I'm sure this is a formatting error on my end. When I try to convert a date to string the conversion is adding 3 hours to the time.
Here's my code:
dateFormatter = [[NSDateFormatter alloc] init];
[dateFormatter setDateFormat:#"MMMM, dd yyyy HH:mm:ss"];
NSDate* dateFromString = [[NSDate alloc] init];
dateFromString = [dateFormatter dateFromString:strToConvert];
Here's some output:
Tuesday, 01 April 2014 | 11:22:12 | KIP_DateManager | convertStringToDate:: | 63 | March, 12 2014 14:22:29
**************************
Tuesday, 01 April 2014 | 11:22:12 | KIP_DateManager | convertStringToDate:: | 68 | 2014-03-12 18:22:29 +0000
**************************
Tuesday, 01 April 2014 | 11:22:12 | KIP_DateManager | convertStringToDate:: | 63 | March, 12 2014 14:16:52
**************************
Tuesday, 01 April 2014 | 11:22:12 | KIP_DateManager | convertStringToDate:: | 68 | 2014-03-12 18:16:52 +0000
**************************
Tuesday, 01 April 2014 | 11:22:12 | KIP_DateManager | convertStringToDate:: | 63 | March, 17 2014 11:14:26
**************************
Tuesday, 01 April 2014 | 11:22:12 | KIP_DateManager | convertStringToDate:: | 68 | 2014-03-17 15:14:26 +0000
Any idea what I am doing wrong?
March, 12 2014 14:22:29
and
2014-03-12 18:22:29 +0000
That's 4 hour difference. Note the parsed date is in +0000 time. You are located in Philadelphia (-0300), with daylight saving time (-1 other hour). Therefore your current time zone is -0400.
Your string doesn't specify a time zone, therefore the system current time zone is used and the date is parsed as 2014-03-12 14:22:29 -0400. However, when you are logging it, you are using [NSDate description] which prints the date with zero time zone.
Your code is giving the correct results but you are testing them wrong.
Related
I have an issue to pull this kind of data. So I need to pull weekly data with these specifications:
The data pull will be scheduled, hence it will involve multiple months
The very first week will start from the first date (1 in every month) -- Green in the pic
The last week doesn't involve dates from the next month -- Red in the pic
The raw data and the desirable output(s) will more or less look like this:
Is there any workaround to do this in BigQuery? Thanks (attached below the data)
+-------------+-------+
| date | sales |
+-------------+-------+
| 1 Oct 2021 | 5 |
+-------------+-------+
| 2 Oct 2021 | 13 |
+-------------+-------+
| 3 Oct 2021 | 75 |
+-------------+-------+
| 4 Oct 2021 | 3 |
+-------------+-------+
| 5 Oct 2021 | 70 |
+-------------+-------+
| 6 Oct 2021 | 85 |
+-------------+-------+
| 7 Oct 2021 | 99 |
+-------------+-------+
| 8 Oct 2021 | 90 |
+-------------+-------+
| 9 Oct 2021 | 68 |
+-------------+-------+
| 10 Oct 2021 | 97 |
+-------------+-------+
| 11 Oct 2021 | 87 |
+-------------+-------+
| 12 Oct 2021 | 56 |
+-------------+-------+
| 13 Oct 2021 | 99 |
+-------------+-------+
| 14 Oct 2021 | 38 |
+-------------+-------+
| 15 Oct 2021 | 6 |
+-------------+-------+
| 16 Oct 2021 | 43 |
+-------------+-------+
| 17 Oct 2021 | 45 |
+-------------+-------+
| 18 Oct 2021 | 90 |
+-------------+-------+
| 19 Oct 2021 | 64 |
+-------------+-------+
| 20 Oct 2021 | 26 |
+-------------+-------+
| 21 Oct 2021 | 24 |
+-------------+-------+
| 22 Oct 2021 | 4 |
+-------------+-------+
| 23 Oct 2021 | 36 |
+-------------+-------+
| 24 Oct 2021 | 68 |
+-------------+-------+
| 25 Oct 2021 | 4 |
+-------------+-------+
| 26 Oct 2021 | 16 |
+-------------+-------+
| 27 Oct 2021 | 30 |
+-------------+-------+
| 28 Oct 2021 | 89 |
+-------------+-------+
| 29 Oct 2021 | 46 |
+-------------+-------+
| 30 Oct 2021 | 28 |
+-------------+-------+
