 40_Day 50_Day RN
------ ------------------- ---------- ---------- ---------- ---------- ---------- ----------
AV626 2016-05-18 00:00:00 0 0 0 0 0 1
DX970 2016-06-23 00:00:00 0 0 0 0 0 1
XC321 2016-04-28 00:00:00 0 0 1 2 2 1
Here is an answer that works for me, I have based it on your answers above, thanks for contributions from MT0 and Sagi:
SELECT CustID,
visit_date,
Prev_Visit ,
COUNT( CASE WHEN (Days_between_visits) <=10 THEN 1 END) AS "0-10_day" ,
COUNT( CASE WHEN (Days_between_visits) BETWEEN 11 AND 20 THEN 1 END) AS "11-20_day" ,
COUNT( CASE WHEN (Days_between_visits) BETWEEN 21 AND 30 THEN 1 END) AS "21-30_day" ,
COUNT( CASE WHEN (Days_between_visits) BETWEEN 31 AND 40 THEN 1 END) AS "31-40_day" ,
COUNT( CASE WHEN (Days_between_visits) BETWEEN 41 AND 50 THEN 1 END) AS "41-50_day" ,
COUNT( CASE WHEN (Days_between_visits) >50 THEN 1 END) AS "51+_day"
FROM
(SELECT CustID,
visit_date,
Lead(T1.visit_date) over (partition BY T1.CustID order by T1.visit_date DESC) AS Prev_visit,
visit_date - Lead(T1.visit_date) over (
partition BY T1.CustID order by T1.visit_date DESC) AS Days_between_visits
FROM T1
) T2
WHERE Days_between_visits >0
GROUP BY T2.CustID ,
T2.visit_date ,
T2.Prev_visit ,
T2.Days_between_visits;
This returns:
CUSTID | VISIT_DATE | PREV_VISIT | DAYS_BETWEEN_VISIT | 0-10_DAY | 11-20_DAY | 21-30_DAY | 31-40_DAY | 41-50_DAY | 51+DAY
XC321 | 2016-05-28 | 2016-04-28 | 30 | | | 1 | | |
XC321 | 2016-06-02 | 2016-05-28 | 5 | 1 | | | | |