| 31 Oct 2021 | 28 |
+-------------+-------+
| 1 Nov 2021 | 47 |
+-------------+-------+
| 2 Nov 2021 | 75 |
+-------------+-------+
| 3 Nov 2021 | 1 |
+-------------+-------+
| 4 Nov 2021 | 26 |
+-------------+-------+
| 5 Nov 2021 | 26 |
+-------------+-------+
| 6 Nov 2021 | 38 |
+-------------+-------+
| 7 Nov 2021 | 79 |
+-------------+-------+
| 8 Nov 2021 | 37 |
+-------------+-------+
| 9 Nov 2021 | 83 |
+-------------+-------+
| 10 Nov 2021 | 97 |
+-------------+-------+
| 11 Nov 2021 | 56 |
+-------------+-------+
| 12 Nov 2021 | 83 |
+-------------+-------+
| 13 Nov 2021 | 14 |
+-------------+-------+
| 14 Nov 2021 | 25 |
+-------------+-------+
| 15 Nov 2021 | 55 |
+-------------+-------+
| 16 Nov 2021 | 16 |
+-------------+-------+
| 17 Nov 2021 | 80 |
+-------------+-------+
| 18 Nov 2021 | 66 |
+-------------+-------+
| 19 Nov 2021 | 25 |
+-------------+-------+
| 20 Nov 2021 | 62 |
+-------------+-------+
| 21 Nov 2021 | 36 |
+-------------+-------+
| 22 Nov 2021 | 33 |
+-------------+-------+
| 23 Nov 2021 | 19 |
+-------------+-------+
| 24 Nov 2021 | 47 |
+-------------+-------+
| 25 Nov 2021 | 14 |
+-------------+-------+
| 26 Nov 2021 | 22 |
+-------------+-------+
| 27 Nov 2021 | 66 |
+-------------+-------+
| 28 Nov 2021 | 15 |
+-------------+-------+
| 29 Nov 2021 | 96 |
+-------------+-------+
| 30 Nov 2021 | 4 |
+-------------+-------+
Consider below approach
with temp as (
select parse_date('%d %B %Y', date) date, sales
from your_table
)
select format_date('%d %B %Y', weeks[ordinal(num)]) start_week, sum(sales) total_sales
from (
select sales, weeks, range_bucket(date, weeks) num
from temp, unnest([struct(generate_date_array(date_trunc(date, month), last_day(date, month), interval 7 day ) as weeks)])
)
group by start_week
if to apply to sample data (as is) in your question - output is
A client (e-commerce store) doesn't possess a very well-built database. For instance, there are many users with a lot of shopping orders (=different IDs) for exactly the same products and on the same day. It is obvious that these seemingly multiple orders are in many cases just one unique order. At least that's what we have decided to work with to simplify the issue. (I am trying to do a basic data analytics.)
My table might look like this:
| Email | OrderID | Order_date | TotalAmount |
| ----------------- | --------- | ---------------- | ---------------- |
|customerA#gmail.com| 1 |Jan 01 2021 1:00PM| 2000 |
|customerA#gmail.com| 2 |Jan 01 2021 1:03PM| 2000 |
|customerA#gmail.com| 3 |Jan 01 2021 1:05PM| 2000 |
|customerA#gmail.com| 4 |Jan 01 2021 1:10PM| 2000 |
|customerA#gmail.com| 5 |Jan 01 2021 1:14PM| 2000 |
|customerA#gmail.com| 6 |Jan 03 2021 3:55PM| 3000 |
|customerA#gmail.com| 7 |Jan 03 2021 4:00PM| 3000 |
|customerA#gmail.com| 8 |Jan 03 2021 4:05PM| 3000 |
|customerB#gmail.com| 9 |Jan 04 2021 2:10PM| 1000 |
|customerB#gmail.com| 10 |Jan 04 2021 2:20PM| 1000 |
|customerB#gmail.com| 11 |Jan 04 2021 2:30PM| 1000 |
|customerB#gmail.com| 12 |Jan 06 2021 5:00PM| 5000 |
|customerC#gmail.com| 13 |Jan 09 2021 3:00PM| 4000 |
|customerC#gmail.com| 14 |Jan 09 2021 3:06PM| 4000 |
And my desired result would look like this:
| Email | OrderID | Order_date | TotalAmount |
| ----------------- | --------- | ---------------- | ---------------- |
|customerA#gmail.com| 5 |Jan 01 2021 1:14PM| 2000 |
|customerA#gmail.com| 8 |Jan 03 2021 4:05PM| 3000 |
|customerA#gmail.com| 11 |Jan 04 2021 2:30PM| 1000 |
|customerA#gmail.com| 12 |Jan 06 2021 5:00PM| 5000 |
|customerA#gmail.com| 14 |Jan 09 2021 3:06PM| 4000 |
I would guess this might be a common problem, but is there a simple solution to this?
Maybe there is, but I certainly don't seem to come up with one any time soon. I'd like to see even a complex solution, btw :-)
Thank you for any kind of help you can provide!
Do you mean this?
WITH
indata(Email,OrderID,Order_ts,TotalAmount) AS (
SELECT 'customerA#gmail.com', 1,TO_TIMESTAMP( 'Jan 01 2021 01:00PM','Mon DD YYYY HH12:MIAM'),2000
UNION ALL SELECT 'customerA#gmail.com', 2,TO_TIMESTAMP( 'Jan 01 2021 01:03PM','Mon DD YYYY HH12:MIAM'),2000
UNION ALL SELECT 'customerA#gmail.com', 3,TO_TIMESTAMP( 'Jan 01 2021 01:05PM','Mon DD YYYY HH12:MIAM'),2000
UNION ALL SELECT 'customerA#gmail.com', 4,TO_TIMESTAMP( 'Jan 01 2021 01:10PM','Mon DD YYYY HH12:MIAM'),2000
UNION ALL SELECT 'customerA#gmail.com', 5,TO_TIMESTAMP( 'Jan 01 2021 01:14PM','Mon DD YYYY HH12:MIAM'),2000
UNION ALL SELECT 'customerA#gmail.com', 6,TO_TIMESTAMP( 'Jan 03 2021 03:55PM','Mon DD YYYY HH12:MIAM'),3000
UNION ALL SELECT 'customerA#gmail.com', 7,TO_TIMESTAMP( 'Jan 03 2021 04:00PM','Mon DD YYYY HH12:MIAM'),3000
UNION ALL SELECT 'customerA#gmail.com', 8,TO_TIMESTAMP( 'Jan 03 2021 04:05PM','Mon DD YYYY HH12:MIAM'),3000
UNION ALL SELECT 'customerB#gmail.com', 9,TO_TIMESTAMP( 'Jan 04 2021 02:10PM','Mon DD YYYY HH12:MIAM'),1000
UNION ALL SELECT 'customerB#gmail.com',10,TO_TIMESTAMP( 'Jan 04 2021 02:20PM','Mon DD YYYY HH12:MIAM'),1000
UNION ALL SELECT 'customerB#gmail.com',11,TO_TIMESTAMP( 'Jan 04 2021 02:30PM','Mon DD YYYY HH12:MIAM'),1000
UNION ALL SELECT 'customerB#gmail.com',12,TO_TIMESTAMP( 'Jan 06 2021 05:00PM','Mon DD YYYY HH12:MIAM'),5000
UNION ALL SELECT 'customerC#gmail.com',13,TO_TIMESTAMP( 'Jan 09 2021 03:00PM','Mon DD YYYY HH12:MIAM'),4000
UNION ALL SELECT 'customerC#gmail.com',14,TO_TIMESTAMP( 'Jan 09 2021 03:06PM','Mon DD YYYY HH12:MIAM'),4000
)
,
-- need a ROW_NUMBER() to identify the last row within the day (order descending to get 1.
-- can't filter by an OLAP function, so in a fullselect, and WHERE cond in the final SELECT
with_rank AS (
SELECT
*
, ROW_NUMBER() OVER(PARTITION BY email,DAY(order_ts) ORDER BY order_ts DESC) AS rank
FROM INDATA
)
SELECT
*
FROM with_rank
WHERE rank = 1;
-- out Email | OrderID | Order_ts | TotalAmount | rank
-- out ---------------------+---------+---------------------+-------------+------
-- out customerA#gmail.com | 5 | 2021-01-01 13:14:00 | 2000 | 1
-- out customerA#gmail.com | 8 | 2021-01-03 16:05:00 | 3000 | 1
-- out customerB#gmail.com | 11 | 2021-01-04 14:30:00 | 1000 | 1
-- out customerB#gmail.com | 12 | 2021-01-06 17:00:00 | 5000 | 1
-- out customerC#gmail.com | 14 | 2021-01-09 15:06:00 | 4000 | 1
Currently my Transaction Table has customer's transaction data for each month. Account_ID identifies the customer's ID. Order_ID is the number of orders that the customer had made. Reporting_week_start_date is the week which begins on Monday where each transaction is made (Date_Purchased).
How do i create a new table to identify the customer_status after each transaction has been made? Note that the new table has the Reporting_week_start_date until current date despite no transactions has been made .
Customer_Status
- New : customers who made their first paid subscription
- Recurring : customers with continuous payment
- Churned : when customers' subscriptions had expired and there's no renewal within the next month/same month
- Reactivated : customers who had churned and then returned to re-subscribe
Transaction Table
Account_ID | Order_ID | Reporting_week_start_date| Date_Purchased | Data_Expired
001 | 1001 | 31 Dec 2018 | 01 Jan 2019 | 08 Jan 2019
001 | 1001 | 07 Jan 2019 | 08 Jan 2019 | 15 Jan 2019
001 | 1001 | 14 Jan 2019 | 15 Jan 2019 | 22 Jan 2019 #Transaction 1
001 | 1001 | 21 Jan 2019 | 22 Jan 2019 | 29 Jan 2019
001 | 1001 | 28 Jan 2019 | 29 Jan 2019 | 31 Jan 2019
001 | 1002 | 28 Jan 2019 | 01 Feb 2019 | 08 Feb 2019
001 | 1002 | 04 Feb 2019 | 08 Feb 2019 | 15 Feb 2019 #Transaction 2
001 | 1002 | 11 Feb 2019 | 15 Feb 2019 | 22 Feb 2019
001 | 1002 | 18 Feb 2019 | 22 Feb 2019 | 28 Feb 2019
001 | 1003 | 25 Feb 2019 | 01 Mar 2019 | 08 Mar 2019
001 | 1003 | 04 Mar 2019 | 08 Mar 2019 | 15 Mar 2019
001 | 1003 | 11 Mar 2019 | 15 Mar 2019 | 22 Mar 2019 #Transaction 3
001 | 1003 | 18 Mar 2019 | 22 Mar 2019 | 29 Mar 2019
001 | 1003 | 25 Mar 2019 | 29 Mar 2019 | 31 Mar 2019
001 | 1004 | 27 May 2019 | 01 Jun 2019 | 08 Jun 2019
001 | 1004 | 03 Jun 2019 | 08 Jun 2019 | 15 Jun 2019 #Transaction 4
001 | 1004 | 10 Jun 2019 | 15 Jun 2019 | 22 Jun 2019
001 | 1004 | 17 Jun 2019 | 22 Jun 2019 | 29 Jun 2019
001 | 1004 | 24 Jun 2019 | 29 Jun 2019 | 30 Jun 2019
Expected Output
Account_ID | Order_ID | Reporting_week_start_date| Customer_status
001 | 1001 | 31 Dec 2018 | New
001 | 1001 | 07 Jan 2019 | New #Transaction 1
001 | 1001 | 14 Jan 2019 | New
001 | 1001 | 21 Jan 2019 | New
001 | 1001 | 28 Jan 2019 | New
001 | 1002 | 28 Jan 2019 | Recurring
001 | 1002 | 04 Feb 2019 | Recurring #Transaction 2
001 | 1002 | 11 Feb 2019 | Recurring
001 | 1002 | 18 Feb 2019 | Recurring
001 | 1003 | 25 Feb 2019 | Churned
001 | 1003 | 04 Mar 2019 | Churned #Transaction 3
001 | 1003 | 11 Mar 2019 | Churned
001 | 1003 | 18 Mar 2019 | Churned
001 | 1003 | 25 Mar 2019 | Churned
001 | - | 1 Apr 2019 | Churned
001 | - | 08 Apr 2019 | Churned
001 | - | 15 Apr 2019 | Churned
001 | - | 22 Apr 2019 | Churned
001 | - | 29 Apr 2019 | Churned
001 | - | 29 Apr 2019 | Churned
001 | - | 06 May 2019 | Churned
001 | - | 13 May 2019 | Churned
001 | - | 20 May 2019 | Churned
001 | - | 27 May 2019 | Churned
001 | 1004 | 27 May 2019 | Reactivated
001 | 1004 | 03 Jun 2019 | Reactivated #Transaction 4
001 | 1004 | 10 Jun 2019 | Reactivated
001 | 1004 | 17 Jun 2019 | Reactivated
001 | 1004 | 24 Jun 2019 | Reactivated'
...
...
...
current date
I think you just want window functions and case logic. Assuming the date you are referring to is Reporting_week_start_date, then the logic looks something like this:
select t.*,
(case when Reporting_week_start_date = min(Reporting_week_start_date) over (partition by account_id)
then 'New'
when Reporting_week_start_date < dateadd(lag(Reporting_week_start_date) over (partition by account_id order by Reporting_week_start_date), interval 1 month)
then 'Recurring'
when Reporting_week_start_date < dateadd(lead(Reporting_week_start_date) over (partition by account_id order by Reporting_week_start_date), interval -1 month)
then 'Churned'
else 'Reactivated'
end) as status
from transactions t;
These are not exactly the rules you have specified. But they seem very reasonable interpretations of what you want to do.
I need Oracle SQL that returns the 'working' week number in year:
no overflowing weeks from one year to another
each week starts from monday
first few days in year are week 01
So the result should be:
2015-12-28 - MON - week 53
2015-12-29 - TUE - week 53
2015-12-30 - WED - week 53
2015-12-31 - THU - week 53
===
2016-01-01 - FRI - week 01 - reseting yearly week counter
2016-01-02 - SAT - week 01
2016-01-03 - SUN - week 01
---
2016-01-04 - MON - week 02 - monday start of new week
2016-01-05 - TUE - week 02
...
2016-12-31 - SAT - week 53
===
2017-01-01 - SUN - week 01 - reseting yearly week counter
2017-01-02 - MON - week 02 - monday start of new week
...
W - week number in a month
WW - week number in a year, week 1 starts at 1st of Jan
IW - week number in a year, according to ISO standard
For your requirement, you need to use combination of IW and WW format. You could combine them using a CASE expression.
If you want to generate the list of dates for entire year, then you could use the row generator method.
SQL> WITH sample_data AS(
2 SELECT DATE '2015-12-28' + LEVEL -1 dt FROM dual
3 CONNECT BY LEVEL <= 15
4 )
5 -- end of sample_data mimicking real table
6 SELECT dt,
7 TO_CHAR(dt, 'DY') DAY,
8 NVL(
9 CASE
10 WHEN dt < DATE '2016-01-01'
11 THEN TO_CHAR(dt, 'IW')
12 WHEN dt >= next_day(TRUNC(DATE '2016-01-01', 'YYYY') - 1, 'Monday')
13 THEN TO_CHAR(dt +7, 'IW')
14 END, '01') week_number
15 FROM sample_data;
DT DAY WEEK_NUMBER
---------- --- -----------
2015-12-28 MON 53
2015-12-29 TUE 53
2015-12-30 WED 53
2015-12-31 THU 53
2016-01-01 FRI 01
2016-01-02 SAT 01
2016-01-03 SUN 01
2016-01-04 MON 02
2016-01-05 TUE 02
2016-01-06 WED 02
2016-01-07 THU 02
2016-01-08 FRI 02
2016-01-09 SAT 02
2016-01-10 SUN 02
2016-01-11 MON 03
15 rows selected.
NOTE:
The value 15 to generate 15 rows and the dates are hard-coded above just for demonstration using the WITH clause since OP did not provide the test case with create and insert statements. In reality, you need to use your table and column names.
An approach could be counting the number of days of the year and divide by 7, with some logic to handle the beginning and the end ot the week and of the year:
with test(date_) as
(
select to_date('23122016', 'ddmmyyyy') + level -1 from dual connect by level < 30
)
SELECT date_,
floor( to_number( to_char(
greatest( least(
trunc(date_, 'iw')+6 ,
add_months( trunc(date_, 'YEAR'),12) -1
),
trunc(date_, 'yyyy')),
'ddd'
)
) /7 +1
) week
FROM test
The LEAST is used to avoid going to the next year, while the GREATEST is useful to avoid going to the previous one.
I found the answer myself, TO_CHAR(date,'IW') format is of no use because the very first week in a year according to this standard (ISO) can start after the New Year but also before it (look at TO_CHAR(TO_DATE('2014-12-31','YYYY-MM-DD'),'IW')=01 the first week that belongs to the next year!)
| DAY | WW | IW | MY
===========+=====+====+====+====
2014-12-28 | SUN | 52 | 52 | 52
2014-12-29 | MON | 52 | 01 | 53
2014-12-30 | TUE | 52 | 01 | 53
2014-12-31 | WED | 52 | 01 | 53
2015-01-01 | THU | 53 | 01 | 53
... | ... | .. | .. | ..
2016-12-31 | THU | 53 | 53 | 01
2016-01-01 | FRI | 01 | 53 | 01
2016-01-02 | SAT | 01 | 53 | 01
2016-01-03 | SUN | 01 | 53 | 01
2016-01-04 | MON | 01 | 01 | 02
2016-01-05 | TUE | 01 | 01 | 02
2016-01-06 | WED | 01 | 01 | 02
2016-01-07 | THU | 01 | 01 | 02
2016-01-08 | FRI | 02 | 01 | 02
The logic is quite simple, let's look at the very first day in year and its offset from monday. If current day is bigger than this first day offset then week number should be incremented by 1.
The number of very first day (offset from monday) is calculated with:
TO_CHAR(TO_DATE(TO_CHAR(dt,'YYYY')||'0101','YYYYMMDD'),'D'))
So the final SQL statement is
WITH DATES AS
(
SELECT DATE '2014-12-25' + LEVEL -1 dt FROM DUAL CONNECT BY LEVEL <= 500
)
SELECT dt,TO_CHAR(dt,'DY') DAY,TO_CHAR(dt,'WW') WW,TO_CHAR(dt,'IW') IW,
CASE WHEN TO_CHAR(dt,'D')<TO_CHAR(TO_DATE(TO_CHAR(dt,'YYYY')||'0101','YYYYMMDD'),'D') THEN
LPAD(TO_CHAR(dt,'WW')+1,2,'0')
ELSE
TO_CHAR(dt,'WW')
END MY
FROM dates
Of course, one can create a function for that purpose like:
CREATE OR REPLACE FUNCTION WorkingWeek(dt IN DATE) RETURN CHAR
IS
BEGIN
IF(TO_CHAR(dt,'D')<TO_CHAR(TO_DATE('0101'||TO_CHAR(dt,'YYYY'),'DDMMYYYY'),'D')) THEN
RETURN LPAD(TO_CHAR(dt,'WW')+1,2,'0');
ELSE
RETURN TO_CHAR(dt,'WW');
END IF;
END WorkingWeek;
/
This may be so last year but I'm using SQL Server 2005
stmpdate intime
----------------------
2014-10-08 08:04:43
2014-10-09 07:57:13
2014-10-10 07:57:14
2014-10-16 07:79:56
2014-10-17 07:45:56
I have this table. It keeps check-in time of the employee, but this employee didn't check-in everyday in the month. So what I want it to be is something like this
stmpdate intime
1 2014-10-01
2 2014-10-02
3 2014-10-03
4 2014-10-04
5 2014-10-05
6 2014-10-06
7 2014-10-07
8 2014-10-08 08:04:43
9 2014-10-09 07:57:13
10 2014-10-10 07:57:14
11 2014-10-11
12 2014-10-12
13 2014-10-13
14 2014-10-14
15 2014-10-15
16 2014-10-16 07:59:56
17 2014-10-17 07:45:56
18 2014-10-18
19 2014-10-19
20 2014-10-20
21 2014-10-21
22 2014-10-22
23 2014-10-23
24 2014-10-24
25 2014-10-25
26 2014-10-26
27 2014-10-27
28 2014-10-28
29 2014-10-29
30 2014-10-30
31 2014-10-31
I tried to make a temp table which contains every date in the month, and then left join it with the first table I mentioned, but it seemed to not work.
declare #datetemp table (
stmpdate varchar(10)
);
insert into #datetemp
SELECT '2014-10-01'
UNION ALL
SELECT '2014-10-02'
UNION ALL
SELECT '2014-10-03'
....
and
SELECT dtt.stmpdate, intime
FROM #datetemp dtt left join v_dayTimesheet
on dtt.stmpdate=v_dayTimesheet.stmpdate
WHERE (emp_no = '001234567')
here is the result of query above
stmpdate intime
2014-10-08 08:04:43
2014-10-09 07:57:13
2014-10-10 07:57:14
2014-10-16 07:59:56
2014-10-17 07:45:56
and here is the result of select * from #datetemp
2014-10-01
2014-10-02
2014-10-03
2014-10-04
2014-10-05
2014-10-06
2014-10-07
2014-10-08
2014-10-09
2014-10-10
2014-10-11
2014-10-12
2014-10-13
2014-10-14
2014-10-15
2014-10-16
2014-10-17
2014-10-18
2014-10-19
2014-10-20
2014-10-21
2014-10-22
2014-10-23
2014-10-24
2014-10-25
2014-10-26
2014-10-27
2014-10-28
2014-10-29
2014-10-30
2014-10-31
you're filtering for only where emp_no has a value. if they didn't check in, it won't return on that row because you just have date info and no employee number. so you have to allow for equal or null.
SELECT dtt.stmpdate, intime
FROM #datetemp dtt
left outer join v_dayTimesheet
on dtt.stmpdate=v_dayTimesheet.stmpdate
WHERE emp_no = '001234567' or emp_no is null
also, for your dates... check this out: http://www.sqlservercurry.com/2010/03/generate-start-and-end-date-range-using.html
DECLARE
#StartDate datetime = '2010-01-01',
#EndDate datetime = '2010-03-01'
;WITH datetemp as
(
SELECT #StartDate as stmpdate
UNION ALL
SELECT DATEADD(day, 1, stmpdate)
FROM datetemp
WHERE DATEADD(day, 1, stmpdate) <= #EndDate
)
SELECT stmpdate
FROM datetemp;
you would then select from datetemp as a normal table. beware, though, a common table expression can only be used once and immediately following the with statement.
just trust me on this one... run this query and see how your blank lines occur:
SELECT dtt.stmpdate, intime, emp_no
FROM #datetemp dtt
left outer join v_dayTimesheet
on dtt.stmpdate=v_dayTimesheet.stmpdate
WHERE emp_no = '001234567' or emp_no is null
all these lines will return with emp_no = 001234567
stmpdate intime
2014-10-08 08:04:43
2014-10-09 07:57:13
2014-10-10 07:57:14
2014-10-16 07:59:56
2014-10-17 07:45:56
and all your blank lines will have null as emp_no.
I got my answer!!
SELECT dtt.stmpdate, intime
FROM #datetemp dtt left join
(
SELECT stmpdate, intime
FROM v_dayTimesheet
WHERE (emp_no = '001234567')
) as vdayTimesheet
on sparedate.stmpdate=vdayTimesheet.stampdate
ORDER BY stmpdate
this is what I want, thanks everyone
SQL Query:
SQLFIDDLEExample
SELECT t2.dt,
isnull(t1.intime, '') intime
FROM
(
SELECT DATEADD(day,number,'2014-10-01') dt
FROM master..spt_values
WHERE Type = 'P'
AND DATEADD(day,number,'2014-10-01') >= '2014-10-01'
AND DATEADD(day,number,'2014-10-01') < '2014-11-01'
) t2
LEFT JOIN Table1 t1
ON t1.stmpdate = t2.dt
Result:
| DT | INTIME |
|--------------------------------|----------|
| October, 01 2014 00:00:00+0000 | |
| October, 02 2014 00:00:00+0000 | |
| October, 03 2014 00:00:00+0000 | |
| October, 04 2014 00:00:00+0000 | |
| October, 05 2014 00:00:00+0000 | |
| October, 06 2014 00:00:00+0000 | |
| October, 07 2014 00:00:00+0000 | |
| October, 08 2014 00:00:00+0000 | 08:04:43 |
| October, 09 2014 00:00:00+0000 | 07:57:13 |
| October, 10 2014 00:00:00+0000 | 07:57:14 |
| October, 11 2014 00:00:00+0000 | |
| October, 12 2014 00:00:00+0000 | |
| October, 13 2014 00:00:00+0000 | |
| October, 14 2014 00:00:00+0000 | |
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| October, 16 2014 00:00:00+0000 | 07:79:56 |
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